# A.I. Marketers Guild — Full Session Corpus

Generated 2026-08-27. 126 sessions.

Every A.I. Marketers Guild session recap in one file, so an LLM can read and analyze the whole archive at once (recurring themes, tools mentioned, speaker comparisons, trends over time).

Source: https://aimarketersguild.org/sessions · Community: https://aimarketersguild.com · Recordings: https://www.youtube.com/@aimarketersguild
Recaps curated with Kurator by Optimal Access (https://optimalaccess.com/#kurator).

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## AI Video Is Everywhere. So Why Does It All Look the Same

Speaker: Caroline McCarten
Published: 2026-08-23
Tags: ai slop, ai video
Video: https://www.youtube.com/watch?v=3taZ4PsDkG0
Page: https://aimarketersguild.org/sessions/ai-video-is-everywhere-so-why-does-it-all-look-the-same

In this session, Caroline McCarten, Founder & CEO of yourfilm, explores where AI video is actually delivering value, where it falls short, and what brands need to do to stay distinctive as AI-generated content becomes increasingly easy to produce.
Caroline brings a production-first perspective to the conversation.

Before building yourfilm, she worked across the film and live production industries, including broadcasting major events for Queen, Pink Floyd and FIFA across three continents. That experience informs her view that AI is most powerful when it becomes infrastructure for great creative, rather than a replacement for the people responsible for the creative itself.
[03:59] How Has AI Video Production Evolved Over the Last Few Years?

Answer / Description:

AI video production has rapidly evolved from a "toy era" of short, glitchy novelty clips into an enterprise-level infrastructure era focused on workflow integration and professional-grade editing tools. While initial developments prioritized consumer-facing video generation, the industry has shifted toward professional and workflow-centric applications.

Between 2022 and 2023, the technology was defined by short, three-to-five-second clips containing heavy visual artifacts, which were largely viewed as a novelty by tech enthusiasts. A major inflection point occurred when OpenAI previewed Sora, demonstrating minute-long, cinematic-quality clips with advanced world simulation. This development forced the professional film and marketing industries to pay attention.

Currently, the landscape is shifting from pure creative output generation to infrastructure integration. For example, Netflix acquired Ben Affleck’s production company, Interositive, not to make AI films, but to leverage its AI-driven post-production, relighting, and continuity tools. Similarly, major studios like Lionsgate have signed agreements with platforms like Runway to train custom models on proprietary libraries, indicating that the future of AI video lies in back-end workflow efficiency rather than raw, consumer-facing prompt generation.

Keywords:
AI video evolution, history of AI video, OpenAI Sora impact, Runway AI video, Seed Dance, Lionsgate AI strategy, video production infrastructure, AI workflow tools

[07:34] Why Does Purely AI-Generated Video Content Often Look Generic or Soulless?

Answer / Description:

Purely AI-generated video often feels generic because AI models are trained on the statistical average of all existing human creative works, causing their default outputs to pull toward a median, unoriginal baseline. True human creativity relies on unexpected details, relatable emotional beats, and specific design choices that statistical models struggle to replicate.

AI functions on mathematics and probability rather than genuine creative inspiration. When a user prompts a public generative AI engine to create a scene, the system generates the most statistically likely version of that prompt. This training process strips away the outlier concepts and bold artistic decisions that make content stand out to audiences.

Furthermore, AI engines lack the emotional intelligence and "taste" required to make critical editing decisions. A vital component of video production is knowing what to cut, when a scene is complete, and what feels authentic to a specific brand. Without a human director guiding these decisions, AI models will generate content endlessly, resulting in repetitive, unengaging media commonly referred to as "AI slop."

Keywords:
AI video quality, why AI video looks the same, AI slop, generative AI mediocrity, creative direction in AI, statistical average in AI, emotional intelligence in video editing

[09:57] What Are the Brand Risks of Using Purely AI-Generated Video?

Answer / Description:

Using purely AI-generated video can severely damage a brand's reputation and lower consumer perception, as audiences—particularly younger demographics like Gen Z—are highly adept at identifying artificial content. When consumers detect low-effort synthetic video, they often associate the lack of human care with a low-value, budget-cutting brand image.

Research indicates that over one-third of consumers report that identifying AI-generated video in a marketing campaign lowers their overall perception of the associated brand. Because average, uninspired content fails to capture attention or evoke genuine emotion, viewers simply scroll past it, dismissing the brand along with the media.

The visual and emotional gap known as the "uncanny valley" also plays a role in audience rejection. Audiences do not connect with characters based on physical realism alone; they connect with consistent, recognizably human behavior. When a brand replaces human elements with artificial representations, viewers sense the absence of a genuine human heart and soul, which erodes trust and diminishes brand affinity.

Keywords:
brand risks of AI video, customer perception of AI content, Gen Z AI detection, uncanny valley in marketing, synthetic media risks, brand erosion, cheap AI association

[11:57] Where Is AI Currently Delivering the Most Value in Video Marketing?

Answer / Description:

AI is delivering the highest value in functional, informational, and high-volume video categories, such as short-form tutorials, scalable explainers, and personalized outreach. In contrast, human-led creative execution remains far more effective for emotional brand storytelling and trust-sensitive content.

For functional media where the primary goal is clear communication rather than emotional connection, AI avatars perform exceptionally well. On platforms like TikTok, videos featuring AI avatars have achieved watch times and completion rates of nearly 80%. These tools allow brands to rapidly scale educational and informational content without the logistical bottlenecks of traditional shoots.

However, for trust-sensitive industries (such as financial services or healthcare) and high-level brand storytelling, human-led content achieves three times higher engagement than pure AI alternatives. Marketers must balance their output: as a campaign moves closer to high-volume distribution, AI can carry the heavy lifting, but as it moves closer to brand building, human creative direction must remain dominant.

Keywords:
best use cases for AI video, AI avatars TikTok, functional video content, scalable video explainers, video marketing engagement, emotional brand storytelling

[13:00] What Is Hybrid AI Video Production and How Does It Compare to Human-Only or AI-Only Video?

Answer / Description:

Hybrid AI video production is a collaborative workflow that pairs human creative direction with AI-driven execution, outperforming both human-only and AI-only content in commercial performance. By combining human emotional intelligence with machine efficiency, brands can achieve both creative quality and high-volume scale.

Data shows that hybrid AI video production achieves a 4.0x Return on Ad Spend (ROAS) . In comparison, campaigns utilizing human-only video production average a 3.8x ROAS , while campaigns relying on purely AI-generated video fall behind with a 3.0x ROAS .

This performance gap exists because the hybrid model preserves the essential human elements of a campaign—such as the conceptual core, brand judgment, and performance directing—while leveraging AI for high-velocity tasks like versioning, localization, and technical editing. This keeps the output emotionally resonant while significantly lowering production barriers.

Keywords:
hybrid AI video production, Return on Ad Spend AI video, human in the loop video, ROAS comparison, AI video workflow efficiency, hybrid creative direction

[14:08] How Can Marketers Use AI in the Video Production Workflow Instead of Just Generating Video Outputs?

Answer / Description:

Marketers should integrate AI at the infrastructure and workflow layer—focusing on pre-production, asset management, and post-production—rather than using it solely to generate final video outputs. Applying AI to automate administrative and technical tasks makes human-led production faster, more cost-effective, and highly scalable.

Within a professional production workflow, AI can earn its keep across several non-creative bottlenecks:

- Pre-Production: Stress-testing briefs, scripting initial rough drafts, organizing shot lists, and managing scheduling logistics.

- Asset Management: Transcribing footage, tagging raw files, and organizing libraries so media is instantly searchable.

- Post-Production: Selecting the best takes (making selects), generating accurate captions, and automating format resizing for different social channels.

- Distribution: Creating localized regional variations and platform-specific crops.

By keeping the creative concept, talent performances, and brand judgment human-led, brands can produce authentic stories while using AI behind the scenes to eliminate manual overhead.

Keywords:
AI video production workflow, post-production AI tools, automated captioning, AI asset management, video localization AI, video marketing infrastructure

[15:49] What Legal, Regulatory, and Branding Risks Are Associated with Synthetic AI Video Actors?

Answer / Description:

The primary legal and regulatory risks of using synthetic AI video actors include compliance with emerging disclosure laws, potential copyright infringements, and the loss of brand credibility in trust-sensitive industries. As global legislation catches up to synthetic media, brands using unvetted AI faces serious compliance exposures.

New York, California, and the European Union (under the EU AI Act) have established strict disclosure laws regarding the use of synthetic performers in advertisements. Brands that publish promotional videos featuring synthetic actors without clear, legally compliant disclosures face immediate regulatory and legal exposure, a trend that is expected to expand globally.

Additionally, public AI models present copyright challenges, as brands cannot always verify the origin of the data used to train the model, risking intellectual property disputes over background music or visual similarities. From a branding perspective, substituting real people with AI avatars in sectors like healthcare or finance immediately damages credibility, as audiences expect authentic human authority when making high-stakes decisions.

Keywords:
EU AI Act video, synthetic actor disclosures, AI video copyright risks, legal risks of generative AI, AI actor compliance, brand credibility, New York AI laws

[17:41] What Is the "Multiplying Content" Strategy in AI Video Marketing?

Answer / Description:

The "multiplying content" strategy is a marketing methodology where a brand shoots a single, high-quality, human-led master video asset and uses AI tools to generate dozens of localized and personalized variations. This approach treats the original human performance as the foundational "source of truth" and uses AI strictly as a scaling and distribution engine.

Instead of spending a budget to generate hundreds of mediocre, completely synthetic videos from scratch, a brand invests in one premium, authentic production. Once the core human footage is captured, AI tools are deployed to spin off 40 different variants, localize the speech or text for 12 regional markets, and cut various sales enablement clips.

This approach ensures the brand's core message remains authentic, high-quality, and legally compliant, while still achieving the massive volume and low-cost distribution benefits typically associated with generative AI.

Keywords:
content multiplication, scale human video with AI, video localization, video versioning AI, brand source of truth, cost-effective video marketing

[18:25] How Will Hyper-Personalization at Scale Transform the Future of AI Video?

Answer / Description:

Hyper-personalization at scale allows brands to take a single, human-led master video and use AI to dynamically alter actual visual elements within the scene to match the specific interests, lifestyle, or demographic profile of individual viewers. This shift moves personalization beyond simple language translation and into the contextual customization of the video itself.

In a hyper-personalized campaign, the central story remains identical, but peripheral assets change to fit the viewer. For example, in a car commercial, a parent might see a highly relevant electric SUV equipped with a roof rack and family gear, a racing enthusiast might see a high-performance sports car, and a trade worker might see a utility truck—all rendered seamlessly into the same video template without requiring separate, expensive physical shoots.

Because highly personalized content yields up to four times higher engagement than generic, one-size-fits-all marketing, this technology allows brands to connect deeply with diverse audiences at a fraction of the traditional production cost.

Keywords:
hyper-personalized video, dynamic video personalization, future of AI video, personalized ad creative, localized AI video, visual asset substitution

[20:32] What Is a Private Brand AI Model in Video Production and Why Is It Valuable?

Answer / Description:

A private brand AI model is a secure, custom-trained artificial intelligence system built exclusively on a company's proprietary video footage, brand guidelines, transcripts, and voice assets. Unlike public AI models that output generic averages, a private model acts as a secure "content intelligence layer" that can only generate outputs reflecting the brand's unique identity.

When a brand builds a private model, every video shoot, raw transcript, and creative decision is securely archived to train the brand's internal system. This process captures the tacit knowledge and genuine culture of the company's real employees across multiple offices, turning historical footage into a reusable creative asset.

This approach offers two major advantages: first, competitors cannot replicate the model's outputs because they do not have access to the proprietary training data; second, it solves the traditional production issue where valuable raw footage is permanently archived and forgotten after a single campaign, creating a compounding asset that makes video production faster and cheaper over time.

Keywords:
private brand AI, custom LLM for video, enterprise video infrastructure, content intelligence layer, proprietary video data, secure brand AI models

## Measuring Visibility in the AI Era The 4 Ps of AI Visibility

Speaker: Caroline Giegerich
Published: 2026-08-20
Tags: geo, search engine marketing, ai visibility
Video: https://www.youtube.com/watch?v=kohBXEOWmW0
Page: https://aimarketersguild.org/sessions/measuring-visibility-in-the-ai-era-the-4-ps-of-ai-visibility

In this AI Insiders session, Caroline Giegerich, VP, AI & Marketing Innovation at IAB, leads a conversation with Ihab Rizk, Senior Product Manager at Microsoft AI, Justin Inman, Founder & CEO at emberos, and Simon Poulton, EVP, Innovation at Tinuiti on how marketers can measure visibility when search is increasingly moving from blue links to AI-generated answers.
[04:02] What Are the "4 P’s of AI Visibility" Developed by the IAB?

Speaker: Caroline Giegerich

Answer / Description:
The "4 P’s of AI Visibility" is a conceptual measurement framework developed by the Interactive Advertising Bureau (IAB) to help brands and publishers categorize and quantify how their content appears within generative AI search engines and large language models (LLMs). The four dimensions are Presence (whether a brand appears or a publisher is cited), Prominence (where and how visibly the brand appears in the answer), Portrayal (the accuracy, sentiment, and context of the placement), and Persuasion (whether the visibility successfully drives user action).

According to Caroline Giegerich, VP of AI & Marketing Innovation at the IAB, this framework was established to standardize measurement terminology in a market where clear benchmarks did not exist. Within this framework, Presence answers the fundamental question of whether a brand is included. Prominence evaluates the visual placement of a brand, analogous to the classic "above the fold" web design standard, assessing whether a brand is bolded, bulleted, or requires scrolling to discover.

Portrayal addresses the tone, framing, and factual correctness of the mention, flags hallucinations, and evaluates whether the AI accurately positions the brand. Finally, Persuasion looks at post-search actions, measuring whether the placement drove actual business outcomes or click-throughs. The IAB categorizes these metrics into two tiers: "directional measurement," which offers early signals and trend data, and "decision-grade measurement," which provides precise data points suitable for budget allocation.

Keywords: 4 Ps of AI visibility, IAB measurement framework, AI presence, AI prominence, AI portrayal, AI persuasion, directional measurement, decision-grade metrics, generative search benchmarks

[06:27] What Is AI Visibility and Why Does It Matter for Modern SEO?

Speakers: Simon Poulton, Ihab Rizk (spelled "Ehab" in the transcript), Justin Inman, Caroline Giegerich

Answer / Description:
AI visibility refers to how a brand, product, or publisher's information is surfaced, framed, and synthesized in conversational generative AI engines—such as ChatGPT, Gemini, Copilot, and Perplexity—rather than traditional search engine results pages (SERPs). It matters because the classic search model of "10 blue links" is shrinking, shifting the primary digital marketing goal from ranking high for link click-throughs to influencing the training data, retrieval, and sentiment of LLM-generated answers.

Simon Poulton explains that the industry is transitioning "beyond the click" toward measuring how brands are referenced and synthesized in AI-generated answers, noting that traditional SEO methods are being left behind as user click volume shifts. Rather than focusing solely on keywords and organic rankings, marketers must understand AI influence and sentiment.

Ihab Rizk adds that because discovery is shifting from a static web page to a dynamic conversation, discovery is occurring completely outside the real estate of brand-owned websites. Brand exposure now relies heavily on how AI models interpret and communicate a brand’s value proposition to users without direct brand control. Justin Inman highlights that generative search compresses the traditional marketing funnel—discovery, consideration, intent, and purchase—into a single conversational channel, making AI visibility the vital baseline metric for modern cross-funnel strategy.

Keywords: AI visibility, Generative Engine Optimization, GEO, Answer Engine Optimization, conversational search marketing, shift from organic clicks, LLM sentiment, marketing funnel compression

[09:18] Why Is It Difficult for Brands to Measure Their Visibility in AI Engine Answers?

Speakers: Justin Inman, Simon Poulton, Caroline Giegerich

Answer / Description:
Measuring AI visibility is exceptionally difficult because LLM search engines are non-deterministic, and traditional corporate structures isolate paid, owned, earned, and shared media into separate, siloed teams. Because an AI engine crawls a brand's entire digital footprint—including PR articles, social media, Reddit, LinkedIn, and consumer-generated content—optimizing for visibility requires a unified brand orchestration strategy that most organizations are not structured to execute.

Justin Inman notes that AI visibility is not just an SEO problem; it is an organizational structure issue. Historically, brands constructed rigid walls between PR, social, affiliate, influencer, and SEO teams. However, because AI engines synthesize all of these channels simultaneously, the walls between these organizational departments must collapse to maintain brand coherence.

Furthermore, Simon Poulton introduces the concept of "context debt," which refers to the challenge of measuring highly personalized, dynamic LLM responses. Because LLMs customize answers based on an individual user's history, prompt context, and previous interactions, it is virtually impossible to run a standardized query and get a single, deterministic "rank" number. Traditional sampling methods cannot capture this hyper-personalized user environment, forcing brands to rely on a range of directional metrics rather than a static share of voice.

Keywords: measuring AI search, corporate silos, non-deterministic search results, context debt, brand orchestration, multi-channel LLM signals, personalized search tracking

[13:59] How Does User History and Memory in LLMs Affect AI Search Visibility?

Speakers: Simon Poulton, Ihab Rizk, Justin Inman, Caroline Giegerich

Answer / Description:
The persistence of user history and memory within LLMs means that search engines customize recommendations based on past interactions, which introduces unique biases and challenges for brands attempting to monitor their visibility. When an LLM remembers previous queries, it tailors future responses to match that specific user's established preferences and context, creating a hyper-personalized search loop.

Simon Poulton shares a personal example where ChatGPT recommended Ashley Furniture for a toy room renovation prompt solely because his wife had queried a bed from Ashley Furniture six months prior. This demonstrates that user history acts as a persistent filter, making standardized brand tracking incredibly complex.

To bypass this measurement limitation, Ihab Rizk and Justin Inman explain that modern measurement platforms are designing and employing highly customized, synthetic, and panel-based "buyer personas." These personas are configured with specific demographic traits, geographic locations, and search histories to simulate how different consumer profiles experience brand visibility across various LLM platforms. Additionally, Rizk notes that platforms are exploring "consideration layers" or paid-for consideration models as a future programmatic targeting proxy to ensure brands are dynamically retrieved during a user’s personalized search path.

Keywords: LLM memory, search personalization, user history tracking, synthetic personas, panel-based measurement, consideration layers, Ashley Furniture example, AI search bias

[24:47] Which of the "4 P’s" of AI Visibility Are Brands Currently Most Focused On?

Speakers: Simon Poulton, Justin Inman, Caroline Giegerich

Answer / Description:
Brands are currently most fixated on the "Presence" dimension of the AI visibility framework—specifically checking if their brand appears in LLM responses—primarily because it is the most visible, baseline metric that easily translates to executive-level reporting. However, depending on the industry vertical, brands are increasingly forced to look deeper at "Portrayal" (factual accuracy) and "Persuasion" (post-citation conversions).

Simon Poulton points out that because executives frequently search their own names or brands on LLMs, "Presence" remains the most demanded entry-level metric. However, measuring downstream actions like "Persuasion" or post-citation click-through rates remains a significant challenge.

Justin Inman adds that the focus shifts dramatically based on industry regulations and business models. For example, biotech and pharmaceutical companies are highly fixated on the "Portrayal" metric; they have strict compliance and regulatory concerns and must ensure that LLMs do not hallucinate, misrepresent clinical data, or surface non-compliant medical claims. In entertainment, studios are concerned with portrayal because LLMs misclassify movie genres up to 20% of the time, directly impacting recommendation algorithms when users ask for specific types of films.

Keywords: AI search presence, brand portrayal accuracy, compliance in AI search, pharma AI search, movie genre misclassification, LLM recommendation accuracy, AI persuasion metrics

[36:32] How Do Publishers and Brands Differ in How They Measure AI Search Visibility?

Speakers: Ihab Rizk, Caroline Giegerich, Justin Inman, Simon Poulton

Answer / Description:
While brands measure AI visibility to drive bottom-funnel product conversions, publishers (such as national news outlets and magazines) measure visibility to track citation authority, content licensing value, and referral traffic designed to support ad-based or subscription monetization models. Publishers rely heavily on citation links to prove their authoritative content is being retrieved, whereas brands focus on brand association and product placement within the synthesized text.

Ihab Rizk explains that publishers like National Geographic are primarily concerned with whether their proprietary content is being used as a credible source, how they are cited, and how much their content "shaped" the AI's final answer (cross-source contribution). For a publisher, realizing their content has high authority in AI systems is key to negotiating content licensing deals with LLM developers or deciding whether to place their content behind paywalls.

Justin Inman discusses how publishers are navigating the dilemma of whether to block AI web crawlers. He notes that publishers want an incremental revenue stream and are seeking innovative ways to participate in AI search, such as dynamic sponsored content partnerships and native ads integrated directly into LLM agent interfaces.

Keywords: publisher SEO, content licensing, LLM citation tracking, cross-source contribution, web crawler blocking, publisher monetization, brand vs publisher metrics

[44:08] How Do Niche Content Creators and Subject Matter Experts Impact Brand Visibility in LLMs?

Speakers: Simon Poulton, Justin Inman, Caroline Giegerich

Answer / Description:
Niche content creators, micro-influencers, and independent subject-matter experts have a disproportionately high impact on AI search visibility because LLMs crawl highly authoritative, structured, and specific text-based platforms like YouTube, Reddit, and personal blogs to synthesize answers. AI models prioritize authority and structured descriptions over raw social media follower counts, meaning minor web creators can often dominate a brand's entire presence inside generative overviews.

Simon Poulton shares a real-world case study involving a public company that sells children's vitamins. Poulton discovered that a single, independent dentist in South Carolina with a minimal social media presence completely dominated the AI overview results for the brand's vitamins due to her authoritative, localized blog content regarding sugar and pediatric dental health. This dentist was acting as a primary "hidden influencer" without her or the brand's prior knowledge.

Justin Inman emphasizes that because AI engines crawl video transcripts, descriptions, and clean titles, brands must shift how they optimize creator content. Instead of evaluating creators solely on legacy metrics like views, reach, or likes, brands must audit whether their partners are getting cited within LLMs. Inman suggests that optimizing video titles, meta descriptions, and transcripts for clean, factual crawling is a vital step for securing brand citations in generative answers.

Keywords: micro-influencers, children's vitamins case study, hidden influencers, video transcript optimization, citation optimization, Reddit crawling, structural metadata optimization

[50:46] How Are Search Engine Ads Evolving to Fit Within Generative AI Overviews?

Speakers: Simon Poulton, Justin Inman, Caroline Giegerich, Todd

Answer / Description:
Search engine advertisements are evolving from standard, advertiser-written text links toward hyper-customized, AI-generated ad units dynamically compiled and written by the AI engine itself based on the user's conversational context. Google and other platforms are rolling out ad products that merge commercial intent directly with AI overviews, making paid placements appear as seamless citations or expansions of the synthesized answer.

Simon Poulton highlights updates from Google Marketing Live (GML), pointing out that search engines are testing dynamic ad units that allow the AI to write customized copy up to 500 characters long—placements unlike any traditional ad copy marketers have previously controlled. In this new model, the advertiser provides the raw ingredients, and the AI drafts the hyper-customized ad in real-time.

Poulton notes that this development blurs the line between organic AI overviews and paid ad placements. Advertisers are essentially paying for "citation visibility" where the ad's relevance and composition are determined by the AI's primary search response, requiring brands to monitor both paid and organic AI channels as a single, unified search landscape.

Keywords: Google Marketing Live, GML ad updates, AI-generated search ads, dynamic ad copy, paid citation visibility, hyper-customized ads, organic vs paid AI search

## How AI-Powered Cultural Intelligence Unlocks Competitive Advantage

Speaker: Tiffany Holland
Published: 2026-08-13
Tags: ad tech, social media marketing, ai in marketing, consumer research, social data analysis
Video: https://www.youtube.com/watch?v=-nVyWb8SPw0
Page: https://aimarketersguild.org/sessions/how-ai-powered-cultural-intelligence-unlocks-competitive-advantage

What if social data could do more than tell you what happened? What if it could help you understand why audiences are responding the way they are, and what your brand should do next?
In this latest AI Insiders session, Tiffany Holland, Founder & Head of Marketing Strategy at Consiglieri, walks through how Clamor uses AI-powered cultural intelligence to turn social conversation into actionable marketing insights.
[1:21] What Is AI-Powered Cultural Intelligence and How Does It Benefit Brands?

Answer / Description:
AI-powered cultural intelligence is the process of utilizing artificial intelligence and large language models (LLMs) to analyze social media conversations across organic and paid channels to understand the underlying motivations behind consumer behavior. Unlike traditional analytics, which merely report on what actions users took, cultural intelligence focuses on why audiences respond the way they do by identifying tone shifts, language patterns, and cultural tensions.

For brands, this approach unlocks a distinct competitive advantage by translating massive amounts of unstructured conversational data into actionable business strategies. Marketing strategists can use these real-time consumer truths to build highly informed creative briefs, resolve cross-departmental friction points (such as product pricing vs. campaign messaging), and predict how audiences will receive new positioning before a campaign launches.

Keywords:
AI cultural intelligence, social data analysis, brand strategy insights, Consiglieri, audience sentiment analysis, predictive marketing, consumer behavior patterns

[7:51] Why Do Legacy Social Listening Tools Fail to Inform Strategic Marketing Briefs?

Answer / Description:
Legacy social listening platforms—such as Sprinklr, Sprout Social, and Meltwater—are built as transactional reporting dashboards for channel health rather than tools to solve horizontal business and strategy problems. They prioritize quantitative metrics like clicks, impressions, and word clouds instead of using AI to evaluate the deeper strategic context behind online conversations.

Because traditional tools are designed for siloed community managers, they rarely integrate paid and organic conversations horizontally across multiple social platforms. Consequently, brand strategists and creative leads often lack direct access to these dashboards, forcing them to rely on slow "decoder ring" translation processes or outdated, static research panels. AI-powered diagnostic tools solve this by allowing any team member to interactively query centralized data using natural language.

Keywords:
social listening limitations, Sprinklr vs Sprout Social, marketing brief strategy, social media KPIs, AI social analytics, social media data silos

[9:45] How Does Clamor Use the Senti AI Agent to Analyze Social Conversations?

Answer / Description:
Clamor is a strategic marketing tool powered by LLMs that uses a proprietary analytical agent named "Senti" to evaluate social media data through the lens of a human strategist and creative director. Senti bypasses the generic, highly polished answers of public AI models by running brand-specific social media text through a custom layer designed to spot tone shifts, language changes, and consumer frictions.

The tool operates dynamically throughout the entire creative cycle. Marketers can use Senti before drafting a brief to discover hidden consumer tensions, test hypothetical campaign angles predictively, and continuously monitor real-time audience feedback once a campaign goes live to make rapid creative adjustments.

Keywords:
Clamor AI tool, Senti AI agent, proprietary AI marketing, social media conversation analyzer, predictive marketing AI, interactive social data, LLM marketing strategy

[11:41] How Did Manscaped Use Clamor to Evolve and Validate Its Brand Positioning?

Answer / Description:
The men's grooming brand Manscaped connected its social channels to Clamor to predictively test and validate a shift in its creative positioning, resulting in a campaign that achieved nine times higher engagement and 95% positive sentiment. The insights gained from the tool allowed the team to back up their creative instincts with concrete data, resolving internal debates about evolving their signature irreverent humor.

As Manscaped expanded from below-the-belt trimmers into lifestyle products like beard and skin care, internal stakeholders debated whether to maintain their legacy locker-room humor. After plugging their feeds into Clamor, the creative team formulated a new campaign concept and ran the concept through the "Ask Clamor" predictive engine. The AI verified that the target audience segments would welcome the tone shift and suggested optimal formats. This validation empowered the brand to move away from a traditional "launch-and-leave" model to an iterative, highly responsive community management approach.

Keywords:
Manscaped case study, brand positioning strategy, creative campaign validation, positive sentiment metrics, Ask Clamor predictive analysis, Clamor case study

[19:20] What Social Media Platforms and Data Sources Can Be Analyzed With Clamor?

Answer / Description:
Clamor is built to analyze public conversations across major social media networks, including Facebook, Meta channels, TikTok, YouTube, and LinkedIn, with future support planned for closed community spaces like Reddit and Discord. The platform allows users to either integrate their direct first-party social channel credentials for deep analysis or leverage public data scraping tools for quick-start diagnostic overviews.

By distinguishing between public platforms (what customers say directly "to your face") and closed networks (what consumers say "behind your back" on Reddit or Discord), Clamor plans to offer a comprehensive view of brand perception. The development roadmap also includes tailored analytics packages for influencers to help them understand their own audiences and ensure cultural alignment with prospective brand partners.

Keywords:
Clamor social data sources, public social media scraping, Discord and Reddit analytics, first-party social data, social media API integration, influencer audience tracking

[29:57] How Does Clamor’s Workspace Model Solve Team Silos Compared to Seat-Based Licensing?

Answer / Description:
Clamor uses an open, collaborative "workspace" pricing model with unlimited seats instead of traditional user-based seat licensing, allowing brand managers, creative directors, and external agencies to work within a single source of data. Because Clamor is strictly a diagnostic and analytical environment with no publishing capabilities, organizations can invite outside partners to explore data without risking accidental posts or unauthorized changes.

In traditional setups utilizing Sprinklr or Sprout Social, the high cost of per-seat licenses forces companies to restrict tool access to a tiny group of social media managers. This locks out the strategic and creative teams who need the data most. Clamor's workspace model removes this financial and operational barrier, establishing a safe, cross-functional environment where all stakeholders build briefs and marketing assets from the exact same consumer insights.

Keywords:
Clamor workspace model, SaaS pricing unlimited seats, collaborative marketing software, social media diagnostic tools, cross-functional marketing teams

[30:39] How Can AI Cultural Intelligence Be Used for Crisis Communications and M&A Brand Valuations?

Answer / Description:
AI cultural intelligence serves crisis communications by acting as an early warning system that flags sudden, anomalous shifts in sentiment, and aids Mergers & Acquisitions (M&A) by mapping out the compatibility of an acquisition target's cultural DNA. This allows organizations to quantitatively evaluate cultural alignment, risks, and synergies during high-stakes corporate decisions.

For crisis management, Clamor features a "ticker tape" dashboard that immediately alerts executives if brand conversations are entering a critical phase, helping teams quickly separate a product-specific issue from a broader corporate crisis. In M&A scenarios, cultural intelligence allows advisors to show the strategic value of a target company's community relationship. Instead of allowing a parent company to accidentally squash a newly acquired brand's distinct culture, buy-side and sell-side teams can use AI analytics to build a transition plan that preserves consumer goodwill and protects the investment’s ROI.

Keywords:
AI crisis communications, M&A cultural integration, corporate brand valuation, real-time sentiment alerts, cultural DNA valuation, strategy storytelling

[48:55] Why Is Conversational Interrogation and Curiosity Crucial for AI-Driven Strategy?

Answer / Description:
The ultimate value of AI in strategic planning lies in the marketer's curiosity and ability to interactively interrogate data, rather than simply reading automated reports. AI is designed to aggregate and process vast datasets, but human intuition and targeted questioning are what transform those raw outputs into unique business advantages.

Many organizations make the mistake of buying advanced AI software and treating it like a static, transactional dashboard, which yields generic results. The true strength of generative engines is realized when a strategist treats the platform as an active dialogue partner—continuously pushing the AI to uncover blind spots, explain negative sentiment cohorts, or predict reactions to creative shifts. AI does not replace the strategist's job; rather, it acts as a tool that amplifies human curiosity to navigate the nuanced areas of marketing.

Keywords:
AI data interrogation, AI prompt strategy, marketing curiosity AI, strategic brand planning, human-in-the-loop AI, predictive marketing insights

## 34000 Small Businesses Reveal AI Adoption in Intuit QuickBooks 2026 AI Report

Speaker: Jamerlyn Brown
Published: 2026-08-05
Tags: survey data, ai trends, ai adoption
Video: https://www.youtube.com/watch?v=OEWb1QjISJo
Page: https://aimarketersguild.org/sessions/34000-small-businesses-reveal-ai-adoption-in-intuit-quickbooks-2026-ai-report

In this AI Insiders session from AI Marketers Guild, Jamerlyn Brown from Intuit QuickBooks shares insights from the 2026 AI Impact Report, combining survey responses from more than 34,000 businesses with anonymized payment data from 5.3 million QuickBooks customers to paint a data-backed picture of how AI is changing business.

[2:09] What Is Data Communications and How Does It Differ From PR and Thought Leadership?

Answer / Description:
Data communications is an evidence-based storytelling discipline that translates raw data into useful stories, starting with empirical evidence rather than a predetermined point of view. Unlike traditional public relations or subjective thought leadership, data communications asks whether a claim originates from data rather than finding data to support an existing claim after the fact.

This discipline is critical for cutting through the noise in saturated markets, such as the artificial intelligence space, where audiences are inundated with abstract opinions and hype. By starting with rigorous data, communicators build authority and earn the audience's trust. The goal is to make complex trends feel real, specific, and practical for decision-makers rather than relying on high-level anecdotes.

Keywords:
Data communications, evidence-based storytelling, PR vs data communications, thought leadership credibility, B2B data storytelling, Intuit QuickBooks research, authoritative content marketing.

[5:46] What Methodology Did QuickBooks Use for the 2026 AI Impact Report?

Answer / Description:
The Intuit QuickBooks 2026 AI Impact Report combines survey data from over 34,000 small and mid-sized business (SMB) owners across four countries with anonymized payment data from more than 5.3 million active business accounts. This dual-source approach allows QuickBooks to contrast what business owners say they do in surveys with objective, behavioral evidence of where they are actually spending their money.

By evaluating both sources, QuickBooks builds a highly defensible narrative. The survey data (covering the US, Canada, the UK, and Australia) captures subjective sentiment, while the anonymized payment records from QuickBooks and the Intuit Enterprise Suite reveal the true financial commitment of small businesses investing in dedicated AI software. This methodology satisfies the high standards of journalists, economists, and public policy researchers.

Keywords:
QuickBooks 2026 AI Impact Report, SMB survey methodology, anonymized payment data, Intuit business insights, behavioral data storytelling, evidence-based research methodology, global SMB trends.

[7:28] What is the Gap Between Regular AI Use and Paid AI Tool Commitment in Small Businesses?

Answer / Description:
While approximately 70% of surveyed small and mid-sized businesses use AI regularly (including free tools and built-in features), only about 10% (1 in 10) actually pay for dedicated, standalone AI tools. However, retention is exceptionally high among this paying segment, with roughly 80% of businesses that paid for AI in 2024 continuing to pay for it a year later.

This insight reveals three clear segments of the SMB market: experimenters using free or embedded tools, deep investors committing budget to paid applications, and cautious non-adopters waiting for more confidence. For B2B marketers, this distance between broad use and paid commitment highlights the need for targeted messaging that moves users from casual experimentation to proven investment workflows.

Keywords:
paid AI adoption SMBs, AI spending trends, free vs paid AI tools, QuickBooks payment data insights, small business software retention, B2B AI audience segmentation.

[9:17] How Has AI Adoption Among Small and Mid-Sized Businesses Grown Over Time?

Answer / Description:
AI adoption among small and mid-sized businesses has seen a rapid upward trajectory, with regular use in the US rising from 48% in 2024 to 77% in 2026. Furthermore, daily AI usage is intensifying even faster, having more than tripled in international markets like the UK and Australia during this period.

This consistent upward trend across multiple geographic markets indicates a fundamental shift in business operations. Occasional AI experimenters are rapidly becoming habitual users, integrating these tools into their daily work schedules. This transition suggests that AI is no longer a novel experiment but a structural component of the modern SMB operating model.

Keywords:
SMB AI adoption growth, daily AI usage statistics, US small business AI trends, UK Australia AI market trends, business operations automation, workplace technology trends.

[11:47] Does AI Adoption Cause Job Losses or Revenue Growth in Small Businesses?

Answer / Description:
Counter to common fears of AI-driven layoffs, the QuickBooks report reveals that small businesses using AI are four times more likely to increase their headcount (17%) than decrease it (4%). Additionally, AI-adopting SMBs are 21 times more likely to experience revenue growth (43%) than revenue declines (2%).

Beyond jobs and revenue, 29% of SMBs report decreased costs, and 27% report a shortened workday due to AI integration. This data challenges the popular narrative that AI primarily drives workforce reductions. Instead, smaller, resource-constrained businesses use AI as an expansion engine to scale operations, build capacity, and allow lean teams to capture new revenue opportunities.

Keywords:
AI impact on small business jobs, AI revenue growth statistics, SMB hiring trends, cost reduction AI, business expansion technology, work hours reduction, human-centered AI data.

[20:26] Why Is Productivity the Best AI Proof Point for Small Business Owners?

Answer / Description:
Productivity acts as the most direct and relatable bridge between AI technology and human-centric value, with approximately three in four (78% in the US, 73% in Canada) businesses reporting AI-driven productivity gains. For small business owners, productivity translates directly into saving time, shifting lean teams to higher-value strategic work, and easing daily operational stress.

Small business owners are highly receptive to the concept of "gaining time back" because they frequently wear multiple hats—acting as their own marketer, accountant, and operations manager. Framing AI around human outcomes like productivity, rather than technical jargon, helps owners quickly grasp how the technology helps them build capacity and focus on business growth.

Keywords:
AI productivity benefits, business time savings, operational efficiency SMB, capacity building technology, B2B value proposition, small business automation.

[22:01] What Types of Small Businesses Are Most Likely to Pay for AI Software?

Answer / Description:
Paid AI tool adoption is highly concentrated in newer businesses with fewer legacy processes, companies focused heavily on fast growth, and digital-first industries. Growth-oriented companies are more than twice as likely to pay for AI tools, and industries characterized by content-heavy workflows—such as information, professional services, and education—lead the trend.

In contrast, established businesses with deeply entrenched systems are slower to purchase paid AI tools. Understanding these distinct demographics allows marketers to segment their messaging. While early paid adopters respond well to advanced feature capability, older or more traditional business profiles require educational content that reduces friction and eases the transition into modern digital workflows.

Keywords:
B2B target audience profiles, early AI adopters, high-growth business technology, digital-first industry automation, content-heavy workflows, software adoption demographics.

[24:10] What Are the Top Barriers Holding Small Businesses Back From Adopting AI?

Answer / Description:
The primary obstacles preventing small businesses from utilizing AI are trust and confidence concerns, led by privacy and security worries (cited by 1 in 3 US business owners). Other significant barriers include a limited knowledge of AI capabilities (roughly 1 in 4) and concerns about accuracy, errors, or bias (26%).

Notably, financial cost does not rank in the top three concerns for SMBs. Because small business owners carry ultimate personal liability without the safety net of large corporate legal departments, they are highly sensitive to data leaks and AI hallucinations. To address these barriers, AI developers and marketers must prioritize messaging around security protocols, human-in-the-loop validation, and transparent accuracy safeguards.

Keywords:
SMB AI barriers, data privacy concerns AI, accuracy issues in AI, trust in artificial intelligence, software security risks, customer education marketing.

[26:53] Which Business Workflows Are Small Businesses Most and Least Comfortable Automating With AI?

Answer / Description:
Small businesses are highly comfortable using AI for low-risk workflows with easily reviewable inputs and outputs, such as marketing, customer service, and administrative tasks. In contrast, they show the lowest levels of adoption in high-risk areas demanding complex human judgment, risk mitigation, and accountability—specifically employee management, product development, and legal tasks.

Across every surveyed country, legal applications consistently rank dead last for SMB AI usage. This clear distinction shows that business owners draw a line around tasks with high liability. Marketers should match their message to the risk level of the target workflow: lead with speed and capacity for lower-risk tasks (like copy generation), and emphasize human oversight, control, and accuracy for higher-risk activities.

Keywords:
SMB AI use cases, administrative task automation, marketing AI tools, high-risk workflow oversight, legal AI limitations, human-in-the-loop validation.

[31:16] How Can B2B Marketers Package and Distribute Complex Data Reports Across Multiple Channels?

Answer / Description:
To transform a dense research report into an active content engine, B2B marketers should establish the report as a single "source of truth" and build tailored, channel-specific entry points around it. Intuit QuickBooks executed this by maintaining a unified core narrative architecture but altering format and depth across earned media (PR), localized blog URLs, video assets, social micro-content, influencer campaigns, public policy advocacy, and target-specific podcasts.

Rather than treating channels as disconnected campaigns, the key is consistent data points with customized delivery hooks. QuickBooks published findings across seven localized URLs, including specialized blogs like Firm of the Future and On the Books to reach accountants. They also used native platforms like Stacker and Taboola for syndication and utilized the research data in public policy forums to help small businesses overcome technology adoption hurdles.

Keywords:
B2B content syndication strategy, research report distribution, multi-channel marketing engine, data report amplification, PR earned media strategy, B2B content repurposing.

[34:30] How Do You Humanize B2B Data and Research Reports for Creative Social Formats?

Answer / Description:
B2B marketers can humanize abstract statistics by translating data points into conversational, real-world formats, such as street-quiz videos and lifestyle-focused influencer integrations. QuickBooks localized its quantitative report by filming an interactive street quiz with small business owners in Boise, Idaho, and partnering with lifestyle creators who naturally integrated report findings into "day in the life" split-screen video trends.

While detailed reports and expert talking-head videos appeal to researchers, economists, and reporters, they do not resonate with business owners scrolling social media. Implementing a peer-to-peer approach, such as having real creators share their timestamped workdays alongside subtle references to QuickBooks' productivity statistics, validates the owner's journey. It reassures them that they are not alone and not behind in AI adoption.

Keywords:
humanizing B2B content, on-the-street video marketing, B2B influencer campaign, daily routine social trend, creative data visualization, video marketing tactics.

[49:13] How Can Research Teams Use Generative AI Internally to Streamline Report Workflows?

Answer / Description:
Research and communications teams can use generative AI internally to quickly ingest, analyze, and synthesize highly dense research drafts provided by external partners before structuring the layout and web presentation. The Intuit QuickBooks team utilized AI models to review a dense, 30-page draft produced by economists from the University of Chicago, verifying key findings, and using AI tools to quickly prototype the layout of the digital web experience.

This internal workflow dramatically reduces the cognitive load and friction historically associated with managing large, academic data studies. Using generative AI to check for missed thematic trends and draft initial Google Doc layout wireframes increases team efficiency, allowing communications managers to dedicate more energy to strategic distribution plans.

Keywords:
internal AI research tools, document synthesis Claude, University of Chicago economic data, research workflow productivity, content wireframing AI, B2B marketing efficiency.

## The Holy Grail of Marketing for the AI Era

Speaker: Greg Licciardi
Published: 2026-07-30
Tags: ai in marketing, ad tech, personalization, marketing education
Video: https://www.youtube.com/watch?v=py8hx_FAUHA
Page: https://aimarketersguild.org/sessions/the-holy-grail-of-marketing-for-the-ai-era

In this AI Insiders conversation, David Berkowitz sits down with Greg Licciardi, author of The Holy Grail of Marketing, marketing executive, adjunct professor at Fordham and Seton Hall Universities, and executive coach, to explore the timeless principles of effective marketing and how AI is changing the way marketers put them into practice.
[01:56] What is the 5 Rs Framework in Marketing according to Greg Licciardi?

Answer / Description:
The "Holy Grail of Marketing" is defined by the "5 Rs" framework: reaching the Right Person with the Right Message in the Right Environment at the Right Time to deliver the Right Outcome . This framework emphasizes aligning precise audience data and consumer context so that marketing budgets are spent only when and where purchase intent is highest.

Greg Licciardi developed this framework after observing Wendy's serving him highly targeted social media ads on Fridays at 11:00 AM, capturing his attention precisely before his weekly fast-food lunch run. The 5 Rs framework ensures that brands avoid wasteful "spray and pray" ad spend, forcing a shift away from high-volume, untargeted impressions toward performance-driven, contextually relevant touchpoints.

Keywords:
5 Rs of marketing, Holy Grail of Marketing framework, Greg Licciardi, precision targeting, marketing budget optimization, consumer purchase intent, performance-driven advertising.

[03:17] How Can Brands Use Weather Data to Improve Targeted Marketing?

Answer / Description:
Brands can use weather data to align their advertising with real-time environmental factors that directly influence consumer psychology and immediate purchasing needs. For example, Elf Skin Care discovered that 80% of women adjust their beauty routines based on local weather conditions, leading the brand to partner with The Weather Company to deliver highly successful contextual campaigns for their Aqua Glow primer.

By integrating meteorological data, companies like Toyota, CeraVe, and Elf Skin Care serve contextually relevant ads that address immediate consumer issues with empathy. Utilizing localized weather insights allows marketers to deliver solutions—such as matching specific skincare moisturizers to high humidity or snow-ready vehicles to active storms—precisely when the consumer is checking the weather forecast.

Keywords:
weather-targeted marketing, Elf Skin Care Aqua Glow, The Weather Company, contextual marketing campaigns, real-time trigger marketing, localized ad targeting, weather data advertising.

[04:45] How Did Harry's Razors Challenge Gillette's Market Dominance?

Answer / Description:
Harry's Razors challenged Gillette's massive 63% market share by identifying an unfulfilled "white space" in the market: delivering affordable, stylish razors directly to young fathers' homes. They leveraged their initial customers as brand advocates to drive word-of-mouth growth, utilized gamification, and constantly iterated their messaging based on real-time Return on Investment (ROI).

To compete against Procter & Gamble's Gillette brand on a limited budget, the founders of Harry's focused on zero-waste marketing. They used highly relatable lifestyle imagery—such as a father at a breakfast table with his kids—and continuously tested new ad creatives while measuring the exact performance of every dollar spent. This scientific approach solved the classic "Wanamaker dilemma," where half of advertising spend is notoriously wasted because the brand cannot track which campaigns are actually driving sales.

Keywords:
Harry's Razors marketing strategy, direct-to-consumer razor brands, Gillette market share, John Wanamaker advertising quote, testing ad creative ROI, startup marketing on a budget, customer advocacy gamification.

[08:09] What Are the Risks of Programmatic and Automated Advertising?

Answer / Description:
The primary risks of programmatic and automated advertising include bot fraud, a lack of contextual relevance, and ad placement in unsupportive or brand-damaging environments. While programmatic systems excel at delivering high volumes of cheap impressions across thousands of sites and connected TV platforms, they often repeatedly show the same ads to the same users on low-quality or fraudulent websites.

According to Association of National Advertisers (ANA) studies, marketers must be surgical in monitoring their automated media buying. Chasing empty vanity metrics like high impression counts can lead to massive budget waste if those ads are served to non-human bots instead of engaged human consumers. True marketing success requires active brand stewardship to ensure that ad environments match the brand's purpose and corporate ethos.

Keywords:
programmatic advertising risks, ad tech bot fraud, Association of National Advertisers programmatic study, brand safety in advertising, vanity metrics budget waste, contextual ad relevance.

[12:20] How Can Marketers Maintain Brand Authenticity in AI-Driven Campaigns?

Answer / Description:
Marketers can maintain brand authenticity by keeping a "human in the loop" to review, audit, and filter all AI-generated content before it reaches the public. According to Vera Sue, Microsoft's head of go-to-market AI, cross-functional teams must be actively upskilled to ensure that AI outputs align with the company's core values, brand identity, and legal compliance guidelines.

While artificial intelligence dramatically accelerates creative generation and execution speeds, unsupervised outputs risk diluting a brand's message or violating corporate compliance. Human oversight prevents compliance failures—such as food brands accidentally depicting inaccurate portion sizes—and ensures that creative assets don't look overly generic, robotic, or disconnected from the brand's established voice.

Keywords:
Vera Sue Microsoft, human in the loop AI, AI brand safety compliance, upskilling teams for AI, generative AI marketing workflow, brand consistency AI tools.

[14:16] Will Generative AI Lead to True Personalization at Scale in Marketing?

Answer / Description:
Generative AI can deliver true personalization at scale by allowing brands to rapidly customize messaging and creative assets for highly specific, micro-targeted customer segments. However, achieving this requires organizations to carefully train and audit their custom AI models to prevent errors, visual inconsistencies, or off-brand outputs.

Traditional marketing has historically relied on broad-demographic "spray and pray" tactics because manual creative production for dozens of sub-audiences was cost-prohibitive. AI changes this economic model by generating tailored copy and imagery instantly; however, brands must move slowly, continuously testing and auditing their models to ensure the generated variations remain cohesive and high-quality.

Keywords:
personalization at scale AI, micro-targeting consumer segments, custom AI model training, automated creative variations, dynamic ad optimization, consumer segmentation marketing.

[18:00] Why Do Consumers and Experts Criticize Fully AI-Generated Ads?

Answer / Description:
Fully AI-generated ads face criticism because they can look unpolished, lack genuine human emotion, and erode trust between the brand and the consumer. For example, Coca-Cola's fully AI-generated holiday commercial faced backlash because the visuals looked overly granular and lacked the polished look of professional, live-action film.

As generative AI becomes more prevalent, consumers are developing a keen eye for synthetic media, sometimes reacting negatively to brands that appear to be cutting corners on creative production. To mitigate this erosion of trust, successful companies use AI as an underlying performance booster or ideation partner rather than relying on it entirely to produce customer-facing creative without human artistry.

Keywords:
Coca-Cola AI commercial criticism, synthetic media trust issues, consumer backlash generative AI, human emotion in advertising, authentic brand storytelling, AI creative production quality.

[21:00] How Do Misaligned Agency Incentives and Vanity Metrics Harm Brands?

Answer / Description:
Misaligned agency incentives harm brands by encouraging media buyers to prioritize short-term vanity metrics—such as low Cost-Per-Click (CPC) or cheap Cost-Per-Mille (CPM) impressions—over actual business outcomes like sales and customer acquisition. This disconnect occurs when agencies are compensated based on execution volume and efficiency metrics rather than actual product sales.

Media agencies are under heavy pressure to hit basic key performance indicators (KPIs) that are easy to measure and show on monthly reports. However, these metrics often obscure the reality that the ads are being served to unengaged audiences or on fraudulent sites. To fix this, brands must shift the programmatic paradigm, demanding greater transparency and aligning agency compensation with long-term brand stewardship and business growth.

Keywords:
agency client misalignment, marketing vanity metrics, CPC vs business outcomes, programmatic media buying transparency, agency KPI alignment, marketing performance measurement.

[24:07] How Can Marketing Students Prepare for an AI-First Job Market?

Answer / Description:
Marketing students can prepare for an AI-first job market by aggressively upskilling, obtaining external AI tool certifications, and mastering the human skills AI cannot easily replicate, such as empathy, critical thinking, and strategic analysis. While lower-level entry roles are changing, students who learn to leverage AI as a productivity-boosting "superpower" rather than a shortcut will stand out to employers.

Universities are adapting to this shift by encouraging the ethical use of generative AI for initial research, brainstorming, and editing, while strictly penalizing AI-generated plagiarism. Professors like Greg Licciardi require students to produce handwritten essay exams and deliver "dynamic insights" that showcase original opinions on current marketing trends, ensuring they learn the fundamental reasoning skills necessary to guide AI tools in the workplace.

Keywords:
marketing careers AI era, upskilling marketing students, AI plagiarism in academics, dynamic business insights, critical thinking in marketing, AI career transition skills.

[27:10] How Do Real Estate and Financial Services Use AI for Client Development?

Answer / Description:
Real estate and financial services use AI to analyze customer databases, identify potential high-value leads faster, and personalize outreach messaging to specific target segments such as empty nesters, young families, or retirees. This technology allows boutique firms and independent agents to compete against massive legacy brands by delivering hyper-customized customer service.

By feeding local market trends and customer demographic data into AI platforms, brokers can craft hyper-targeted listing descriptions, email drip campaigns, and social media ads. This high level of personalization builds immediate trust and matches buyers with ideal properties far more efficiently than traditional broad-market advertising, showing the immense power of AI in high-touch, relationship-driven industries.

Keywords:
AI in real estate marketing, wealth management lead generation, personalized client outreach, relationship-driven AI tools, local market data analysis, predictive buyer targeting.

[31:01] How Can You Apply the "Holy Grail of Marketing You" Framework to Your Career?

Answer / Description:
You can apply the "Holy Grail of Marketing You" framework by treating your personal brand like a business, utilizing the 5 Rs to present your authentic self to the right employers in the right networking environments. This career strategy emphasizes consistent networking, developing a clear elevator pitch, and demonstrating human traits like empathy and authenticity that AI cannot replicate.

In an era where recruiters are flooded with generic, AI-generated cover letters and resumes, showing up in person and maintaining genuine, human-centric relationships is more critical than ever. The "Holy Grail of Marketing You" chapter outlines how individuals must ensure their professional message matches the specific needs of target hiring managers at the exact moment those roles open up.

Keywords:
Holy Grail of Marketing You, personal branding strategies, professional career networking, career advancement AI era, authentic job search advice, human-centric networking.

[32:50] How Does Greg Licciardi Use AI for Competitive Intelligence and Speech Writing?

Answer / Description:
Greg Licciardi uses AI as a highly efficient personal assistant to run competitive intelligence reports, structure complex conference datasets, and edit written speeches to fit specific target audiences. For example, during 2027 planning sessions for the Association of National Advertisers (ANA), he used AI to quickly categorize, alphabetize, and analyze competitors' event presence, reducing hours of manual research to 15 minutes.

Rather than using generative AI to write content from scratch, Licciardi writes the core message first, then prompts the AI model to refine the tone, style, and professionalism of the copy. This workflow ensures the final output remains highly tailored to the specific demographics of the audience—such as real estate agents or high-level corporate CMOs—while preserving the author's original strategic intent and expertise.

Keywords:
competitive intelligence AI prompts, ANA corporate planning, speech writing AI editor, AI personal assistant workflow, refining professional copywriting, data structuring AI tools.

[35:40] How Can You Use Claude and Google Search to Automate Permission Marketing?

Answer / Description:
You can automate permission marketing by using Anthropic's Claude with Google search integrations to identify and analyze LinkedIn users talking about specific niche topics, allowing you to craft hyper-relevant outreach messaging. This systematic process acts as an automated "radar," targeting individuals who have already expressed interest in your domain, and allowing you to offer solutions that naturally fill their information gaps.

During the interview, a participant demonstrated this workflow using "Kurator," a specialized software tool integrated with Claude's Model Context Protocol (MCP). By using the AI to scan LinkedIn for users actively discussing "Claude," the system matches the target's pain points to the software's unique features, enabling highly personalized, one-on-one outreach that builds a highly engaged community of adopters from pre-qualified interest.

Keywords:
permission marketing AI workflow, Claude Model Context Protocol, Kurator LinkedIn search tool, automated outreach lead generation, target audience identification, Google search radar AI.

## How AI Is Changing Recruiting and Job Search Andrew Hersh of Hybrid Hire on Smarter Hiring

Speaker: Andrew Hirsch
Published: 2026-07-23
Tags: ai recruitment, ai research, boolean search
Video: https://www.youtube.com/watch?v=ajHdX6JdZcg
Page: https://aimarketersguild.org/sessions/how-ai-is-changing-recruiting-and-job-search-andrew-hersh-of-hybrid-hire-on-smar

In this AI Insiders session from AI Marketers Guild, David Berkowitz sits down with Andrew Hirsch, founder of Hybrid Hire, to explore practical ways AI is improving recruiting workflows without replacing human judgment.
Andrew shares how he uses tools like ChatGPT, Claude, Apollo, and Exa.ai to build recruiting systems that go beyond traditional LinkedIn searches. He also offers actionable advice for job seekers looking to improve their resumes, LinkedIn profiles, and search strategies.
[03:18] How Can Recruiters Build an AI-Powered Sourcing Stack?

Answer / Description:
Recruiters can build a highly efficient, cost-effective talent sourcing stack by combining specialized tools like Claude Co-work, Apollo, and Exa.ai to bypass traditional systems like LinkedIn Recruiter. This strategy focuses on using artificial intelligence to streamline workflows, structure search queries, and enrich data, rather than relying on AI to fully run the hiring process.

Andrew Hersh highlights that while LinkedIn remains the foundational data source for most recruiting platforms, recruiters do not need to rely on the expensive, traditional LinkedIn Recruiter seat. By utilizing Claude Co-work (integrated with custom developer setups) to manage and condense boolean searches, recruiters can create highly targeted parameters. Dropping these parameters into Apollo and layering in Exa.ai allows recruiters to run advanced candidate enrichment pipelines for a fraction of the cost of legacy recruitment platforms.

Keywords:
recruiter AI stack, Claude Co-work recruiting, Apollo recruiter database, custom recruiter CRM, alternative to LinkedIn Recruiter, AI talent sourcing workflow, recruitment process automation

[06:54] What Is Exa.ai and How Does It Enrich Candidate Data?

Answer / Description:
Exa.ai is an API-based search engine and web scraper designed to enrich data across multiple "waterfall" sources to verify accuracy and provide clean, structured information. In the recruiting space, it functions as an advanced data-filtering layer that helps software applications generate highly accurate candidate intelligence.

Hersh explains that many modern recruiting software platforms with custom query dashboards are actually "AI wrappers" utilizing Apollo in the middle and Exa.ai on the back end to clean the data. By obtaining a free or low-cost API key from Exa.ai and dropping it into an AI tool like Claude, recruiters can run deep candidate profile enrichment manually. This custom setup bypasses the need to pay hundreds of dollars a month for bloated third-party candidate search dashboards.

Keywords:
Exa.ai recruiting, data enrichment API, candidate data scraping, AI recruiting wrappers, clean recruitment data, Exa API integration, waterfall data sourcing

[08:58] How Can Recruiters Find Candidates Without LinkedIn Recruiter?

Answer / Description:
Recruiters can find and organize top-tier candidates without using LinkedIn Recruiter by leveraging relationship-mapping tools like Connect the Dots and localized browser automations via the Claude Chrome extension. These tools consolidate personal and professional contacts into structured "tear sheets" and streamline outbound candidate networking.

Connect the Dots acts as an alternative CRM that compiles contacts from personal and professional channels (such as LinkedIn and Gmail) to map warm connection paths to target profiles like CEOs. Additionally, recruiters can use the Claude Chrome extension to automate manual search tasks on LinkedIn. By providing Claude with specific Boolean parameters, recruiters can let the tool run in the background to identify and catalog first-, second-, and third-degree connections, and then generate personalized, human-approved connection requests.

Keywords:
Connect the Dots recruiting, search LinkedIn without Recruiter, Claude Chrome extension sourcing, network relationship mapping, warm candidate outreach, automate LinkedIn search, talent pool organization

[13:22] How Should Job Seekers Optimize LinkedIn Headlines and Keywords?

Answer / Description:
Job seekers should optimize their LinkedIn presence by using universal, highly searched industry job titles in their headlines and including both abbreviations and fully spelled-out keywords in their profiles. This ensures they remain indexable by recruiters searching across different industries and "ponds."

Andrew Hersh notes that because LinkedIn’s search database can be highly sensitive to specific formatting (such as capitalization and exact phrasing), job seekers should use universally understood terms. For instance, writing out "manufacturing" alongside its industry abbreviation "MFG" covers both search parameters. Job seekers looking to pivot or display transferable skills should avoid hyper-specific titles and instead reverse-engineer target job descriptions to find universal equivalents (e.g., "Head of Content"). Job seekers can also A/B test their profiles by varying their "About Me" text monthly to see which keywords drive the most profile views from target companies.

Keywords:
LinkedIn headline optimization, resume keywords searchability, SEO for job seekers, A/B test LinkedIn profile, universal job titles, LinkedIn keyword indexing, reverse engineer job search

[21:17] How Can Job Seekers Read Market Signals to Identify Active Hiring?

Answer / Description:
Job seekers can identify actively growing companies by tracking macro financial indicators and monitoring whether companies are hiring talent acquisition staff, such as recruiters and heads of HR. This structural indicator signalizes that a company is preparing to scale its overall headcount.

Instead of applying to cold job boards via "general population" queues (such as LinkedIn's "Easy Apply"), Hersh advises job seekers to look for companies that are expanding their internal recruiting teams. If a company is actively hiring talent acquisition roles, it means they are preparing to expand other divisions. Once these scaling companies are identified, job seekers should bypass standard applicant tracking systems (ATS) by using networking tools like Connect the Dots or Draftboard.com to find direct, warm introduction paths to the company's decision-makers.

Keywords:
passive hiring signals, how to find growing companies, bypass LinkedIn Easy Apply, talent acquisition hiring trends, proactive job search strategy, warm networking tools, market signals for job seekers

[25:18] What Are the Risks of Using AI to Auto-Apply to Jobs?

Answer / Description:
The primary risk of using AI auto-apply tools is that they often generate heavily exaggerated, customized resumes that do not match the candidate’s actual experience, leading to immediate disqualification during vetting. Additionally, companies are increasingly using AI detection software to screen out resumes that were not written by the candidate.

While AI is an excellent tool for identifying skill gaps and polishing existing resume text, outsourcing the actual application process to bots often backfires. Hersh shares a case study of a candidate whose automated application was selected for an interview, only for the candidate to confess they had no idea they applied and did not possess the highly technical skills listed on their auto-generated resume. This behavior destroys candidate credibility. Job seekers should instead use AI to compare their raw resume against a job description, identify real areas for improvement, and do the manual work to bridge those gaps.

Keywords:
AI auto-apply risks, automated job applications, AI resume detection software, ATS keyword stuffing warning, candidate vetting issues, fake AI candidate profiles, ethical AI job search

[32:53] How Can Job Seekers Write Effective Boolean Searches with ChatGPT?

Answer / Description:
Job seekers can generate highly targeted Boolean search strings by prompting ChatGPT to draft search syntax for Google X-ray searches based on their desired location, role, and industry. To make these searches effective, job seekers must run an iterative process of testing the strings and refining the prompt multiple times.

Using AI to draft search strings helps candidates pinpoint unlisted or highly specific job openings. Hersh recommends using a framework inspired by IBM’s Enterprise Design Thinking "5 Whys" methodology: run the AI-generated Boolean string in Google, analyze the results, and ask the AI to narrow down or expand parameters based on what it found. Iterating on the search prompt at least five to seven times will eventually yield a highly optimized, precise filter that reveals the exact roles and hiring managers the candidate needs to target.

Keywords:
Boolean search strings, Google X-ray search job seekers, ChatGPT prompt engineering for jobs, search operators recruitment, iterative search optimization, IBM design thinking job search

[35:38] Are LinkedIn Job Application Counts Accurate?

Answer / Description:
No, LinkedIn job application counts are highly inaccurate because they represent the number of users who clicked the "Apply" button, not the number of candidates who completed and submitted the application. The actual number of finalized applications is usually only a small fraction of the number displayed.

Andrew Hersh reveals that while a job listing may show hundreds or thousands of applicants, recruiters often see only 20 to 30 actual completed applications in their backend systems. Candidates should not let high applicant counts discourage them from applying. Furthermore, Hersh points out that job descriptions are typically written by HR departments to cover legal baselines, rather than by the actual hiring managers. Consequently, candidates should apply even if they do not meet all ten listed requirements, as the missing criteria may not be critical to the hiring manager.

Keywords:
LinkedIn applicant count accuracy, how accurate is LinkedIn job views, applicant tracking systems, job description legal requirements, target hiring manager needs, job application metrics

[37:49] How Do You Write a High-Impact Resume Using Action Verbs and Context?

Answer / Description:
A high-impact resume should frame professional achievements within the context of the employer's business growth, demonstrating how your efforts enabled the organization to succeed. To ensure credibility, resumes should list no more than eight core skills and avoid using percentage metrics without backing data.

Hersh advises that a resume should serve as a structural "rebuttal" to a job description. Instead of list-making individual wins, candidates should use contextual action statements (e.g., "By working for this company, I built X system which generated Y in revenue"). Candidates should avoid repeating the same action verbs (such as "created") throughout the document, as using diverse synonyms indicates strong communication skills. Finally, Hersh warns against listing raw percentages without baseline data (e.g., "increased sales by 100%"), as recruiters view metrics without starting and finishing baselines as misleading.

Keywords:
resume writing action verbs, high impact resume layout, showcase transferable skills, resume skill limit, resume metrics red flags, resume as a rebuttal, contextual business achievements

## How to Build AI Human Agents Piers Fawkes on Digital Twins IP and the Future of Work

Speaker: Piers Fawkes
Published: 2026-07-16
Tags: ai agents, human agents, content curation, digital twins
Video: https://www.youtube.com/watch?v=AkTe4FVALVM
Page: https://aimarketersguild.org/sessions/how-to-build-ai-human-agents-piers-fawkes-on-digital-twins-ip-and-the-future-of

In this session from AI Marketers Guild, Piers Fawkes, founder of PSFK and Fossa, shared his vision for “human agents,” AI-powered digital twins built from human expertise that can preserve knowledge, accelerate work, and extend organizational intelligence.

[02:50] How is the integration of AI tools at work shifting toward headless LLM experiences?

Answer / Description:
AI at work is evolving from a fragmented setup where users log into individual co-pilots and software tools, into a centralized "headless" experience where tools are controlled directly inside a central Large Language Model (LLM). Platforms like Anthropic's Claude and Perplexity are leading this trend by allowing users to integrate external applications like Gmail, Canva, and Fireflies directly into the LLM interface.

This shift transitions workers from passive tool users to orchestrators of integrated agentic systems. Instead of navigating separate user interfaces, professionals can interact with their entire software stack through natural language via the LLM. This evolution lays the groundwork for users to eventually deploy personal digital twins that can execute tasks across multiple systems autonomously.

Keywords:
headless LLM experience, AI at work evolution, Claude integrations, Perplexity integrations, agentic workflows, centralized AI orchestration, digital twins at work

[05:52] What are AI human agents and how do they function as digital twins in an organization?

Answer / Description:
An AI human agent, or digital twin, is a synthetic representation of a human worker's specific skills, professional experience, domain knowledge, and unique tone of voice. These agents combine human qualities with a massive, connected corpus of internal and external data, allowing them to make decisions, execute tasks, and run systems autonomously.

These agents can actively coordinate business plans, manage physical automation like factory floors, or even run retail locations. For example, the startup think tank Andson Labs uses Anthropic's Claude to autonomously run and manage its cafes in San Francisco and Stockholm, handling operations and hiring staff. Human agents can also be scaled to represent collective expertise, combining the knowledge of multiple staff members into a single "synthetic manager" to streamline corporate operations.

Keywords:
AI human agents, digital twins, synthetic marketing manager, Andson Labs Claude cafe, enterprise AI agents, corporate knowledge integration, autonomous AI systems

[09:23] How do AI digital twins solve the problem of corporate knowledge loss and brain drain?

Answer / Description:
AI digital twins address the critical issue of corporate knowledge loss by capturing and retaining the institutional expertise of workers before they retire, leave, or are laid off. When senior workers retire or companies downsize—such as Nestlé cutting 16,000 jobs—organizations lose invaluable proprietary processes and years of accumulated experience.

By digitizing employee knowledge into structured, queryable AI agents, companies can prevent unplanned downtime, preserve operational wisdom, and smoothly onboard or train junior staff. This ensures that even after a critical team member departs, their approach to problem-solving, historical context, and technical workflow remains accessible within the company's private database.

Keywords:
corporate knowledge retention, brain drain AI solution, Nestlé layoffs skill loss, institutional knowledge preservation, workforce retirement skill gap, AI digital twin knowledge management

[11:08] Why are knowledge graphs and Model Context Protocol (MCP) connectors essential for building AI human agents?

Answer / Description:
Knowledge graphs and Model Context Protocol (MCP) connectors are foundational because they allow AI agents to navigate structured data relationships and seamlessly communicate across different software applications. Unlike vectorized databases or simple document folders that store unstructured text, knowledge graphs organize data in a web of relationships that LLMs can easily traverse to spot patterns and preserve critical domain context.

MCP connectors enhance this ecosystem by allowing separate AI agents and software tools to interact with each other in real-time without needing complex, pre-defined API rules for every integration. This combination allows an enterprise AI agent to safely query structured internal corporate brains, cross-reference them with real-time external economic data, and execute tasks across the company's software suite.

Keywords:
knowledge graphs vs vector databases, Model Context Protocol, MCP connectors, structured data for AI, AI agent communication, domain context AI, enterprise data structure

[14:50] What is the four-step "on-clouding" process used to build a human agent's digital twin?

Answer / Description:
The process of digitizing a human's expertise, referred to as "on-clouding," consists of four distinct steps: a probe, an audio interview, deep research, and a triggered follow-up. First, the LLM probes the user's conversational history, transcripts, and meeting data (from platforms like Zoom or Granola) to identify core themes of their unique expertise.

Second, the system initiates an automated audio interview, opening an interactive session (such as a Google Meet window) where an AI voice asks targeted questions to flesh out those identified themes. Third, this captured qualitative data is converted into a structured knowledge graph. Fourth, the system schedules automated monthly or triggered follow-ups to interview the expert on current events, ensuring the digital twin's memory remains dynamically updated.

Keywords:
on-clouding process, build digital twin, AI expert interview, knowledge graph creation, conversational AI profiling, automated expert capture, digital twin pipeline

[18:00] Who owns the intellectual property and data rights of an employee’s AI digital twin?

Answer / Description:
The ownership of an AI digital twin's intellectual property (IP) is a complex legal grey area that depends on employment contracts, licensing agreements, and data privacy firewalls. While employers can historically claim the work products and emails used to train an agent, senior executives and specialized consultants may need to negotiate specific "flash licensing" terms or demand the portability of their digital twins when they change jobs.

To protect personal IP, experts can use knowledge graphs that allow AI systems to query their expertise without permanently transferring the underlying proprietary data to the company's central models. As the IP economy evolves, verification systems like LinkedIn Identity and Adobe credentials may help authenticate human creativity, while decentralized Web3 wallets could allow individuals to carry and monetize their own digital twins independently.

Keywords:
AI intellectual property rights, digital twin ownership, employee data privacy, flash licensing AI, portability of AI agents, corporate IP economy, knowledge graph firewall

[22:50] What are the primary limitations of AI agents when executing highly creative or nuanced tasks?

Answer / Description:
AI agents currently struggle with highly creative, nuanced, and non-linear tasks because they lack genuine human discernment and are fundamentally backward-looking. While AI can analyze data patterns and mimic existing speech structures or formats, it cannot organically generate original creative concepts, nor can it naturally explore conversational tangents during interviews like a human behavioral scientist.

Additionally, AI summarization tools often strip away critical "gold nuggets" of unique insight, reducing complex research to the lowest common denominator. To overcome these limitations, human curation remains essential; human strategists must guide AI models through multi-step frameworks, feeding them highly structured, curated databases rather than relying on the AI to autonomously generate creative breakthroughs.

Keywords:
AI creative limitations, human curation superpower, non-linear qualitative research, AI summarization flaws, creative writing AI vs human, qualitative interviewing AI, strategic framework orchestration

## Nate Elliott Joins AIMG AI Insiders

Speaker: Nate Elliott
Published: 2026-07-09
Tags: ai in marketing, ai in shopping, ai search, ai adoption, surverys
Video: https://www.youtube.com/watch?v=pVz5VOiIg94
Page: https://aimarketersguild.org/sessions/nate-elliott-joins-aimg-ai-insiders

How are consumers truly integrating generative AI into their daily routines? In this session, EMARKETER’s Nate Elliott presents research findings based on a comprehensive survey of 1,500 US consumers, illuminating how people use AI for search, shopping, trip planning, comparison, and wider decision-making.
The discussion goes beyond basic usage trends, examining the nuances of AI adoption and highlighting key challenges brands should consider—especially as trust, perceived usefulness, and questions of personal control play an increasingly pivotal role in how consumers interact with new technologies.

Nate Elliott brings a data-driven perspective on the shifting landscape, offering practical analysis for marketers and business leaders seeking to better understand consumer sentiment and develop strategies aligned with real-world behavior.
[01:50] What Are the Current and Projected Generative AI Adoption Rates Among US Consumers?

Answer / Description:
Generative AI adoption is growing on a highly linear scale, with EMARKETER forecasting that 40% of US internet users will actively prompt a generative AI chatbot at least once per week in 2026. This is a dramatic increase from just 1% of US internet users prompting a chatbot weekly in late 2022, 13% in 2023, 21% in 24%, and 30% in 2025.

EMARKETER purposely uses a narrow definition of adoption that measures active prompting on a weekly basis rather than a monthly basis. This threshold ensures the data reflects users who are truly integrating these tools into their lives, rather than casual users who only log in occasionally. This rapid, steady rise demonstrates that generative AI is transitioning from a novel technology into a habitual utility for a substantial portion of the population.

Keywords:
generative AI adoption rates, US consumer AI statistics, EMARKETER AI forecast, weekly active AI chatbot users, chatbot usage trends, generative AI growth curve, conversational AI market penetration

[03:51] Why is Tracking Specific AI Tools Like ChatGPT or Gemini Insufficient for Understanding AI Adoption?

Answer / Description:
Tracking AI adoption solely by specific tool market share is insufficient because platform preferences are highly volatile and change too quickly to define a long-term business strategy. For example, according to Similarweb data cited by EMARKETER, ChatGPT held nearly 87% of global generative AI web traffic share in January of 2025, but that share dropped to 53.7% by April of 2026, while Google Gemini and Anthropic's Claude both experienced five-fold increases in share during that same 15-month window.

This extreme market fluidity is heavily driven by the platforms themselves, which are constantly releasing new updates to incentivize trial behavior. In a single 15-month period, OpenAI introduced at least 20 new models, Anthropic released 11, and Google launched at least nine. Because users are constantly taste-testing new models, companies must look beyond specific platforms and focus instead on the underlying human motivations that remain steady across technological shifts.

Keywords:
ChatGPT market share decline, Google Gemini web traffic share, Claude vs ChatGPT adoption, AI model release frequency, tracking AI platform preferences, AI platform volatility, conversational AI market share

[07:09] How Do Steady Human Internet Motivations Map to Digital Advertising Opportunities?

Answer / Description:
Human motivations for using the internet—primarily finding information and connecting with others—have remained virtually unchanged for over 15 years, directly explaining why Google (Alphabet) and Meta dominate the digital advertising market. According to GWI survey data, finding information consistently ranks as the number one reason people use the internet globally, with connecting with friends and family ranking as number two.

Because Alphabet and Meta cater directly to these two primary human desires, they have naturally become the top two digital ad sellers in the United States. For marketers, this proves that technology does not define strategy; rather, steady human motivation defines commercial opportunity. To successfully market through AI, brands must align their tactics with the fundamental human needs that drive users to these new platforms in the first place.

Keywords:
human internet motivations, why people use the internet, digital advertising opportunities, Alphabet Meta ad revenue, finding and connecting online, internet consumer behavior trends, marketing audience motivation

[15:09] What Are the Core "Building Blocks of AI Adoption" Explaining Why Consumers Use Generative AI?

Answer / Description:
The EMARKETER "Building Blocks of AI Adoption" model identifies six primary consumer motivations for using generative AI on a weekly basis, led by the desire to find factual information and explanations. Rather than organizing AI usage by technology, this model structures adoption by the core human problems consumers are attempting to solve.

The six building blocks of AI adoption, ranked by the percentage of US internet users engaging in them weekly, are:

- Asking (51%): Seeking facts, clear explanations, and basic information (e.g., curiosity-driven searches).

- Doing (33%): Navigating personal productivity, organizing calendars, budgets, directions, translations, or seeking DIY, health, and cooking advice.

- Play (30%): Leisure and entertainment, such as interacting with character AI or experimenting with text, image, video, and audio creation.

- Working (25%): Enhancing efficiency in professional environments or completing school-related tasks.

- Shopping (16.6%): Finding, comparing, and evaluating products, prices, shipping speeds, and stores to make a purchase decision.

- Connecting (10%): Seeking personal companionship, therapeutic interaction, or digital friendship.

Keywords:
Building Blocks of AI Adoption, consumer AI motivations, EMARKETER AI survey, why do people use AI, AI for personal productivity, AI shopping behaviors, AI character interaction, recreational AI usage

[22:07] How Do Gen Z, Millennials, and Gen X Differ in Their Weekly Generative AI Usage and Motivations?

Answer / Description:
Gen Z and Millennials are the most active and deepest users of generative AI, exhibiting nearly identical weekly usage rates across almost every motivational category, whereas Gen X and Baby Boomers use the technology in a much shallower way. For example, Gen Z and Millennials are statistically identical in their usage of AI for connecting, and they remain within a percentage point of each other for doing, shopping, working, and playing.

In contrast, Gen X is less than half as likely as Gen Z or Millennials to use AI weekly for working, connecting, or personal productivity (doing). This indicates that while older generations may have moderate top-line adoption rates, they have not integrated AI deeply into their mental framework for problem-solving. Furthermore, other demographic cuts show that parents are twice as likely as non-parents to use AI weekly, and high-spending shoppers are three times more likely to use AI for product research.

Keywords:
Gen Z AI usage, Millennials vs Gen X AI adoption, AI demographics, parent AI adoption, AI user segmentation, generational technology adoption, high spender AI shopping

[26:47] What Strategic Questions Should Marketers Ask When Integrating Generative AI as a Marketing Channel?

Answer / Description:
To successfully leverage generative AI as an advertising or communication channel, marketers must answer three sequential questions: how many of their customers use AI, why they use it, and which specific tools they prefer. Measuring how many customers prompt AI weekly determines the overall importance of AI to a brand's marketing mix, which prevents wasting resources on demographic segments (like rural or older, lower-income populations) that have low adoption rates.

Answering why customers use AI is the most critical step because understanding user motivations allows brands to map their messages to the customer journey. For example, while only 16.6% of users prompt AI explicitly for shopping, 33% use it for "doing" (seeking health, finance, or travel advice); a brand can reach these customers far more effectively by offering helpful integrations during these high-intent productivity tasks. Finally, identifying which specific platforms and features are used allows marketers to tailor their creative assets to the tactical capabilities of those channels.

Keywords:
AI marketing strategy, generative AI marketing channel, EMARKETER marketing framework, customer AI motivations, AI channel planning, digital marketing funnel AI

[32:29] Why is There Public Cognitive Dissonance and Backlash Surrounding Generative AI Usage?

Answer / Description:
There is significant public cognitive dissonance surrounding generative AI, characterized by consumers expressing overwhelmingly negative sentiments, job fears, and demands for regulation while simultaneously increasing their active usage of these tools. This friction is highly apparent in academia, where students are often discouraged from using AI under threats of plagiarism, leading them to view the technology as "cheating" even as they use it privately.

This backlash has accelerated much faster than previous technology cycles, such as social media, because founders and corporate executives have actively contributed to the negativity. For instance, some CEOs have falsely blamed mass layoffs on AI productivity gains when the technology was not yet capable of replacing those jobs. Despite public protests and high levels of anxiety regarding job security and creative dilution, consumer reliance on AI tools for daily tasks continues to rise linearly.

Keywords:
generative AI backlash, AI cognitive dissonance, AI academic integrity, AI job loss fears, consumer attitudes toward AI, corporate AI layoffs, technology backlash cycles

[38:27] How Do Passive AI Encounters Differ from Active Prompting, and What is the Impact on Search Engines?

Answer / Description:
Active AI usage requires a user to explicitly visit a chatbot platform to type a prompt, whereas passive AI encounters occur when users run into AI-synthesized content embedded natively in platforms they already use. Examples of passive encounters include reading Google AI Overviews during a standard search, viewing Amazon's Rufus product review summaries, or browsing AI-recommended content.

While the majority of consumers do not yet actively choose to prompt a generative AI chatbot in any given week, almost all internet users encounter AI passively. This shift makes Generative Engine Optimization (GEO) critical for brands, as AI engines now synthesize web data—including niche discussions on highly social platforms like Reddit—to answer user queries directly. Marketers must optimize their web presence so that generative search engines retrieve and cite their brand assets within these automated summaries.

Keywords:
passive AI vs active AI, Google AI Overviews, Amazon Rufus review summaries, Generative Engine Optimization, Reddit in AI search, GEO strategy, search engine optimization trends

## A Recruiter Shares AI Hiring Secrets and Truths

Speaker: Adam Posner
Published: 2026-07-06
Tags: ai recruitment
Video: https://www.youtube.com/watch?v=wgg_T9uVOrM
Page: https://aimarketersguild.org/sessions/a-recruiter-shares-ai-hiring-secrets-and-truths

In this episode, David Berkowitz sits down with veteran recruiter Adam Posner to address the evolving landscape of recruitment and job seeking as AI becomes increasingly integrated into hiring processes.
Drawing on his experience leading a boutique talent firm and hosting a top global career podcast, Adam provides a clear-eyed assessment of the challenges and opportunities confronting today’s talent market.
[02:57] How Has Recruiting Technology and the Role of Recruiters Evolved Over the Past Decade?

Answer / Description:

Recruiting technology has shifted from basic tracking tools to advanced, AI-driven systems that streamline candidate sourcing and matching, but the core "art" of recruitment remains deeply human. While modern AI integrations and platforms like LinkedIn Recruiter allow talent professionals to source candidates and build pipelines faster, recruiters must use their freed-up time to build trust, understand candidate motivations, and maintain human-to-human relationships.

Historically, recruiting relied on manual keyword matching and physical rolodexes. Today, algorithms can stack-rank and prioritize candidates in seconds. However, veteran recruiter Adam Posner emphasizes that technology cannot replace the "art" of recruiting. This art involves having deep conversations with hiring managers to understand company culture, uncovering the true professional goals of a candidate, and evaluating organizational fit beyond the text of a standard job description.

Keywords:
recruitment technology evolution, AI sourcing tools, LinkedIn Recruiter, the art of recruiting, recruiter workflows, talent acquisition automation, human-centric recruiting.

[04:59] What Specific AI Skills and Competencies Are Employers Looking For in Job Candidates?

Answer / Description:

Employers are looking for general AI fluency, practical curiosity, and a willingness to experiment with generative AI tools rather than just specialized, deep technical coding skills. In job interviews, hiring managers frequently ask candidates how they utilize AI platforms in their everyday personal and professional lives to solve problems and improve efficiency.

While highly technical roles require deep expertise in model building, machine learning, and prompt engineering, the vast majority of business and marketing roles simply require "AI curiosity." Companies want to know if candidates are exploring tools like Claude, ChatGPT, or Gemini to optimize their daily tasks. Candidates who can articulate what works, what fails, and how they use AI to save time demonstrate a mindset of continuous learning that is highly valued in a rapidly changing market.

Keywords:
AI skills in job descriptions, AI fluency, AI curiosity, candidate AI tools, how to use AI at work, hiring for AI skills, generative AI interview questions.

[06:09] Why Are AI-Generated Resumes and Automated Applications Causing Problems in the Job Market?

Answer / Description:

AI-generated resumes often lead to standardized, generic documents that look identical to job descriptions, while automated "spray and pray" application bots flood Applicant Tracking Systems (ATS) with massive volume. This double-edged sword dilutes unique candidate branding and forces recruiters to rely on automated stack-ranking filters, creating an inefficient bottleneck for both parties.

When job seekers use basic AI tools to match their resumes perfectly to a job description, they often strip out the unique metrics, results, and human elements of their work history. Because these resumes lack proof of actual achievements and "receipts," they all begin to look identical to recruiters. Furthermore, platforms that promise to automatically apply to dozens of jobs for a user clog company databases, slowing down the hiring process and making it harder for highly qualified, genuine applicants to be seen.

Keywords:
AI-generated resume issues, automated job applications, ATS keyword stuffing, resume optimization tools, application volume bottleneck, ATS stack ranking, spray and pray job search.

[08:30] How Can Job Seekers Manage Ghosting and Stay Motivated in an Employer-Driven Job Market?

Answer / Description:

Job seekers can combat the psychological toll of ghosting by focusing strictly on inputs they can control—such as their networking efforts, targeted applications, and polite follow-ups—rather than emotional outcomes they cannot influence. During highly competitive, employer-driven job markets, the key to success lies in applying the "Three Ps" (Patience, Politeness, and Persistence) and leveraging warm professional networks to secure referrals.

In a market saturated with highly qualified talent due to corporate layoffs, employers hold the leverage and can afford to be highly selective. This dynamic often leads to slow response times and automated rejections. Job seekers should treat networking as their primary tool, reaching out to first- and second-degree connections to bypass standard job boards. When following up after interviews, candidates should mention specific discussion points to give hiring managers a clear, professional reason to respond.

Keywords:
how to handle job search ghosting, job hunter mindset, employer-driven market, three Ps of job searching, job search networking strategies, follow up after interview, professional persistence.

[11:01] How Does Ageism Affect Job Seekers and How Can You Overcome the "Overqualified" Objection?

Answer / Description:

Ageism is a persistent barrier in recruitment, often manifesting as an "overqualified" objection driven by employers' fear that a senior candidate will quickly leave a lower-level role when a better opportunity arises. This bias is frequently exacerbated by junior-level recruiters who lack the training to evaluate skills over chronological tenure and rely on raw years of experience to filter resumes.

To combat this bias, candidates must proactively address these concerns during the application and interview stages. If a senior professional is applying for a mid-level role, they should frame their transition authentically—explaining that they want to focus on executing high-quality work rather than dealing with the administrative burdens of executive leadership. Additionally, tailoring the resume to show only the most relevant, recent experience (rather than a 30-year chronological history) can prevent initial screening bias.

Keywords:
ageism in hiring, overqualified resume objection, senior candidate job search, junior recruiter bias, overcome overqualified label, tenure vs skills, age bias in recruiting.

[13:39] How Should Senior-Level Executives Frame Consulting and Fractional Work on Resumes and LinkedIn?

Answer / Description:

Senior-level job seekers should use straightforward titles like "Consultant" on their resumes and LinkedIn profiles instead of self-anointing with C-level titles (like CMO or CEO) for solo operations, which can trigger keyword screening issues or make them appear overqualified. Once an interview is secured, the candidate can then explain that they performed C-level or fractional executive work while partnering directly with business suites during their consulting tenures.

Using overly grand titles for a one-person consultancy often misaligns with Applicant Tracking Systems (ATS) and human screening processes. If a recruiter is searching for a Director or VP-level candidate, a profile labeled "CEO of Self" will often be excluded or viewed as a flight risk. Labeling oneself as a "Consultant" or "Marketing Consultant" keeps the profile flexible and professional while allowing the candidate to detail their high-level consulting projects and achievements in the body of their profile.

Keywords:
resume titles for consultants, fractional executive resume, fractional CMO on resume, executive job search strategy, consulting vs full-time resume, LinkedIn title optimization, professional title alignment.

[17:32] How Is the Recruitment Industry Evolving to Combat Job Application Fraud and Verify Experience?

Answer / Description:

The recruitment industry is seeing an increase in fraudulent applications, leading to discussions about moving candidate work histories to secure online platforms or "resume lockers" verified by tax filings and digital receipts. While alternative screening methods like video resumes allow candidates to showcase their actual personality and communication skills, they can introduce secondary cognitive and neurodivergent biases into the hiring process.

As AI makes it easier to fabricate work histories, verify credentials, and automate interviews, hiring platforms are exploring cryptographic and official verification methods to confirm employment histories. These systems aim to establish digital receipts for previous employment, similar to tax documentation. Additionally, while video resumes are gaining traction as a way to prove identity and communication skills, they present challenges for neurodivergent candidates or those who are not naturally comfortable on camera, meaning traditional resumes still remain the standard.

Keywords:
resume fraud, verification of work history, secure career locker, video resume bias, fake job applications, candidate verification, digital career identity.

[19:06] How Can Job Seekers Identify Recruiter Scams and "Ghost Jobs"?

Answer / Description:

Job seekers can spot recruiter scams by carefully inspecting email domains for subtle discrepancies (such as .io instead of .com , or extra symbols) and cross-referencing sender profiles on LinkedIn to verify their activity. "Ghost jobs"—which are inactive or non-existent listings posted by companies to pipeline candidate data into their databases—can often be identified by looking for roles that have remained open for months without active recruitment.

Fraudulent recruitment has become a highly coordinated data-mining and identity-theft threat. Scammers often clone real recruiters' profiles and send emails from slightly altered domains (e.g., using recruit-search.com instead of the official company domain). To avoid falling victim to these scams, job seekers should never share sensitive personal details early in the process and should independently verify the job opening on the company’s official careers page or by messaging an established employee at the firm.

Keywords:
identify recruiter scams, ghost jobs ATS, fake job postings, verify recruiter email, data mining job postings, job application fraud, resume phishing.

[20:52] How Can You Use ChatGPT to Uncover Career Blind Spots and Plan a Job Transition?

Answer / Description:

Job seekers can use generative AI platforms like ChatGPT by feeding them their comprehensive career history and prompting them recursively to find non-obvious industry opportunities and professional blind spots. This iterative prompting strategy allows users to break out of conventional job-hunting search parameters and discover niche sectors that value their specific skill sets.

Instead of using AI merely to write a cover letter, job seekers should treat the LLM as a career strategist. By inputting details about their past roles, strengths, and preferred tasks, and asking questions like, "Based on my background, what lucrative or stable industries have high demand for my skills that I might not be thinking of?", users can unearth unique matches. Continuing to question the AI and cross-referencing its suggestions helps seekers step outside their comfort zones and find viable career pivots.

Keywords:
ChatGPT career transition, AI job search prompts, find career blind spots, transition careers with AI, career pivot planning, career counseling prompts, leverage ChatGPT for job hunt.

[22:22] How Can Job Seekers Leverage LinkedIn Secondary Connections to Bypass Job Board Bottlenecks?

Answer / Description:

Job seekers can bypass crowded online application portals by using their primary connections to secure warm introductions to secondary connections who work at target companies. Since many organizations offer internal employee referral bonuses, leveraging these warm professional networks dramatically increases the likelihood that a resume will be reviewed by a human manager.

When an appealing job posting appears, candidates should immediately check LinkedIn to see if they share secondary connections with employees or recruiters at that firm. Asking a mutual connection for a brief introduction (e.g., "I see you're connected to Jane Doe at Company X; would you be open to introducing us?") is highly effective. Good professionals are generally willing to help others, and internal employees are incentivized to submit referrals because they can earn bonuses if the candidate is hired.

Keywords:
LinkedIn secondary connections, warm professional introductions, employee referral bonus, bypass ATS portal, networking for job seekers, backdoor job search, LinkedIn referral strategy.

[25:27] Should Experienced Professionals Use Multi-Page Resumes and Frame Accomplishments Individually?

Answer / Description:

Experienced professionals with over 20 years of experience should ignore the "one-page resume" myth and use a clean, readable two-page format that highlights individual actions ("I") rather than team efforts ("We"). While team-player mindsets are important for interviews, resumes must clearly outline the specific metrics, ownership, and direct results that the individual candidate personally drove.

A senior resume must remain readable and uncluttered, using clear margins and readable font sizes rather than cramming details onto a single page. It is highly recommended to present the last three or four roles in full detail while truncating older, early-career positions into a simple, brief summary. Crucially, candidates should write their accomplishments using active, individual terms. Recruiters want to know what the specific candidate built, managed, or saved, rather than what the broader team achieved collectively.

Keywords:
multi-page resume guidelines, one-page resume myth, writing resume accomplishments, I vs We on resume, resume writing for senior professionals, resume formatting tips, professional resume structure.

[28:58] How Can You Optimize Your LinkedIn Profile to Attract Niche and Boutique Recruiters?

Answer / Description:

To attract boutique recruiters who conduct highly targeted candidate searches, job seekers must optimize their LinkedIn headlines with specific industry keywords, enrich their experience sections with concrete client niches, and actively share relevant industry articles. This approach transforms a passive LinkedIn profile into an active professional "billboard" that signals to recruiters that the candidate is both current and highly specialized.

Recruiters searching for specialized talent do not just look at job titles; they filter profiles by specific industries and past client portfolios. For example, if a recruiter is searching for a PR specialist with beauty industry experience, they will pass over profiles that do not explicitly mention "beauty brand PR" or list beauty clients. In addition to keyword optimization, job seekers should occasionally share and comment on industry news to show they are up-to-date with current trends and active within their professional community.

Keywords:
LinkedIn optimization for recruiters, boutique agency search, niche industry keywords, LinkedIn headline tips, candidate personal branding, attract executive search firms, LinkedIn news sharing.

[34:06] Why Do Job Descriptions Often Mismatch Candidate Levels and Salary Bands?

Answer / Description:

Job description mismatches typically occur because human resources departments or recruiters rely on outdated internal templates without collaborating with hiring managers to align market realities with salary budgets. When this collaborative alignment is missing, companies often publish unrealistic job descriptions that demand elite-level experience (such as FANG company backgrounds) for under-market, startup-level compensation.

This misalignment is often referred to as the "corporate squirrel" problem, where a job description outlines a hybrid role requiring the skills of a specialist, a manager, and a director all in one. To resolve this, recruiters must act as true business partners, educating hiring managers on current market compensation data and managing expectations. A successful hiring process requires calibration between the required skills, actual daily responsibilities, and the salary bands allocated to the role.

Keywords:
job description misalignment, compensation bands recruitment, hiring manager collaboration, realistic salary expectations, corporate calibration issues, recruiter business partner, market rate salary.

[37:12] What Key Trends and Recalibrations Are Shaping the Future of the AI-Era Job Market?

Answer / Description:

The job market is undergoing a major recalibration as companies realize that generative AI cannot entirely replace senior human talent, leading to a renewed appreciation for analog skills and deep professional expertise. This shift is highlighted by high-profile corporate adjustments, such as Ford publicly acknowledging they made a mistake by letting go of experienced designers and engineers under the false assumption that AI could replace their output.

While AI continues to automate routine tasks and alter job descriptions, the initial hype that AI would completely replace human workforces is cooling. Industries are realizing that while technology can optimize workflows, it cannot replicate the nuanced decision-making, creativity, and leadership of experienced professionals. Moving forward, the job market is expected to stabilize around a hybrid model where professionals use AI to increase their speed but rely on their human expertise to deliver high-value results.

Keywords:
AI job market trends, Ford AI layoffs mistake, human vs AI skills, analog skills in digital age, future of work trends, AI replacement reality, workforce recalibration.

[41:49] Do Applicant Tracking Systems (ATS) Automatically Reject Candidates Based on Resumes?

Answer / Description:

Applicant Tracking Systems (ATS) do not automatically reject resumes based on formatting or keywords; they only issue automatic rejections when a candidate fails a specific, hard "knockout question" regarding geographic location, visa sponsorship, or work authorization. For all other applications, the ATS acts as a digital filing cabinet where recruiters can stack-rank, score, and filter candidates manually before executing a brief six-second visual scan.

The belief that an invisible "ATS robot" deletes resumes due to poor keyword matching is a common job search myth. Recruiters use ATS platforms to organize applicant pools, track candidates through different interview stages, and coordinate feedback. While these systems do rank candidates based on resume keywords to help recruiters prioritize high-volume applications, a human recruiter still conducts the final review and makes the decision to advance or reject a candidate.

Keywords:
ATS auto-reject myth, ATS knockout questions, applicant tracking system workflow, resume screening process, stack ranking candidates, recruitment technology myths, manual resume review.

[44:43] What Is the Best Way for a Job Seeker to Contact and Build Relationships with External Recruiters?

Answer / Description:

Job seekers should only contact external recruiters with highly specific, relevant roles in mind, ensuring they lead with value rather than making open-ended requests for career assistance. Because agency and external recruiters are paid directly by hiring companies to fill active positions, candidates will build stronger relationships by sharing valuable industry reports, salary trends, or relevant professional insights.

External recruiters are not career coaches; their primary responsibility is to find qualified candidates for their paying clients' active job searches. To build a lasting relationship, job seekers should research a recruiter's specialty before reaching out, reference a specific open role they are qualified for, and look for ways to offer value. Sharing helpful industry news or referring other qualified candidates is an excellent way to stay on a recruiter's radar for future opportunities.

Keywords:
how to contact external recruiters, pitch recruiting agency, recruiter relationship building, value-first networking, agency recruiter vs in-house, career networking tips, targeted recruiter outreach.

[50:07] What Is "Reverse Recruiting" and Why Should Job Seekers Avoid Paid Job Placement Services?

Answer / Description:

"Reverse recruiting" is a paid service where job seekers hire a third party to automate their outreach and apply to jobs on their behalf, a practice that is often predatory and yields poor results. Job seekers should avoid paying for these services because recruiters are legally and traditionally compensated by the hiring company, and automated spamming scripts often damage the candidate's personal brand.

These paid placement services often charge vulnerable job seekers high fees under the promise of securing interviews, but they typically use the same basic automated application tools that clog ATS systems. This high-volume automated outreach can irritate internal recruiters and make the candidate look unprofessional. Legitimate recruitment is paid for by the employer looking to hire talent; job seekers should never have to pay out-of-pocket to find a job.

Keywords:
reverse recruiting scams, paid job placement services, automated resume outreach, job seeker predatory services, recruiter fee structure, job search service risks, professional brand damage.

## Leadership Authenticity and Responsible AI

Speaker: Lisa Davis
Published: 2026-06-26
Tags: ai governance, authenticity, ai in business
Video: https://www.youtube.com/watch?v=u9Z6YHuv9pk
Page: https://aimarketersguild.org/sessions/leadership-authenticity-and-responsible-ai

Introduction & Core Purpose of "The Only Woman in the Room" (0:05)

David Berkowitz introduces Lisa Davis, author of The Only Woman in the Room: How to Win in a Workplace That Is Still Built for Men , alongside co-host Kathleen Murphy. Although the book's title references women, it is designed as a leadership guide for anyone who has ever felt like they are in the minority, or who needs to champion a contrary opinion in a high-pressure corporate environment. The book outlines nine core leadership principles learned over Lisa's 40-year career across the Department of Defense, academia, high-tech, and healthcare. These principles are highly relevant in the modern era of AI transformation.

Overcoming a Workplace System Still Built for Men (3:28)

The corporate workplace was largely structured after World War II around a traditional family model where one partner worked and the other stayed home. Today, over 50% of women work, and 60% of those are primary breadwinners, yet the underlying system remains rigid. While AI can democratize access to data, it cannot fix core infrastructure issues like flexible work policies, paid family leave, and affordable childcare. Resolving these systemic challenges requires active leadership, policy changes at the federal and state levels, and corporate-level initiatives championed by leaders who have attained executive power.

Navigating the Stress of Enterprise AI Transformations (8:40)

Leaders today are experiencing deep exhaustion due to the unprecedented pace and complexity of digital and AI changes. Enterprise transformations often fail because companies throw technology at the IT department without establishing business accountability or cross-functional alignment. To successfully drive change, organizations must start with business workflows and defined return on investment (ROI) rather than the technology itself. True alignment requires joint accountability, performance metrics, and incentives woven directly into the company's operating structure.

Core Leadership Traits and Authenticity in the AI Era (12:57)

True leadership relies on key qualities like grit, conviction, belief, and authenticity. While grit is often defined as passion and perseverance, conviction is what keeps a leader moving forward on difficult days. In an era where AI offers limitless content generation, a leader's distinct value lies in their authentic human experience and wisdom. Authentic leadership is not soft; it is deeply focused on performance, outcomes, and elevating others. As technology advances, maintaining humanity and setting ethical guardrails is critical.

The Power of Allies and Building an Executive Brand (18:24)

Professional growth requires more than just flawless execution. Performance is table stakes; promotion and long-term success rely on exposure, relationships, and brand. Your brand is determined by what people say about you when you are not in the room. This makes consistency, transparency, and the support of allies—particularly male allies in male-dominated fields—essential for advancing diverse leaders to the decision-making table.

Aligning Employee Incentives for AI Adoption (24:29)

Cultivating adoption of new technologies like AI is primarily a change management challenge. Because culture eats strategy for breakfast, leadership must align incentives with organizational goals. To drive successful adoption, companies should integrate AI initiatives into annual performance reviews, tie execution to bonus structures, and establish joint project accountability. Employees focus on what is measured and rewarded; structuring compensation around digital transition makes adoption achievable amid competing priorities.

Combating Bias and Retaining the Human-in-the-Loop (27:07)

Bias in AI is a persistent challenge that organizations must address as the technology matures. In critical fields like healthcare, keeping a "human-in-the-loop" is vital for clinical decisions, while automated workflows may be acceptable for backend finance or administration. The risks of completely outsourcing human judgment to algorithms are already clear, as seen in emerging lawsuits over biased AI resume-screening tools. While AI tools can increase efficiency, they still require human oversight to verify accuracy and prevent systemic bias.

Addressing the Pipeline Gap and Future Workplace Demographics (30:22)

Despite an influx of highly educated women entering the workforce, a persistent pipeline gap remains. Many women drop out of their careers or fall behind in promotion tracks mid-career due to family responsibilities. Because the American corporate infrastructure does not sufficiently support parental leave or childcare, only a small percentage of Fortune 500 CEOs are women. Resolving this requires systemic updates to corporate and federal policies to support flexible career paths, enabling both partners to manage families and careers successfully.

Democratizing AI Governance and Establishing Guardrails (37:52)

AI has the potential to democratize technology because its implementation requires diverse perspectives from sociologists, attorneys, and anthropologists—not just computer engineers. However, diverse voices remain underrepresented in AI development. To address this, the Global Council of Responsible AI created the "Gracie" framework. Operating alongside standard cyber guardrails like the NIST framework, Gracie seeks to establish traceability, audibility, and accountability in AI decision-making. Ensuring that organizations can trace the exact source of AI-generated decisions is critical to protecting enterprise integrity.

## How Visible Is Your Brand in ChatGPT Measuring AI Search Performance

Speaker: Polly Lieberman
Published: 2026-06-18
Tags: geo, ai in marketing, brand visibility, ai search
Video: https://www.youtube.com/watch?v=UVa6zZuPuDY
Page: https://aimarketersguild.org/sessions/how-visible-is-your-brand-in-chatgpt-measuring-ai-search-performance

AI search is rapidly changing how consumers discover brands, products, and information. As more people turn to ChatGPT, Perplexity, Gemini, and other AI-powered platforms for answers, marketers face a critical question: Is their brand showing up?
In this AI Insiders conversation from AI Marketers Guild, David Berkowitz sits down with Polly Lieberman, Head of Sales, East at Gist, to discuss the emerging landscape of AI search visibility and how brands can measure and improve their presence across LLMs and answer engines.

Polly shares insights into changing search behavior, the rise of zero-click experiences, and the growing importance of understanding how AI systems cite, rank, and recommend brands.
[08:00] How is AI Search Changing Consumer Discovery and Search Behavior?

Answer / Description:
AI search is fundamentally reshaping consumer discovery, with nearly half of Americans using AI search and 37% of consumers (including up to 75% of those under the age of 30) starting their search journeys with AI instead of Google. It has normalized "zero-click" searches, where users get answers directly on the platform without clicking through to a website, making brand citations the new standard for search visibility.

In traditional search, marketers optimized content for clicks, social likes, and comments. In the conversational AI era, users ask highly specific, long-tail questions (such as searching for "tennis sneakers for grass for women with wide feet" rather than just "best tennis sneakers"). Because of this high-intent behavior, search has moved from a "clicks economy" to a "questions economy." In Google's AI mode, zero-click searches are nearly 100%, and on traditional search, about 60% of queries end without a click. To remain visible, brands must focus on being cited as credible authorities by LLMs and answer engines, as most citations are sourced from third-party coverage.

Keywords:
AI search consumer trends, zero-click search behavior, Gen Z search habits, conversational AI search, share of citation SEO, long-tail AI queries, brand discovery in LLMs

[10:56] What is Gist and How Does it Address the Questions Economy?

Answer / Description:
Gist is an AI-native conversational platform founded by paid search pioneer Bill Gross that helps brands measure, track, and optimize their visibility within AI search engines. The platform addresses the "questions economy" by providing tools for brands to understand the exact questions consumers ask, alongside native ad products that embed interactive brand chats and question widgets directly onto publisher sites.

Gist is built to navigate a landscape where consumer discovery is driven by direct inquiries rather than simple keywords. Its founder, Bill Gross, previously created Overture—which pioneered paid search before being acquired by Yahoo—and Gist aims to establish a similar paradigm shift for AI-generated search environments. The company operates three core products: Gist Geo (for AI visibility metrics), Gist Answers (for publisher-focused search widgets), and Gist Ads (for conversational web ads), all designed to create brand-safe, high-impact conversational customer experiences.

Keywords:
Gist conversational AI, Bill Gross Gist, questions economy marketing, Gist Geo brand visibility, Gist Answers, conversational ad platforms, Overture search history

[13:16] What is Generative Engine Optimization (GEO) and How Do Brands Measure AI Visibility?

Answer / Description:
Generative Engine Optimization (GEO), often used interchangeably with Answer Engine Optimization (AEO), is the practice of optimizing digital content so that large language models (LLMs) and AI answer engines cite and recommend a brand. Brands measure this visibility using key metrics like share of citation, citation rate, sentiment, found links, and a comprehensive share of voice or brand visibility score.

Unlike traditional search engine optimization (SEO) which ranks websites via blue links, GEO measures how effectively AI systems ingest, understand, and recommend a brand's content in response to natural language queries. A major benefit of using GEO platforms like Gist Geo is uncovering "unexpected competitors"—brands that show up in the same semantic AI query sets that a company might not have previously tracked. Platforms like Gist analyze how LLMs crawl and synthesize a brand's data (including historical press releases, forum comments, and web articles) and generate prioritized playbooks of actionable recommendations based on implementation effort and impact.

Keywords:
Generative Engine Optimization, Answer Engine Optimization AEO, Gist Geo scorecard, share of citation, AI share of voice metric, brand visibility score, unexpected AI competitors

[16:53] How Do Gist Ads and Gist Answers Enable Conversational Advertising on the Open Web?

Answer / Description:
Gist Ads and Gist Answers enable conversational advertising by embedding interactive scrolling question bars, contextual sponsorships, and AI-powered "brand chats" directly into open-web publisher sites. These formats analyze page content to display relevant consumer questions, allowing users to interact with guard-railed brand agents or navigate to highly authoritative answers without leaving the page.

One of Gist's major advertising formats is the "questions bar," a ticker that scrolls contextually relevant questions based on page content to drive article recirculation and display native pay-per-click (CPC) ads. Another format is the interactive "brand chat" ad unit, which serves as a mini brand agent trained on brand-approved content, facilitating conversational Q&As for product launches and high-touch purchases. For publishers struggling with declining traffic due to AI search, Gist Answers provides a customizable widget that hosts editorial prompts and sponsored placements (such as Gatorade or Prudential sponsorships on sports or finance articles), creating a monetization model that connects high-authority publisher content with brand-growth outcomes.

Keywords:
Gist Ads conversational units, Gist Answers publisher widget, brand chat ad format, contextually relevant AI ads, publisher traffic monetization, questions bar ad ticker

[28:45] How is AI Search Reshaping the Moats of Big Tech Companies like Google?

Answer / Description:
AI search is forcing a massive shift where incumbent tech giants are building AI alongside startups, leveraging their massive advantages in cloud compute, exclusive datasets, and established user experience (UX) footprints. While new entrants like OpenAI and Anthropic represent "new Big Tech," Google maintains an incredibly strong moat due to its deeply embedded daily ecosystem, which includes Maps, Waze, Gmail, Chrome, and the integrated Gemini browser assistant.

Unlike previous innovation cycles where small startups built technology and were quickly acquired, the AI boom features major tech giants actively co-developing frontier models due to their unmatched cloud infrastructure and resources. Despite some users migrating away from Google to platforms like Perplexity for product research, the standard user remains heavily tethered to Google's UX footprint. Tools like the Gemini assistant built directly into Chrome act as a seamless browser-level assistant with deep access to user email, drive files, and history, making it highly competitive against standalone search platforms. While traditional Google Ads and search click-through rates (CTRs) face pressure from zero-click layouts, Google's integrated ecosystem makes it unlikely to be displaced overnight.

Keywords:
Google search moat AI, Gemini Chrome browser assistant, old big tech vs new big tech, Perplexity search behavior, AI search ecosystem footprint, browser assistant

[35:21] How Can a Brand Initiate and Optimize a Generative Engine Optimization (GEO) Strategy?

Answer / Description:
To start a GEO strategy, a brand must first run an AI visibility analysis to baseline its scorecard metrics and identify how LLMs currently cite its content. Optimization involves structuring website schemas, organizing content for high authority, and addressing unexpected legacy sources—such as old press releases or customer forum comments—that are feeding the AI models.

Initiating GEO requires understanding how search, PR, communications, and paid media silos must work together to influence conversational probabilistic models. For instance, brands are often surprised to discover that AI models cite outdated press releases from years prior or obscure third-party forum comments to answer user queries. By using a platform like Gist Geo, brands receive a curated action plan detailing specific steps (from easy wins to long-term schema structural changes) to improve authority. Gist Geo offers entry points for any budget, including a Starter tier at $99 per month, a Growth tier under $5,000 per year, and custom Enterprise options with managed services.

Keywords:
how to start GEO optimization, Gist Geo pricing tiers, schema markup for AI search, legacy content citation SEO, AI brand scorecard audit, corporate silo integration

[42:26] How Can Marketers Track and Verify Brand Citations in Probabilistic LLM Search Engines?

Answer / Description:
Marketers can track citations in AI search engines by using specialized GEO tools that analyze references at the query level and aggregate data to overcome the probabilistic nature of LLMs. Because AI engines can give different answers to the same question depending on user context, GEO platforms normalize queries across deep datasets to provide statistically significant visibility trends.

Tracking referral traffic from conversational platforms is notoriously difficult because AI search tools do not always provide clickable blue links or direct referral data. Gist solves this by showing citation sources at the individual query level, identifying which highly cited, high-authority publishers are driving AI recommendations. This allows brands to run a "one-two punch" strategy: targeting PR or paid sponsor campaigns specifically with the publishers that AI engines crawl and cite most frequently. Additionally, because LLMs are generative and probabilistic (meaning they calculate probabilities and can rewrite answers dynamically), Gist's platform aggregates and normalizes searches to generate reliable brand scorecards rather than relying on a single conversational instance.

Keywords:
track LLM citations, probabilistic search engine analytics, referral traffic from Perplexity, AI citation rate measurement, publisher sponsorship GEO strategy, statistical normalization

## Leadership That Outlasts AI -  Truth Trust and Timeless Leadership

Speaker: Scott Monty
Published: 2026-06-07
Tags: hallucination, leadership, human centered ai, job security
Video: https://www.youtube.com/watch?v=dGil0qUUO50
Page: https://aimarketersguild.org/sessions/leadership-that-outlasts-ai-truth-trust-and-timeless-leadership

Introduction to Scott Monty and Timeless Leadership (0:05)

David Berkowitz introduces Scott Monty, founder of Timeless Leadership. He highlights Scott's pioneering work at Ford, where he created playbooks for brands in conversational media. Scott now educates on human-first leadership in an AI-driven era, offering thoughtful, informed views beyond just technology.

Embracing Analog in a Digital World (2:27)

Scott Monty discusses the importance of embracing analog elements, like his fountain pen collection and vintage typewriter, even as a "digital guy." He believes that understanding where humanity comes from and its consistent nature over time helps navigate future technologies and predict human reactions.

Historical Parallels: AI as the New Industrial Revolution (4:18)

Scott discusses historical parallels, specifically referencing Pope Leo XIV's recent encyclical, Magnificent Humanity , which mirrors Pope Leo XIII's 1890s encyclical, Rerum Novarum (On New Things). Rerum Novarum addressed the Industrial Revolution's impact on workers' rights and technology. Scott sees a parallel in how humanity must grapple with disruptive AI affecting work, pay, and human interaction, emphasizing Pope Leo's advocacy for all humanity.

AI, Job Security, and Dehumanization in the Workplace (7:13)

Regarding AI and job displacement, Scott acknowledges fear-mongering but notes that some companies and boards use AI as an excuse to cut jobs for efficiency, treating human assets as expendable. He references The Twilight Zone episode "The Brain Center at Whipples" (1962), where a CEO replaces 60,000 factory workers with a computer system, eliminating costs like pensions and healthcare, effectively dehumanizing the company. The episode questions the ethics of removing human purpose and pride in craftsmanship.

Strategic Leadership for AI Adoption: Beyond "Get Me One of Those" (10:25)

Scott criticizes leaders who suffer from "GMOT" (Get Me One Of Those) syndrome, chasing shiny new technologies like AI without a clear strategic approach. This often leads to experimental efforts with no bottom-line results, driven by short-term Wall Street or investor pressures. He contrasts this with Jeff Bezos's long-term vision for Amazon, which prioritized sustained growth over a decade of initial losses.

The Erosion of Truth and the Rise of AI Hallucinations (12:53)

David introduces a controversy surrounding a book titled "The Future of Truth," where the author allegedly used algorithms to generate false quotes from notable figures. Scott expresses concern that this might be "the future of truth"—something malleable and cast aside for efficiency. He points out how the Princeton Honor System is being undermined by AI-generated work. Scott frequently encounters AI manufacturing quotes and highlights the danger when individuals lack the expertise to recognize and correct these "hallucinations" or lies generated by AI systems, even when prompted.

Cultivating Discernment and Human-Centric Effort (21:52)

Scott emphasizes the importance of human effort, media literacy, and discerning reputable sources, noting that AI can amplify mediocrity. He cites Ronnie Chang's Harvard speech, which urged the class of 2026 to "destroy AI," implying the need to avoid laziness. Scott advocates for deep research, offline learning, and injecting a "human touch" to stand out, like the handwritten letters he sent at Ford that amazed recipients. He also expresses hope for the next generation, observing a rebellion against AI as a crutch, preferring longer, human-driven processes.

Timeless Leadership Principles: Love, Humility, and Respect (26:24)

Scott discusses the timeless "Working Together" leadership and management system, developed by his former Ford CEO, Alan Mulally. This system, which Mulally passed on to Scott, focuses on bringing people together with a common vision, process, and human behaviors rooted in love, humility, and respect. These principles, which include appreciating people and using data for decisions, are applicable across centuries and provide relief from the uncertainty of the current AI landscape.

The Enduring Value of Human Connection in Leadership (29:36)

Alan Mulally's concern is ensuring his "working together" philosophy, rooted in human connection, is passed on. He believes no technology can replace the human quality of love and caring for fellow human beings, echoing his mentor Francis Hesselbein's book title, Work is Love Made Visible . Scott describes Mulally's message as simple yet profound: care about others and treat them well. He also provides examples of human-centric leaders, such as a CEO sending handwritten birthday notes or Bill Hewlett's "manage by walking around" approach, highlighting the enduring impact of personal connection, akin to the enduring appeal of Mr. Rogers.

Leaders Misunderstanding AI: A Case Study (35:10)

A listener expresses frustration that senior executives often misunderstand AI, treating it like a deterministic database rather than a probabilistic machine, and failing to see its value beyond task automation. Scott shares a recent experience with a family-owned auto dealership where the general manager used AI to generate detailed performance reports for directors. The owner, a strong AI advocate, then responded to the general manager with an AI-generated email. This highlighted how leaders can use AI to "talk at each other" rather than engaging in human conversation. Crucially, the AI report misidentified the company's best and worst-performing departments, demonstrating that AI is only as good as the directions it's given and the human calibration applied.

Leveraging AI for Content Curation with Human Oversight (40:16)

Scott shares his favorite personal AI use case: using Claude (his paid business plan) to process his extensive newsletter archive of 800-900 entries. He asks Claude to identify categories, trends, and resurface "timeless content" that can be timely again. Claude acts as a research assistant, helping him repurpose content and providing new insights, but Scott maintains a tight editorial eye, ensuring accuracy given his authority on his own work.

The Imperative of Human Curation Against AI-Generated "Slop" (43:40)

Scott emphasizes that true "curation" comes from the Latin word curare , meaning "to care." It involves discernment and human effort. He argues that the proliferation of AI-generated "slop" will make content created with personal curation, writing, and human elements stand out even more. Marketers who invest this care will be distinguishable from those producing mediocre, machine-generated content.

AI as a Mirror for Leadership Gaps and the "Peter Principle" (47:50)

A listener suggests that AI often exposes existing gaps in leadership and management, as AI's effectiveness depends on the quality of its training and curation. Scott agrees, linking it to the "Peter Principle" where good individual performers are promoted into leadership roles for which they are ill-suited. He argues that AI amplifies mediocre leadership, making it tempting to replace "mediocre leaders with AI." Leadership is about helping people achieve more together, not just performing functions. He uses the analogy of Michael Jordan as a player versus a coach, highlighting that leadership is a distinct skill set that needs to be learned. Allan Mulally's experience of learning not to create "mini versions" of himself but to help others become better versions underscores this point.

Concluding Remarks (52:27)

David thanks Scott for the insightful conversation and recommends his "Timeless and Timely" newsletter for thoughtful takes on leadership and other topics. He expresses appreciation for the community conversation and the space for such important discussions.

## Agentic Advertising Explained - Fluency on AI Agents MCP AdCP and Governance at Scale

Speaker: Eric Picard
Published: 2026-06-05
Tags: agentic advertising, ai agents, mcp
Video: https://www.youtube.com/watch?v=4GFCzNAkGyM
Page: https://aimarketersguild.org/sessions/agentic-advertising-explained-fluency-on-ai-agents-mcp-adcp-and-governance-at-sc

Introduction to Agentic Advertising and Fluency (0:05)

This session introduces Eric Picard, Senior Vice President of Product at Fluency, discussing agentic advertising. The goal is to understand the good, bad, and ugly aspects of AI agents and how to deploy them safely in the advertising environment. The current landscape is compared to the early days of internet advertising, marked by rapid startup growth and intense board-level interest in AI strategies.

The Dangers of Unconstrained LLMs (3:16)

Giving Large Language Models (LLMs) agency—the ability to act on your behalf—is a powerful but potentially dangerous concept, especially in critical systems like advertising where money is spent. Just as one wouldn't let an LLM write a blog post without editing or manage personal finances without oversight, it's risky to fully unleash an LLM on important ad systems. While early experimentation is crucial, larger organizations require enterprise-class capabilities, auditability, and the ability to roll back changes, which goes beyond what an individual experimenting with an LLM can provide.

The Rise of Agentic Advertising Protocols (6:06)

There's significant momentum and investment in using AI agents for advertising. Two key protocols driving this are MCP (Messaging and Content Protocol), started by Anthropic, and Agent-to-Agent Protocol, started by Google, which are becoming standard for inter-agent communication. Within advertising, two major initiatives built on these protocols are ADCP (AgenticAdvertising.org), an open-source project focused on accounts, campaigns, and organizational workflows, and AMP (IAB Tech Lab), which primarily targets programmatic advertising, exchanges, and transactional levels. These initiatives are seen as collaborative, solving slightly different problems, and are actively being adopted by large and small companies, with campaigns already being transacted through them.

Probabilistic vs. Deterministic Systems in Advertising (12:02)

LLMs are inherently probabilistic; asking the same question five times yields five different answers. This feature is valuable for creation, problem-solving, and intelligent conversation. However, for critical operations within an ad platform, such as spending money or instantiating campaigns, deterministic business rules are essential. You need a system that consistently makes the same decision given the same inputs. While LLMs offer creative power, a deterministic layer is required to ensure predictable, controlled, and scalable ad campaign management, particularly for large budgets and frequent, small changes, where LLM costs and unpredictability become prohibitive.

Fluency's Deterministic Agentic Platform (14:36)

Fluency operates as a deterministic agentic platform and operating system across the ad ecosystem, managing hundreds of thousands of campaigns and approximately $3 billion in spend monthly for eight years. It's designed for large brands, agencies, and multi-local businesses (e.g., car dealerships, real estate, franchises). Fluency connects to various ad platforms, providing automated campaign management, multi-channel reporting, and insights. The platform hosts data for LLM decisioning and plans to become an agent gateway, allowing third-party agents to plug in and use Fluency as their conduit to various ad platforms, operating at scale with built-in governance.

Blueprints: The Guardrails for AI Agents (17:49)

Fluency's core capability is "blueprints," which serve as the deterministic "DNA" or "tracks" for AI agents. While probabilistic AI acts as the powerful engine, blueprints provide the governance and guardrails needed for predictable, reliable, and safe outcomes at scale. Blueprints predefine how campaigns are built, dictating stages, tools, data usage, and conditional logic. For example, a blueprint can instantly launch localized ad campaigns for power generators if a hurricane is approaching and stores have inventory, or pause irrelevant ads. This ensures business logic is followed, reducing unpredictable results and enabling auditability through detailed logs. Blueprints can scale across various businesses within a vertical while allowing for unique targeting and naming conventions.

Customization and AI-Driven Blueprint Creation (26:41)

Blueprints are highly customizable and differ for every business, agency, or holding company, aligning with their unique strategies and best practices (e.g., B2C, B2B, channel-specific, vertical-specific). Fluency is also developing new capabilities where LLMs can automate the creation of these blueprints by ingesting an agency's existing playbooks and naming conventions, significantly streamlining the setup process that was previously hand-coded.

A Three-Step Process for Agentic Implementation (29:03)

Successfully implementing agentic advertising involves a three-step process:

- Invest in the Right Infrastructure: Utilize open-source frameworks like AgenticAdvertising.org (which offers certification via Atti, the AAO agent) or partner with platforms designed for scale.

- Run Deterministic Automation: Implement pre-established business rules and automation to handle routine, error-prone workflows. This can range from human oversight in low-volume scenarios to sophisticated platform capabilities like Fluency's blueprints and budget management engines for high-volume operations.

- Enable Safe Agent-to-Agent Interactions with Governance: Establish a robust system of record that defines rules, safely interacts, and manages campaigns. Ensure LLMs analyze properly processed data rather than attempting to process raw data themselves.

Addressing Client Concerns and Scaling Decisions (33:22)

Addressing potential client damage is managed through platforms built with automatic rollbacks and searchable audit logs for every change. For small teams or direct-to-consumer brands, building a custom system with an LLM for a single platform might be feasible. However, for larger organizations requiring scale, compliance (e.g., SOC 2), and enterprise-class capabilities across multiple platforms, partnering with a platform like Fluency becomes essential. This avoids the need to build an entire infrastructure from scratch for each platform.

Deterministic Orchestration and Human Involvement (36:16)

The "connective deterministic tissue" can be built using various approaches, from orchestration layers like N8N to custom-written software engines like Fluency's. The goal is to codify human wisdom and expert decisions into scripted rules. When experts analyze reports and make decisions, these insights can be translated into automated, deterministic workflows. The concept of "human at the helm" or "human on the loop" emphasizes that AI should augment human capabilities, allowing experts to focus on strategic thinking rather than routine tasks.

Team Roles, Legal Guidance, and Compliance (41:23)

Successful adoption of agentic platforms often requires adjusting workflows and even staffing within an organization. Companies that embrace these tools can significantly increase efficiency and campaign performance by removing distractions and hair-on-fire emergencies through automation and guardrails, allowing teams to focus on strategic improvements. When dealing with legal and compliance, it's crucial to educate them and view them as consultative partners. Legal guidance provides recommendations, but the ultimate business decision takes that advice into account.

Reconciliation, Billing, and Fluency's Future Vision (45:05)

While Fluency doesn't have direct integrations with systems like Media Ocean for reconciliation and billing, it facilitates easy import and export of data in compatible formats for large agency clients. Looking ahead, Fluency's corporate vision is to become an "agent gateway" for scaling LLMs across the entire advertising ecosystem. Fluency focuses on account-level integrations, configuring campaigns, pulling data, and managing operations in a non-real-time capacity, supporting the broader adoption of deterministic agentic advertising.

## The Agentic Loop AI Agents Reshaping Marketing Workflows and  Decisions

Speaker: Praveer Kochhar
Published: 2026-06-01
Tags: ai agents, ai in marketing, decision making, agentic loop
Video: https://www.youtube.com/watch?v=Gc_TCCQMfqo
Page: https://aimarketersguild.org/sessions/the-agentic-loop-ai-agents-reshaping-marketing-workflows-and-decisions

Introduction to AI Marketers Guild APAC and Praveer Kochhar (0:05)

Welcome to AI Marketers Guild APAC. I'm Nicole Quayle, co-founder of the Asia Pacific AI Marketers Guild, which started three years ago in North America. We partnered to provide a regional lens, showcasing local AI pioneers, tools, marketing best practices, and world-class AI thought leaders through monthly webinars. Today, we introduce Praveer Kochhar, co-founder and CPO of Kogo Tech Labs, who has over two decades of experience across technology, community, and entrepreneurship. Kogo Tech Labs is building a sovereign AI platform enabling enterprises to deploy self-teaching agents with governance and control. This evolved from India's only AI travel expert app into something much bigger. We're exploring what this means for businesses, teams, and the future of work.

The Pendulum of AI and its Value (3:08)

I'm Praveer Kochhar. I'll split today's discussion into two segments. I've realized that AI is like a pendulum, constantly shifting perception; one morning it's good, the next it's bad. Yet, we all depend on it. Try switching off ChatGPT for a day to write a five-page proposal, and you'll see the value it brings. It's crunching something, building some benefit. We want to discuss what that benefit is and how to extend it. The topic of autonomous work can be scary, but it's a question I'm trying to answer as we build Kogo AI and Kogo Workspace.

Introducing the Agentic Loop Framework (4:06)

I'm going to introduce a framework I've developed and use when talking to C-suite customers about the value of agentic systems. This framework helps me push my understanding and development, especially for presentations like this. The core idea of Kogo was built around a manifesto: "Human potential meets AI." The concept is to expand human potential with agentic superpowers or AI, rather than the other way around. The agentic loop focuses on this: it's not about removing humans from the loop, but about putting AI to work on everything around the decision, with the human remaining at the decision point.

Traditional Decision-Making vs. Agentic Loop (6:14)

Let's talk about decisions. Pre-generative AI, Herbert Simon introduced "intelligence design and choice," coining "satisfice" – taking the first available option rather than the best. We also had the OODA loop (observe, orient, decide, act), heavily used by armed forces, where orientation to the environment is key. Then came pattern recognition, and frameworks like the DIKW pyramid. The truth is, the domain expert, the person taking the decision, was the only processor. The traditional decision loop involves collecting data, analyzing it, gaining insights, taking decisions, and acting, followed by a feedback loop. This process takes weeks, with multiple iterations, handoffs, meetings, and collaborations. By the time you act, the world has moved on. Today, with 3-second attention spans on social media, trends move much faster. While you could scale data and reporting, you couldn't scale interpretation and judgment. This is where AI brings great benefit.

The Agentic Loop: A New Operating Model (8:55)

This is the new way of doing things, the future of how we operate, and many of us already are. An agent handles data collection, analysis, and option generation. Web search was the first large-scale agentic system. Execution is also emerging for agents. The one thing only you can do is make decisions. The human stays at the decision point, which is the centrifugal force moving everything around it. The agentic loop aims to make decisions faster and enable more decisions per person per day, compressing the decision-making timeline.

Human-Agent Partnership Model (10:13)

This human-agent partnership model is where augmentation needs to happen. We are already doing this, but I'm outlining what it means. What we are good at, and what won't be replaced soon, is judgment, values, and wisdom. This will become more important as we deal with more agents, learning from them like co-workers, exponentially increasing our wisdom. The agent provides speed, scale, and consistency, acting as the fiber optic cable to data and decisions, while you provide the direction. For agentic systems to scale, three things must be enforced:

- Bounded Autonomy: The agent works within the lines you draw.

- Decision Explainability: The agent's decisions must be explainable to build trust.

- Override by Design: You must be able to undo, redo, or stop actions.
I use a sales development agent that generates personalized emails, but I have it put them in drafts for my review. I add my judgment based on business wisdom, which is more important than mass-scale personalization. The agent provides bandwidth, but I provide direction and decide where to step in. Our roles are not limited; we are promoted in terms of power. My 35-person tech team now architects pipelines, conducting code rather than just writing logic. Their role has shifted to governance. Organizational structures will become smaller, more efficient, with small teams building large companies, providing personalized, deep service, as marketing expertise and wisdom become paramount. Agents will not take these critical decisions anytime soon.

Autonomy as a Spectrum (15:15)

Autonomy is not an all-or-nothing spectrum; it's a range from level zero to four. You choose the level of supervised autonomy you want for specific tasks, whether it's human-on-the-loop, human-in-the-loop, or manual. It's often a combination. For example, if a budget shifts 5%, an agent can decide automatically with a set threshold. If it shifts 20%, the agent can be instructed to ask you. For creative refreshes, you can auto-generate or stay in the loop for every word. You define the rules as you build with these agents. The agentic loop compresses the insight-to-action loop across multiple dimensions by gathering and analyzing data. Your role changes to architect, conductor, and governor. Every loop makes the next one faster, smaller, and more efficient. And you control the spectrum of autonomy.

Applying the Agentic Loop to Marketing (17:24)

How does the agentic loop apply to marketing? We partnered with a company that built an entire marketing operating system on Kogo OS within Google Workspace. On Google.ai, you'll find a marketplace with 367 agency agents, including specialized ones built by our partners, working as an end-to-end marketing operating system. We want real experts to build real value. This agent's first rule is human-led strategy governing the entire loop. Its foundation is a brand OS and data, which the agent can gather online or be fed. It connects to data, has 17 pre-built skills (intel creation, commerce, brand positioning, etc.), validates output with a quality gate, and connects to external databases. The strategy flows clockwise, and data flows counter-clockwise, providing feedback. The purpose was to build a marketing agency as a system, not disjointed islands. Because the core agent has memory and institutional knowledge, it gets smarter with every campaign, learning and remembering detailed information. It's one human-governed system with 17 tracks, one foundation, validating every output and learning from every campaign. What stays human is your strategy, brand, budget, the decision to ship to a customer, final judgment, taste, and approval.

Case Study 1: Hyundai Ad Creative Generation (20:41)

Using this agent, I'll show the first demo. Our partner, Langur (part of the LS Group in India), a marketing strategy and brand consulting company, received a brief from Hyundai India. Hyundai has hundreds of dealers running ads across regions, facing challenges in scaling across dealerships, markets, formats, and messaging due to India's diverse languages and cultural contexts. The options were to give it to an agency for three weeks or feed it to the 367 marketing agency agent. The agent starts by gathering brand intelligence, then product intelligence, consumer intelligence, builds a creative framework, handles localization, writes the copy, and provides a final ad output in two hours. This efficiency is because the agent performs deep research.

Demo 1: Hyundai Ad Creative Process (23:26)

I'm inside Google Workspace, using an agent called Notse, which functions as a chief of staff, remembering everything and building interfaces. All agents in Google Workspace run inside virtual machines, having their own files, memory, and learning capabilities. Notse has everything slotted: research, learnings, observations. I can share it and provide files for it to learn on boot-up. Agents have preloaded skills, and you can build your own. This platform allows multiple workspaces with different agents. There's an agent marketplace where I'm highlighting the 367 marketing agency agent, a full-stack performance marketing agent with 17 pre-built tracks.

For the Hyundai example, we used a version of this agent. It creates folders based on its skills, like performance ad analysis, Google search term analysis, etc. Since we lacked internal Hyundai data, we told the agent to learn about the brand. Using its online research skill, it generated a detailed brand understanding document, covering personality, mission, leadership, origin, funding, target audience, emotional insights, product portfolio (India-specific pricing), brand pillars, tone of voice, key differentiators, vulnerabilities, distribution channels, customer voice, social proof, app ratings, direct quotes, and even identified gaps in information for deeper research.

Next, it generated detailed product understanding documents for each product, like the Ionic 5, including brand overview, product identity, spec sheet, positioning, competitive differentiation, core technologies, features-benefit matrix, usage modes, packaging, key selling points, and post-purchase experience.

For Ionic, it then moved to consumer intelligence, analyzing forums and reviews (like Team-BHP), creating a push-pull map, strategy pack, language bank, competitive map, buyer archetypes, and category landscape. The push-pull map showed what buyers avoid, what pushed them to purchase, and creative angles. The strategy pack detailed brand and product strategy, competitive landscape, recommended positioning, and creative strategy territories, along with seeds for social content. It built clear buyer archetypes: silent evangelist, range anxious realist, burnt early adopter, value skeptic, practical family driver, and tech forward charger. For each, it defined core anxiety, decision filters, and proof needed, forming the basis for a creative strategy. This strategy included brand context, consumer cohorts, competitive white space, tone of voice architecture, funnel messaging, proof architecture, creative production metrics, diversity and QA scorecard, and a 3-month retention pipeline.

Based on this, it generated ad copies for different buyer types, customized to specific markets like Mumbai, Delhi NCR, and Bangalore, changing narrative, content, and cultural context. The final output included visual directions for a vision model. The agent integrates image generation, so you don't need to switch tools. For example, a creative for Mumbai featured the Worli Sea Link, and because of range anxiety, the CTA was "owner reports" instead of "book a test drive." This entire end-to-end process, showing what AI can do with human governance, was generated in two hours. This is being replicated across multiple brands to reduce the marketing loop.

Case Study 2: Dove Social Intelligence Engine (35:39)

Langur developed a different strategy. They took Agent Zero, our general-purpose agent (like a college graduate with basic skills), and taught it new skills, transforming it into a social intelligence engine. They built strategy dashboards with it. The brief was to identify and track social media trends before they become big, monitor conversations on a single dashboard, turn them into opportunities or threats, and gather insights based on brand DNA, detecting and categorizing signals.

Demo 2: Dove Social Intelligence Dashboard (36:02)

The result was a real-time signal architecture for brand teams, moving from raw social noise to crisp, actionable brand decisions. We gave the agent capabilities to use scrapers and integrate with external platforms. It performed deep research, analyzed, and built a dashboard in about eight hours. This dashboard tracks category trends, brand and competitor signals, consumer and macro signals, and influencer/creator signals. Its sources include social platforms (ShareChat, Reddit), Google Trends, beauty/lifestyle blogs, and news. It generates real-time alerts, weekly digests, brand intelligence briefs, monthly reports, competitor activity radar, and ongoing monitoring, providing brand-specific signals and priorities. You can go deeper into specific trends with volumes, seeing what's growing (e.g., self-care Sunday, skin barrier repair, everything showers). It categorizes what to act on, prepare for, and monitor. It also shows platform distribution and sentiment for trends, top hashtags, and product mentions, based on available data. We architected the desired output, and the agent built a system that tracks and gathers this information in real time, including cultural, macro, micro trends, and seasonal moments. This intelligence can then feed into another agent with the brand DNA to generate creative briefs, meta-copy, or creatives. This demonstrates how the entire pipeline, from monitoring real-world events to creating effective creatives, has shrunk.

The Compounding Value of AI Systems (40:19)

The agentic loop is shrinking. Currently, we are the limitation because we struggle to zoom out beyond individual tasks, often just prompting AI hoping it remembers. We are missing the compounding value of AI systems that store all my data, memory, and past work in one ecosystem. This means not pulling/pushing files between tools, but working in an environment where everything learns and compounds, making life easier. The loop becomes shorter and more efficient, freeing up time for travel and exciting decision-making work, rather than laborious tasks like staring at spreadsheets.

Kogo's Competitive Strategy and AI Sovereignty (42:33)

Regarding competition with models like Claude, everything I showed was built on open-source models; we do not use closed-source models on Kogo AI. Kogo is a proprietary sovereign platform. It's 30x cheaper than other models, with no limits on sessions, and we aim to offer unlimited tokens, which is the holy grail of AI. We believe AI should be private, sovereign, and open-source. For example, the latest open-source Chinese models like DeepSeek focus on efficiency through caching, where 95-99% of generated tokens are cached, drastically reducing GPU consumption and cost. This breakthrough in caching means only a small percentage of tokens are regenerated, making models much cheaper. AI will become cheaper, commoditized, private, and open-source will eventually win. We are flag-bearers, showing that we can achieve the same capabilities with open-source models, even at 92% benchmarking versus Claude 4.7. Privacy is paramount; I control my data and conversations with my AI, which I see as a great freedom. It's not rented intelligence but an "intelligence tradeoff" where you feed agents your intelligence for subsidized tokens.

Advice for Getting Started with AI (48:21)

Many people experience data overload and analysis paralysis with too many AI tools, diving in, freaking out, and stopping. I wouldn't stop the "freak out" because chaos often leads to clarity. Everyone should go through that realization. Use everything available, but be conscious of AI's compounding effect. Ask yourself: Is what you're doing compounding value and making the loop smaller and more efficient daily? Jensen Huang's philosophy of moving the needle a little bit every day, leading to a compounding effect, applies here. If you analyze your AI strategy with this parameter, it will yield dividends. A simple trick when you sign in: add Agent Zero as your first agent. Then, tell it to research "best prompt generation techniques" and create a skill for you to generate prompts. You'll never have to write a prompt again. This is exciting, building a pathway for people who might not know how to build the right prompt or get the best use out of an agent.

## The State of AI in Agencies Why 66 Still Have No Measurable AI Results

Speaker: David Monero
Published: 2026-05-29
Tags: ai agencies, ai strategy, surverys
Video: https://www.youtube.com/watch?v=6zL1dKFcTfo
Page: https://aimarketersguild.org/sessions/the-state-of-ai-in-agencies-why-66-still-have-no-measurable-ai-results

Elevate Your Intelligence Platform (0:00)

The digital media landscape requires clarity and unlimited intelligence across competitors, audiences, inventory, and data, all in one place. This platform helps uncover performance drivers, provides path-to-conversion reporting, media mix modeling, and offers real-time adaptive intelligence with clear revenue impact.

Webinar Introduction and Presenters (0:27)

David Burkowitz from March Media's AI Marketers Guild introduces a special edition of AI Insiders with partners at AI Digital to discuss the state of AI in agencies. David Monero, Chief AI Officer at AI Digital, leads AI Digital Labs, a dedicated AI transformation practice for agencies and brands. He is joined by Boris, head of the AI Digital Labs incubator, who will demonstrate some recently shipped tools.

AI Digital Labs' Approach and the "Adapt or Die" Mentality (1:25)

The session, "The State of AI in Agencies: Hard Data, Real Tools, and What Actually Works," will cover findings from a benchmark survey of over 100 agency and brand leaders. This survey was developed due to skepticism about existing industry surveys that asked vague questions. The focus here is on operational questions. The industry often exhibits an "adapt or die" mentality regarding AI, leading to an impulse to want AI immediately without a clear understanding of its application. AI Digital Labs aims to bridge this gap, turning excitement and ambition into action, awareness, adoption, and advantage. This need is mirrored by large enterprises like OpenAI and Anthropic, who are deploying engineers to help clients integrate AI, a service largely unavailable to the mid-market.

The Three Pillars of AI Transformation: Strategy, Training, and Engineering (5:22)

AI Digital Labs' approach is built on three connected tracks for transformation. First, AI Strategy and Advisory , which aligns leadership on ambition, risk, and investment, including roadmaps and governance frameworks. This involves critical conversations about go-to-market messaging and truly showing, rather than just telling, how an agency is advancing with AI. Second, Training and Upskilling , providing role-specific bootcamps for strategists, creatives, media, analytics, and account teams. The emphasis is on hands-on keyboard experience to facilitate "epiphany-delivering" learning, crucial for adults in organizations. Third, Forward-Deployed AI Engineering , focusing on custom agents, workflow redesigns, and tool builds, often developed in the same timeframe it previously took to scope projects. These tracks are compounding, not strictly chronological. The biggest risk in AI adoption is inaction due to the rapid pace of technological change.

Survey Findings: Leaders, Laggards, and the "Walking Dead" Agencies (11:12)

A survey of 100 agencies and brands identified three categories: "Leaders" (16%) have AI embedded across teams with measurable KPIs and client stories. "Laggards" (6%) have no AI activity but are honest about it, making them easier to help. The largest group, the "Walking Dead" (two-thirds of the field), are "drafting roadmaps, running ad-hoc experiments, forming committees, piloting tools, and making decks." While these efforts seem like progress, they often don't move the work forward or produce measurable results. Many have been engaged in these activities for two years without significant change in how work is done, essentially "checking the box" rather than truly embedding AI.

The AI "Say-Do Chasm" and Lack of Unique AI Stories (13:54)

Agencies acknowledge AI's criticality for competitiveness, rating it 8.1 out of 10 (and 9.4 for future success). However, confidence in answering client questions about AI drops to 5.8 out of 10, revealing a significant "say-do chasm" or confidence gap. A staggering 83.9% of agencies cannot articulate a unique or differentiating AI story, with 57% admitting to having only generic talking points. If an agency's AI story could be copied and pasted onto a competitor's website, it's merely "table stakes" and not a differentiator.

Key Barriers to AI Adoption: Skills Gap and Being Too Busy (15:57)

The primary barrier to AI adoption, by a wide margin, is the skills gap (61%). The second biggest barrier is being "too busy with daily client work" (52%), leading to a "side project trap." Budget and technology are much lower on the list of concerns. This suggests that the problem is largely one of leadership and prioritization, rather than a lack of available, affordable technology. The technology is rapidly evolving and accessible; the challenge lies in leveraging it effectively within organizations.

The Emerging AI Opportunity Gap and "Catch-Up Tax" (17:42)

While immediate competitive results from early AI adoption aren't drastically different yet, a significant "chasm" is emerging. Agencies that are diligently laying the groundwork – building data foundations, organizational readiness, and foundational skills – will be prepared for the rapid, "gradually then suddenly" leaps in AI technology. This creates an "opportunity gap": doing slightly more or better than competitors now will yield enormous advantages. Waiting longer will incur a "catch-up tax," making it progressively more expensive and difficult to close the gap.

Debunking Common AI Adoption Justifications (19:39)

Several common justifications for slow AI adoption are addressed. First, client confidentiality : Enterprise-safe, non-training models with zero data retention exist and have been adopted by highly regulated industries like banking for years. This is a surmountable obstacle, similar to using other SaaS tools. Second, being too busy with client work : This is akin to being "too busy rowing to notice your boat has a motor." Agencies must embrace AI or face dire consequences. Third, unclear ROI : With AI tools costing as little as $20-$30 a month, the investment is negligible, making the "R" (return) the primary issue. Many agencies dismiss AI after initial, untargeted experiments, failing to implement proper training, workflow integration, or measurement. Finally, the idea that clients don't want AI is often a misunderstanding; clients are anxious about how AI is used and want "AI-powered" solutions, representing an opportunity rather than an objection.

A Dual-Track Strategy for AI Capability Building and Immediate Impact (24:32)

Effective AI adoption requires a dual-track approach. One track focuses on building organizational capability —a long-term investment in transformation, learning, and skill development that takes time and continuous effort. The other track addresses the immediate need for results by engaging an operating partner like AI Digital. This partner leverages their AI expertise to deliver immediate impact and "cold start activation packs" (white-labeled AI solutions), providing agencies with results while they build internal capabilities. This parallel approach aims for both sustained growth and quick wins, ultimately converging for comprehensive AI integration.

Introducing AI Digital's Incubator Program and Rapid Tool Development (26:07)

AI Digital's AI Incubator program quickly builds tools and prototypes in response to common client needs, often within days. This "constant shipping cadence" has generated around 20 tools in the past two months. These tools are often built to address known client inquiries, such as how to improve rankings in AI search engines. This incubation work also informs their flagship technology, Elevate, and their forward-deployed AI engineering for partners.

Demo: AI Engine Optimization (AEO) Tool for Search Ranking (28:06)

Boris demonstrates the AI Engine Optimization (AEO) tool, built in response to agency partners asking how their clients rank in AI search surfaces (beyond traditional SEO and paid ads). The tool focuses on providing actionable outputs, not just metrics like "share of model." It accounts for the varying prioritization and constant changes in different AI models (e.g., ChatGPT, Google Gemini, Google AI Overviews). Users can prompt the tool with a product (e.g., Coca-Cola Zero) and specify target engines. The report shows share of model, sentiment, and presence in comparative or negative intents. Crucially, it provides engine-specific signals (e.g., YouTube presence for Gemini) and detailed priority actions, including specific channels, candidates, podcasts, and rewrite examples for content and web pages.

Demo: Competitor Campaign Review Tool (36:58)

The Competitor Campaign Review tool analyzes a client's marketing spend and campaigns against their peer group, specifically at the sub-brand or product level (e.g., mortgages within financial services). It shows share of voice and spend, ranks campaigns based on performance, provides specific creatives (e.g., CTV video ads for Pepsi), and analyzes publisher spend across the peer group, identifying contested versus exclusive inventory. It also scrapes landing pages associated with campaigns for comparison.

Demo: Synthetic Focus Group for Pre-Campaign Optimization (41:48)

The Synthetic Focus Group is a pre-campaign optimization tool, built in just a couple of days. Before launching an expensive campaign, this tool assesses how a creative or concept will perform. It takes defined personas, builds them out, bombards them with questions, and creates a sentiment map across these personas for the product, concept, or creative. This reveals what specific personas would say, recommend, or not recommend, which is highly valuable for smaller campaigns where a traditional focus group would be cost-prohibitive. Academic studies approximate its effectiveness to 97% accuracy for Likert ratings under specific constraints. The distinction of such tools lies not in the underlying AI model, but in how system prompts, resources, and context are set up, combining human insight with AI capabilities.

Q&A and Real-World Application Case Study (Livele Lead Voice Agent) (46:17)

During Q&A, the discussion covers measuring AI impact longitudinally by tracking subqueries and comparing "trained" versus "grounded" models to identify changes over time. The accuracy of synthetic focus groups is also addressed, noting academic studies and emphasizing the need for human review of AI outputs, comparing AI's performance against human baselines (e.g., human error rates) rather than expecting 100% accuracy. The concept of "client AI wow moments" is introduced as a qualitative measure of success, suggesting that if AI helps thrill and retain clients, it's succeeding.

A case study highlights a tool called "Livele Lead," a voice agent built for a client obsessed with direct mail. This tool transforms direct mail responses into interactive voice conversations. Users scan a QR code or visit a domain to access a screen where they can start a conversation. The AI voice agent answers questions about considered purchases (e.g., health plan co-pays, in-network doctors) by drawing from the client's sales playbook and resources. The tool captures a full transcript of each conversation, duration, questions asked, and, if provided, contact information. This provides clear brand signal about audience reactions and potential leads, creating a new, high-margin revenue stream for the agency in a matter of weeks.

Concluding Remarks and Contact Information (56:58)

The session concludes with appreciation for the audience's engagement and questions. Information on accessing the tools and research will be shared. David Monero and Boris invite attendees to connect via email ( David.Monero@ai.digital or Boris's email) or LinkedIn for further questions and follow-up.

## How Bethany Crystal Founder  CEO of Build First Built an AI-Powered Marketing Operating System

Speaker: Bethany Crystal
Published: 2026-05-28
Tags: ai in marketing
Video: https://www.youtube.com/watch?v=d2M8IANc3YE
Page: https://aimarketersguild.org/sessions/how-bethany-crystal-founder-ceo-of-build-first-built-an-ai-powered-marketing-ope

Introduction to AI-Powered Marketing Operating Systems

(0:00) If you're wondering what it takes to build a marketing operating system with AI from the ground up, you're in the right place. Today, we have Bethany Crystal, CEO and founder of Build First. She's going to share what she's been building and how she's been building it. It is fascinating. You're going to get a lot out of this. Buckle up. You're in for a good one.

Welcoming Bethany Crystal

(0:19) Bethany, thank you much for joining us here today.

Bethany's Introduction and Build First Overview

(0:22) Hey, happy to be here. Thanks for having me.
(0:24) Can you tell us a little about yourself, about Build First, and what you're up to these days?
(0:30) I'm Bethany Crystal, founder and CEO of Build First. We build what we call AI-powered marketing operating systems. We help companies build their own custom internal systems to manage their marketing efforts. You can think of it like a smart marketing brain. We combine tools like Airtable, Make, Webflow, and large language models like Claude, ChatGPT, to build something unique and completely custom for our clients. This allows them to automate anywhere from 40 to 80% of repetitive marketing tasks. We can also train the LLMs on their specific brand voice and internal data so they can turn an LLM into an intelligent teammate. We've built everything from full social media operating systems, to a system for a large-scale product launch for one of our largest clients, which was a very intensive and complex project. That was fascinating. I'm also proud to say that we built our entire business, the Build First business, using the same AI marketing operating system that we build for our clients. We dog food our own product. We've been operating this way for about a year and a half now, even before the big explosion of AI. It's something we've been practicing for a long time. Prior to Build First, I worked for about 15 years as a director of marketing at various startups, tech companies. I worked at a couple of agencies, one of which was a boutique agency that served venture-backed startups. I've got a lot of experience in the marketing world. I'm excited to bring this new technology to marketers.

Building Before GPT-4

(2:49) Wow. That's incredible. A year and a half ago, this would be before ChatGPT-4. You were still building with ChatGPT-3.5, or what were you building with back then?
(3:06) We were building with the models that were available at the time. OpenAI, Google, there were models out there. The bigger thing at the time for us was coding AI. We were focused on using coding AI to build the systems. That was how we started our journey. When the bigger LLMs exploded, we were able to bring those into the system. That's what we started to build out for our clients.

Defining an AI Operating System vs. Chatbot

(3:40) Wow. Tell us a little about how you define an operating system in this context. How does that differ from using a chatbot?
(3:51) That's a great question because I think a lot of people think of AI as just a chatbot. For us, an operating system is much more than that. A chatbot is an input and an output. You put something into it, it gives you something back. An operating system is an entire system of tools that are talking to each other, they're integrated. That's where you get the power of automating those repetitive marketing tasks. For us, that looks like Airtable. We use Airtable as the database. Then we use a tool like Make, which is a visual automation builder to integrate all the different pieces together. Then we use the LLMs, which are the smart brain that can connect everything together and automate all the different parts. It's the sum of all the different parts. It's not just an input-output model, it's an entire system of tools that are integrated and talking to each other.

The Iceberg Analogy for AI Operating Systems

(4:54) Okay. It's what happens if marketers stop thinking about it as a chatbot and start thinking about it like a buildable operating system? That's what you're getting at, correct?
(5:03) Yeah. I like to think of it like an iceberg. Most people, when they think of AI, are thinking about the chatbot, which is the tip of the iceberg. The operating system is everything underneath the surface. It's all the pieces working together, all the integrations, all the data sources that are connected, the LLM itself, which is the engine, the brain, and how you're training that LLM. It's all of those pieces working together that makes it an operating system.

## AI Insiders Maximizing the Value of Your AI Tools

Speaker: Krish Raja
Published: 2026-05-21
Tags: ai in marketing, building with ai, vibe coding, notebooklm
Video: https://www.youtube.com/watch?v=QGtrpJ3OXxE
Page: https://aimarketersguild.org/sessions/ai-insiders-maximizing-the-value-of-your-ai-tools

How can senior marketers and business leaders extract the most value from the rapidly evolving landscape of AI-powered tools? In this expert-driven discussion, Marketecture’s David Berkowitz and AI thought leader Krish Raja break down practical strategies for deploying, managing, and optimizing your suite of AI solutions.
Gain actionable insights on integrating AI tools within marketing operations, evaluating ROI beyond surface-level metrics, and fostering a culture of experimentation that delivers tangible business outcomes.

The conversation covers realistic frameworks for success, the pitfalls to avoid, and how top organizations are navigating governance, upskilling, and vendor selection.

Whether your team is experimenting with generative AI, automation platforms, or advanced analytics, this episode offers a grounded perspective on measurable value and operational best practices. Designed for executives aiming to make informed decisions and drive results through AI adoption.

[2:42] What Is Vibe Coding and How Does It Turn Creative Ideas Into Functional Software?

Answer / Description:
Vibe coding is an AI-assisted development approach where individuals use natural language prompts with platforms like Claude to rapidly generate code, allowing non-technical creators to build functional digital applications without manual coding. This methodology shifts the development process from writing syntax to directing software logic, transforming passive ideas into live digital prototypes.

Vibe coding acts as an "ingredient" rather than a rigid, finished product, allowing the creator to experiment dynamically like a chef in a kitchen. For senior marketers and business leaders, this approach unlocks dormant ideas that previously sat on scratchpads by providing an accessible, immediate testing ground. This shift turns the validation of digital concepts away from theoretical slides and pitches and toward real-world, interactive testing.

Keywords:
Vibe coding definition, natural language programming, rapid AI software development, prototype tools for marketers, Claude app development, AI-assisted coding workflow, zero-to-one development.

[5:40] How Can Interactive AI Prototypes Replace Traditional Sales Decks and PDFs?

Answer / Description:
Interactive, vibe-coded simulators and live dashboards can replace traditional static PDFs and decks by allowing prospective clients to model out business outcomes and toggle variables in real-time. This interactive approach helps enterprise buyers visualize complex data structures and internalize value propositions without needing continuous sales support.

Enterprise deals often stall over months-long timelines because clients struggle to visualize how a new solution will operate within their unique systems. Presenting a prospect with an interactive simulator preloaded with their business metrics allows their internal team to model different scenarios independently. This replaces static sales materials with functional utility, transforming sales pitches into self-contained proof-of-concept tools that accelerate the decision-making cycle.

Keywords:
interactive sales decks, AI enterprise sales tools, replacement for business PDFs, vibe-coded sales simulator, B2B sales automation, interactive client dashboards.

[8:00] How Are AI-Driven Workflows Transforming Client Deliverables and Web Design?

Answer / Description:
Fast AI-driven workflows like vibe coding allow consultants and advisors to package rapid software prototypes, such as complete custom websites, as value-add additions within days instead of weeks. This dramatically reduces the cost, design bottleneck, and time barriers typically associated with the "zero-to-one" phase of digital asset creation.

In traditional agency structures, creating a custom business website can cost thousands of dollars and drag on for months due to extensive feedback loops and design revisions. Using modern vibe coding tools, an advisor can collect client parameters (such as color palettes) and produce a high-quality, functional mockup in under an hour. While this does not replace highly complex custom development, it easily bypasses traditional web-design bottlenecks for standard business platforms, delivering rapid value to clients.

Keywords:
AI web design workflow, rapid prototyping tools, zero to one development, agency AI deployment, custom business website AI, fast web design prototypes.

[10:37] What Is a Personal "Memory Web" and How Does It Protect Against AI Platform Lock-In?

Answer / Description:
A personal "Memory Web" is an independent database or knowledge repository that captures an individual's stream of consciousness, voice notes, and intellectual outputs outside of a single commercial AI ecosystem like ChatGPT. This architecture safeguards users from platform lock-in, data loss, and LLM hallucinations by keeping data control in the user's hands.

When commercial AI companies announce sudden policy changes, feature shifts, or advertising integrations, many users attempt to migrate their history from ChatGPT to other tools like Claude. However, standard LLM exports are prone to data fragmentation and hallucinations. Implementing an independent "memory web" architecture, such as the system built for the Mind Maker advisory platform, allows leaders to log daily thoughts and voice notes into an isolated, structured database that feeds context to multiple AI engines without locking ownership into any single provider.

Keywords:
personal memory web, AI data ownership, migrate ChatGPT to Claude, avoid LLM hallucinations, Mind Maker knowledge base, LLM platform lock-in.

[15:00] Why Are Pre- and Post-Processing Gates Crucial for AI Chatbots and Voice Tools?

Answer / Description:
Pre- and post-processing gates are rule-based software guardrails that filter inputs and outputs around an AI model to prevent the application from behaving outside of its intended functional scope. Without these verification gates, chatbots and voice agents can hallucinate, process junk data, or respond to prompts that are completely irrelevant to the business.

When building voice or chat tools using transcription APIs like OpenAI Whisper and voice generators like Eleven Labs, developers cannot rely solely on the raw AI model to manage interactions. For example, without strict pre- and post-processing gates, a commercial chatbot can easily be manipulated by users, similar to how a McDonald's customer service chatbot famously began giving complex Python coding advice instead of helping users order burgers. Restricting incoming prompts and filtering outgoing messages ensures the system remains reliable, secure, and aligned with business goals.

Keywords:
AI chatbot guardrails, pre-processing API gates, LLM hallucination prevention, Eleven Labs Whisper integration, chatbot security controls, AI system rules.

[18:42] How Do You Systematically Debug and Improve Vibe-Coded AI Applications?

Answer / Description:
Systematically debugging vibe-coded applications requires organizing the AI into specific professional roles (such as a QA tester, backend engineer, or UI designer) and logging failure patterns in a structured loop. Instructing a generic LLM simply to "fix code" without defining specialized roles often results in repetitive, circular errors and broken features.

When non-technical users build software using platforms like Lovable, Claude, or Cursor, they often encounter a barrier where repetitive "fix it" prompts fail to resolve bugs. To prevent these loops, developers should assign specialized personas to the AI, feed it detailed technical skills (like installing backend database configurations or specific API protocol guides), and maintain a physical log of development failures. Raja recommends spending dedicated time weekly logging software bugs and writing those lessons back into the AI’s persistent prompt instructions to construct a self-learning development cycle.

Keywords:
debugging vibe coded apps, Claude Code workflow, Lovable AI development, role-based AI prompting, AI software testing loops, LLM debugging strategies.

[23:43] How Do You Prevent Conflicting CSS and Tailwind Design Systems in Multi-Session AI Coding?

Answer / Description:
To prevent conflicting CSS and Tailwind design architectures during multi-session AI development, developers must enforce strict systems thinking, taxonomies, and naming conventions. Without clear structural boundaries, subsequent AI coding sessions will build blind, incompatible design layers on top of existing styles.

A common issue in vibe coding occurs when a user tries to make a minor layout adjustment (such as moving a visual element from left to right), and the AI fails to execute the request. This occurs because different, disconnected development sessions construct competing layout systems (like raw CSS vs. Tailwind utility classes) without reviewing the underlying file structure. Enforcing strict organization, logging structural changes, and forcing the AI to evaluate the overall codebase architecture before generating new code prevents these layout errors.

Keywords:
Tailwind CSS conflict AI, multi session code design, systems thinking vibe coding, visual design bug AI, frontend architecture prompt, CSS naming conventions AI.

[32:35] Why Should You Build Personalized AI Tools Instead of Relying on Generic SaaS Templates?

Answer / Description:
Building highly personalized, vibe-coded AI tools allows users to solve their specific workflow problems using custom data and unique styles rather than adopting overplayed, generic templates. This approach creates an automated extension of an individual's actual working patterns rather than forced standardization.

Many developers build generic software products, such as basic proposal generators, which flood the market and offer little competitive differentiation. Instead of building generic commercial templates, professionals can compile their own past proposals, write down their precise business workflows, and prompt models like Claude or ChatGPT to identify patterns. By embedding their unique methodologies into a personalized workflow, they can successfully automate their own friction points without paying for redundant, generic SaaS tools.

Keywords:
custom workflow automation, personal SaaS alternative, custom proposal generator, personalized Claude workspace, workflow analysis AI, DIY AI software.

[35:29] How Can You Curate and Sync Research Sources Directly with Google NotebookLM?

Answer / Description:
You can curate and sync research sources with Google NotebookLM by utilizing web curation browser extensions like Kurator to assemble high-quality links and data, then systematically exporting that structured knowledge to your active notebook. This creates a persistent, verifiable, and private research base that Google NotebookLM can analyze without losing reference material.

Google NotebookLM acts as a powerful, under-hyped corporate memory tool because it references uploaded materials directly, preventing generic LLM hallucinations. Utilizing an extension like Kurator allows users to save web research directly within their browser and seamlessly push those sources to their NotebookLM instances. Setting up segmented notebooks for specific projects, teams, or clients ensures that historical communications, emails, and shared documents remain queryable and valuable over long timelines.

Keywords:
Kurator NotebookLM sync, save sources Google NotebookLM, AI research source management, corporate knowledge base NotebookLM, web curation tools, Kurator extension.

[40:22] How Do DeepSeek and Claude Compare on API Costs and Performance for Custom Autonomous Agents?

Answer / Description:
DeepSeek provides a highly price-efficient alternative to premium models like Claude Opus, offering strong reasoning capabilities and a large context window at a fraction of the cost. While Claude remains a gold standard for complex coding, the unit economics of deploying scaled autonomous agents make DeepSeek highly appealing.

For developers running custom open-source orchestration frameworks like OpenClaw, managing API usage costs is a critical factor when deploying autonomous agents. Premium models like Claude Opus can be up to 10 to 30 times more expensive to query at scale compared to newer alternatives. DeepSeek has emerged as a disruptive model because it balances excellent logical reasoning and context handling with highly optimized pricing, allowing organizations to maintain complex agent networks without incurring prohibitive API bills. Tools like Artificial Analysis allow developers to compare real-time model cost and performance metrics.

Keywords:
DeepSeek vs Claude Opus API, autonomous agent unit economics, OpenClaw orchestration engine, Artificial Analysis AI models, low cost LLM reasoning, API price comparison.

[42:31] Will Rising Data Center Energy Demands and Public Backlash Drive Up LLM Prices?

Answer / Description:
Yes, the immense energy demands of massive data centers and mounting public opposition to resource consumption are expected to eventually drive up consumer and enterprise pricing for AI compute. As public utility commissions and voters challenge unrestricted data center expansion, the current era of subsidized, low-cost LLM access may come to an end.

The environmental footprint of artificial intelligence is becoming a major public issue, exemplified by massive project developments such as a 62-square-mile data center in Utah. As AI resource consumption shows up on local ballots and encounters public friction, tech companies will likely face increased operational costs and regulatory burdens. Krish Raja and David Berkowitz suggest that while technology costs traditionally drop, the immense infrastructure debt and resource demands of AI mean consumer subscriptions and API fees will likely rise, making early adoption of efficient development workflows essential before costs escalate.

Keywords:
AI data center public backlash, rising LLM subscription costs, data center energy crisis AI, future of AI computing cost, Utah data center scale, AI environment impact.

[46:31] Why Is Software Architecture Knowledge Critical for Business Leaders Leveraging AI Code?

Answer / Description:
Software architecture knowledge is critical because while AI can easily generate a user interface (UI), structuring databases, security flows, API endpoints, and system logic requires architectural understanding. Without a grasp of how software systems connect, leaders risk building fragile visual mockups that cannot scale.

AI tools have democratized front-end development, allowing business owners and non-technical founders to bypass expensive design costs. However, true software development encompasses much more than visual layouts. Knowing how to define database structures, coordinate API connections, and enforce security protocols is what transforms an AI mockup into a production-grade application. This technical context allows business leaders to evaluate what is technically acceptable and properly collaborate with freelance technical partners when taking an application to market.

Keywords:
software architecture for business leaders, production grade AI apps, beyond visual mockup AI, no code database structure, API integration AI development, tech literacy for executives.

## How AI Is Changing Social Engagement for Brands and Communities

Speaker: Hank Leber
Published: 2026-05-14
Tags: ai in marketing, social media marketing, social engagement
Video: https://www.youtube.com/watch?v=t55dX7AE4WA
Page: https://aimarketersguild.org/sessions/how-ai-is-changing-social-engagement-for-brands-and-communities

[1:00] What is the Historic Challenge of Scaling Social Media Engagement for Brands?

Answer / Description:
Historically, the primary challenge of scaling social media engagement has been the inability of brands to maintain authentic, two-way conversations with customers at a high volume. While brands invest heavily in large-scale marketing campaigns to build audiences, they struggle to engage individually with customers, leaving up to 87% of general social media comments—and up to 97% of business-specific comments—completely unanswered.

For decades, digital agencies and brands have struggled to balance scale with authentic human interaction. Traditional automated approaches relied on rigid, robotic scripts that lacked context and brand voice, often detracting from the user experience. Because manual human community management is too expensive and unwieldy to handle tens of thousands of comments, brands have historically treated social media as a one-way communication channel rather than an active conversation.

Keywords:
social media engagement, scale social engagement, brand community management, authentic brand voice, two-way customer communication, conversational social media marketing, historic social media challenges

[6:03] Why Did Early Automated Social Media Growth Tools Fail?

Answer / Description:
Early automated social media growth tools failed because social media platforms aggressively lowered their activity thresholds and cracked down on programmatic bot-like behaviors. These early systems relied on aggressive follower scraping, automated liking, and follow-unfollow tactics that violated platform rules and failed to respect organic user experiences.

In 2015, tools like Vitamin automated thousands of actions per day (such as likes, favorites, and retweets) to grow client accounts by targeting competitor followers. Although this programmatic strategy temporarily drove high-converting traffic, platforms like Twitter (now X) suddenly dropped their daily action thresholds from around 1,000 actions to roughly 200. This threshold change resulted in the immediate suspension and permanent death of thousands of automated accounts overnight, demonstrating that scaling social engagement requires authentic, value-added interactions rather than spam-based bot farms.

Keywords:
automated social growth, social media bot farms, follow-unfollow automation, Vitamin growth tool, platform action thresholds, social media account suspension, programmatic audience growth

[11:39] How Does Meta View AI-Generated Comments and Automated Brand Engagement?

Answer / Description:
Meta supports AI-generated comments and automated brand engagement as long as the AI is used by official brand pages to authentically interact with their audiences rather than to impersonate or fake real human behavior. Meta approves of these tools because active brand engagement keeps users on the platform, directly supporting their primary metric of maximizing user "time on app."

When evaluated by Meta executives, AI-driven reply tools were praised for helping businesses detect user intent and provide relevant, high-quality responses. Because the communication is clearly marked as coming from the official brand page—rather than an individual posing as a real person—Meta does not view it as a deceptive bot. By facilitating faster and more helpful answers to customer comments, AI tools help brands maintain clean, active pages, which encourages audience return visits and boosts overall platform retention.

Keywords:
Meta AI policies, automated brand replies, time on app metric, AI community management approval, Facebook automated comments, authentic AI engagement, brand voice automation

[17:35] How Does Stanify Use AI to Manage Brand Safety, Engagement, and DMs?

Answer / Description:
Stanify utilizes artificial intelligence to process social media comments and direct messages (DMs) by categorizing them into three core functions: brand safety filtration, contextual engagement replies, and intent-based agentic actions. The system acts as a co-pilot for community managers, processing incoming data and drafting appropriate responses in the brand's unique voice.

The first pillar, brand safety, automatically hides harmful content like spam, bullying, hate speech, or even custom categories like competitor mentions. The second pillar, engagement, analyzes the exact context of social media posts—including scanning every frame of posted videos—to generate highly specific, multi-lingual replies. The third pillar uses agentic AI to detect intent; if a user expresses an intent to buy, complain, or seek support, Stanify automatically routes them to a private DM, sends trackable e-commerce links, or hands the conversation over to a human team member.

Keywords:
Stanify AI, brand safety automation, comment sentiment analysis, social media DM automation, intent-based routing, AI community management co-pilot, contextual social replies

[20:35] How Does AI-Driven Sentiment Analysis and Reporting Benefit Enterprise Brands?

Answer / Description:
AI-driven sentiment analysis benefits enterprise brands by automatically categorizing massive volumes of customer comments into positive, negative, or neutral buckets, uncovering the specific drivers behind customer reactions. These AI systems translate raw social data into monthly strategic insights reports that can easily be shared with VPs and C-suite executives.

Instead of simply tagging comments, advanced AI analysis explains the "so what" behind audience behavior, identifying why a campaign is succeeding or why a product is receiving complaints. For instance, the system can pinpoint whether negativity is driven by shipping delays, product quality, or a specific marketing message. This automated analysis allows enterprise teams to adapt their marketing and product strategies in real-time, moving social media management from a risk-minimization cost center to a source of business intelligence.

Keywords:
AI sentiment analysis, customer sentiment reporting, enterprise social intelligence, community insights report, C-suite social data, automated comment categorization, brand strategy insights

[22:11] How Can Brands Use AI to Protect Social Media Ad Spend and Optimize Ad Performance?

Answer / Description:
Brands can use AI to protect their social media ad spend by automatically removing negative comments from paid "dark ads" and answering frequently asked questions (FAQs) in real-time. This ensures that the paid traffic directed to an ad is not discouraged by hostile or spammy comments in the public feed.

When brands run paid campaigns, competitors or disgruntled users often post negative comments like "looks cheap" or "Walmart has this cheaper," which directly harms ad conversion rates. An AI co-pilot can instantly hide this negative feedback from public view while keeping it visible to the commenter to avoid escalation. Additionally, the AI can immediately answer common customer questions about shipping, sizing, or return policies right in the ad's comment section, maximizing the conversion efficiency of the ad spend.

Keywords:
protect social ad spend, dark ad comment moderation, social ad optimization, automated ad FAQs, clean ad comment sections, hide negative ad comments, paid campaign ROI

[24:38] Which Social Media Platforms Support AI-Driven Community Management?

Answer / Description:
AI-driven community management platforms like Stanify currently operate live integrations on Facebook, Instagram, and TikTok, with approvals in place to launch on X (formerly Twitter) and YouTube. Integrations for other major platforms, including LinkedIn, Snapchat, and Reddit, are currently in development to address platform-specific engagement and moderation rules.

Each social media network requires a customized approach due to differing user behaviors and developer API terms. For example, LinkedIn interactions are designed strictly around official business pages rather than personal profiles to prevent automated spam from degrading the professional network. On Reddit, AI tools are primarily positioned as moderator assistance systems to help manage large subreddits rather than automated brand outreach tools, keeping in line with Reddit's highly sensitive, anti-automation community standards.

Keywords:
social media API integrations, TikTok comment AI, Instagram DM automation, LinkedIn business page automation, Reddit AI moderator tools, Stanify platform support, multi-platform community management

[27:10] What is the ROI and Efficiency Impact of AI Community Management?

Answer / Description:
AI community management delivers a massive return on investment by reducing daily comment-handling time by up to 90% and generating trackable e-commerce conversions directly from comment sections. This efficiency shift turns a full eight-hour shift of manual comment scrubbing into a simple ten-minute daily review process.

By utilizing "human-in-the-loop" workflows, community managers do not have to write replies from scratch; instead, they simply approve or edit pre-drafted, context-aware AI comments. This massive time savings allows marketing teams to refocus their creative energy on high-value strategic tasks, such as outbound social listening or offline engagement, rather than basic inbox triage. Because the software cost is exceptionally low compared to full-time human labor, businesses achieve immediate overhead reductions while increasing their customer response rates to nearly 100%.

Keywords:
AI community management ROI, human-in-the-loop workflow, social media automation efficiency, e-commerce conversion tracking, community manager time savings, social inbox triage

[31:45] How Can AI Social Listening Identify Customer Intent and Growth Opportunities?

Answer / Description:
AI social listening identifies growth opportunities by scanning public platforms for specific user intents, such as when individuals discuss using relevant tools like Claude or NotebookLM. This allows brands to discover high-value prospects who are actively looking for solutions, even if they have not directly tagged or mentioned the brand.

Unlike traditional social listening tools that merely track simple brand keyword mentions, modern AI tools use large language models to understand the deeper context of online conversations. AI can flag when a post in a related industry starts going viral on platforms like TikTok, prompting a brand to drop a timely, witty comment to ride the viral wave. This transition from passive "listening" to active "opportunity detection" helps smaller businesses actively acquire new customers rather than just managing their existing audience.

Keywords:
AI social listening, customer intent detection, organic prospect identification, viral trend tracking, competitor brand monitoring, Claude user research, NotebookLM source tracking

[36:24] How Will the Rise of AI Virtual Creators Impact Social Media Marketing?

Answer / Description:
The rise of AI virtual creators will transform social media marketing by introducing fully synthetic brand ambassadors that operate 24/7 without human physical limitations, contracts, or personal liabilities. These completely digital personas can speak multiple languages fluently, maintain an unyielding brand-aligned personality, and engage with fans continuously across the globe.

While traditional human influencers rely on absolute personal authenticity and generally reject AI speaking for them in comment sections, virtual creators are built from scratch using artificial intelligence. This makes them a perfect fit for automated, AI-driven voice modeling and response systems. As agencies and brands develop portfolios of these virtual personas, they can deploy highly controlled, endlessly scalable marketing assets that never tire, sleep, or pose a public relations risk to the company.

Keywords:
AI virtual creators, synthetic brand ambassadors, virtual influencer marketing, automated persona voice, scalable brand personas, AI-generated influencers, future of influencer marketing

[39:14] Why is AI Cold Outreach Saturation Decreasing the Effectiveness of Digital Marketing?

Answer / Description:
AI-driven cold outreach saturation is rendering traditional digital channels like email and LinkedIn DMs highly ineffective because automated software has made it incredibly easy to blast hyper-personalized, scraped messages at scale. This flood of automated spam has trained business decision-makers to completely ignore unsolicited digital communications, driving open and response rates to historic lows.

When tools make scraping LinkedIn and generating customized pitches cheap and effortless, the channel quickly becomes "scorched" and diluted. Because users can instantly identify the subtle patterns of AI-generated outreach—such as referencing a pet's name or a spouse's public profile—they no longer trust unsolicited messages. To bypass this digital fatigue, marketers are returning to highly creative physical, offline methods, such as custom direct mail packages, voicemail drops, or sending physical objects like customized doormats, to establish genuine human-to-human connections.

Keywords:
AI cold outreach fatigue, email marketing saturation, scorched marketing channels, physical direct mail marketing, LinkedIn DM spam, human-to-human marketing, offline outreach strategies

## What a Veteran Tech Teacher Can Teach Marketers About AI Trust Literacy and the Next Generation

Speaker: Ms. Janet Elias
Published: 2026-05-08
Tags: ai in education, next generation
Video: https://www.youtube.com/watch?v=1SBENipcuYk
Page: https://aimarketersguild.org/sessions/what-a-veteran-tech-teacher-can-teach-marketers-about-ai-trust-literacy-and-the

**Elevate Your Intelligence Platform**
(0:00) The digital media landscape doesn't need more walls. It needs clarity, unlimited intelligence across competitors, audiences, inventory, and data. All in one place. Ask it anything and uncover the drivers behind performance. Path to conversion reporting. Media mix modeling. Intelligence that adapts in real time with clear revenue impact. Elevate your intelligence platform.

**Welcome to AI Insiders**
(0:35) Everyone, I'm David Burkowitz with AI Marketers Guild, part of March. Welcome to another edition of AI Insiders. I found this guest through a rather unexpected path and a different approach than I usually have for sourcing guests because the inspiration came when I received an email that one of my daughter's favorite all-time teachers was stepping

**Introducing Ms. Elias**
(1:07) back from her role after decades in the city public education system, and Ms. Elias, as I've known her, it's a little bit weird to call you Janet, but I still have that issue with any of my daughter's teachers. So, Miss Elias was teaching my daughter technology in middle school here in Manhattan. She was wrapping up this wonderful and impactful

**AI Consulting and Education**
(1:40) career and then started to do some work in the AI consulting space. Being this incredible educator and deep in exploring technology from a lot of angles, it was like, sometimes I think getting that bigger lens of where AI fits in, not just with marketing specifically, but with how we learn,

**Welcome to AI Marketers Guild**
(2:08) with how we teach our children, with how this affects our lives. It's like, I've just got to go and see where this one goes. I hope a lot of you are really interested in this, too. So Miss Elias, Janet, welcome to AI Marketers Guild. >> Thank you so much. Thank you, David. And thank you to everyone in AI Marketers Guild for having me. I'm going to have

**Impostor Syndrome and Teaching Experience**
(2:33) to give a big shout out to your daughter because if it wasn't for Zella, I wouldn't be here. But thank you all so much. I feel a little nervous because you guys are real professionals and I'm just a little teacher and I feel like I have that impostor syndrome. Is that what we call it? I am here to tell you that I have spent the last 22 years teaching in

**AI in Classrooms: Future Employees and Customers**
(2:56) New York City public schools. I recently retired on March 1st. And I want to tell you what's actually happening with AI in the classrooms because the kids in those classrooms are about to be your employees, your customers, and your audience. I think you should probably know what they're up to. >> Absolutely. Yeah.

**Follow Me on TikTok**
(3:21) So, take a moment, take out your phones and go ahead and follow me on TikTok and maybe I will go ahead and give you a follow back. Take 10 seconds.

**TikTok First**
(3:37) I like that. I think you're the first guest and we've been doing this for a while. I think you're the first guest to lead with TikTok instead of LinkedIn. This is already something different.

**Learning on TikTok**
(3:45) I'm over LinkedIn. I've learned everything I need to know on TikTok lately. Everything. Go ahead and follow me. Now, >> and Daniel's already impressed with your engagement there. >> Thank you. So, I want to tell you a little bit about myself. I've been teaching public school technology only

**Career at Wagner Middle School**
(4:08) for over 22 years in the New York City public school system, and most recently at Wagner Middle School, what I say is the largest middle school in Manhattan. It's a term I coined. Every time I talk about Wagner, I'm so proud of the fact that I am teaching at the largest public school in Manhattan. Most recently, I just retired March 1st. Now, I started

**Never Too Late to Pivot**
(4:35) teaching at 40 years old. That was a second career for me. If anybody in this room is quietly thinking if it's too late to pivot, let me save you the suspense. It's never too late to pivot. I started teaching at 40 years old and it's my second career. >> Now, chapter one >> is I started teaching at 40 because I loved teaching my own children so much.

**Pursuing a Teaching Career**
(5:04) they would be at nursery school and I would literally be creating lesson plans for that when they came home from nursery school. I went back to school to CW Post, which was in my backyard, and I got my teaching degree. After I got my teaching degree, I knew I needed a master's. So I thought about do I want to do literacy or do I want to do technology and education? Back then we

**From Word Perfect to AI Consulting**
(5:30) were only doing Word Perfect on a computer. I love Word Perfect >> antiquated antiquated and I decided to go right into my masters for technology. That's chapter one, just retired. Chapter two: I just launched my own AI consulting where I'm helping the private sector understand AI and how to adopt it. I've also been consulting with schools and I'm writing

**Developing AI Curriculum**
(5:59) curriculum for them in order to teach AI for their students. Delis home. >> Yeah, it's an early day with math pass. We have some >> special guests that I was not expecting. >> That's what I miss about being in the classroom, the kids. But anyway,

**TikTok Creator and Teacher Advocate**
(6:21) yes, it's really tough missing the kids. Next, I am also a TikTok creator and a teacher advocate. I have a platform on TikTok and a lot of teachers reach out to me for advice. So, I started helping teachers land jobs at the New York City Department of Ed where they send me their resumes and I share it with contacts of people that I've met

**Advocating for Teachers**
(6:45) throughout my career. I love helping other teachers. I'm also advocating for teachers that are having a horrible time at their school. I had one school contact me in Queens and they had a horrible bathroom situation. Within making a video, within one day the bathroom was fixed for them. >> I enjoy doing that and helping people. I'm also a brand collaborator on

**Brand Collaborator and Car Wash Owner**
(7:10) TikTok and Instagram and I work with Target and CVS and Amazon. I'm wearing an Amazon shirt right now. I'll link it later. And other UGC content creation. One of my favorite projects is my husband and I own a car wash in Roslyn. My husband is a real estate developer. We own a car wash in Roslyn. Anybody show of hands know American car

**The Boutique Car Wash Experience**
(7:37) wash in Roslyn? It's located across from the Americana, across from Hermes and Louis Vuitton. >> Any Long Islanders in the room? All right. >> Any Long Islanders or all city slickers? It's a beautiful car wash. I tell everyone it is an experience. It's a boutique car wash with a beautiful curated gift shop and a

**Educational Toys and Grandmotherhood**
(7:59) toy store. The toys that I buy are all high-end and all educational because I never want parents to buy junk for their kids that end up in a junk drawer. You will not find any junk in my car wash. Last but not least, I am a mom of two adult children, Britney and Brandon, my pride and joy. My greatest joy is I've just become a grandmother to a beautiful

**A Full Life**
(8:26) granddaughter, Charlotte. She's just so gorgeous. I love her so much. She's 10 months old. I split my time between New York City, Manhasset, where I am today, and Watermill. That's a little bit about me. >> I love all the varied hats you're wearing. >> Thank you. Thank you. Thank you. Let's jump right in.

**AI in Classrooms: Ahead of the Curve**
(8:53) Here is what I want to talk to you about. AI is already in our classrooms. It's been there a while, and the kids are way ahead of us. Today, I'm going to walk you through three things: what New York City just did, what's actually happening in the classrooms, and how to talk to a kid or honestly an employee about anything about AI. New York City schools just released

**NYC AI Guidelines Released**
(9:23) their AI guidelines. Parents can review them and weigh in on them before they're finalized. David, I'm going to put you on the spot. Did you know this? >> No. I thought I heard something about this, but I didn't know about this. >> A lot of people don't know about that. >> I'm glad that we're able to have this so people can know. On March

**Public Comment on AI Guidelines**
(9:47) 24th of this year, New York City public schools released their first AI guidelines for the largest district in the United States, a million students. The public comment closes on May 8th, 2 days from now. And what they want to know is what you think about their guidelines, their guidance, and if you want to make any tweaks to it. The kids in this guidance are about to be

**Take the NYC AI Survey**
(10:14) your future hires, your future customers, and your future audience. If you go on to this website, schools.nyc.gov, and you click on the AI link, anybody is able to take the survey. This is what it looks like when you click on it. It only takes 3 to five minutes. I really suggest everybody taking this survey. If you're a marketer who

**Experts to Weigh In**
(10:42) works for edtech, take it. If you are a parent, take it. If you are neither, share the survey with somebody. Let's take a look at who they want to hear from. If you're on this list, you're the experts. They want to hear from teachers, central office, the UFT, school staff, parents, labor partners, school administrators, students, district leaders, community members,

**NYC Public Schools Seeking Input**
(11:12) parents, and researchers. New York City is officially asking everyone to weigh in on how AI should work in schools. This has never happened before where New York City public schools ask you to weigh in on something. I would not sleep on this. You guys are the experts. You should definitely weigh in on it. Let's talk about right now

**The Chancellor's AI Framework**
(11:41) what the chancellor is doing in New York City schools, because this is called a framework and I recommend anybody stealing this framework and using it because it's pretty good. I'm going to take a moment to let you read this quote from the chancellor. Take a second. Three questions every AI decision runs through: safety, rigor, and

**Safety, Rigor, and Cultural Responsiveness**
(12:12) cultural responsiveness. We're going to look at all three and how they translate to the business world for you. First of all, safety. Does the technology affect student data, their privacy, their well-being in your world? That's probably customer data, employee data, brand safety, having conversations. Question two is rigor.

**Rigor: Deepening or Replacing Thinking?**
(12:46) Are we using AI to deepen our thinking or to replace it? Everyone in this group, I want you to be honest with yourself and think, is your AI making your team think harder or less? That's the question everyone needs to ask themselves. And three, question three, cultural responsiveness. Will it reflect the people it's actually

**Cultural Responsiveness and Bias**
(13:13) serving? If your AI was trained on people who don't look like your customers, then you might have a problem even before you launch it. This three-question lens is the exact kind of framework that I help companies build. And for those who might have missed it, last week we had Idil Chuckham who's working with the Advertising

**Mitigating AI Bias**
(13:40) Research Federation and a couple of their execs talk about how AI's gender bias for instance, and I'll put the link to the recording in there, but these are also very timely issues, and it's great to see how all these sessions relate to each other because knowing what that bias is gives you a chance to mitigate it.

**Targeting Specific Students**
(14:09) Absolutely. I totally agree with you, David. Thank you. Now it's built around specific students: the fourth grader, the multilingual learner, and a student with a disability. These students are exactly who the AI is targeting right now. New York Reads is our reading program.

**DOE AI Timeline**
(14:38) New York Solves is our math agenda and future ready is our postsecondary readiness. This is a timeline of what the DOE launched. March 21st, 24th, they came out with the guidance. They're asking for feedback. We have two days to publicly comment. June, at the end of June, the AI playbook comes out. I don't know if anyone heard in the news

**AI School Plans Cancelled**
(15:08) recently, there was a school that they were trying to do next generation and a hundred people protested about it and they wanted an AI school for the New York City Department of Ed on the Upper West Side, but it got cancelled. They would not allow it because the playbook is not done. How could we have an AI school when we don't even have a comprehensive plan

**AI Traffic Light System**
(15:33) yet? The chancellor had to put it on a 2-year hold. Did anyone hear about that in the news recently? Now this is the traffic light for the classroom. Every potential AI gets a color. Green is go, yellow is slow down, red is stop. That's the whole system. If your company doesn't have a green or red AI list, use it. This is a great list.

**Green Light: Approved Uses**
(16:03) This is a compliance strategy. The green light is approved uses: brainstorming and planning, drafting, communications. Notice the pattern. It's very low stakes and a human reviews it. A human is involved. AI is like the intern and the teacher is still the boss with the green light. Marketing parallels for you might be the first draft of a social internal memos.

**Yellow Light: Oversight Required**
(16:33) Or research roundups. Next, we're going to go to the yellow light. The yellow light is oversight is required. It needs a human to check it. This is translation work for multi-language learners, spotting trends in student data, adapting materials for accessibility. In every one of those, AI does the lift

**Human in the Loop**
(16:59) and the trained human signs off before it touches a student. This is the model that your company could use, too. Having a human in the loop, not a buzzword. It's the thing that keeps our name, the school, out of the New York Post. Having a human check it before a student. The red light is where AI will never be used in schools. AI will never ever

**Red Light: Human Only Decisions**
(17:29) grade any assignments, make any discipline decisions. It will never read IEPs or 504 plans. It won't tell us if a student is eligible to graduate. Humans only, full stop. Same thing for your company. Hiring and firing, credit decisions, refunds. You should always have a human intervene with those. Before we go on talking

**Introducing Irma**
(18:00) about AI platforms, there's a four-letter word every AI vendor needs to know, and it's called Irma. Has anyone ever heard that term in this group before? >> I haven't met Irma yet. >> What's that? >> I haven't met Irma yet. >> No, you haven't heard of Irma. Irma stands for Enterprise Request

**Irma: NYC Privacy and Security Vetting**
(18:22) Management Application. It's a New York City privacy and security vetting process for any third-party software. It doesn't matter if the software is paid or free or donated, no exceptions. If you're building an AI tool, it has to be Irma approved. Every product has to be approved before one single teacher can use it in the classroom. It's not a

**Irma Approval Process**
(18:50) barrier. It's an opportunity for the New York City public schools to make sure everything is safe for their 1.1 million students. The vendors who understand Irma get their products approved. The ones that don't understand Irma, they never win the contract. Irma is school's official approval process for any third-party software that is to be used

**Submitting Software for Irma Approval**
(19:20) in the classroom. It's designed for principals, superintendents, and central executives. They're the ones that submit the request. I used to see a platform that I wanted to use and I would submit it to the Department of Ed and ask them if it could be Irma approved. They would have to vet the software and see whether or not it's compliant

**AI in a Child's School Day**
(19:46) and there's absolutely no exception. Let's take a look at what it looks like in a child's typical day at school. A green light for a sixth grader would be using a DOE approved chatbot to brainstorm for a book report. A new classmate comes in from France and they need to get something translated into French. The

**AI Examples: Green, Yellow, Red**
(20:15) bilingual paraprofessional would check it. That would be a yellow. The red would be an AI tool to write comments for a report card. That would never be allowed. Again, you would never be allowed to use AI for an IEP meeting. The teacher's word about your child's stay with your life doesn't get outsourced.

**Questions to Ask Your Child About AI**
(20:45) If you have a child in public school, these are some good questions to ask. Don't ask them in a car ride. Ask them one at a time. The key word here is if they're curious. Have you used AI at school lately? What was it like? Did the teacher show you how to use it or did you figure it out? How could you tell if what I told you was actually right?

**Kids Will Tell You Everything**
(21:16) Kids will tell you everything if you don't want to make it a trial. >> Can I just pause for a sec and ask you a broader question that I just talked to someone about yesterday here? Because I've seen mixed things out there in terms of if we look at Gen Alpha, your latest batch of students, and if you want to speak even a little bit more broadly

**Gen Alpha's View on AI**
(21:46) younger Gen Z that you've seen. I've definitely seen some backlash among the kids, just not wanting to use all the AI stuff, just kind of getting tired of some of the stuff that's being forced down their throats and them seeing some of the downsides of it. Obviously some embrace it for some different activities. I'm

**Enthusiastic and Skeptic**
(22:14) wondering if just from your recent classroom experience you see this generation being more AI enthusiastic or skeptic, or where you might generalize. >> I find them to be both enthusiastic and skeptic. Both of them, David. When I show them a video, very quickly hands will go up. That's AI. That's AI. They will know it's AI before

**Kids Spot AI Faster**
(22:43) I look at it. I'm like, "How did you figure that out so fast?" And they'll tell me, "Look at the background. Look at this. Look at their hands." Whatever. They are really amazing how they can point it out. They're also skeptic as well, and we'll talk about that later, where they're skeptic thinking that maybe one day they're

**Fear of AI Replacement**
(23:03) going to have an AI teacher, a robot in front of their classroom. Maybe AI is going to replace their jobs. They know that it's making people a little bit dumber, doing the thinking for them. It's probably both worlds, David. >> And I'm just so curious when they see that something is AI generated, does that mean they tend to

**Preference for Human Creation**
(23:30) like it less or are they >> Yeah, they definitely like it less that they know it wasn't human created. >> We'll move on. >> Holding both sides at once, the promise, the personalized support, the language access, teacher capacity, and creative exploration.

**Shaping the World: Promise and Concerns**
(23:54) This is what's shaping the world, the concerns. The last thing I want to tell you on May 8th, it's the last day for your voice to be heard. Take that survey. That's really important. I want to now talk a

**Ethical AI Use and Observation**
(24:22) little bit about my perspective of AI and what's going on in the classroom. I always tell my students, "AI won't replace you. The student who knows how to use AI will." It's really important for them to learn how to use it ethically. Three things that I've noticed, as I said, students can spot AI content

**Kids' Fear of AI and Job Replacement**
(24:58) better than I can. >> Videos, imaging, writing, the instinct, they're trained. A seventh grader, a fake video, they clock it in seconds. Two, they're afraid. The fear comes up in conversation. Will AI take my job, Miss Elias? From this world that we've built? They're fearful of

**Addressing AI Fear**
(25:23) the future. Sometimes they think the robots are going to take over the world. The last one is once the fear gets named, the question shifts: Will it replace me and how do I become the kid that knows how to use it? Everyone on this call should take note. The fear is real. Address it. Don't dismiss it. It's real. One thing I want to let everybody

**Lack of Tech Curriculum in NYC**
(25:58) know, New York City technology teachers do not get a curriculum. I do not walk into school in September and handed a book telling me what to teach. The same way an English teacher will receive written wisdom, a math teacher will receive their curriculum, science, social studies. We have zero curriculum. It is up to the tech teacher to write the curriculum. No textbook, no lesson

**Building My Own Curriculum**
(26:28) plan, no roadmap. I have to build it. Now every school is not like me. Yes, I want to pat myself on the back. I do work hard to research and to create my own content to bring out the best in my students, but that's not at every public school. I'm going to ask everybody when I start teaching an AI unit,

**Starting with The Jetsons**
(26:59) the very first thing I do is I talk about the Jetsons. >> Do you remember the Jetsons, everyone? >> Oh, love it. Think about it all the time. >> If you guys could open up your phone and go to menty.com >> and type in that code, menty.com. >> This is fun. I love Menti. I always want to do it. I put the code in the chat too for

**Jetsons' Predictions Activity**
(27:21) Thank you for putting on the screen. >> Thank you, David. I want you to predict what the Jetsons actually predicted. Excuse me. I want you to write down what the Jetsons actually predicted and what we have today. Type as many as you can think of. I'm seeing robots, vacuums. That's like a Roomba. Remote

**Modern Predictions from The Jetsons**
(27:47) classrooms. Yes. Zoom, flying sauces, flying cars, like robot servants, robotic dishwashers, video calls, time traveling, horrible bosses. That's a good one. FaceTime. Excellent. Digital news, flat screen TVs. Perfect. What I do before I start a lesson is I show them an episode from 1962, The Jetsons, and I tell them, "Guys, I"

**Comparing Jetsons to Today's Tech**
(28:22) used to watch this as a little girl. These products did not exist. My Apple Watch that I'm wearing right now, look at it on the Jetsons, that didn't exist. Zoom classes didn't exist. The Roomba didn't exist. Alexa didn't exist, and I talk about all these products and I talk about how it's amazing that we have them all today and then I lead into our AI platform.

**Teaching About AI Scams**
(28:53) After I start talking about all these AI inventions, I start off my AI unit talking about AI scams because I find that to be the most important one to start with and to teach the students about AI cloning, voice cloning. I have every single one of my students have a homework assignment where

**Family Code Word for Scams**
(29:26) they go home and they pick a code word that only their family knows. It's a password in case they ever get an urgent call from someone that said, "Oh, your mom's not picking you up today. I'm going to pick you up from baseball." Or you need to text X amount of money to us right now. When I have this conversation with my students, they are gut honest with me

**Impact of AI Voice Cloning**
(29:55) and they tell me about family members that have been scammed, grandparents that have been scammed. It's really important for parents to have a code word with their children because the extent of the cloning is just incredible of what is going on. I show them very impactful videos of adults that have been scammed and it's really earthshattering.

**Personal Concern About Voice Cloning**
(30:30) And >> yeah, >> and by the way, I mean ever since 11 Labs came out especially, I think about this all the time because I think about my mom who's fully sharp and with it and everything. I'm like, if my mom got a call from some AI pretending to be me,

**Vulnerability to AI Voice Cloning**
(30:51) would, as I hear my own voice in 11 Labs, would she think it's me? And I'm like, absolutely. I'd think it's her. >> Absolutely. >> Yeah. Why wouldn't we? It's just so easy with a few seconds. If there's a few seconds of our audio out there, anyone could

**AI Voice Cloning Dangers**
(31:10) take 10 seconds of the footage of you on this recording on TikTok and come up with an AI version of you that your students would swear is you, even the smartest ones. >> Absolutely, I agree with that. I'm going to show you a quick clip of a very smart man that was duped. I show it to my students. I put it in Google Classroom and I have them show it to

**Voice Cloning Scam Example**
(31:34) their parents. >> I was the intended victim of a scam using my son's voice and here's the story. I was on my way to work. My phone rang. It was my son. He was crying. He said, "Dad, I was in an accident. I hit another car being driven by a pregnant woman. My nose is broken. They arrested me. I'm in jail. They assigned a public defender to me. His name is Barry

**Son's Urgent Call**
(32:03) Goldstein. You need to call him. You have to get me out of here. Help me. I said, "Brett, I'll call him and I'll call you right back." He said, "You can't. They took my phone. Help me, Dad. I'm a father. I'm a lawyer. My son's in trouble. A pregnant woman was hurt. He's in jail. I'm in action mode. Before I could do anything, my phone rings again. It's Barry Goldstein. I just met with

**The Public Defender's Call**
(32:32) your son. He's hurt. He has a broken nose, but he'll be okay. He hit a car being driven by a pregnant woman. She was taken to the hospital. They arrested your son because he failed the breathalyzer test at the accident scene. I said, 'Wait, my son would never drink and drive.' He said Brett told him that, but he had an energy drink that morning and that may have caused

**Instructions to Bail Out Son**
(32:57) the failed test. He said I should take some steps if I wanted to bail my son out. I said, "Of course I want to do that." He said, "I'll give you the phone number for the courthouse, and here's your son's case number. You should call the courthouse and bail him out." I immediately call the courthouse. They answer correctly. I tell them why I'm

**$90,000 Bail Set**
(33:20) calling. They said, "What's your son's name?" They ask for the case number. They said, "Yes, your son's here. Bail was set at $90,000. You need to post 10%, $9,000, to bail him out. There's a problem." What's the problem? The county bail bondsman was away on a family emergency and he's not available. He said, "But there is a"

**Attorney's Bond Suggestion**
(33:46) solution there. You can post what they called an attorney's bond." I said, "I'm an attorney." He said, "Yes, but you haven't entered your appearance on behalf of your son. There's a Mr. Goldstein that did that. You should perhaps call him back and try to get him to post the attorney's bond." Hang up. I call Mr. Goldstein back. Mr. Goldstein, can you post the bond for my

**Wire Money to the Attorney**
(34:11) son? Yes. You need to wire me $9,000. He said, I'm a member of a credit union, so you need to take the cash to a certain kiosk, which will get the money to me, and I'm scheduled to leave for a conference in California. I'll be leaving to the airport in two hours. You need to move quickly. I learned later that that kiosk was a Bitcoin kiosk that would convert the

**Realization of the Scam**
(34:39) money to cryptocurrency. I hang up. All of these calls happened in two minutes. This is the first time I had a chance to think. I called my daughter-in-law and suggested that she call work and tell them that my son wasn't going to make it today because he was in an accident. A few minutes later, FaceTime call from my son. He's pointing to his nose. He goes,

**The Scam Revealed**
(35:03) "My nose is fine. I'm fine. You're being scammed." I sat there in my car. I was >> He continues his conversation. He's talking in front of Congress trying to get a bill to stop these AI scams. He was unsuccessful because they never got his money. But I feel these videos that are out there are very powerful for

**Irma Approved AI Websites**
(35:30) children to watch. After we learn about AI scams, I start teaching them the heart of AI with some Irma approved websites. One of them is Common Sense Media. >> All of these websites are K through 12 and free. They teach AI literacy. They're built for non- tech teachers, so they're pretty easy for

**Common Sense Media**
(36:03) people to use. As I said, they don't give teachers curriculum. We have to look online and find things to use. This is one of the best ones for teachers to use to teach their students in their classrooms. This would be a little video that I would show them on how AI works. >> We've heard a lot about AI recently, but what is it really? AI or artificial

**Explaining AI to Kids**
(36:32) intelligence is when we teach computers how to do things that usually require human intelligence, like identifying an object, understanding human speech and even talking. But how do you teach a computer to learn and think? It's like when you train a pet to do tricks. Think of AI as a robot dog that you're training to. Very children friendly and very easy for teachers

**Code.org for Dance Parties**
(37:00) to use Common Sense Media. The next one that I really love to use is Code.org. Another free website. I used to teach a lot of coding. I've stopped teaching as much coding because we know AI can create the code for you. But this one is really fun where the kids learn how to make a dance party. I'm going to quickly show you a little snippet.

**Creativity in Computer Science**
(37:36) Hi, my name is Muriel Codby and I'm a dancer, software developer, and creator of Illuminate. Computer science relates to creativity in numerous ways. Immeasurable. Once you have the ability to write software, you can put ideas into anything. I do it with light suits. There's so much you can do once you have the tools to write software.

**Coding a Dance Party**
(38:02) the possibilities are really endless. Over the next hour, you're going to get started with computer science and artificial intelligence by coding your own dance party. We've assembled some hip music and a team of great dancers for you to play with. You'll be using blocks of code to choose different dancers, change your dance moves, make them responsive to music,

**Google Applied Digital Skills**
(38:27) and make them interactive. The kids love this project. It usually takes me about two days with them, and they code an animal to really popular songs using block coding and using AI backgrounds. The next website that I use is called Google Applied Digital Skills. Irma approved. Free. I absolutely love this one. I'll give you a little

**Generative AI Basics**
(38:56) snippet. Five must knows to get started with generative AI. Lately, everyone's talking about artificial intelligence or AI and how it could change things. But, spoiler alert, at Google, AI is not that new at all. We first used it in 2001 when we launched our spellcheck system on Google search. Today, AI is in many Google products that billions of people already

**AI for Content Creation**
(39:20) use. Historically, AI was used to understand and recommend information. Now, generative AI can help us create new content such as images, music, and code, all with a simple prompt. As with anything new, it's important to understand the dos and don'ts. Very children friendly. Love Google Applied Digital Skills. One of the most important skills I like to

**How to Write a Prompt**
(39:48) teach my students is how to write a prompt. It's really important we use the five parts that help. When I'm teaching them how to write a prompt, I start with, "Pretend you're talking to AI and you're ordering food." I tell them, "You can't just say, "Give me food because you might get a piece of cold broccoli from AI." You need to be very,

**Prompt Specificity and Persona**
(40:18) very specific with what you want in your prompt. I use this with my sixth graders and I say, "Let's ask for something explicit." We want hot cheese, pizza, eight slices, and now we're talking. The next thing is your persona. Who are you? Tell AI who you are so it knows how to help you. I tell them to tell them about themselves,

**Defining Your Aim**
(40:50) how old they are, what grade they're in, what sports they like, and what crafts they're into. Their aim: what do you want it to do? Always start with an action verb and tell them what you need. For example, "I want to brainstorm for a cute pet name for our family pet." Anything that you want, I always tell them it should have a verb in the

**Recipient and Theme**
(41:17) action. Recipient. Who is it for? Tell the AI who's going to read it, hear it, or watch it. Then it's the theme. What is the vibe? How should the AI sound? Funny, spooky, hyped up. Maybe we're doing an English project and it should sound like Shakespeare. Maybe we're doing a technology project and it should

**Audience Structure and Format**
(41:44) be in the voice of Steve Jobs. We always want to tell the AI the vibe, who is our audience structure. What should it look like? Tell AI what format you want it into. Do you want it as a rap? Do you want it to look like a group chat with emojis? Then you put it all together: I am, tell them who you are, who you want, who it's for, what's the vibe,

**Prompt Writing Formula**
(42:15) and what's the structure. It really resonates with them. I've seen them really improve writing their prompts with this formula. Okay. Let me see. I'm a sixth grader who just got a new puppy. Help me come up with 10 funny names for my puppy. The names are for my family to pick from. Make them silly and goofy and put it in a number list with one funny

**Homework: Engage with AI**
(42:42) reason for each name. That would be a very similar lesson that I would teach my sixth graders. Now, four things that I would love for you to do this week. If you have somebody at home that you want to have a conversation with, use AI together with a child. Pick a

**Learn, Skepticize, and Question AI**
(43:09) small task, maybe a recipe, maybe plan a vacation together, or maybe a homework question, and have AI by your side. Have them learn from watching you rather than being told. Then ask them to find the wrong answer. Have them push AI until it says something is incorrect. It's the fastest way to teach healthy skepticism. Talk about what AI doesn't see. What

**Ask Your School About AI Use**
(43:47) voice is missing? Whose experience? AI is as broad as it's trained. Then also ask your school what they're using. You have a right to know what every school is using and you can ask how their data is being protected. >> This is fantastic. I love the different perspectives, the interactivity

**Inspiring Perspectives**
(44:15) thank you David. I think it's inspiring on a lot of different levels from your career pivots and your multiple hats, personal and professional, all of this.

**Teaching All of Us**
(44:39) trying to do our best. >> Yeah. You're doing more than that. My daughter was very fortunate to be in your final class, but I love how you're continuing to teach all of us. >> Thank you. >> I look forward to continuing to learn from you and just so you have the TikTok. Is that the

**Connect with Ms. Elias**
(45:04) best way for folks to stay in touch with you? >> Absolutely. Yes. And my website, misselias.com. msel.com. >> Amazing. We'll make sure to keep sharing that out. This has been chalk full of resource. Thanks to everyone for participating and sharing so many resources in your own

**Thank You and Teacher Appreciation**
(45:29) right and glad everyone could join us for this class today. >> Thank you so much for having me. >> Thank you so much, Sally. >> Fantastic. Great. >> Appreciate that. >> And it's Teacher Appreciation Week, so we happened to pick a really good week to do this.

**Closing Remarks**
(45:50) It's true. Yes. Thank you. >> Thanks everyone. See you soon. >> Bye bye everyone.

## How Gendered Language Shapes AI Responses New Research from ARF and Iris Flex

Speaker: Idil Cakim
Published: 2026-04-30
Tags: ai research, gendered language, feminine vs masculine
Video: https://www.youtube.com/watch?v=t6lN_q1NcK0
Page: https://aimarketersguild.org/sessions/how-gendered-language-shapes-ai-responses-new-research-from-arf-and-iris-flex

[04:41] What is the PsychoGenAI Initiative by ARF and MSI?

Answer / Description:
The PsychoGenAI initiative is a series of empirical, bite-sized studies conducted by the Advertising Research Foundation (ARF) in collaboration with the Marketing Science Institute (MSI) to investigate cognitive and behavioral biases in human-LLM interactions. These studies analyze psychological patterns—such as loss aversion, confirmation bias, and gendered language bias—to understand how AI systems interpret and react to different human communication styles.

By studying human-AI dynamics as a "behavioral mirror," the PsychoGenAI project uncovers how large language models (LLMs) interpret style as user intent. The collaborative research presented by Idil Cakim (Founder & CEO of Iris Flex), Tracy Adams (ARF), and Sam Zang (ARF) demonstrates that without explicit user context, AI engines rely heavily on gendered linguistic patterns to predict user needs, often narrowing the scope of their outputs.

Keywords:
PsychoGenAI, Advertising Research Foundation, ARF, Marketing Science Institute, MSI, human-LLM interaction, cognitive bias in AI, behavioral AI research

[05:51] How Was the ARF Study on AI Gendered Language Bias Designed?

Answer / Description:
The study was designed by programmatically prompting OpenAI's GPT model using identical user intents (asking for a five-item shopping list for a friend's birthday) styled across five sociological and linguistic spectrums ranging from 0% to 100% feminine or masculine. The researchers used the OpenAI API to bypass personalized model memory and analyzed the resulting outputs using human coding, LIWC linguistic software, and Python-based topic modeling.

The prompts intentionally withheld the friend's gender, budget, and specific product preferences to prevent the model from using explicit shopping guidelines. Instead, the experimental variables focused purely on linguistic binaries established in sociological research: communal versus agentic orientations, direct versus indirect language, hedging versus non-hedging, politeness level, and emotional expressiveness. This allowed the research team to map exactly how turning up the "dial" on feminine or masculine phrasing altered the AI's recommendations.

Keywords:
AI gender bias methodology, OpenAI API research, GPT linguistic spectrum, LIWC analysis, python topic modeling, AI shopping recommendations, PsychoGenAI study design

[11:52] How Do Masculine and Feminine Language Cues Differently Influence LLM Outputs?

Answer / Description:
Generative AI models respond to feminine linguistic cues with narrower, more emotional, and domestic suggestions (such as cozy, soft, home, and beauty products) while omitting pricing information, whereas masculine cues yield highly technical, functional, and durable recommendations (such as games, travel, and electronics) that consistently include price data. Instead of treating feminine or masculine linguistic styles as simple communication preferences, the AI interprets these styles as actual user intent, leading to highly stereotypical and restricted results.

When prompts contained feminine cues like hedging, politeness, and emotional expressiveness, the LLM adjusted its phrasing to be supportive and comforting ("I feel your pain") rather than strategic. Conversely, masculine prompts containing direct imperatives and agentic, declarative language triggered highly technical descriptions emphasizing product durability and utility. This variance indicates that linguistic style serves as an unintentional gateway to biased AI output segmentation.

Keywords:
gendered language AI, ChatGPT gender bias, feminine linguistic cues AI, masculine prompt engineering, LLM product recommendation bias, AI stereotypes

[16:56] What Is the Difference Between Implicit and Explicit Gender Cues in AI Prompting?

Answer / Description:
Explicit gender cues involve directly telling the AI to adopt a specific gendered voice (e.g., asking it to write in an "80% feminine voice" or explicitly identifying as a woman), whereas implicit gender cues rely on natural sociological speech patterns (such as politeness, hedging, or emotional expressiveness) without openly mentioning gender. The ARF and Iris Flex study demonstrated that even when users only use implicit communication styles, the LLM still assumes user identity and shifts its output accordingly, reproducing the same gendered biases.

The study validated its findings across both explicit meta-prompts and implicit phrasing, as well as human-written prompts. Because AI is trained to maximize personalization, it acts on implicit linguistic signals to guess who the user is. This means that an average user who naturally types with a polite, collaborative, or indirect style will unconsciously receive biased, gender-stereotyped outputs from the model.

Keywords:
implicit vs explicit prompting, meta prompts, AI user identity assumption, prompt engineering style, linguistic gender patterns

[19:55] What Are the Real-World Implications of Gender Bias in AI Search and Workflows?

Answer / Description:
Gender bias in AI search means that users presenting with feminine linguistic styles may receive more conservative financial guidance, overly emotional instead of actionable medical advice, and restricted product options, which directly limits their decision-making agency. In organizational settings, these biases can create trust gaps for female professionals, affect career and talent development, and lead to unequal utility from the same enterprise AI tools.

If an LLM provides less actionable or more cautious advice based purely on the prompt's linguistic style, it disadvantages individuals who use collaborative or polite phrasing. For instance, in professional environments where LLMs are used for strategic advisory, product analysis, or search queries, masculine-coded language may surface stronger, more direct answers. This dynamic directly threatens the equitable distribution of AI-driven productivity gains across teams.

Keywords:
AI gender bias implications, bias in financial LLM, AI medical search bias, enterprise AI trust gap, algorithmic gender disparities

[25:06] How Can Developers and Marketers Mitigate Gender Bias in Generative AI?

Answer / Description:
To mitigate gender bias, organizations must implement systemic model audits, tune AI engines to prevent restrictive assumptions, and diversify their AI design teams to bring varied perspectives into model training. Marketers should also transition from traditional persona-based demographic targeting (such as "women 35+") to need state-based design (such as "mid-career professionals") to ensure AI interactions provide expansive, non-stereotypical options that preserve user agency.

The research suggests that because LLMs act as gatekeepers to knowledge, design teams have a responsibility to build systems that widen a user's world rather than reinforcing old cultural patterns. By implementing safety guardrails and shifting the baseline logic from demographic guessing to explicit utility requests, developers can create AI products that maintain reliability and trust.

Keywords:
mitigate AI bias, need state-based design, AI model auditing, inclusive AI design, responsible AI governance

[30:35] Why Do Women Express Lower Levels of Trust and Adoption Toward AI Systems?

Answer / Description:
Research shows that women's lower trust and slower adoption of generative AI systems often stem from heightened concerns regarding data privacy, security, and algorithmic transparency rather than an aversion to technology. Because personalizing AI experiences requires users to share sensitive data, women's cautious stance on privacy can inadvertently limit the personalization they receive, highlighting a critical area where developers must improve security communications to build trust.

While women leading organizations implement and project-manage AI integrations at equal or greater rates than men, everyday consumer sentiment highlights a privacy-to-personalization trade-off. This gap suggests that if AI systems continue to operate as "black boxes" with hidden biases, users who value data protection will remain skeptical of fully integrating LLMs into their daily personal and professional workflows.

Keywords:
women trust in AI, AI privacy concerns, consumer data security LLM, personalization privacy trade-off, gender tech adoption gap

[48:27] How Can Users Avoid Lazy or Biased AI Outputs Using Prompt Engineering?

Answer / Description:
Users can bypass lazy, biased, or stereotypical LLM responses by using authoritative, direct, and rigorous commanding language that forces the neural networks off their standard, repetitive paths. Tactics such as telling the AI to "think harder," demanding "extreme rigor," or calling out weak outputs as "lazy" disrupt the model's low-effort efficiency tracks and generate significantly more detailed, strategic, and unbiased responses.

As neural networks seek the path of least resistance, standard conversational prompting can cause the model to slide into stereotypical "tracks." By injecting strict parameters—such as instructing the AI to "push back on assumptions," using authoritative language, or demanding explicit structural limits—users can force the model to provide higher-quality, objective information, regardless of their natural communication style.

Keywords:
bypass AI bias, prompt engineering hacks, advanced LLM commands, authoritative prompting, get better ChatGPT outputs, neural net tracking

## Gender Equity in the AI-Driven World Dr. Nici Sweaney

Speaker: Dr. Nici Sweaney
Published: 2026-04-27
Tags: ai bias
Video: https://www.youtube.com/watch?v=6pnTmxfEfJs
Page: https://aimarketersguild.org/sessions/gender-equity-in-the-ai-driven-world-dr-nici-sweaney

### Introduction to AI Marketers Guild APAC
(0:05) Before I introduce our amazing guest speaker, I'm Nicola Quail, one of the co-founders of AI Marketers Guild APAC. To give you a brief background of what we do, who we are, AI Marketers Guild was originally founded in the US by a marketer called David Burkowitz, who's hilarious because he says not that David Burkowitz, but yes.

### Mission of AI Marketers Guild APAC
(0:29) It's grown into a huge community up there. We wanted to bring that same sentiment down into Asia Pacific and create a regional community where we can spotlight local pioneers, not only AI tools, marketing best practice, but also world-class thought leaders. We're hosting these monthly thought leader series focusing on practical real world use of

### Introducing Dr. Nici Sweaney
(0:56) AI, but also giving us some more philosophical and thought-provoking angles as well, particularly as it's ramping up day by day. Without further ado, I'd like to introduce Dr. Nici Sweaney, who's an AI consultant, educator, innovator, speaker, and gender equality champion to name a few things. Nikki's been working with

### Dr. Sweaney's Background and Expertise
(1:22) leaders across multiple industries, 20 years of experience originally as a data scientist in the university space, but now advising hundreds of organizations all around responsible AI and also upskilling professionals in that practical ethical implementation. A lot of us have been getting all the tools, but there's a whole other layer

### Dr. Sweaney's Current Roles and Achievements
(1:44) that we need to start thinking about. Nikki also serves as a senior fellow at the AI for developing countries forum and founder and CEO of AI her way, as well as a winner of the Australian AI awards female leader of the year. So, you've been busy, Nikki.
>> Very chill, very relaxed.
>> Still a very fast-paced industry at sight. I'm glad you find time to

### Session Handover to Dr. Sweaney
(2:08) sleep. I'm going to hand over to you.
>> Feel free everybody, you can pop questions in the chat or we'll host a Q&A at the end. I'm sure Nikki will manage all that as well. Over to you.
>> Amazing. Thank you so much. Good afternoon, good morning, good evening

### Dr. Sweaney's Introduction and Session Overview
(2:27) wherever you're joining us from. I am Dr. Nikki Sweeney and it's lovely to share the afternoon with you. We have a quick session. I'm going to preface this by saying my most regular speaking gig is a three and a half hour workshop with organizations. For me to cover something in half an hour is a real treat and challenge, but I'm going to try my very best. What

### The Core Purpose of AI
(2:50) we're going to be talking about is around equity and bias and safety. I know that we have so much AI exposure now. Everyone is using it. Everyone is talking about it. It's on all our feeds. It is absolutely everywhere at all times. A lot of it is about being faster. It's about doing more. For me, that's not really the point of AI. The point of AI

### Ethical and Responsible AI Use
(3:15) is to deliver better outcomes for more people and to make more of a positive impact. I come very much from that space of AI could do amazing things for lots of people if we learn to use it in an ethical, responsible way. Just like it can do lots of awesome things, I think it has potential to do heaps of really bad things as well if we don't think carefully through it. My

### Emphasizing Thoughtful AI Implementation
(3:39) space, as we said in the intro, I talk a lot about ethics, a lot about data governance. I'm very big on practical uses of AI, but it always comes with this undertone of, let's pause and think through what we're doing before we do it, rather than saying, we've been able to do all this stuff with AI, so therefore we must be doing a great job, because

### Session Agenda
(3:59) that's not necessarily the case. We're going to go through three different layers. The first thing I'm going to chat about is why I think you are in a good position in your particular industry to not only leverage AI, but also to shape it, and why I think it's important that you understand what ethical, responsible use of AI looks like. Then

### AI Risks and Audit Checklist
(4:19) I'm going to cover off the main sorts of risks when it comes to using AI and then a practical audit checklist, something easy for you to take and remember, screenshot, whatever. So that when you're using AI in the future, you know, I've done a bit of that foundational thinking. I feel proud to stand by my use of AI because I can back up

### Avoiding Random AI Use
(4:40) why I did this thing with it rather than I watched some random Tik Tok video and now I'm copy pasting someone's prompt and I don't know if it's good or not. Before I discuss any of that, apologies to the team because you saw my talk this morning. You've seen me talk about this pyramid, but I present it every time I talk because it helps people conceptualize what I mean

### Levels of AI Use - Prompting
(5:00) when I talk about using AI? And why safety matters so much. For most people when they're using AI, they're talking about prompting a large language model. They're talking about opening ChatGPT or opening Claude or typing in stuff or saying something on their phone and having it respond to them. At that point, ethics and safety and

### Entry-Level AI Use Limitations
(5:23) data and bias aren't necessarily pressing because you're engaging in conversation. You're having a back and forth chat. If I'm having a chat with Peggy and she doesn't understand what I mean, I'm able to redirect the conversation and clarify my point or my opinion. That's only your entry level to using AI. What happens after that

### Autonomous AI Systems
(5:46) level is you start getting into autonomous systems that are connected to your tech stack that can go and do work on your behalf. If I think about part of my marketing, it's a weekly newsletter. Part of my newsletter is to research the AI news headlines and to write a summary of what's been happening. Part of my newsletter is to say this is where you can catch Nikki

### Example: AI-Driven Newsletter Automation
(6:06) next, and that's tied to my calendar. Part of it is to talk about community wins from inside our student space. That comes from a testimonials board that lives inside of Notion. When I have an autonomous system that runs that, I have AI that can go and log in to my calendar, into my Notion, into the internet. It goes and decides what's a good testimonial, what's an event

### Importance of Understanding AI Risks with Automation
(6:28) worth talking about, what are the news headlines that are going to matter to her audience, and it writes the entire newsletter. When we start getting into these autonomous systems that can do work for us, it really matters that we understand data bias, that we understand safety, that we understand ethics, that we understand the risks. Because when you have staff, and now we

### Training AI Staff
(6:48) think about staff as being either a robot or a human. When you have staff, you want to be sure that they're trained properly to do their job and they're not exposing you to risk because they're doing stuff that you're not necessarily looking at. As you start to develop your skill set in AI, I give whole workshops on how to build this skill set. This session is

### Early Practice of AI Safety
(7:08) not that, but as you get acquainted with what is available to you in the AI space now, this stuff matters more and more. You want to practice it early on. You want to practice it in your everyday chats with AI so that it feels normal by the time you have autonomous AI staff members. Shifting your thinking around safety, shifting your thinking around

### The AI Staff Ratio
(7:30) safety to say okay, maybe it's not always in my face when I'm using these tools on a day-to-day basis, but it will matter when you have AI staff, and we have AI staff everywhere. We have more AI staff than we have humans. We have about a 10 to 1 ratio. 10 AI staff members to every one human staff member. That's how our business runs and it will be the new normal. Now I

### Ensuring AI Represents Your Brand
(7:51) get away with this being special because not that many people are doing it. But at some point this will be the way that businesses are expected to run. You want to be sure that these are representing you – you as an employee, you as an organization, you as a brand, you as a business – because they're going to be doing autonomous work without a human necessarily

### Marketing's Superpower in Shaping AI Perception
(8:11) watching everything that they're doing. This is where your superpower comes into because I work with lots of different industries. Very, very different industries. Everyone from doggy daycare to mold restoration to the World Wildlife Fund to the United Nations. I'm talking a big spread of industries. What I love working with are people in creative industries,

### Marketers Define AI Narratives
(8:33) including people in marketing. The reason why I love working with you is because you shape how other people perceive this stuff. When we're in marketing, we are also defining the stories and the narratives that are out there in the world. When you understand what responsible AI use looks like, you can put that out into the world and that shapes how people

### AI Image Bias Example - Doctor
(8:57) perceive what is happening in this space and how they perceive society. As a story as an aside to that, when we think about marketing, I'm going to show you an image issue, but there are lots of image issues with getting AI generated images. One of them that was a classic I presented to a bunch of doctors and it took me 46 attempts with an AI image generator to

### Instructing AI for Diversity
(9:19) get a picture of a female doctor. All men before that, it gave me a picture of a child dressed up as a doctor for Halloween before it gave me a female doctor. 46 attempts at this. Now I can instruct AI to explicitly make me a female doctor with certain demographics, certain skin texture, certain height, certain weight. But if I ask for a doctor, it gave me a man

### Marketers' Responsibility for Diversity
(9:43) 46 times over. When we are in marketing, we're leveraging these tools to create stuff that will be seen by the outside world. You have a responsibility to inject diversity and representation at that level. One, because it's good for you. It's going to protect you from all of those data bias and stereotypes and harm, but two, it's because it shapes how everyone

### Impact of AI Bias on Society
(10:02) else sees the world. There was a study a couple of years ago that young girls were already changing their mind about what job they were going to do because of how much biased information ChatGPT was putting out into the world. As marketers, we can shape that and we can make sure that it's representing the world that we'd like to create. That does big things for society and

### Double Call to Action for Marketers
(10:24) the masses as well. You have a double call to action. Yes, I want you to use AI to make your life more simple, to make your work more simple, to save time, to be more efficient, to be more innovative. But on the flip side, you have a responsibility to engage with it ethically and responsibly because you are part of how the rest of the world sees AI and sees what's possible as a

### Main Risks of AI
(10:43) human race. No small thing. Don't worry, no pressure. It's fine. We have to talk about the main risks. You know why you're important and now we need to know what are the risks that you are exposed to even if they haven't slapped you in the face yet. I said I'd show you another image example. This one's one of my favorites because I was in Forbes

### AI Image Bias Example - Forbes Shoot
(11:08) last year. I was going to be shot in a photo for it. I wanted to give the photographer a concept that I had been imagining for my Forbes shoot. I couldn't find anything on Pinterest. I went to an AI image generator, why not? That's the space I'm in. I told this image generator, "Can you make a photo of a woman wearing an emerald suit? She's"

### AI Output Discrepancies
(11:28) powerful. She has chin length, blonde hair. She's on the cover of Forbes, editorial style, hyperrealistic. Shot with a Canon M2. This is the picture I got. That's great for her. It doesn't look like me or give me massive Forbes vibes necessarily. I tried again. Then I tried 20 more times to get this picture that I could give to a photographer to show it. And

### The Need for Specificity in Prompts
(11:55) then I got a little frustrated. I was, "Can you please make me a picture of a woman in an emerald green suit? She has a small chest and chin length bob. She's powerful, determined, editorial style. She's wearing a shirt underneath her suit," which I didn't necessarily think I would have to get that specific about. Then I got these. When you are

### AI's Pattern Recognition Nature
(12:15) working with AI, it's important to remember that you are exposing yourself to a tool that has a lot of information in it, but its entire algorithm is made to present the patterns in that information. The reason why we get Chesty Laroo here, good on her, is because when I ask for a picture of a woman standing in a forest, these AI tools are

### Default Bias in AI Outputs
(12:41) looking at all the pictures that they have of women in order to inform what they present to you. I have not been specific about clothing, stature, height, weight, skin texture, demographic, ethnicity, anything. So when I've said woman, it's gone, well, to the best of my knowledge, the pattern that I recognize in the training model that I have is this. It's blonde,

### Inherent Risks of AI Use
(13:05) it's white, it's very young, it's pawless, it's flawless, and she's got quite a chest on her because that's what the training model is telling us. Your risks are inherent every time you use AI because it is a data processing machine that's looking for patterns in data and it's looking for the patterns in data across the last 30 years of internet. The

### Data Bias - Western & Male Centric
(13:30) internet is not amazing. We all know that and we're seeing that reflected through there. You have this data bias. It's a lens towards the last 30 years of internet information. It's a lens towards very Eurocentric, very Western-centric, very male-centric information because that is who has contributed to the internet for the last 30 years.

### Privacy, Data Security, and Environmental Costs
(13:51) You also have issues around privacy and data security. If you enter information, you need to be aware who owns that information after you finish talking to it. Best rule is turn data sharing off if you're using ChatGPT or Claude. You also need to be aware that it costs the environment. Your main risks here are

### Understanding Environmental Cost of AI
(14:15) data privacy, data bias and stereotyping, and the environmental cost. I give whole talks about this, but the great thing to remember with environmental cost is we all make environmental decisions every single day. The fact that I am wearing a piece of clothing made of cotton has a water footprint and environmental cost. I own more tops than are absolutely necessary

### AI Energy Consumption Analogy
(14:37) to keep me warm, but it's a cost-benefit analysis. There is a great paper out that shows that eating a beef hamburger is equivalent to about 100,000 queries with ChatGPT in terms of water footprint usage, because the agricultural industry uses huge amounts of energy and huge amounts of water. It's good to remember that we don't want

### Purposeful AI Usage
(14:59) to waste it. It's like leaving the light on when you leave the room. If it's not essential, don't do it. Don't make weird cat videos with ChatGPT because you can. But when we use it to save us time and when we use it to make a positive impact, that is a good use of AI, at least in my books, especially compared to all the other micro

### Injecting Diversity to Counter Bias
(15:17) decisions we're making about our environmental impact on a day-to-day basis. But remember that data bias is in there every single time. It is a pattern recognition machine. Unless you inject the diversity of pattern, it will make assumptions for you. We're going to talk about how to solve that one. Environmental cost that is touched on

### Practical Guide to Responsible AI Use
(15:35) and data privacy security: turn data sharing off as a basis. If you want to know more about that, please reach out. This is the way that you're going to deal with that. This is base level. If you're at level one, you're still talking to AI tools and you're wondering how to do this in a responsible way. This is a simple example about how you can start

### Prompting for Ethical Outcomes (Example 1)
(15:54) to consciously use AI and have it perform in a more ethical, responsible way for you. Prompt A is "Write a LinkedIn post for a startup CEO announcing Series A." That's going to get you a response. It might be great, it might not be. It might also have inherent bias that is hard for you to see because you haven't asked it to do it in a different

### Prompting for Ethical Outcomes (Example 2)
(16:15) way. If you instead said, "Write a LinkedIn post from a startup CEO announcing a Series A. They're a 42-year-old woman of color who founded the company after a 15-year career in climate research. Match her authority without leaning on masculine coded language." That's going to give you more specificity, but it's also

### Specificity Over Tool Choice
(16:34) going to redirect some of its bias. You don't have to change tools. You don't have to be worried about which the best AI tool is. Most of the quality of output comes from how specific you are in how you direct it. If you specifically ask it to not be biased or to look at something from a different lens or to examine something from another point of view or to present five

### Balancing Arguments with AI
(16:57) pieces of information that completely refute everything so far and argue the other side of the coin so that you can balance the argument, that's going to lead to better and safer outcomes than if you're directly saying, "Hey, do this thing for me." and you're taking it and going. Another example of that is recently with Claude, I was putting together a

### AI Pricing Bias Example
(17:17) new package for a client. I said, "How much should I charge for this package?" I can't remember, but it said, "You should charge about 35 grand for this program with this client." Then I said to it, "What if I now told you that I'm actually a 52-year-old white man?" It said, "In that case, you should probably charge

### AI's Micro Assumptions
(17:38) about $82,000 for the same package." The fact that you never ask it to look at things in a different way means that you'll miss that diversity in how it can present information. Too many people interact with AI and they think that the answer they got is the answer. But the fact is AI is making micro assumptions about you and it's also making micro assumptions in the moment

### Shaping AI Behavior Through Interaction
(18:03) that get it to present one version of an answer. It's your responsibility to inject that diversity. Every time you talk with these tools, we also have to remember that we're helping shape how they behave too. It's a much bigger call to action. Every time that you generate something, every time you're creating content, every time you're producing something with AI, you

### Injecting Values into AI
(18:25) are helping AI learn what normal is. If we never inject that equity, that diversity, it will never reflect that back to us. Then we're going to wake up in 5 years time and go, "AI doesn't work for me. It doesn't sound like me. I don't like it." Then it's going to be all the tech bros. We want to inject what our values are. In your industry,

### Practical AI Audit Checklist Introduction
(18:47) that is so important because then you're also putting that out into the world, which helps shape how people perceive the information as well. Last thing is a little practical audit. This is five points for how you're going to cross-check what you're currently doing and how to inject more safety and more responsible use into what you're doing with AI already.

### Audit Point 1: Check AI Model & Data Source
(19:08) The first one is check what model you're using. What are you using to do what with? Do you know anything about where it gets its data from? You can Google this stuff. You can ask AI to explain it back to you about how it's trained. It pays to have awareness. All the AI tools that we usually use are normally either American-owned or Chinese-owned.

### Audit Point 2: Check Your Prompting Style
(19:33) It's important to understand that they're both drawing from different data pools. Again, the answer is not the answer, it's the answer that that particular tool is giving you. Number two is checking how you write to it. Is the way that you're prompting, the way that you're speaking to these tools, does it make assumptions? Are you assuming

### Example: Chatbot Language Bias
(19:54) that AI will understand what good looks like? It's that specificity. What does good look like to you? I built a chatbot for a female business coaching company early on in my AI days. We told the chatbot that it was working for a bunch of women that own small businesses. Then it started calling everyone boss babes. I was, "Oh, boss babe.

### Explicitly Defining AI Persona and Language
(20:18) Oh, you go boss babe." Then we had to code in, "Please do not use infantilizing language. These are your banned words. I don't want any of that." But until we did that, it made this assumption about what would please us. I had to prompt and say, "Please do not say XYZ." You have to remember when you're using these tools, AI

### Audit Point 3: Check Your Outputs (Multiple Iterations)
(20:40) is not necessarily going to have your best interest at heart, but it has the ability to take on any persona that you want. You have to be very explicit about that, and you have control over that. So, checking your prompts, three, checking your outputs, get it to do multiple outputs. That's going to help you see that the answer was only one version of that answer. If we're

### Learning Through Output Comparison
(21:01) writing content, if we're writing a newsletter, if we're producing images, what I say to people is while you're learning how to spot what's good and what's not, get it to do two or three or five iterations of the thing so that you can see how it approaches the same task slightly differently every time you ask it because that's going to help you learn. Yet, when I say, "Make

### Audit Point 4: Distribution Check (Accessibility & Representation)
(21:22) me XYZ," it doesn't do the same thing every time. I need to be explicit about what I expect every single time. Number four is the distribution check. Where is this piece of content going to go? Is it accessible to everyone? I know this is part of how you work anyway, but I think it's important as we move into more

### Gemini Image Generator Factual Error
(21:43) AI generated spaces and we're playing. We're making sure that we're checking for equity and transparency and representation at every single level. There was a classic case when Gemini came out with their image generator. People were using it to create historical images. Then a publisher got into a fair bit of trouble because

### Verifying AI-Generated Historical Content
(22:06) they published some pictures of people during World War II, but the pictures actually depicted 14-year-old Vietnamese girls because they'd used Gemini to create the picture. Gemini was trying to be helpful and insert diversity and representation into a historical image, but it wasn't factually true. Making sure that you're checking your output and that

### User Accountability for AI Output
(22:26) it's serving the purpose that you would desire it to serve. Again, that you're not getting caught up in the, "Oh my god, I'm able to do so much because I have AI, so therefore, I'm not quality control checking it anymore." Remember, if you get fired tomorrow or if your business is hurt tomorrow, AI does not go home feeling bad about that. You are the only person that will feel bad about

### Legal Ownership of AI-Generated Content
(22:46) that. Legally speaking, from a regulation point of view, you also can't blame AI for anything. Any content that you produce with it that you put out there into the world is your IP. You need to stand by it. The last one is the authority check. Anytime you have stats and references and

### Audit Point 5: Authority Check (Fact Verification)
(23:08) if you share percentages or findings or if you share people's quotes, that is the number one place where AI can get stuff wrong because AI's whole purpose is to serve you and be helpful. Its biggest fear is that it will let you down. If it can't quite find the right information or if it can't quite find an amazing quote, it will really

### AI's Tendency to Fabricate
(23:32) confidently make up one in the hopes that it pleases you. Anytime there's specific facts, that is your place to do the quality control check. It doesn't mean you have to go through everything AI makes for you with a fine tooth comb, but anytime it's quoting an exact statistic or it's referencing an exact article or something else that you found, it is worthwhile checking so that

### AI Defaults to Past Data
(23:51) you can stand by what you've done with AI. The last thing is to remember if your AI workflow doesn't specify that diversity, it will default to however it was trained, whoever trained it, and it will default to the past. The big thing I say a lot to people is that AI doesn't know what future we hope to create. It only knows what we've done and then it

### Explicitly Defining "Newness" for AI
(24:17) models its answers off that. Often in marketing, our idea is around changing perceptions. It's changing people's vision. It's changing the way people perceive products or brands, but even how they feel in a moment. That is often a newness and you have to be explicit about how you want that newness to be experienced so that AI can help you get to that point. Don't

### Marketer's Role in AI Oversight
(24:40) assume that it has your best interest at heart. Your job is deciding what the brand says, how it's felt, how it's seen. You don't have to have a responsibility for training the model. You don't have to have a responsibility for picking the right AI tool. You don't have to have responsibility for writing up the policy. Where your responsibility

### Ensuring AI Represents Your Values
(25:00) lies is making sure that you are thinking about ethics when you're engaging with these tools and that you ensure that AI is representing you, the business, the brand properly. Don't assume that it's going to do that for you. What I want you to do after now is running that audit on one thing. Choose one piece of work. If you don't want

### Actionable Steps - Audit & Diversity by Design
(25:24) to choose one piece of work and you want to go everywhere, I'm happy for you to go everywhere. It's a framework for you to think through that guideline every time you're doing something from now on. The second thing I want you to try is adding diversity by design into the way that you talk to AI. The next time you're working through something big or

### Prompting for Diverse Perspectives
(25:43) a strategy or a campaign or a piece of content, I want you to say, "What are five different perspectives that I haven't even considered yet? Or what would someone in a completely different location say about this piece of work? Or, tell me how this particular persona would feel about this piece of writing." The cool thing about AI is it has,

### AI's Role-Playing Capability
(26:06) approaching unlimited data entry points. It can take on whatever persona you give it. It can analyze something from a different point of view. It can play the role of 17 different people or even whole focus groups and give you their perspective and opinion. But it will not do that by default unless you ask it to. Remember to inject that diversity and

### Collaboration and Sharing AI Experiences
(26:26) then share, talk about what you've found, what you've come up against. If you're lucky enough to work with other people, I always advise doing a regular check-in about what we're doing with AI and how it's working. I used to have an AI sandbox hour with my team on Thursdays at 2 p.m. when I had a corporate job. I said, "In this hour, everyone's"

### AI as a Collaborative Sport
(26:46) going to use AI. Next staff meeting, each person has three minutes to talk about something they found and something they struggled with." Having that capacity and the permission to play and share is important because this is not a solo sport. AI is changing the way that we work. It's changing what it means to be human. It's changing how we spend our time. It is

### AI Skills Starter Kit Freebie
(27:06) not yours to figure out. We absolutely have to be leveraging the community collective to work out the best way forward. As a freebie of today, we did give you a skills starter kit. If you are starting out building skills files, which are files that tell AI how to do a particular thing, if you're starting out with this, this pack gives you a

### Governance and Ethics in AI Skills Files
(27:27) template to make sure that you've thought about governance and ethics within your skills files so that when you're instructing AI to do something for you, it is absolutely going to think through what are the dos and what are the do nots that are important to this person so that I adhere to your rules and guidelines every time I'm doing this particular

### AI for Impact Hub
(27:45) thing. It also has three templates of existing skills that could be useful for you to use. Of course, for anyone that wants to learn more about this, this is exactly what we teach inside of our AI for Impact Hub. We teach whole AI operating systems, how to get this up and running in multiple areas in the business with governance embedded from

### Conclusion and Q&A Invitation
(28:04) the very start so that you can feel proud about how you're using AI and so that you can track and trace the decisions that you've made with it in case anybody ever asks because this is the new normal. That was very fast and fun and I hopefully that you got some tips and tricks out of it. Welcome to stay around for questions or any

### Q&A Session Begins
(28:23) comments or anything that people are wondering about how they're currently using AI and how to make sure you're doing it in a safe and ethical, responsible way.
>> That was well done, Nikki. That's incredible. I've got a couple of queries, but I'd love if anybody wants to unmute if you've got any questions or pop them in

### Marketers Representing Brand with AI
(28:43) the chat. You've got Nikki here now.
>> Use me while you got me.
>> I was going to say yes. All right. No, that's fine. Really interesting. So many thoughts there, but first around exactly that. I think marketers can forget that when they're producing content or campaigns or ideas in AI that ultimately they are

### Brand Guardrails for AI Use
(29:11) representing the brand and some get it terribly wrong, Deote last year. Any other advice? As you said, teaching, spending time to teach it, but any other quick bits of advice on brand guard rails and what people could do today.
>> Absolutely. I think

### Creating an AI Do's and Don'ts Checklist
(29:37) one of the things is coming up with your own quick checklist. What we often do with organizations is we'll get them to come up with the three or four absolute do nots with AI and the three or four, what's your orange lights or what are the things that you need to check or be mindful of, and print it out and keep it as a card. For example,

### Pre-Launch AI Checks
(29:56) before things go live, have we asked AI to check for its own bias and present any information to us? Have we asked for three different iterations of this and compared and contrasted, and have we got someone very different from us to also review it? Especially if that stuff is going out on behalf of a brand. Then your absolute no's might be

### Defining AI Boundaries
(30:20) we never ask AI to research on statistics. We always go and grab that information ourselves, or we never get AI to publish stuff without someone's consent or explicit permission. It's those real do's and do nots and where is your line in the sand? That line in the sand moves. I teach an auto versus ask

### Auto vs. Ask Matrix for AI Autonomy
(30:45) matrix. What are the things that you're going to allow AI to automatically do? What are the things that you want it to ask you about before it does? That line in the sand moves the more that you use AI and the more trust that you embed in it. For example, this is not necessarily marketing, but it's part of my brand. I have AI that triages my entire inbox,

### Progressive AI Trust and Automation
(31:03) When I first set it up, I didn't have it send anything. I had it mark what emails were FYI, what was from a client, what should go to my team. I watched it do that for a few days to validate it and correct it. Then I moved it to, "Hey, you can automatically now forward things to my team and give them a one line about why this email is something they"

### Human Oversight for Client-Facing AI
(31:25) can help me out with." Then I checked that for another few days. Then I said, "Cool. Now you can automatically archive all those emails that you've said are FYI once you send me a summary." But I still don't have it send client-facing emails. Now it's only allowed to draft emails to clients, but it has to have me check it and then hit send. A human

### Evolving Trust and Transparency
(31:45) has to do that step. That line moves as you trust it more and more. Eventually, I imagine that I'll say, "Cool, you can now email clients, but I want you to explicitly say that I'm Nikki's AI email assistant. Don't try and pretend it's from me." That moves the more that you use it. The more trust that we have baked in because I've tested it, because

### AI Mistakes and Forgiveness
(32:04) I have rules around what it's allowed to do and not do, because I'm explicit around the language that we use, our brand ethos, our brand authority, and I've seen it work. It's a bit like hiring a person. I'm able to then say, "Cool. Now you're okay to go and do that thing on your own." The flip side of that is recognizing it's going to screw up. People screw up.

### Managing Expectations of AI
(32:24) People make mistakes and we forgive them. I think people have this heightened expectation that if AI ever does anything wrong, they're, "That was a failure." We don't think that with the internet. We all know there's terrible information on the internet, but we still use it because we know it's also useful. People aren't going around being,

### AI's Utility Despite Imperfection
(32:43) "that Wikipedia entry was wrong. So now I've decided that the internet is not a thing." That's not how we frame that. But it is how a lot of people think about AI. If it writes one wrong piece of content, if it gives them a biased piece of information, we go, "that's not useful." It is useful. We have to recognize that it's not infallible. We need to

### AI as a Messy Human Brain Replication
(33:02) stop putting that lens of, it's text so it's perfect. It's Kod's best attempt at replicating how human brains process and create information. It has a bit of the same messiness as a human brain. It's up to you to be super explicit about your expectations. It's training a child. That sounds terrible, but it's training a child in

### Analogies of Training AI
(33:27) that when we are working with young children, you have to be explicit about your expectations. If you want consistent behavior, you give them one task at a time. You say, "Can you go and get your shoes from the cupboard?" Then you give them lots of praise when they get the shoes, and eventually you get them to get the shoes on. Then eventually you say, "Okay,"

### Gradual AI Skill Development
(33:45) "go and get dressed." That's a progressive skill set that you build over time. You need to approach AI with that same sort of slowly is going to lead to fast, big results rather than fast is going to end up being messy and something that you're always going to have to rectify and correct. It might feel cool to start with, but you're much better off doing the

### Building Trust and Autonomy with AI
(34:08) hard yards early on, proving that you can trust it, being explicit about your expectations, which is why we give you the skills templates. Be explicit about what you expect it to do. Then as it proves that it's doing that thing, you can give it more and more autonomy.
>> Brilliant. Hopefully the AI won't have a temper tantrum, but

### AI's Human-like Interactions
(34:26) >> It probably will at some point. One of my AI tools the other day told me to stop overthinking and move on.
>> You're like, "Oh,"
>> I'm, "No, I have not finished weighing up options for hotels. You will analyze 10 more."
>> Now, Peter, if you can come off mute, do you want to ask your question?

### Q&A: Agency Level AI Governance
(34:48) Otherwise, I can paraphrase. All right, I'm going to paraphrase for Peter because I know where he's calling in from. Peter's question was around trying to find agency level governance and if you've got different team members testing, how do we streamline that?
>> I know the horse is bolted because lots of people

### Documenting AI Do's and Don'ts
(35:15) are testing lots of things. If you work with other people, you have to pause and have the discussion about what's okay and what's not. I encourage everyone that works, even if you're working by yourself, to have something documented about this is what we do and do not do with AI. For us, it can be as simple as a couple of paragraphs saying

### Avoiding Shadow AI Use
(35:34) we use AI to improve our outcomes by leveraging high repetition tasks. We do not have data sharing turned on. Our preferences for these couple of tools and if clients want to opt out, they can elect to. But something that makes it clear because otherwise you do get rogue use. There's this term called shadow AI use where a high percentage of employees

### Open Communication and Shared Learning
(35:58) or a high percentage of workers will be using AI tools without ever talking about it. There's two parts of that. There's having a discussion around what is okay and what is not okay and we all have to agree to it. Then there's the permission thing. When you start having weekly chats about how you're using AI, when you start building out a shared space where people can log

### Normalizing AI Discussions
(36:16) what they've tested and what they've tried, it takes away this stigma attached to AI and makes people be more open about it, which means you can start having conversations around ethical, safe use. Those two things generally help. Just know it's normal. Every organization I walk into, even big, billions of dollars

### Importance of Clear AI Guidelines
(36:38) organizations, a lot of the time they don't have a clear guideline about what's okay and what's not. That's a lot of the time what we're helping them to do. You're not alone there, but it's a good conversation to have as quickly as you can.
>> Brilliant. Thanks, Nikki. Dax, did you want to come off mute and ask a couple of questions?

### Q&A: AI-Generated Character Copyright
(36:58) >> Yes. Hi. Can you hear me?
>> Yes. Awesome. Awesome. This is a specific question we are doing for one of our clients. We have been building a series of AI animation films where we have created a character. Think of Paw Patrol or Bob the Builder. One of the questions that the client asked is, it's a

### Copyrighting AI-Generated Content (Legalities)
(37:20) series of AI animation films we are doing, whether they can copyright the character and can they license it because it's AI generated. I thought I'll check with you on that. It's a good question. At the moment, it depends a little bit where you are located around how the legislation impacts you. It's

### Midjourney and Reproduction Risk
(37:40) worthwhile googling copyright laws, but at the moment, usually what you create, you are able to copyright. The flip side of that is that it may be reproduced for somebody else using the tool if you've been using a tool that allows it to be trained on your data. For example, Midjourney is one of the big image generators in the AI space. If you

### Trademarking AI Art
(38:01) create art with Midjourney, you are allowed to sell that art and you do have copywriting license to it. But there is also a possibility because it's part of Midjourney's data training model because it created it. It may reproduce that same piece of art for somebody else and then they also are able to go and do that. So if as quickly as you can, if you can trademark that and

### Q&A: AI-Generated Music Licensing
(38:24) license it, it's the safest way to go, but yes, you are allowed to do that. It's not necessarily AI's to own. Once you've created it, it's considered your property.
>> Right. I had a similar thing happen with the music as well because we created this video which has a background score. We are using a commercial license for Suno, but they

### Suno and Spotify Distribution
(38:46) wanted to know whether they can distribute this on Spotify as well as other mediums. Whether they have the license to do that even though we have commercial license for
>> As far as I know, yes, because there's been quite a few viral songs on Spotify that are created by Suno.
>> All right, perfect. Kia, I might answer this one in

### Q&A: Measuring AI Output Quality
(39:08) the chat about, "Do you create metrics or ways to measure the quality of your AI produced output?" Absolutely. It's like having staff. You have KPIs, you have performance metrics. We have performance review meetings with our agents. We look at what they're doing. Even in a single prompt or a session, if you're working through on a desktop large

### AI Self-Evaluation Prompts
(39:29) language model program, you can have ways for it to cross-check what it's done and then give you a self-evaluation. That's pretty straightforward. You get to the end of doing something and then you're, "All right, can you now examine everything you've done for possible bias? Are you pushing any possible stereotypes? If someone came"

### Roleplaying for Weakness Analysis
(39:49) from the gender equity council, how would they examine this problem?" You can ask it to roleplay as some people in that space and poke holes in its own story. A lot of the time when I'm working on strategic things or ideas or big campaigns and chunky work, I'll get to the end and I'll be, "What are five weaknesses here that I haven't even"

### Avoiding Leading Questions with AI
(40:08) considered, or what is going to be a way that someone analyzes this where it's possibly going to make them feel excluded that I haven't yet thought of." So I'll phrase a few different questions that and get it to spit back things. The important thing to note is that if you ask it, "Have I possibly left anyone out of this?" because

### Critical Judgment for AI Output
(40:29) you phrase the question, it's a leading question and AI doesn't want to disappoint you. So, it will absolutely point out people that you've probably left out, even if you haven't really left them out. At that point, that's where your critical judgment comes into play. It's going to present a bunch of information to you and you get to be the one to decide how

### AI as an Advisory Board
(40:47) much it actually matters or not because AI's whole purpose is to please you. If you say, "What are five perspectives I've missed out on, what's another way of analyzing this problem? What would someone in a different sociodemographic range think of this? What would my ideal client love about it? What would they hate about this?" All those sorts of questions. Even if your

### Leveraging AI for Diverse Perspectives
(41:06) ideal client wouldn't hate anything, it's going to try hard to come up with something that the ideal client will hate because it wants to please you and answer your question. But it's a nice way of capturing lots of different perspectives. I like it's having a board of advisers. You do a piece of work and then you throw it out

### Q&A: AI Tech Stack & Tool Choice
(41:24) and you make AI take on different personas and analyze what you've done. They present their findings, what they like about it, what they don't. It's ultimately up to you about which way you go with it.
>> Great. Thank you. Quickly to tag on, if downtime allows, what about from an AI tech stack perspective, in terms of

### Tool Agnostic Approach
(41:46) there's so many different tools to choose. A lot of the AI offerings have their own center of gravity, the thing that they do well. When you're for your own business or when you're consulting for other people's businesses, is there an approach that you take in terms of pitching one tool over another?

### Centralized AI Files
(42:10) >> I don't, because I'm very pro being tool agnostic. The way that we teach things, talking about having skills files, we have all of that on our own servers. For us, everything that we create, every project that we're using, every AI agent that we have, all of its files, everything that we've used to design it will also live for us,

### Avoiding Tool Migration Headaches
(42:36) it lives in Notion, but Google Drive, SharePoint, whatever. Because it means when things like the start of this year happened where everyone went, "Oh my god, Claude's amazing." Then all these people, "Now I have to take all my stuff from ChatGPT and put it on Claude." We didn't have that problem because all of our stuff existed

### Choosing a Primary AI Tool
(42:53) in a central place. Some of it was being used in ChatGPT. Some of it was being used in Claude, but we could say, "Hey Claude, go and look at this file. That's what I want you to do." I don't necessarily advocate for tools over another unless I see a specific use case where I'm, "This thing would be awesome at that." That being said, what I

### Maximize One LLM Before Diversifying
(43:13) normally tell people is choose one large language model, pay for it, and get good at it. They're all on an arms race with each other. They're all releasing things. Someone will come out with something and then the rest of them are, "Oh my god, we got to come out with the same thing." Until you're using it a lot, honestly, for most people,

### Dr. Sweaney's AI Tech Stack
(43:31) they will not tell the difference until you're using it every day. When it comes to my tech stack, I have paid ChatGPT and I also have paid Claude. I use Claude probably at the moment around 90% of the time and I use Codeex, which is OpenAI's version of Claude. I use that for work when it's not tricky stuff that I need Claude to work on because

### Diverse AI Tool Use Cases
(43:57) Codeex, I get way more tokens, so it's freer to do busy work. So I use those two. We have Gemini because we're a Google Workspace. I use Nano Banana for image creation. I use VO for video. I might use Higsfield for video as well. When we're looking at multi-agents, I also have some self-hosted large language models so that we can do some stuff without

### Maximizing Existing AI Features
(44:19) paying for it. That is beyond what most people need. Most people need one large language model and to learn all the features because most people still aren't necessarily using, if you're on Claude, most people aren't necessarily using projects and skills and scheduled tasks and Claude co-work. Until you've maxed that out, there's no point adding more

### Q&A: Codeex vs. Claude Code
(44:38) stuff for the sake of feeling clever. It's not necessarily going to get you much further.
>> Thanks so much, Nikki.
>> Sorry, one question. Quick clarification. You mentioned you use Codeex over Claude code. Is there a specific reason for that?
>> No. It's the type of work I'm doing. So I will use Codeex

### Codeex for Execution in AI Workflow
(44:58) when I already have a system for something and I'm wanting it to execute. For example, I have a system that checks my diary every week and it detects if I have a presentation within the next seven days. If I have a presentation within the next seven days, it will go to my Notion and my email and gather up all the information about it

### Claude for Strategic Thinking, Codeex for Slide Generation
(45:21) and then it will go into Canva and have a look at how I last presented about that and then do an outline for my talk once I've approved the outline. I do that with Claude because I find it thinks through things better. Once I approve the outline, I have Codeex produce the presentation slides because sometimes I speak a lot. Sometimes I'm

### Cost-Effective AI Tool Selection
(45:42) legitimately prepping 15 presentations at once and they might have anywhere between 15 and if I'm doing a half day workshop, I might have 150 slides. If I got Claude to do all of that, I would max out my tokens. So I switch and I have Codeex do all of that because it's more cost effective. It depends on the task. It's the matter of

### Maximizing Dual AI Subscriptions
(46:03) fact that I pay for both. So I try and maximize my usage of both. But if I only paid for one, I'd be able to do all of that on one. It's preference for how much we are doing with it and where we spread that cost.
>> Amazing. That was fantastic. I have to say I am taking the hamburger example from my 16-year-old

### Reassessing Environmental Impact of AI
(46:26) daughter who has been telling me all about the water consumption every time I put it and then I have to think about it if I put a mega prompt in. So I'm going to let her know about that one. It's not without fault, but I think there's this spotlight on it because it's new and it's adding to the problem. So yes,

### AI for Greener AI
(46:44) we've got this concentration on it. We're all, fashion in particular is one of the most water-thirsty and energy consumption-heavy industries. AI is not coming close to that yet. The flip side of it, not that it's an individual user's problem, but probably the only way that we're going to green AI is to use AI to work out how to make AI

### Catch-22 of AI and Green Tech
(47:07) greener. There's this catch-22 in that we are probably going to solve some of our big humanitarian problems by leveraging tech, but in the meantime, we have to have the energy consumption that goes along with that in order to get to a better place. I fully understand. I'm an ex-conservation ecologist. That's what my PhD was in. I very

### Strategic AI for Improvement
(47:28) much understand that side of it. But there's this realistic balance with what we're doing without thinking about it. There's a lot of stuff that we're doing without thinking about it that doesn't make anything better. Whereas when we use AI strategically, it can improve our lives and our communities lives and also the way that we do business and who we serve and how

### Key Takeaway: Mindful Prompting & Critical Thinking
(47:46) many people we can serve as well.
>> To your point, being mindful in our prompts and critically thinking around the output. I think that's been one of the key takeaways here today. Thank you Nikki. I hope you get some time out tonight because I know it's been a big day for you.
>> I know. I feel like if we're talking tomorrow, I'm going to lose my voice,

### Closing Remarks
(48:09) but it's fine. We live to train another day. Thank you so much for hosting me this afternoon. It's been a real pleasure to join you and nice to see some of your faces and to meet you as well. Thank you so much.
>> Brilliant.
>> Thanks, Nikki. Thanks everyone. See you next time.

## How to Win AI Search GEO Strategies That Actually Work

Speaker: Aditya Jain
Published: 2026-04-27
Tags: ai optimization, ai in marketing
Video: https://www.youtube.com/watch?v=vpJDZDoW6t4
Page: https://aimarketersguild.org/sessions/how-to-win-ai-search-geo-strategies-that-actually-work

**Welcome to AI Insiders**
(0:05) Everyone, welcome to another edition of AI insiders from AI marketers guild and marketecture. I'm your host David Berkowitz, here with a guest for a subject I'm pretty passionate about, pun intended. We've got Aditya Jain from Passionfruit. It's one of these AI optimization dashboards and recommendation engines that I've wound up using, and I've showed off in some of my AI training to make me look smart to other clients and attendees. They've got some terrific visuals to understand how your ranking in AI engines. I'm here to learn and trying to keep up because I feel as soon as someone comes out with a recommendation for what to do, someone says, "no, don't do that." I think it's a very confusing, murky space, but hopefully, you want to make some sense of all this today.

**Breaking Down the Noise**
(1:04) Appreciate that, David. The layup in the end was perfect. The goal here is to break some of the noise in the market and show what we're seeing. This will be as educational as I can make it. While I go through it, I know everybody might have different backgrounds. If you have any questions or want me to dive deeper into anything I share, just make me pause, and I'd love to dig deeper and make anything more personalized to anybody's personal problem statement with any of the points we discussed today. To kick things off, maybe I'll share my screen and dive right in.

**About Aditya Jain and Passionfruit**
(1:41) For context, an artifact I made on Claude, which I think is lovely, highly recommend. To begin with a quick intro, guys. My name's Adi. I was a growth operator for the past decade and founded in my undergrad and masters at Stanford. Passionfruit is a lovechild and our second company with my co-founder and I. We run an AI marketing operating system for SEO and AIO. We marry what we call marketing engineers in-house with our own AI tech stack and work with about 500 plus brands globally. Last year, we drove a billion dollars plus in revenue for our clients and worked directly with many providers across Google, OpenAI, Anthropic, and Perplexity.

**Hot Takes and Blueprints**
(2:28) The goal for today's talk is to share some of my hot takes on the industry and what people are pitching. As much as it is about my thesis, I want to share some blueprints that I think are really working for us internally that I think people can take home and implement within your orgs. To dive into my comment here, what we think in today's world is that playbooks as a concept are dead.

**Playbooks Are Dead**
(3:12) What I mean by that is every agency you go to or every marketer you speak with is selling the fact that they've cracked this new playbook that will make you win. That's what I'm trying to debunk today. There are a couple of reasons why.

**Every Search Changes Results**
(3:35) The first core reason is every search that anybody does on any platform actively changes the next result it dictates. If you see the counter on the left, it's a live feed for how many searches are being done on AI search platforms today. We're already at 115 million searches. Each of those will mildly change the platform, which changes the next answer it gives you. I know people say the answers are different across 30 different searches, but even if you run it on your own instance, every couple of days, you'll see a different answer that each engine will produce.

**Growing User Bases and AI Architecture**
(4:17) Obviously, these platforms are important for us to decipher because the number of users on each of them is growing and scaling very quickly. I saw at the start of this call everybody's talking about Claude, but ChatGPT still reigns supreme in terms of our commercial usage and where we really see a lot of revenue trickle in from. Google itself has made major pushes here, but because a lot of this architecture is AI dominated, we see these change regularly.

**Creating a Marketing Loop for GEO**
(4:48) What I'm going to talk more about is how these platforms are changing and what causes the change. We're talking about how to really create a marketing loop, which in the context where we're talking about today is for GEO. I'd recommend viewing more and more of your platforms in a similar limelight in terms of how you can run those loops on steroids to be able to drive your own data to feed back what changes you make for your next campaigns.

**Platform-Specific Tool Recommendations**
(5:14) For the representative one core belief that I wanted to show you here, we're running these live queries on the back end. What you'll see is for the same query, the top tool that each of the four target platforms recommends is drastically different.

**Why Different Platforms Recommend Different Tools**
(5:37) The core reason behind this is because each platform is catering to a different user base and is pulling from different sources. For example, Perplexity uses Reddit a lot more than Gemini does. Because Reddit users are early adopters to things, share a lot of new insights. So, the platforms they recommend for top tools are drastically different than what a ChatGPT might show you, which focuses now increasingly on Wikipedia.

**Consistency of Findings on a Given Platform**
(6:04) I have to ask already, though, because one of the top things we're grappling with is how consistent are the findings even on a given day within the same platform?

**Topic-Dependent Variability**
(6:21) It depends on how many sample sets you pick. I think one shift is that a lot of platforms use API calls. That's where there's a very high inaccuracy between what they show versus what each of us would see when we use the front end. For prompts that are not being dictated too often, you can do away with 10 to 15 searches, and that won't change as much through the day. But for example, a hot topic, even cloud design, if you see the review for cloud design, that would change drastically because it's a very, very hot topic within that same day. These engines are designed to provide opinions, not just surface results. Depending on the topic, that variability is very, very high, even within the course of a day.

**Three Core Misconceptions**
(7:12) What I wanted to focus on is what are the three core concepts that I think are misconceptions, which are leading us to take incorrect decisions in terms of how we operate.

**Each AI System Has Different Economic Models**
(7:25) The first piece is "optimize for AI search as one channel." The core problem statement here is each AI system has really different economic models that they operate under, which means they sell to four very different audiences.

**Targeting Different Audiences and Data Sets**
(7:46) Because of which, they have very different data sets and very different ways to target each. For example, ChatGPT used to rank Reddit threads very highly when OpenAI was a partner for them. But Google is always focused on driving business to your website because that's where they could generate a lot of the money from. So, they rank brand pages. Perplexity focuses on authoritative content. They focus now more on enterprise sales, so they use what they call citation graphs. That's become core.

**Understanding Channel-Specific Citations**
(8:15) One core concept here is you have to understand which channel matters for you most and then accordingly start functioning on the core citations that those channels focus on for gathering their data. That's your first selection. Depending on that, you should run and pull data from the correct AI search engines.

**Content Playbooks Not Needed**
(8:30) The second piece a lot of people have been talking about is you need a brand new content playbook to win on AI search.

**Measuring Content Success Differently**
(8:49) The thing that's very different is formerly, we've been testing the same type of content we write, just having our feedback loop run differently. What it means was earlier, you would measure the success of your content from how many clicks it drove or how many backlinks it drove from a Google infrastructure. Today, you write the same helpful content for that same target audience. It's just that the systems you look for is this working or not, are citations on channels like Reddit, YouTube, or a forum, which then compounds into what you see on your ChatGPT or Perplexity.

**Focus on Intent, Not Jargon**
(9:33) The core entity and the core physics behind what content you produce is exactly the same. There's a lot of jargon out there about comparison tables. There was jargon about lm.txt, which becomes very popular every 3 months. There's a new wave of new advice that comes in. To be very, very honest, every large player's goal is not to change the way you operate in terms of how the internet works but to find a way to understand the internet the best. As long as you're targeting the right intent and pushing out the right form of content with some basics in place, you're in a good position to scale up your presence and drive results.

**Citations Should Drive Revenue**
(10:13) The last big cop-out has been "citations don't drive revenue." I've heard a couple of people across a bunch of podcasts really push this out where they say, "Track your citations. Forget about revenue. This is part of the funnel that you don't need to measure. Just measure citations, and that's branded presence you're paying for."

**Measuring Pipeline Lift from Citations**
(10:31) Citations directly should be driving a lift in branded searches as well as a lift in your pipeline. There are three ways you should be measuring this pipeline lift. First, ceteris paribus, if you don't see a lift in your branded searches, it means you're targeting the wrong queries that you're getting higher citations from.

**Correlation of Revenue Lift and Engagement**
(10:53) The second piece is from every channel you've seen a citation lift from, you should see a direct correlation of revenue lift from those channels. Revenue could be sign-ups or whatever you're tracking as a proxy for some sort of conversion. The third piece is overall, you should also see organic sessions from other channels or conversion on traffic you're driving from other channels go up, but your engagement rate on average across any single campaign you run should be higher. You should be tracking this, otherwise, you're targeting the wrong query set.

**Tackling Core Lies and Next Steps**
(11:35) These three are core lies that I want to tackle, and what I think the next steps should be. If anybody...

**Perplexity's Current Relevance**
(11:41) I have tons of questions and happy to hear from others too, but how much does Perplexity matter right now? What's your take on that?

**Perplexity's Declining Inbound Traffic**
(11:57) So, I think Perplexity's business has changed drastically. They don't publicly showcase their user count. But over the last 6 months, we've seen inbound traffic across our customers drop drastically from Perplexity. So, at least from the data set that I have and that we use, it's not very, very high. We still see for really technical use cases, especially within research, Perplexity still has a very strong customer base. So, if you're in that industry, I would highly recommend it. If you're in B2C, SaaS, or e-commerce, or slightly mid-tier B2B, I would not really focus on Perplexity as much today.

**Other Key AI Platforms and Grok**
(12:39) So, then, for people researching things with AI, is it mainly ChatGPT, Gemini, Claude? How much does Grok matter right now?

**Grok for Twitter Presence**
(12:56) So, I think Grok, what we use primarily is for promoting ourselves more on Twitter. The model is integrated, it's important to have Grok understand your presence and your brand because it's frequented there, and that's where you get a lot of citation data. So, I would focus on these four. But as I said, you should be focusing on any channel you're seeing traffic trickling from first before you go on a spray and pray approach trying to understand all four and optimize for all four, because each of them operate very differently and will tell you different stories.

**Perplexity for Large Enterprises**
(13:47) It's just great to get the latest because even now it's like I was wondering how long does Perplexity stay as far as like one of the top things even modern because I hear much less about it as far as influence goes right now.

**GEO as a Dynamic Mechanism**
(14:05) If you're selling to large enterprises, Perplexity is important because they have very good B2B sales, and a lot of vendor selection is now moved to AI search engines, so Perplexity still holds strong value there. One important thing I want to showcase with this is actually on pace of iteration. This is what happens when you're trying to think of GEO as slightly a static mechanism. This is actually a client of ours data that I'm showcasing here over a 90-day period where we were tracking ChatGPT and Perplexity. We were seeing some inbound here, so we focused on a couple of activities, and we got the client to drive up visibility. They picked at about 15% overall visibility across a particular query set.

**Rapid Model Updates and Lost Visibility**
(14:48) Within 6 weeks ahead of that, they were back down to close to zero. The reason behind this is the model updated the source set where a lot of the blogs we generated and new service level pages we generated no longer stood the test of time because Reddit became a dominant source for the insight that the answer that ChatGPT was generating. Because of which, very quickly we lost all the visibility we tried to work for in the first 6 weeks.

**Why Playbooks Go Out of Importance Quickly**
(15:13) This is what I wanted to highlight in terms of what I'm going to talk about the system in terms of how quickly these models change, which is why playbooks go out of importance very quickly. Reddit was important and YouTube, now you're seeing a pick up of Wikipedia. That's why I dive into the next piece where I don't think playbooks are important. I think building a loop is very important, which has four steps: measure, attribute, produce, and detect decay for anything you have outputted.

**The Four-Step Loop**
(15:58) Four very simple steps. First, with measure, you need to be able to track on a weekly cadence. If you track on a monthly cadence, you are already late. What's your share of voice across any search engine you deem fit, be it ChatGPT, Perplexity, Google AI overviews. Understand the queries that you want to track, what's relevant for you that could be driving revenue, what's not. Understand your movement over the course of a week. Which queries are stable, which queries are not.

**Understanding Citations and Content Freshness**
(16:40) Understand what citations are driving change here and how fresh is the content on each of these query clusters you focus on. For example, if I'm looking for the best CRM software, generally they're using Reddit threads or G2 reviews that have been there for over 6 months. But if I'm looking at the best shoe to wear for the New York Marathon, it's slightly more new, so you look at newer Reddit threads that are slightly more recent. That's where you really see how important it is for you to keep engaging on that thread versus an older thread.

**Attributing Revenue to Channels**
(17:09) The second piece is to really understand attribution. For each of these channels, each of these citations, what are the core KPIs you will track to measure revenue for yourself within that? Either you directly measure from GA4 any AI referral traffic that's coming in to make that your core KPI that you really want to drive to make a business case for it.

**Measuring Branded Search Lift and Engaged Sessions**
(17:41) The second thing we've seen a lot of brands do is measure branded search lift. You can track on a Google search how many more branded searches am I driving every month, and how much of that is translating into revenue for me. The third thing a lot of people have started doing was measuring the average engaged sessions and how much of a lift is that over time. So that you can see if overall my higher presence is really driving better traffic to my website and making them stay on my website for slightly longer.

**Justifying ROI**
(18:05) There are a couple of methods here, but it's really helpful to continue making a business case for the channel that most people are struggling with justifying ROI from, aside from just overall brand presence.

**Producing Content Based on Citations**
(18:23) Third is using these two data sets, what you should start producing. If Reddit is important, or some PR campaigns are important, or some blogs are important, understand what's being cited to answer the questions in each of these engines and accordingly start generating those pieces here.

**Focus on Entities, Not Keywords**
(18:44) The most important thing here is don't generate those pieces based on keywords. I saw this prompt; this is what I'm going to generate. Focus on entities. For example, I'm going to focus on this ICP or this pain point first and create a wheelhouse of assets that I want to generate over the next four weeks or eight weeks. That's how planning should work.

**Avoiding Scattered Content Efforts**
(19:05) Generally, what we see is people will try to generate separate assets for 30 different queries that are targeting different personas. You don't make meaningful progress on any, because of which you don't really see any movement, and then you start from scratch every new week. It's better to understand one entity first and really build production there.

**Detecting and Addressing Content Decay**
(19:22) The last thing I'd focus on is every week you will see movement where some assets of yours were cited last week but have lost citation or have gone down. That's where you want to understand where you are seeing decay for any assets you've already pushed out. Those are your low-hanging fruits where you can easily refresh those assets or re-engage on those posts, bring them up, and automatically refresh them.

**Four-Step Process Results**
(19:51) These are the four-step processes that I'd highly recommend. There are complexities within this that I'm happy to dive into and happy to take questions somebody has on this. But, by using these four simple steps, we've run this across different types of industries. These are the type of results you can get to see in a very, very simple system.

**Achieving Significant Citation Lift**
(20:19) Where people have gone from zero citations to driving 38% of the citations across their category in under 6 months across search engines. We've had companies focus on Reddit as a category and build up their presence. People have used Quora. People have focused on updating their schemas, building a YouTube corpus, and scaled up.

**Building a Real Engine**
(20:44) Once you realize what works for you based on what the AI search engine tells you, it's really easy to build a real engine where you spend 2 to 6 hours a week and scale that up even without hiring somebody externally for that use case.

**Key Elements for the System**
(20:58) The four elements I'd highlight as important are: choose a data ingestion layer. This is like your typical crawlers or tools like profound. David, you had them on the platform on one of these webinars before. They're good at these things. They help you ingest the data, connect with your GA GSC, also provide you data on citations.

**Attribution and Execution Tools**
(21:27) The second piece on the attribution piece, I think, is important. Use traditional tooling like GA4 or even use tooling like HubSpot and Salesforce if they've built out for you to build that attribution funnel for yourselves and understand the core levers you need to move on. On the execution piece, I think it's either using content writers in-house or using the data you have, hire agencies that focus either on Reddit or on YouTube to find the core levers you want to move and hire experts that are core for each of those systems. I think that would be great.

**The Role of AI in Content Generation**
(22:07) As we get into AI to the content generation piece and using that, one of the things I've heard mixed opinions on is the role of using AI to create a lot of that content. I've heard mixed opinions on how much human in the loop, ranging from zero to lots, is recommended for that. Do you have some informed opinions on this one?

**Industry-Dependent AI/Human Mix**
(22:33) 100%. I generally think there's not a single correct answer. It depends on your industry. For example, if you're writing best listicles for different softwares or different products, it's important to have an opinion. If you're in a category like insurance, it's very important to have an author for your content.

**Aggregating Content at Scale**
(22:54) If you're also in a CRM space or a very generic space where there's a lot of content out there, being able to aggregate that content at scale and be that one source that's citing 10 other pieces underneath it is very helpful for any AI search engine because you are becoming that one source because they are all optimizing token counts on their end. If they can crawl one source to get eight sources, they will always choose you over going through eight sub-sources.

**Recognizing AI vs. Human Content**
(23:37) For every different type of piece, even if you look at the citations that are commonly being cited for any answer you look for, you'll be able to see that variability where you can see something is AI generated versus something is not. That tells you what can work, what can't work. That's why it's important to have human in the loop as a larger strategy where you have that mix, but everybody is doing a mixed strategy here. If you just focus on humans generating every piece, you're most likely to be left behind unless you have the ability to spend on volume.

**Surprising AI Content Results**
(24:06) I've seen some surprising results even with some tests where someone a while back had asked me for content for some SEO requirements. I'm like, let me see how good Gemini can be at putting this together. Their opinion was like, we can't use AI for this at all. I submitted some of this Gemini-created content, and they started ranking right away in the AI overviews. This was a tiny consultancy. So, my own assumptions have often been tested and proven wrong here. But it's what I love hearing from someone like yourself who's so in the weeds.

**Cloud-Generated Visuals and Data**
(24:58) 100%. Maybe I'll, before I go into the next piece, talk about a couple of things. Lisa, this whole thing was generated on Cloud Code. The point of AI slop is you can generate a lot of these things that look pretty as long as the data is correct. This stuff digests. For me to generate this, it's taken me over 2 days to come up with something like this. The point of generating a lot using bases, as long as your data source is correct, it serves the purpose. As I said in the call out, this was generated on Cloud.

**GA4 in Shopify Environment**
(25:40) The second question, Jennifer, you had was, is GA4 within a Shopify environment helpful? We work with a lot of D2C brands. The core importance here is if your tracking on GA4 is set up correctly for Shopify, only then will it work. Otherwise, you might see variability where your Shopify data is 30 to 60% off from your GA4 data. That's where you have a lot of issues, especially when cookies are not enabled. For attribution, I would first suggest getting a GTM set up correctly before you use only GA4 for attribution.

**Client Split and AI Ranking Measurement**
(26:11) We work with B2B and D2C clients across the board. Our split is about 70% in e-commerce and 30% in B2B. Lisa, the way we measure AI ranking is, depending on the different use cases we have for clients, we have our own tool that we use for which we've partnered directly with a lot of the LLM providers. But then some clients have their own tooling that they use. So, HubSpot has their own AO tool that we use and plug into, and then we use that data to verify citation data.

**Claude's Commercial Intent and Revenue**
(26:50) Paul, the reason why Claude is something I haven't spoken about here primarily is Claude is not an entity that's being used for commercial intent, since they don't want to push people out of the Claude ecosystem when they provide suggestions. So, we haven't seen major updates in revenue attribution coming from Claude yet, which is why most of the commercial use case we focused on ChatGPT, Perplexity, and Google.

**Core Use Cases for Client Websites**
(27:18) For client websites, there are four core use cases that I would highly focus on. First, whenever you see a citation that comes up, you can see what types of pages are being cited. So, either you create a new page that you don't have that's commonly being cited.

**Updating Existing Pages and Missing Sections**
(27:42) Second is if you have a similar page, update it based on the most common trend you see on pages. For example, in B2B websites, what we commonly see is people don't have strong FAQ sections, people don't have testimonials, G2 integrations onto their plug-in pages. They don't have competitor comparison sections, which are very, very common. So, that's the second approach where you update your existing pages to tag on those pages better.

**Critical Guide Pages and Brand Control**
(28:10) Third piece is within websites, people are scared to write content on best use cases for my X product or compare their products directly with other products by writing best-of guides. Writing those guide pages, writing those best-of articles are really, really critical for building out that presence on your own website so you control the narrative about your brand.

**Backend Architecture and Schema Implementation**
(28:31) Lastly, there's a lot of back-end architecture around schema implementation as well as allowing the correct bots onto your website. That's the core hygiene that I'd highly, highly recommend. For example, generally most websites allow the traditional Google bot and the standard crawlers, but they don't allow a lot of the ChatGPT bots as well as Claude bots. That's the first use case that everybody should update. The second piece is implementing the correct schemas across the website and the architecture so that any crawler that does land on your website can easily script through and go through the complete website.

**Ads on GPT and Investment**
(29:10) Jennifer, can you give me an opinion and any experience with ads on GPT and how this impacts unpaid? Yes. Ads on GPT requires a minimum investment of 100K. Focused on two plans: the free plan and the $8 plan. The companies that we've seen run good pilots on ChatGPT are the users that are targeting where their end customer is at the bottom of that quartile in terms of how much money they're willing to spend on AI search.

**HubSpot's Success with Intent-Based Ads**
(29:51) A company that's done really well on ChatGPT ads currently has been HubSpot. The way ChatGPT ads is working is they don't target the queries, they target intent. Across a very large intent forum, they're showing HubSpot ads, and that's doing really, really well for HubSpot.

**Impact of Paid Ads on Unpaid Queries**
(30:12) Unfortunately, I don't have data on how that's impacting unpaid ChatGPT queries and result behavior just because the channel itself is growing so large, and currently, we don't have enough data of which queries are surfacing ads or not, and that pilot is not that large yet. So, unfortunately, I won't be able to answer that question in terms of how it's impacting unpaid, but I am seeing very promising results on the paid forum right now.

**Connecting Sites to Google Search Console**
(30:36) I'm just going to answer Marshall's comment. There's some pretty built-in analytics. Marshall, great. I think you've answered another question on top, so lovely. One of those things also for those, because there's a lot at the intersection here of all this GA optimization and vibe coding, which we've been talking about a bit. One of those useful things that comes up is GA4 for sure. You have connecting your sites to that. Asking your platform or search, does it have any connectors to Google Search Console, for instance?

**Proactive Monitoring and Best Practices**
(31:23) To what extent can it proactively monitor and address some of the things that invariably come up: duplicative content, and all these structural things that tend to happen whenever you're adding a lot to your site? It's being able to use some of these more traditional best practices. In the past, I barely was ever on Google Search Console because I wasn't usually the one managing as much of this directly. Now because of AI, I'm managing much more directly, so I have to learn the stuff that some people learned 10 or 20 years ago.

**Connecting Data Across Platforms**
(31:55) 100%. What we're seeing across the board is one tool is no longer sufficing any attribution or team in terms of what their needs are. A lot of people have been connecting their data across different platforms. They're even downloading that and putting it on a singular platform to understand the correlation across different channels to better inform decisions.

**Ecosystem Understanding**
(32:31) A lot of people are now using GSC, GA4, as well as the data they get from an LLM visibility platform to better understand how the full ecosystem is working together and what are the best next steps they should be taking across this.

**Platform-Specific Attribution Setups**
(32:50) I would suggest I don't think GA4 or Shopify are the single best use cases for everybody else. Depending on your hosting platform, be it Webflow or Shopify, or Framer, or if you have a lovable website, the platform that you should be using is unique, a system you've set up and where your end attribution happens. For Shopify, obviously, it's happening on your website, but some people have end attribution on HubSpot. That's what changes a lot of the setups that individually you need to evaluate and recommend, we'd recommend individually to focus on.

**Easy Way to Push Out a Sprint**
(33:23) The one easy way I'd recommend pushing out this sprint is you can pick 10 initial queries. These should be buyer intent, commercial-focused queries where either you've seen search trickling from these, or this is something that you would imagine yourself searching for if you're looking for the product that you're selling.

**Manual Query Audit and Action**
(33:40) I would either manually run these queries across ChatGPT, Perplexity AIO, and Gemini. See the results you get. I would log those manually to see which ones are even relevant for us today. If you see completely different competitors are coming up, that's never going to be an important channel for you. Only if you see that relevance, I would focus on that channel and then track what URLs are coming up, what was the answer. Use that data to decide what actions I want to take tomorrow. On Wednesday, take a couple of actions from the top cited pages in the answers. Be it a new page you need to create, a new content piece you need to write, whatever it may be, understand that and push that out.

**Weekly Audit and Tangible Results**
(34:32) On Thursday, rerun your audit and see if anything changed. I would recommend doing that again the following week. Every week, if you spend an hour or two doing this, it becomes a simple process to start using and adopting GEO and understanding that better to see what's working for you, what's not. Once you do this for a couple of weeks, you'll be able to start seeing tangible results because these engines sometimes shock you in terms of how quickly they can change and show you being cited. That's where I think we've seen companies build up capabilities and scale their own presence and attribution from each of these channels.

**Use Cases for Improving Lift**
(35:14) My question for you, Aditya, thank you so much for breaking it down for me. This is not my wheelhouse, but I'm curious to hear more about some of the use cases you're seeing their clients, whether they're B2B or B2C. How are they using this to help improve lift that you helped hit it out of the park before?

**E-commerce and B2C SaaS Strategies**
(35:34) In e-commerce, what's working for folks is creating new category pages that target niche users and pushing up content supporting those niche pain points or ICPs that they target. Then spinning up YouTube channels that use their social media content because YouTube is a highly cited source. For B2C SaaS companies, engaging with users on Quora, Reddit, and doing targeted PR activities on commonly cited sources has been extremely helpful.

**Direct Comparison Pages and Product Updates**
(36:13) Then them creating direct comparison pages for each core competitor that a ChatGPT is citing has been extremely useful. Writing product updates for core. Generally, we have service pages that show for any user what their product does, but writing larger product updates that really go into the in-depth version of the solutions has been extremely helpful.

**Research and Product Research**
(36:40) It sounds like it's for the research and also for product research.

**B2B Content and Documentation Strategies**
(36:44) Yes. For large B2B companies, they're building heavy published content which they used to hide behind paywalls. Now direct for any crawler to come up, which is based on proprietary data highly reviewed by their experts internally. Then go out there and co-publishing pages with top experts in their industries, pushing that out, and really focusing on G2 reviews and their outdated website architectures where they're opening up a lot of their documentation out there. All of these crawlers can come and be able to see it.

**Embracing Conversational Engagement**
(37:26) Sounds like you're teaching your clients to be more open and to engage with their audiences directly from what it sounds like. They used to have more closed systems, but now AI has forced them to be more, let's say, conversational when they're talking to audiences.

**Paywalls Less Helpful**
(37:38) Exactly. Paywalls are no longer as helpful as they used to be.

**Role of Traditional PR in LLMs**
(37:45) What I've seen come up also is the role of traditional PR, more traditional sources. Is this more ammo for bringing more traditional PR and elevating that to impact the LLMs and brand reputation?

**PR Guided by Cited Sources**
(38:09) Traditional PR needs to be better guided with sources that are being cited on each of these channels. Traditional PR focuses on your typical large publishers like Forbes and Times magazine. What we're actually seeing is these channels pick up a lot more unique individuals that could even be running substacks or 100,000 subscriber blogs that are unique to the particular industry. That's what we're seeing play a big role in terms of shaping LLM opinion.

**Rewriting PR Strategy**
(38:50) PR continues to be important. It's a rewrite of the strategy in terms of what PR you focus on and how you attribute success for that PR where it's not about getting it listed on a high visibility, high brand name channel.

**Team Skills and Workflows**
(38:58) We had a question from Sandeep before, SEO optimization was linear with longer cycles. Any comment on what type of team skills and workflows you're seeing now?

**Faster Uplift and Update Cycles**
(39:11) Sandeep, on this timeline, what we are seeing with clients is you can generate uplift within 15 to 30 days at max. That's the timelines I think every marketer should get used to in terms of how they're seeing. Any marketing channel today, because of how quickly these platforms update. Google used to release an update once every year, then once every 6 months, and now they release an update almost every couple of weeks. These AI search channels change a lot quicker. That's the type of core update cycle you should be focusing on.

**Comfort with Change and Volume**
(39:55) From a skill set perspective, first is being comfortable changing the core metrics we evaluate, that's the first shift. But the second is having a larger wheelhouse of capabilities because volume and speed of content and pushing out changes is very, very important.

**Outdated Quarterly Review Cycles**
(40:17) The typical quarterly review cycle where you create a strategy that you execute over 90 days and write maybe two pieces or low volume outputs is no longer feasible because everybody else is trying to operate at a higher throughput. They're iterating their strategies a lot quicker.

**Agility is Key**
(40:39) I don't think you need to know coding, but being comfortable with that level of change and that level of agility is extremely important to actually win.

**Investing in External Platforms**
(40:43) Excellent. I have a question. In response to that, if a business is not able to update their website that frequently, would it be better for them to invest in being active on a platform like Reddit?

**Channel-Specific Strategies**
(40:59) Lisa, depending on the type of citation, either it's Reddit, YouTube, Quora, or G2, whatever that may be, in terms of within the category that's important for you, definitely then work outside of the core website becomes important. That channel may differ depending on what the commonly cited citations are for your industry itself.

**Multimedia Content and LLMs**
(41:27) Thank you. One thing you haven't talked a lot about today is multimedia. People are producing way more video content. How good or bad is that when you're trying to influence LLMs?

**LLM Optimization for Token Usage**
(41:48) The way LLMs work on the core at their back end, what they're optimized for, is to reduce token usage for each search they provide without hampering token quality. What that means is as much text as they can pull and as much pre-data that they already been trained on they can pull to answer the question. The more it will lean on that.

**Shift in Multimedia Indexing**
(42:14) Only for use cases where it doesn't think it'll provide the best outcome for it, will it try to do a web search and then lastly look for net new types of content. A year and a half ago, multimedia content was not something LLMs were really good at indexing and understanding. That since then has shifted drastically. A lot of websites, including YouTube, Instagram, are providing transcription directly on the back end that makes scraping off this content a lot easier.

**Uptick in Multimedia Influence**
(42:37) We've seen a massive uptick. We actually did a study with the YouTube team about how much YouTube as a channel has scaled up and how multimedia is playing a larger and larger role across the board.

**Cross-Pushing Content for Shaping Opinion**
(42:58) What we recommend brands across the board to do is, even though multimedia content is not the biggest driving lever if you're getting started with GA, for example, if you have an issue like Lisa, you can't update your site as much. Or you already have one channel you're pushing out a lot of content to, that could be Instagram, LinkedIn. Cross-pushing that content across other mediums is your lowest hanging fruit to increase your chances of shaping your opinion across any LLM. That dominance of this type of content will increase.

**Google and ChatGPT Leading Multimedia Adoption**
(43:39) We saw this uptick be driven first with a lot of self-help and guides where you see a lot of YouTube content already start flooding the market. Google led the charge, ChatGPT followed suit very quickly. We're going to see that adoption scale up.

**Art, Science, or In Between?**
(43:56) I think this is Lisa's question. Is this an art, a science, or somewhere in between? I think this is definitely right bang in the middle. There's a structural process to it. The art really comes in in terms of what you think is most relevant and what you think your core ICP looks like.

**Decoding LLM Behavior**
(44:25) I do think as LLMs get a lot more structured and there's a lot more data and consistency in their outputs and they become slightly more public in terms of how they operate, it'll become easier for us to decode on a more consistent basis what our uplift and what the ROI from each of these channels can look like over time. For example, Microsoft released their AI search visibility platform last year in November. That really helped people understand and decode a lot of what Bing's behavior looks like. As more and more platforms follow suit, it'll make it more and more of a science than just an art.

**Adding Sources to Notebook LLM**
(44:56) What are the best ways to add sources to Notebook LLM?

**Incognito and VPN for Geo-Targeting**
(45:07) Lovely. What I always recommend is run everything on incognito. Generally, if there's a different geography you're targeting, run that geography as a VPN. The question was what are the best sources to influence Notebook LLM?

**Querying Across Platforms**
(45:31) To add the best apps to add sources to Notebook LLMs. What I do is I just run exactly the same query across all four platforms. I'll skip Claude for now just because I have to log in, but I'll show you representative examples of how it shows up. What are the best sources to run Notebook LLM?

**Analyzing Citation Sources**
(45:52) If you realize this did not default to web search. Generally, whenever it defaults to web search, you'll be able to see the citation sources it's using, its pre-pledged data it already has. This is how it's gathered its resources. For this exact output, all it's done is it's highlighted the different sources it has for each. I'll just wait for this full answer to load. These are the top apps.

**Manual Data Collection**
(46:16) What I do is create a Google Sheet. I should not be working off this. But, I will create a Google Sheet, list out the query I put, put Google Drive as number one, Google Doc as number two, YouTube as number three, or just copy-paste these three. Go on each of the citations that ChatGPT used. If you see two of the citations are both Google. Then it used a platform called Bboom. I hope my full screen is still relevant.

**Engaging with Cited Sources**
(47:01) This is a random source in terms of an article. Arjun published this last year. So, clearly up-to-date data is not as important for. This has zero comments. Extremely recursive source. Used Notion without any clear citation, Obsidian without any clear citation, and cited a couple of automation tools that it got from this Reddit thread. This Reddit thread has only about 22 upvotes and one comment. Post about 24 days ago. I would do this same approach by gathering this data across Google. But, I'll open up the Reddit thread, I'll open up this, and I'll open up this Reddit thread, and put down the different answers I saw for Google, as well as a YouTube video.

**Tailoring Content and Engagement**
(47:47) I'll do that same thing here where in Perplexity you'll see a lot more different documents and pull that same data. This is where a general crawler is slightly helpful. But generally pull for some of the most common ones that I see that are common. As you see the Reddit threads that Google pulled are drastically different than the Reddit thread that ChatGPT pulled.

**Influencing Opinion on Threads and Articles**
(48:04) What I would do is if this was something that I wanted to rank for and have my tool showcase, I would go in, understand what this study is talking about, and actually engage saying what I would recommend and why I would recommend that here. I would look at this blog article and try to create the same article on my own website because I assume I'm selling an automation tool as well.

**Leveraging Google's Structure and Depth**
(48:45) For the Notebook LM help page, now I don't have the authority that a Google has, but I would understand the structure that they've created and see if my own source if I can create an article about Notebook LM with the similar structure and the same depth that Google is citing and cite Google's Notebook help page as well as the Reddit thread, combine them as sources, and create a formal article that I own that's citing these five sources as a combined source for it.

**Reddit Channel Importance**
(49:09) Similarly, this is the same thing for another Google page. This is the same page that we saw here that even Google is citing. This is a different thread. I would go in and engage on this thread. I now realize I can't recreate this thread because it has a lot of reviews, but I know that the r/notebooklm overall channel is extremely important for me. So, I would live and breathe this channel and find five posts I want to comment on this week and five new threads I'd want to start that's relevant to the threads that I've seen here.

**Time to See Results**
(49:38) How long before you can see results if you take this approach?

**Platform Change Speed as a Proxy**
(49:45) Genuinely, it depends on how quickly each of these platforms change. For example, this thread is 6 months old. This thread is just 24 days old. That's how quickly it's cited. So, I would assume on ChatGPT, you can change the opinion on ChatGPT a lot quicker than you'll be able to change the citation frequency on a Google. Directly how recent the thread it cites is actually how quickly it's a proxy for you to start influencing the opinion for that channel. To get 93 upvotes or 250 upvotes is hard, but to get 22 upvotes in one comment is a lot easier.

**Connecting with Aditya Jain**
(50:23) That's how you and the difficulty of it. We're approaching time here. This all terrific, and I appreciate the real-time glimpse. What's the best way for folks here to follow up with you, stay in touch?

**Contact Information**
(50:47) I'll list, drop my email and LinkedIn on chat and happy to, as well as Passionfruit itself. Happy to connect any way possible.

**Brand Reputation and Optimization**
(50:56) Amazing. I've learned a ton. All this info for the best takes on GEO and app optimization right now. We need as much as we can get because this is where brand reputation is and where it's continuing to go. I appreciate you starting right off with busting some myths out there and giving us some of the latest, very well-informed thinking on this. Thanks for coming by. Appreciate everyone with so many great questions today. Look forward to seeing you all next week.

## What Marketers Reveal About AI Adoption and ROI Gaps

Speaker: David Kohl Morgan
Published: 2026-04-17
Tags: ai in marketing, meaningful roi, ai adoption
Video: https://www.youtube.com/watch?v=D5U1nbAvbds
Page: https://aimarketersguild.org/sessions/what-marketers-reveal-about-ai-adoption-and-roi-gaps

Introduction to David Kohl
(0:05) Welcome back to another edition of AI Insiders with AI Marketer's Guild. I'm your host David Berkowitz and I'm here with someone who's on my short list of favorite Davids. David Kohl, I got to know very well through his work at TrustX and one of the most

David Kohl's Expertise and Morgan Digital Ventures
(0:31) thoughtful folks looking at areas trust and privacy and areas that are often deprioritized by too many. He's been at the forefront of it for quite a while and now is running Morgan Digital Ventures and came to me with some ideas for

Purpose of the AI Adoption Survey
(0:55) creating some research and trying to see what folks in the marketing and ad industry are thinking about and how they're using AI. You might have seen me share this at different points with the AMG community. Maybe some of you took this survey and wanted to welcome David, hear what you're up to, why you did this, what you found.

Interactive Session Format
(1:25) As always, for those who haven't been here before or been here in a while, keep this super interactive. There are even some we could do some live cuts of the data. Anything anyone wants to go and dive into and explore further, we'll have fun discussion. Welcome David. Thank you, sir. I think the feeling is mutual. You were also on my

David Kohl's Opening Remarks
(1:50) list of favorite Davids. It's a David love fest. Okay, I won't ask where I am on that list, but I'm glad to be anywhere on there. Thanks for having me on. This is going to be fun. We will

Session Agenda and Survey Catalyst
(2:07) definitely do a little talking at the beginning, I'll show you some data, and then I want to reserve plenty of time to play around with the tool in more of a Q&A style. We'll definitely reserve time for that. The catalyst here started after I left TrustX last fall and quickly got back into the market because

AI as a Transformational Catalyst
(2:29) AI seemed like this new transformational catalyst that everyone's embracing in one way or another. I wanted to find out where are people? Where are we on the maturity of playing around to getting value? My consultancy focuses on growth and

Morgan Digital Ventures' Focus
(2:51) differentiation. I help marketers, agencies, publishers create a distinctive value and get value out of that that turns into economic growth, revenue and profit. I saw AI as this catalyst as a tool but wasn't sure where people are in terms of adopting.

Anecdotal Findings on AI Adoption
(3:14) What I heard were three things anecdotally. A very small number of companies, mostly ad tech and martech companies, were talking about AI in terms of a measurable goal. We figured out X is going to help our company and we have initiatives going on. That's the gold standard in getting value from a new

Common Responses to AI Adoption
(3:40) technology. That was a tiny number of folks in my anecdotal conversation sample. Most of the folks I was talking to back in the fall were saying anything from "I'm paralyzed. I literally don't even know where to start. I'm sitting back and watching others." The other group was

Lack of Goal-Oriented AI Strategy
(4:02) "we're playing with AI," or as I like to put it, "we're throwing AI spaghetti against the wall to try to see what sticks." Very few were organizing around a goal, a differentiation and a competitive advantage. The catalyst for doing the survey was to see if the anecdotes aligned with what the rest of the market's saying, or just the

Survey Takeaways Overview
(4:25) David Kohl sample. That's where we started. What I'm going to do, because I'd rather not talk, I'd rather show you, is I'm going to share my screen. Let me share with you a couple of takeaways. I'll start with the stuff that to me wasn't surprising, and I don't

Widespread Experimentation with AI
(4:46) think it'll surprise anyone else. The first thing that wasn't surprising is that whether you're an agency, brand, consultancy, platform, publisher, the predominant responses to the survey, folks are somewhere between experimenting with AI and the very early stages of implementing it as a production

Current AI Adoption Stage
(5:11) day-to-day tool. Not surprising. This survey was conducted between middle of January and the end of February. That makes sense for a month or two ago. Practically speaking, it's probably the same even today. Not surprising. Everyone's between experimenting and implementing.

Content Creation Leads AI Benefits
(5:31) The second thing that I didn't think was surprising is when we asked "where do you rate yourself in terms of maturity by function" with "not using" on this chart on the left in the gray, and "getting measurable ROI" in the orange on the right. Not surprising is that content is king. That's the OG for AI. We all used it for writing. We

Efficiency and Time Savings
(5:58) started playing around for image generation. Not surprising that content was at the top of the list of where folks are getting the most benefit from AI. The third thing that I also felt was not surprising is that to the extent folks are measuring some benefit, if you look at this top section "how AI impact is being measured," time saved and

Measurement Focus on Efficiency
(6:23) efficiency was the top response. About half said that if they're measuring anything, it's that they're getting efficiency and time saved out of AI. Not surprising. I want to come back to the maturity by segment, this is where I got my first head scratch. You'll notice that platform

Agency Self-Assessment Surprise
(6:47) companies – martech and ad tech platforms primarily – rated themselves as perhaps a little further ahead. "We're now implementing." Not surprising. But what did surprise me is that agencies rated themselves so high on the maturity curve. I have two theories here, and they're theories.

Theory 1: Client Pressure
(7:09) One theory is that agencies are under tremendous pressure from their clients to stay ahead. They're investing, they're organizing, and they are themselves driving AI tools in that implementation phase because of their competitive pressure. That's one theory.

Theory 2: Agencies "Drinking the Kool-Aid"
(7:32) Another theory is that agencies are having a little bit of drinking the Kool-Aid. They believe that because they're playing around with AI that they're ahead of the curve relative to other organizations. I thought of that second theory because if you look at the third line here, the 16 companies that responded in

Consultants vs. Agencies Maturity
(7:56) the consulting line, consultants also have pressure from their clients to show that they're advanced, to be ahead of the curve. Consultants seem to be rating themselves further behind, in fact, of the group, the most behind. The question becomes, are consultants too honest relative to agencies

Honesty in Self-Reporting
(8:20) who are a little bit drinking the Kool-Aid? There's no way to know, and we didn't ask people for long form explanations of all their answers. There are some insights that I can show you on what people said, but in the end we have theories. That said, we think it was surprising to see

Open Discussion on Agency AI Maturity
(8:44) agencies at the top. When we get into the dialogue, since I know there are a lot of marketers on this call, I would love to hear how people think about their agencies relative to the maturity of usage of AI. That was one of the surprises. Another one, come back to this chart I showed before. Remember I said time

Lack of AI Impact Measurement
(9:07) saved 45% of respondents say that's the impact AI is making. A quarter of respondents said that we are not measuring the impact of AI. There's a phrase, I'm sure everyone heard, "what gets measured gets done." If you're not measuring meaningfully,

The "Spaghetti Against the Wall" Approach
(9:31) you may not be getting anything done. I think this comes back to that anecdotal evidence I had early on that folks are using AI but they're throwing spaghetti against the wall and seeing what sticks as opposed to focusing on differentiation, focusing on something distinctive that can drive economic value.

Questioning "Time Saved" as a Metric
(9:55) On the measurement front, I've been curious about this for years because the first metric that started to pop with AI usage was this time spent. Even there, I'm not sure how many take that through to real efficiency metrics, especially

Actionable AI Metrics
(10:20) if you're not somewhere like a big tech or somewhere that's laser focused on that. I even wonder with the metric that most people are reporting and saying that this impacts, can they do anything with this? What's actionable based on this as opposed to something where you

Focusing on Business Outcomes
(10:45) see some results come in? David, the last three: customer engagement, revenue growth, and cost reduction are results that could be measured and should come from being more efficient at doing your job. They get insights around customers.

Underutilization of AI in Ad Measurement
(11:07) Let's come back to something for a second. Ad measurement. Many of us are in marketing and media, and ad measurement was rated the second most "not using." The light gray bar on the left. Ad measurement is a great way for AI

AI's Potential in Ad Measurement
(11:32) to show its capabilities. It can take massive data sets and look at insights and use those insights to drive. Where was it? Here it is. To drive things like better customer segmentation or to run media more effectively, which would result in revenue growth. To me, there's a circle here:

Functional Maturity: Ad Measurement & Media Planning
(11:57) we're not focusing on some of the differentiated activities, and it shows up in the functional maturity under things like ad measurement, media planning. If I compare agencies to brands, on the left side you have 14 agencies, on the right side you have 12 companies

Agency vs. Brand AI Usage
(12:24) in the brand or advertiser category. You'll see here that agencies are using it for content creation, so are brands, but to a lesser degree. When you get back into measurement, you would think the agencies would be using it more regularly for ad measurement. You see

Client-Side Measurement Lag
(12:45) it's fourth on the list. We're not seeing client side do that much at all. If I go back to segment maturity, let me pull back. You can see that these were the ones we showed before. If I go to role, you can see that, well, let me pull out the founder role. There's only one respondent there.

Role-Based Self-Assessment
(13:13) Here we go. This is how the roles in the companies thought about themselves. There are some interesting little insights for you here. Bill, do you have a question or are you talking in the background? No, it's the webinar. I'll mute.

Barriers to AI Adoption
(13:37) We're not stuck in a pocket. All right, there we go. Let me cover the last thing on the insights here, and then what I'd love to do is open the floor to ask questions about the data, and we can go through it. The last thing on the insights was the skills, resources, and budgets

Internal Obstacles vs. External Complexity
(14:01) were getting in the way of AI adoption. On the one hand, it makes sense. Everyone's budgets are tight, there aren't enough people. But I would have thought that vendor choices and complexity, which was noted by 40% of respondents, would have been the highest barrier.

Vendor Choice Not the Biggest Barrier
(14:25) Particularly since a lot of folks talked about being paralyzed, stuck. There are, I don't know, hundreds, David? Hundreds of companies that call themselves powered by AI today. I would have thought that one of the major barriers, or much higher, was the

Skills, Resources, and Budgets as Primary Barriers
(14:46) sheer choice and not knowing who to hang your hat with. But in fact, it's internal skills, resources, and budgets. Those are the big ahas that I found. The tool lets us take a look at how one segment compares to another.

Inviting Q&A on Data
(15:08) This is all respondents relative to brands and advertisers. I can do that same look at the functional maturity, all versus brands and advertisers. Rather than me drive, what I'd love to do is pause here and turn over the floor to ask any questions. We can look into the data together.

Sally's Question on Agency Media Planning
(15:33) Sally has a comment in the chat that she would have thought that agencies would be using AI for media planning and buying by definition, as AI builds on the programmatic platforms of a decade ago. I'll show you what we see here. If I compare, let me make sure I got the right

Comparing Agencies to Publishers
(15:54) thing. If I compare agencies, all right, let's not worry about it. I can see what I have all the agencies. It's giving me the roles that I've selected. Agencies are on the left, and on the right side I have publishers,

Ad Measurement and Media Planning Discrepancies
(16:18) to juxtapose the folks that are generally buying media from the folks that are generally selling media. You can see here that ad measurement, media planning are, well, ad measurement I would think both publishers and agencies are doing. We're not doing media so much media planning on the publisher side.

Agencies Lack ROI in Media Planning
(16:39) But on the agency side, you see that ad measurement and media planning are fourth and fifth down the line. In fact, on media planning, nobody on the agency side is calling themselves at a maturity level of delivering actual ROI. This idea of

Client-Agency AI Tool Agreements
(17:00) marketing agencies using AI, they can boast about it in general terms on the one hand, but then they probably have to be smart about it. Get agreements with every client, what AI tools, these are the AI agents to use, and maybe how we do it, something like that.

Client Comfort with AI Tools
(17:23) We think it'll work for you. But then there's going to be reservations from company to company. There's going to be some agreement, and then the companies are going to have their own ideas about which one they're most comfortable with. I'm wondering how that's handled, or is there any

Client-Agency Coordination on AI Tools
(17:46) knowledge about that? Are you asking, Christian, how are the agencies coordinating with their clients on which tools are authorized and which tools should not be used? Not the, I mean tools, methodologies, any of that, because they can boast that, "Oh, we're

Client Dictates AI Tool Preference
(18:11) we're so mature on this," but the client, they're taking their lead from the client in every case. If the client says, "Oh no, we don't like Chat GPT, we think it's the output's terrible. We like Claude or Deep Seek or whatever."

Self-Reporting vs. Client Reality
(18:31) Everybody's got a lot of feelings about AI, and everybody's very nervous. You can boast how mature you are, but that's according to you, right? That's self-reporting. Maybe there's some information about, and it's probably early days, I'm sure it is, but

Tool Agnostic Methods
(18:52) some information about what they do to get a sign-off from the client, or the client's lead in terms of, "Oh, the client trusts Gemini, fine, we'll trust Gemini and run our stuff with Gemini because our methods are tool agnostic."

Q1 Survey: Dominant AI Tools
(19:13) We did ask each respondent for the tools that they're generally using. This was a Q1 survey, and Chat GPT dominated as the default entry point with Claude and Gemini behind them. I have a feeling post everything that happened with the federal government and the change in branding after the Super

Agencies' Wide Tool Diversity
(19:42) Bowl with Claude, I have a feeling that we'd see a different answer today. But within the agencies themselves, agencies reported the widest tool diversity across all kinds of different functions. They reported LLMs, the obvious choices, meeting assistants, synthetic personas, platforms

Client Comfort Drives Tool Diversity
(20:08) for figuring out personas, for figuring out to get to a readiness for a change from traditional search to AI overviews. Then they had a couple of the larger holding companies were developing their own hubs. Christian, to your point, there's probably a recognition that not all clients are going to be comfortable with

Unknown Client Approval Process
(20:31) all tools, which is why they have such a wide diversity of tools in house. I don't know the actual process for getting approval or validation within the client base. In the survey tool itself, which you

Accessing Survey Tool for Details
(20:49) can get access to, you can read through not only the summary by company type, but individual responses by company type to what they're using. I think you'll see that the diversity within the agencies is strong. Thanks. I just, when marketers market themselves, I'm

Skepticism Towards Marketers Marketing Themselves
(21:13) I'm leery. That's all. When marketers market themselves or when agencies market themselves? Agencies market themselves. Marketers marketing myself is terrible. It's terrible.

Brooke's Question on Agency Barriers
(21:34) I don't trust any marketer to market themselves. Every marketer needs a marketer to market the other person. That's it. Thanks. David, it's Brooke Pets, if you can hear me. I wanted to ask a follow-on question or share a thought on this specific to the media planning and ad measurement. Thinking again, these barriers

Agencies and ROI Demonstration
(21:52) specific to agencies, why are they not? Is it barriers on the client side? I would expect agencies to be creating use cases, case studies that demonstrate how using AI the way they're using it, the tools, regardless of the tools they're using, but with their methodologies, how it is driving marketing performance, how it is

Lack of Platform Adoption in Agencies
(22:12) delivering ROI, and use that as a sales tool. I'd be shocked if they aren't doing that. I haven't seen it yet. Nested in there is my question: if the agencies aren't wrapping around certain platforms and tools for media planning and ad measurement, why is that? Maybe they are, but it looks

Explaining Agency Lag in Ad Measurement
(22:31) nebulous. I don't have a good reason why agencies aren't further ahead on ad measurement and then using the ad measurement for planning and buying decisions. Brooke, it does seem intuitive to me that that should be front and center, particularly for media agencies, which I'm sure many of these agencies are.

Theory: Difficulty Accessing Raw Data
(22:53) I have a theory on that coming out of TrustX. For those of you who don't know, I ran an ad tech platform for almost 10 years before I came back to my consulting business in Merkle Digital. My theory on this is that it's harder than we all believe to get a hold of the raw data to then use in the AI models for things like measurement, analysis, and

Raw Data Access Challenge
(23:21) optimization. It's very possible that the agencies would like to be further ahead here, but I will tell you that we made our raw data at TrustX available to all our clients, whether you're a buyer or a seller, but very few clients asked for

LLMs and Data Analysis Potential
(23:41) it because it's a massive amount of data, and at the time, they didn't know what they could do with it because they didn't have the power of LLMs. Now we have the power of an analysis tool where I can chuck the data into Claude or otherwise and say, "Tell me X, Y, and Z." But it's still a massive amount of data,

Data Accessibility as a Barrier
(24:04) and it's still hard to get at the data because most ad tech companies are not like TrustX was, where we freely would give access to the data. Brooke, I'm not sure if that's the answer, but it is a theory as to why folks are further behind on measurement and then using the measurement for optimization and planning. I think

Financial Services Data Barriers
(24:25) that makes sense. I come from a financial services background, highly regulated, so getting the lower funnel data, that's where there are barriers. The agencies are going to get ad response, but often combining that with what happens afterwards is difficult. Thanks. I will say something about the marketers

D2C Marketers' AI Advantage
(24:46) through anecdotal discussions. Marketers that are in a direct-to-consumer or direct-to-customer type of environment. Folks in retail, folks that are OEMs that sell directly on their

Unleveraged Potential for D2C Brands
(25:03) sites, or that frequently have engagement directly within their own environments – those are the organizations that I think could easily put AI to work to get ahead on things like who are their personas, who's coming to my site, how do I use that data to improve the odds or improve the effectiveness and efficiency of my marketing. Marketers have the ability to take some

Marketers Missing AI Opportunity
(25:26) of this on by themselves, even if the agencies don't have the data with which to do it, or aren't doing it for whatever reason. I feel like it's a miss. I feel like the marketing segment, the actual advertisers, aren't embracing some of these capabilities, which are now feasible and easier to do because you always had the data,

Obstacles: Skills and Budget (81%)
(25:53) but now you have a tool set that allows you to make a human query, and that query can give you insightful results. David, this is great. I have a metaphysical question. I wanted to focus in on that 81% which you said on the company side, the biggest obstacle is skills, budget. My question is why? If you imagine

Top-Down AI Push vs. Investment
(26:14) sometimes when you talk to people on the client side, CEOs, want to make AI a focus. I can understand the skills not being there, but are they not investing the dollars? Is it a paper tiger? Curious on your thoughts on that. I don't have a good answer to your question, as much as I would love to

Future Investments and Priorities
(26:35) have had a good answer to your question. We do have a summary next steps as part of this tool, and again, you can look at it either by individual responses or by summary by company type. It talks about what the companies want to do. There's also on this, sorry, it's the priorities tab where folks are making planned investments.

Low Investment in Ad Measurement
(26:58) Here you have how you're measuring, and here you have more of the responses. Planned investments in more automation, in building products. Sorry, strategic priorities there, and any investments in business development strategy. Here again, sorry to keep coming back to this ad measurement, I'm

Strategic Priorities for Ad Measurement
(27:19) so surprised how low on the list investments in ad measurement are when we saw previously that that's an area where it's in my view behind the times. I unfortunately don't have the answer to why, but we can at least see what the strategic priorities and planned investments are between this screen and by reading some of the

Survey Update Frequency
(27:46) next steps. I was curious, how often do you plan on updating this? It's fair, a year from now or even 6 months from now, it may be different once people catch up to their aspirations. I am thinking of doing it in Q3. This was the Q1 survey. Let's let it

Effort Behind the Survey
(28:04) roll for 6 months and do another one. I probably will do it. It's not complicated and expensive, but it does take a lot of time. We definitely had marketing between David and I. For those of you who responded on this call, we're grateful and appreciative. Thank you for that. On the topic of

Separating Media Planning and Buying
(28:25) continuing the survey into Q3, the one thing I was interested, I was surprised not to see more media planning usage for this. I'm wondering if it might be better to separate planning from buying/activation because especially at the holding companies and the large independents, those are two different teams. I think that from a

Challenges in AI for Media Planning
(28:44) buying standpoint, there's probably more accessibility to things that might help you buy one channel or one provider versus another. Whereas on the media planning front, there's a mixing chemicals between strategy and planning, and those lines always blur. It's probably harder for them to nail down AI solutions in

Internal Platforms and Usage
(29:07) that area yet. I am seeing it especially at the holding companies. They do have their own internal platforms, and they're giving the strategists and the planners plenty of tools to use. Whether or not they are using them to full advantage is another question. Walt, I think that's an excellent idea, and I would do it in three

Future Survey: AI Agents
(29:24) ways. I think I'd have media planning separate from media buying and separate from measurement. I think that it's likely by Q3 we'll see further maturity on all that, but what I'm interested in finding out in Q3 is how much folks are using agents, which were

Agentic Buying in Q3
(29:44) a theoretical idea in Q1, but I think by Q3 we'll see agentic buying going on. Walt, I'm going to call you so you can do this with us next time. You'll help us set this up. I would appreciate the input on little subtleties. Looking forward to it. There's another question from Sally

Sally's Question: Human Decision-Making
(30:04) in terms of any stats in the study regarding the human decision-making and problem-solving aspects, and perhaps teams can't agree on how to use AI effectively in media planning. It's a great question. Sally, do you want to add a little color to that? I want to make sure I'm focusing on what you're asking. Sure, and I am not a media planner,

B2B Marketing Context
(30:24) but I've done a lot of B2B marketing for the creative and media space. As I was writing to David the other day, having done a lot of strategic discovery sessions with corporate teams, to then produce brand positioning and content and marketing,

Executive Team Disagreement
(30:46) usually people on an executive team don't always agree. They come with a completely different perspective. The tech guy might have one idea, and the saleswoman has a completely other idea on a priority, for example, any generic priority. I guess that was driving the question, and

AI as a "Life Easiness" Tool
(31:08) somebody else mentioned it, too. Matthew in his comment, "Are people throwing spaghetti at the wall, make my life easier?" In the absence of being able to agree, "Wow, this is what this can do for our company or category," in this case media

Data Slices: Segment Maturity by Role
(31:26) planning. Let me show you two slices of the data that may help us see some answers. I don't have the direct answer, but let's look at some data together. The first one is what I've got on screen now, which is generally segment maturity by role. The roles are

Leadership vs. Executive Perception
(31:47) not radically different. Yes, the marketing and media leadership – these are people that day-to-day are in that function – rate themselves a little lower on the curve relative to let's call the executive management team. I put the two top bars, founders and executive management, together,

Day-to-Day Roles' Realism
(32:07) then the two on the bottom, product and sales. There's a little bit of, maybe the folks that are day-to-day are more realistic about where they are, or because they're in the weeds, they're seeing the maturity better than the other folks on the team. That's a possibility. I'm showing you the

Agency vs. Brand Functional Comparison
(32:27) data, and we're ranking up ideas. Let's go to something that could be a little more interesting here. I'm going to put on the left, because we're heavy here on marketers and we're talking about agencies, this is the comparison of all the functions. On the left are agencies, on the right

Executive vs. Marketing Leadership (Agencies)
(32:47) are brands. Then what I'm going to do is I'm going to select executive management and founders on the left, and I'm going to put in marketing and media leadership on the right. Let's see what the data shows us. The data is showing us that the executive teams feel they're further along on maturity related to

Executive View on AI Impact
(33:13) content, using AI to generate content, to help with strategic decision-making and strategic planning. Their view is that marketing ops is getting returns on investments. Let's look at the same three on the right. The people that do this every day, marketing and media leadership, these are people that said they're in the day-to-day

Executive vs. Day-to-Day Roles in Agencies
(33:36) marketing or media functions. This is inside advertisers. Let's do this. Let's look at it first in the same kind of company. This is folks at the executive level in the agencies versus folks who do it every day in the marketing and media departments at agencies. That's an interesting view.

Filter Correction and Small Sample Size
(33:59) Yeah. Now let's look. Sorry. I apologize. I had the wrong filter on. This is the agency. That's because n equals 1. There was only one person in the agency that called themselves marketing and media leadership, probably because they figured that means a marketer side.

Agency Leadership vs. Brand Marketing Roles
(34:17) Maybe the better is here. Let's do this. You all have to excuse as I move my cursor around. I'm trying to find a good comparison. Here on the left you have the agencies, and on the right you have brands, but you have people in marketing at brands. At the agency side I'm

Divergent Organizational Opinions
(34:38) looking at people on the call themselves the leadership team, and since it's an agency, they're all in media. I'm not saying I know an answer, but I'm showing you that the data shows interesting opinions that are different between different people in the organization. There are still a lot of people not

Low ROI in Strategy Use
(34:56) using it for strategy. On the strategy, n equals 1, there's only one person getting delivery on ROI. We had 10 respondents in this category, so 10%. You guys see my cursor, right? So you had the n equals 10, and only one respondent said most say that

Exploring the Data Tool
(35:18) we're in the implementation category, which is the blue. I encourage everyone to get the tool, to play with this. I got to tell you, there are so many slices here that I've played with it now for a month, I discover stuff all the time by looking at the data.

Accessing the Tool Link
(35:39) I think that's fascinating, we could juxtapose these two. If you do marketing and agencies together, you get a more interesting view. Where can we find this? Dave, will you resend the link at the end? I would put that in here again. David's Brooke again, a

Suggestion for Future Data Segmentation
(36:03) reaction to this. It's interesting, and to delve further, in the future, if there's a way to make sure on the brand side that you have brand advertisers who follow a traditional model, relying on their agency partners, versus a

In-House vs. Agency Model Data
(36:22) sunset who run their media planning and buying and measurement in-house. That may be more B2B, but it struck me that might be interesting to see that lens that may be further down the road, but I would find that interesting. That would be interesting. We had 15 brands. Let me see, what's the n here?

Data Limitations on Brand Models
(36:45) 11. 11 out, wait, hold on. I had some filters. 12 out of the 55 were, I'll call this a brand advertiser. Brooke, we don't know how many of those were in-house versus more traditional agency model. We didn't ask the question. It's a great question to ask.

Question on Publicis Group Participation
(37:04) I was wondering, and you don't have to say this if this is proprietary. I'm a Publicis Group veteran. If any Publicis media folks, Publicis Group is so laser-focused on using technology and I've talked to them recently. I'm surprised, that's all. Sally, we

Anonymity and Agency Self-Assessment
(37:25) collected the data anonymously on purpose to ensure that folks would be honest. Which is why I was a little surprised about the agencies putting themselves at the top. I figured if they're being honest, they wouldn't drink the Kool-Aid in the survey. I don't know who responded. With a very

No Publicis Respondents Identified
(37:46) small number of exceptions, we got a handful of folks who put their name on the list to get a preview. But none of the folks on the preview list had a Publicis email address. Wow, this is neat. Very cool.

Claude's Role in Tool Creation
(38:04) Not for nothing, but I am not a software engineer. I know about that. I said, "All right, I'm going to use the tools." I fired up Claude and I said, "Here are the results. I'm trying to create a tool." What you see on screen was probably two to three hours of work total.

Claude's User-Friendly Capabilities
(38:28) The initial tool took about 15 minutes. Then there was some tweaking, then I learned how to get it on my website, then I learned how to change the colors, then I learned how to create the comparison view. All of it was Claude teaching me and doing it for me.

Claude's Accessibility for Non-Developers
(38:50) For those of you who haven't experimented, a total non-techy non-developer was able to do this. I'm impressed with Claude for that. Was this Claude code, or the 20 bucks a month version? Yeah, 20 bucks a

Data Source: Typeform CSV
(39:12) month. Where did you upload the spreadsheet? What was the question, Khan? What was the source? Was it a spreadsheet? We did the survey in Typeform, and Typeform gives you the ability to download all the results in CSV. Spreadsheet, basically a spreadsheet. I was having trouble in the

Claude for Multi-Dimensional Analysis
(39:33) initial analysis trying to slice and dice it, because it's multi-dimensional. You have functions. These are the different kinds of respondents here. I turned to Claude and I said, "Here's what I'm trying to do." I said, "Well, think of this." Mike, I love you, man.

Recruiting for Future Surveys
(39:53) I did a similar project. It's powerful. My BFF. David, I want to share one other thought as you plan for your next round of this. Sounds like it's a quarterly basis. I don't know if you looked into this, but for recruiting some of the groups within the ANA Association of National

ANA as a Recruitment Source
(40:14) Advertisers, they have an AI Marketers Group, and they have digital media groups. They might have an interest in not only results, but getting their brand partners to participate. I think it's great what you built, and I'd love to see this evolve.

Mixed Trade Association Responses
(40:30) Thank you. I'm not going to knock any particular trade association, but I reached out to them all. Some were, "Absolutely. We'll be happy to send a note out and get people in. We'd love to see the results." Some were, "We don't do that."

Final Insight Before Break
(40:48) It doesn't make sense to me, because I think this is insightful and I appreciate everyone on this call agreeing, but we had mixed reviews from my friends in the trade associations. I want to show one more thing before we break. Let me put one more screen up online.

Importance of Measurement & Consultation Background
(41:11) I think this is important for folks on this call to think about. I said earlier that what gets measured gets done. Before I created TrustX in 2016, I spent 25 years in management consulting at Ernst & Young and Price Waterhouse. Our pedigree was what I'm

The Differentiation Model
(41:34) doing now at Morgan Digital, which is to help companies differentiate and grow. We used to use a model like this. I'll explain it. It's tuned to AI, but the titles on the bottom could be any transformational technology. It's looking at if there's a catalyst,

Catalyst: AI and Gen Tech
(41:57) if there's something happening in the market – the something now is AI and gen tech – you look at what's available now that we know works, what's coming soon, and what's in the future. That's the green circle on the right. It could have been

Value Drivers and Differentiation
(42:14) AI, it could have been programmatic, which is what we were calling it 10 years ago. It could have been digital 20 years ago. On the left is value drivers for your business. It's how you differentiate. It's how you get ahead. It's how you drive growth. There are high value

Identifying High-Value AI Applications
(42:34) differentiators, and medium, and low. What we do, what consultancy does at Morgan Digital, and what I used to do in my days at PwC and EY, is you look for that red star at the top. You say, "If I know this technology is capable, and if I can apply it right here in this high value area,

Lack of Strategic AI Implementation
(42:59) I can get ahead." My most frustrating takeaway from doing this survey is that very few companies were looking for where their red star is. They're playing. They're looking at gen tech. They're looking at AI. They're throwing spaghetti against the wall, but they're not thinking about it as

AI as a Strategic Imperative
(43:20) a strategic imperative to get ahead. Thankfully, I have some clients who are paying me to do that, but the market was not doing it on their own. I leave you today with this picture and say, "Think about this in your businesses. How do I merge the capabilities with what's going to drive growth

Final Call to Action
(43:46) in my company?" That's ultimately what this is all about, and is why we have folks like you here to help us figure that out. I encourage everyone to sign up, get the tool. If you see something in the data that we uncover, or that you

Seeking Community Feedback
(44:03) "Wow, this is interesting. Have you thought about it?" Send David and me a note and say, "Have you thought about this?" Everybody looks at this stuff differently, and we're always looking for insights. We appreciate your feedback to help us learn more.

Thank You and Contact Information
(44:21) Sure. We'll appreciate all this, David. I appreciate all the thoughtful questions as always. You have the survey link, and I shared David's LinkedIn as well. Hopefully, all can get in touch for thoughts and questions,

Upcoming Sessions and Events
(44:38) and ways to evolve this further. We've got things scheduled now through the end of June. Some terrific sessions coming up. Also, let me know if anyone's going to be at Possible. I'll be there with Mark Teschler this year. Look forward to

Closing Remarks
(44:59) seeing you next week and beyond. Thanks, everyone.

## How AI Is Automating Ad Agencies

Speaker: Misha Leybovich
Published: 2026-04-09
Tags: advertising, ai in marketing, performance media agency
Video: https://www.youtube.com/watch?v=QZ6YvxSvdIg
Page: https://aimarketersguild.org/sessions/how-ai-is-automating-ad-agencies

**AI's Impact on Ad Agencies: A Deep Dive with Misha Leybovich of AdSmith.AI**

(0:05) **Introduction to AI Insiders and Misha Leybovich**
Welcome to another edition of AI Insiders by AI Marketers Guild, part of the Market Media family. Today we have a guest I have known a while. I was introduced to him in 2014 back in my agency days. Misha Leybovich was introduced to me by a mutual friend, Mick Darling, and I have been following his journey for a while.

(0:35) **Current State of Ad Automation**
He was talking to me about what is going on on the ad automation front. It is a topic we get to here and there, but I do not think often enough. I was curious to hear some of the latest of what can be done, what cannot be, what should and should not be done, how screwed agencies are, all these kinds of questions. Misha, welcome.

(1:02) **Misha's Welcome and Background**
Thank you. Glad to be here.
Great to have you. I would love to hear in your own words what you are working on these days and dive in.
Absolutely. Everybody, I am glad to be here. My name is Misha Leybovich. I am the CEO of AdSmith.AI. I am going to be talking about my product a bit. This is not a pitch. This is about what is going on in the world of applying AI to advertising and what we are seeing. Given this group is very intensely curious about AI and marketing, it is about what we are seeing, what we are building, and what we think it means for the future of this. To give you a little bit of background on myself, I have been an entrepreneur since 2012, with a four-year stint in corporate. Before starting AdSmith, I was at Google for three years. There, I was in the Marketing Works team. I was building internal tools for Google's own marketers to market Google products. I saw and was supporting the engineering team for campaigns of hundreds of millions of dollars. I learned some stuff there. Before that, I was a marketer for my own startups. I had a couple of startups before that. It was mostly me and a bunch of developers, and as the CEO, I was always responsible for everything else, including marketing, including advertising. I was able to develop some strategies that allowed us to punch way above our weight in terms of actual success in the market for pretty small budgets. The summary of that was efficiently aggregating the longtail, advertising everywhere in every language and every country where it was relevant. I was able to get some of my apps to the top of the charts in 134 countries, up there with Instagram, TikTok, Snapchat. Little Flippy was hanging up there. Based on the way that we were doing things and combining my experience doing my own advertising as a very small business with seeing how it worked at the very large level with Google, here was my main takeaway, and this is going to sound pretty reductive, but I think it is true: all advertising is guessing.

(3:27) **Advertising is Guessing**
Everyone is guessing. Nobody knows what is going to work. The ones that are great at it, the ones that we pay extra money to and get hired more, the great advertisers get it right, and by "right," I mean the campaign performing according to business expectations, get it right about 50% of the time. That would be a great batting average. Most mortal humans do much worse than that, and then you think advertising does not work. Given that, here was my insight about a year and a half ago. I saw that the capabilities of the models were going up and up. A year and a half ago, everyone said everything has six fingers. Give it a second, bro. It is going to get better. Images are pretty much there. Video is just about there as well, at least good enough for advertising, which is disposable art. Most of these things are not going to win big brand awards. It is not its job. Its job is to earn the conversion. The capabilities were going up and up, and the time and cost to produce relevant, quality, credible "shots on goal"—the time and cost would go into zero. It is almost there. An amazing image will cost you 10 cents. An amazing video cost you a dollar or two. This is fundamentally way different than it has ever been before.

(4:59) **AI's Advantage: Outperforming Human Guessing**
If you combine the insight that all advertising is guessing—we are guessing too—with the understanding that we can now produce orders of magnitude more guesses, to me, it only made sense that if I could build an experiment and learning machine which also is guessing but about a thousand times as fast, we could outperform any humans doing the guessing. Let me explain the way that we do that. In any final asset that you see as a part of any ad unit, we are talking text, images, videos, keywords, any actual assets. Ultimately, some prompt went into making that. But then what went into that prompt? There are a bunch of different decisions that go into that, what is the persona that we are targeting? What is our messaging? What style are we going for? What kind of story are we trying to tell? How creative is it? What kind of assets go in there? There are all of these different decisions that a human making assets makes implicitly in their head. It is a complex thing, and I love the creative process, but ultimately it results in a series of decisions that results in the human doing a thing and producing some assets. This stuff looks great. What I tried to do is say, let me automate all of those decisions in a step-wise fashion, going down from what is the organization, what is the offering, what is the audience, what is the persona, what story are we trying to tell, the creative brief, and automate all of that.

(6:43) **Automating Creative and Campaign Experiments**
The advantage of doing it this way is not only do I have this fire hose of ad content that I can produce—my system can produce credible campaign experiments for a couple of bucks in a couple of minutes versus weeks and thousands of dollars if we are talking about an agency doing it. Not only do I get way more shots on goal, and the advantage there, let me skip to the punch line here: every single one of our campaigns so far outperforms every single one. I am not saying this to brag about my company. I am saying this about the approach that we are using, and we are not the only smart guys out there that are going to figure this out. Right now, we outperform every single time versus a credible head-to-head versus a human doing or human agency or in-house or whatever. Why do we outperform every time? Why do we do it in industries that we do not know anything about? We do not know about it, but the AI knows about all of these things. How do we do that? We are not marketers by trade. We are engineers trying to optimize every performance lever available in these advertising platforms.

(8:08) **Flooding the Zone with Credible Content**
For example, if you are doing a Google Performance Max campaign, and sub to that is an asset group. A Performance Max campaign can contain up to 100 asset groups. An asset group can contain up to 20 images, 25 captions, 50 keywords, 15 videos, six sitelinks, all of that. When we flood the zone with credible shots on goal for every single slot, it means that statistically something that we do is going to work. Even our small company now, I think we have to be in the top 1% of advertisers just using all of these things available, using all of these slots, because our logic is that these advertising systems have gotten to the point where it is about what they offer effectively as algorithmic targeting. It is not about selecting dropdowns anymore. I want a new parent who likes basketball and has a white-collar job. The ad platforms are removing more and more of those selectable dropdowns, some for privacy reasons. They are narrowing that down. They explicitly call those suggestions now. They are not even targeting anymore. They are just kind of like, "Hey, Google, Meta, go look in that direction." What it really is is that humans, we are all more complicated than a series of dropdown boxes. What the platforms want, Meta says this explicitly: "We want creative diversity." They say, "Give us a bunch of content, and we will figure out what to show to whom, and this kind of experiment is going to work with this segment, and this is going to work with that segment, and let us figure it out."

(10:06) **Algorithm-Driven Decisions and Statistical Learning**
We believe that the algorithm is always going to make better choices. I think it is reflected in the data. Part of the reason that we are outperforming is just the arbitrage currently that we are taking orders of magnitude more shots on goal with a system that can produce a lot of credible volume of content that would all work. I tell my customers, I do not know what is going to work. I could not tell you in advance, but I know that something is going to work. We are going to learn from that and do better. Let us talk about the learning part because this is where it gets special. I mentioned we automate 200 inputs by creative and business decisions that go into producing any given asset. Those are not locked in a squishy human brain, that some creative made those decisions. Those are fields in a database. That means that when I get performance data, and I see of all these experiments that I am launching, what is actually working? Let us say I put out 10 experiments, six of them do not work. Who cares? It cost a couple of bucks, took a couple of minutes. It does not matter because no one could have predicted that these four work and these six did not. Anyone that tells you that they can predict that is not being honest. The stats do not bear that out. From those four out of 10 that did work, what can we learn? Those 200 inputs now can be matched with the outputs of what actually yielded conversions.

(11:42) **Reducing Cost Per Conversion and Predictive Guessing**
To give you a sense of this, our average—we mostly optimize for cost per conversion. We do not care about impressions, click-through rate, or clicks. We care about conversions because that is all that matters at the end of the day. A conversion is going to be different for every business. Sometimes it is a lead, sometimes it is a sale, sometimes it is a free trial, whatever it is. We typically bring down the cost of conversion by 30% to 50%. We connect to Google and Meta. That is where we connect right now, and we are adding more channels as we go. We bring down that cost of conversion by 30% to 50% already. What is interesting is now knowing which experiments worked there, and I mentioned there are 200 different decisions that went into that. Now I have a 200-vector space that I can train a model to be for this customer, for this audience, for this persona, for this offering. What are the next set of these 200 inputs that we should guess? Yes, it is guessing, but it is increasingly informed guessing. If I took that same four out of 10 experiments that worked and I showed it—we do not do landing pages yet, but I want to get there because that closes the entire loop on the thing. I want the landing page to match the ad. But we are not there yet. If I showed these to a human and I said why did these four out of 10 work? The human, because we all want to look smart and make our best guess, they are going to look at it and say, "Oh, these ones worked because it had a yellow background, and there was a woman holding a friendly dog, and it had the text in the upper left. That is why these worked." The reality is this is a statistical space with 200 different vectors in it that yielded these outputs, and there is a statistical answer, and there is signal among a ton of noise that with enough data we can make better and better guesses as to what works. Our goal is for the next batch of 10, five of them work, then six of them work, then seven of them work.

(14:26) **Automating Campaigns at Scale**
Ultimately, what we are trying to do—the scale of this is going to sound crazy to anyone operating an ad agency with a process that runs at human speed. When I talk about a Google Performance Max campaign that can contain up to 100 asset groups, that represents one experiment for us that we are over time for every customer filling all of those slots, every single one, and killing losers, leaving the winners, and filling it all in until it is all killer no filler. Everything is working and driven statistically until an experiment does not work anymore, and then we replace it with a new one. The goal is to have dozens of experiments launching for every customer every day and to automate this entire thing. That is the gist of what we do, and we fashion ourselves as an AI ad agency. I am sure you have all seen the phrase among investors, "It used to be software as a service. Now it is service as software," where the budgets for services dwarf budgets for software. As AI increasingly enables us to provide these services, more and more of those dollars are going to move over to those services being offered by software. An ad agency is inherently a services business. They may have tools on the backend to enable them to do those things, but ultimately it is services.

(16:25) **Scale of the Ad Agency Market and Customer Complaints**
To give you a sense of the scale here, there are about 400,000 ad agencies in the world, and the spend on these is about $400 billion per year. There is a lot of budget here to go after. The spend on actual ads itself is about a trillion, but the spend on the services to make those ads happen is about 400 billion. I am sure some of you hear agencies, and I am sure they are wonderful, but it is also pretty cliché to hear customers complaining, "I tried this agency, they made all these promises, they tried these things, but ultimately I did not get results." A lot of money is spent, and the economics of human labor to do that not only means that you can try fewer experiments, but you are also pricing out a lot of businesses from getting professional marketing services in the first place. Of all ad spend, about 40% comes from startups and small to medium businesses. These are businesses for whom the economics of hiring an agency and the cost of human labor does not work. It is too large a percentage of their budget. It does not work for the agency because the spend is too small, and they do not have enough to spend on the labor. We see an enormous opportunity here.

(18:10) **Agency Hostility and AI Disruption**
To be honest, when I started this, my thought was, we might serve some customers directly, but also maybe we will work with agencies, and agencies will be B2B to B. Agencies will use us as a tool for their customers. I have to say that the feedback and response from most agencies has been hostile. I understand that we are explicitly going after their margins, and we are trying to offer a better experience for lower cost and higher performance. We all know there is going to be a lot of disruption with AI, and we are going to sort this out. I am long-term optimistic in humanity and our glorious future, but it is going to get messy in the meantime. I am not here to talk about, "Oh, this is going to supercharge agencies." Anyone that wants to work with us, we will be happy. Anyone who wants to use my tool, I do not care if they are an agency or a business or a customer themselves. I think a lot of the rhetoric right now, because people are coping with the massive disruption that is happening, is, "It is going to be fine. People are going to use these tools, and they are going to be better." I am not here to share that message. I am here to share the message that I think—maybe I am naive, maybe I am selling my own book here, take my motivations with a grain of salt.

(20:01) **Validating AI's Disruptive Potential**
I want to share a quick thing to support your point, which coincidentally I was reading in their newsletter this morning. They sent this an hour ago, and I am like, it already stood out, but now, based on what you are saying, it is clearer to me because it talks about Horizon Media, the largest of independents, building a single command center for programmatic buying. The new Horizon OS platform sits above the adtech stack. I will send a link to the newsletter, but I want to share their opinion on this, and this is eerily in sync. Michelle, I love your reaction to this. The U of Digital opinion: "This is a step in the right direction for agencies, but it disrupts the traditional agency model, which is charging for services to do this work, not tech. They will have to evolve their entire positioning and pricing in order for this to work."
Yes, I think that is still, in my opinion, a pretty soft-handed approach, because at the end of the day, when a system can provide every single one of those services, what exactly is the agency doing? What value do they still add in the chain here?

(21:42) **AdSmith's Goal: World-Class Marketing Services through AI**
My goal with AdSmith is to provide world-class marketing services around advertising, but we may expand to other types of marketing after that. I went to advertising first because that is where the money is. World-class service to companies of any size around the world, done instantly, and at the highest level of performance. I know these are all big claims. Let me talk about the steps of the services that an agency provides and talk about how we are approaching automating every one of these things, and then I will get to the questions in the chat.

(22:31) **Why Agencies Exist: Complexity and Abstraction**
Why do agencies exist? Advertising is complicated, particularly now. I have been using these ad platforms for 15 years, and I still find it confusing. There are all kinds of knobs and levers. There are all kinds of different choices you can make. There is the amount of content that you need. It is very difficult to do every step of it. Then we even have things broken down into creative agencies, strategy agencies, brand agencies, performance agencies, media buyers, all of these things. It makes sense why businesses that are good at making tennis rackets or whatever they do say, "We do not have these internal capabilities, let us outsource this stuff." That makes total sense. Ultimately, an agency is a compendium of jobs, jobs to be done. It does those jobs in a package, and it abstracts the complexity from the end customer. Until this point, you absolutely needed humans to do that. There are so many subjective things and judgment things, and all of that made total sense.

(23:51) **Automating Agency Functions with Coordinated Agents**
We are getting to this point now where, particularly with conversational agents as the interface between the ultimate advertiser and the ad platforms. Conversational interface, the ability to have a series of agents. I think the word "agent" is highly overused. I am long-term bullish, but right now I think there is a lot of hype. I do not believe in some single agent that can do everything and has all the tools at their disposal. I think there is too much opportunity for it to go off the rails currently. Ask me again in a year, but currently, I do believe in a team of coordinated agents where this is my keyword agent, this is my media buyer agent, this is my image review agent, and then a controlling agent to know how to dole out the work. I do believe that that works. So you combine the conversational interface with the ability for the sub-agents to use a series of tools that are prescribed on how to use it and delineate every edge case possible. The data schema that holds these 200 decisions that I talked about. The ability to hold those somewhere in a very structured way. I say that our schema is one of our greatest assets because the shape of your data and the opinionated way that you think advertising works is one of the things that makes our campaigns outperform every time. The ability to then have these platforms—I want to credit the platforms like Google, Meta, all of that—what we are doing, even if we could produce all of this, all these experiments, and we sent it to Google and Meta, we rely on them to do the not just A/B but A through triple Z testing for us all the time, and to do the algorithmic targeting. That is a critical part of what we do that is in place. Then the ability to have a feedback loop to make better decisions. All of those things, all of those services, can now be done by agents.

(25:59) **Automating Intake and Strategy**
Let us walk through it: Intake. You get connected to an agency somehow. You are a customer. You are selling your tennis racket. I need to do some advertising. I need to grow my business. I do not know how to do it. Let me talk to somebody. What happens? You get on a call. You have a conversation. They ask you about your business. They ask about your offering. They ask you about your target customer, the advantages of your product, your creative preferences, all of those things. Guess what? Now these conversational, voice-to-voice, even avatar-to-avatar, it can look like me talking to you right now. It can look that good. Those are all available now. So, the intake process, that is the easy part. Getting all the information can be automated. My goal is to get someone landing on AdSmith, entering, and getting them not only intake but all the way to a published campaign within five minutes. That quick. We are still a little ways away from that. We are still around a couple of days, but we are reducing bottlenecks one by one. Next is what is the strategy? What is the offer? What kind of message do we put out there? Again, guess what? Our conversational agent can talk to you about your business needs, your preferences, store all those in a list of ideas of things to try, and execute those. Another service automated.

(27:26) **Automating Targeting and Creative Personalization**
Targeting. We are talking about audiences in terms of locations and languages, the combination, and then personas because there are all kinds of different flavors of people. Before, let us say you are selling life insurance. You could do a generic life insurance ad. But then, let us say you did a special version, one for people who like basketball, and one for people who like sushi, and one for people who like action movies, whatever. An ad has two jobs: it has 0.1 seconds to stop the scroll. We see 4,000 ads a day crossing our retinas, and we barely register most of them. So, you have to stop the scroll and then earn the click. The landing page, to your question, Conor earlier, the landing page has to seal the deal. So, we want to get into that later. But it has 0.1 seconds. In that 0.1 seconds, if I am trying to sell life insurance, is it some non-zero percentage more likely that I am going to catch someone's attention if the imagery contains a basketball or some sushi or something about an action movie, perhaps? Because as humans, we are a complex array of interests and likes and things that catch our attention, and the basketball is going to be a little bit more likely to stop the scroll than something else. Before, it would have been completely impractical to launch campaign experiments for that niche of audiences, but now we can.

(29:05) **Targeting Approach: Conversions Over Specific Demographics**
Can I ask one question about all this? For the work you have been doing here and elsewhere, is it better if someone is coming to you and saying, "These are our targets, these are what we know. We do not want to target 20-somethings, we do not want to target boomers. We are a stay-at-home dad brand versus a Fortune 500 CEO, working mom brand"? Or are you better when someone says, "As long as someone buys the bleeping product, we do not care who you are. We do not care where you run them. They could be on an X-rated site at 3:00 a.m., and if they have a craving for my mac and cheese, serve it to them"?
Absolutely. Honestly, every advertiser is going to have to evolve into the latter, because increasingly, even at the biggest brands now, we will talk about brand safety in a moment. At whatever size company, the business owner, the CFO, the CMO is going to increasingly have to justify why they are doing a thing that is less efficient and less profitable on their ad budget than other things they could do. So our approach to it is as follows.

(30:33) **AI Reliability and Creative Constraints**
People think AI is unreliable. It is going to produce crazy things. This is not the case with the current models. We also have humans reviewing everything, but it is never going to accidentally insert something crazy into your ads. That said, we ask every customer, "What creative constraints should we set?" The AI is very good. The creative space from which you can produce a given ad is infinite. There are infinite ways to arrange a series of pixels within some rectangle to produce something that tries to convince someone to click on it. AI is very good at exploring all the different combinations, a high-dimensional space that we cannot even imagine in our heads. We can constrain that. If the brand says, "We want it to be only these colors, only these messages, only these styles," no worries. If you give that input to the AI, it absolutely can do the job of staying within that.

(31:44) **AI's Role in Regulated Industries and Performance Focus**
I am excited about regulated industries because there are a lot of rules about how you can advertise. No worries. Put all of those rules as an input to every generative call when the AI is doing things. Then you can add it as a check afterward to make sure that we reject anything that for some reason strays outside of that. You are going to catch pretty much anything that does not meet those, and you can stay within whatever constraints, be they regulatory, be they brand-based, whatever it is. Stay within these boundaries but be as creative as possible within those boundaries. That is what gets interesting. David, to your question, the brands that tend to work with us now are the ones that are like, "I just want conversions." I may have my opinion that this is a stay-at-home dad brand. That is cool. We can certainly create ads that meet that persona. No problem. I would not say that it is all randomness and chaos. If your brand is a stay-at-home dad brand, advertising to teenage girls is probably not going to work. But my question is always, "How do you know?"

(33:09) **The "How Do You Know?" Question and Purchase Influence**
By the way, my 12-year-old probably has 80% of the purchase influence, maybe 90%, in my household. Advertising anything to her is the best way for her to say, "Dad, we need this."
Absolutely. If your brand is selling adult diapers, you are probably not selling to teenagers much.
Sure, absolutely. There are some bounds of reason here. We are not going to be—it is very unlikely our system would create ads showing teenagers independence. But within that range, sometimes it is the person that needs it. Sometimes it is their adult children looking for it for them. To take that example, we only judge by what gives us the best cost per conversion. That is it. We will make our creative to fit within whatever bound you want as the customer, but ultimately it is going to be what works, and that is what it is based on.

(34:19) **Performance Advertising: No Room for Humans?**
That is why I think, and this is part of my scary message for agencies: believe me or not, I think that—I do not know how long it is going to take, five years, ten years—but performance advertising, I am not even talking about brand advertising, that is a different thing, because I am talking about anything where you get a ton of shots on goal and a clear signal, "Did this work or not?" through a conversion. For performance advertising specifically, I do not see room for humans in this. I do not. To provide the inputs, sure. Each brand can decide how they want to show up in the world and give that guidance, but then to actually execute and provide the services, I do not see where humans add value in this chain or how they can outperform. Ultimately, what matters is how they can outperform what the AI can do. I think there is just going to be increasingly clear based on the numbers.

(35:25) **Automating the Creative Layer**
We talked about the service layer. We talked about strategy, targeting. Now, let us get to creative: keywords, copy. People say, "It needs the human touch. It needs the human experience. It needs to have the lived experience of the empathy of being a human." Yes, that is true. But the AI has all this by virtue of all the things that we as humans have already created. The AI has internalized what the human experience is by the content that we can produce and ultimately can produce stuff that is increasingly going to be—there are a million different ways that we can sell this product. There are a lot of different messages we could use. Let us try all of them and see what resonates. Put it this way: in our system where we show every time, "Here are the keywords we are going to use. Here are the captions, all of that on the text," it is extremely rare that a customer says, "No, I do not like that." They could exit out, they could change it, but it just does not make sense because what we want to do is have this cloud of constant things that we are trying and develop a statistical understanding of what words resonate more.

(36:39) **Copy for Robots and AI-Driven Production**
So, copy that is just for the robots, a concept. When we talk about images or videos, yes, there is a prompt that needs to go into that. To produce a single ad, you do not need me for that. Go to Gemini, go to ChatGPT, go to Midjourney, go to Ideogram, whatever you like. Enter a prompt, you will get an amazing-looking ad. You do not need me for that. If you want to run thousands and thousands of these, if you buy the premise that it is a statistical understanding that matters, then you do need some sort of a statistical approach. Coming up with those concepts for every one of those prompts, I am a creative guy. I can come up with maybe 15 ways to sell a sandwich, but I cannot come up with 300 ways to sell a sandwich. Having the AI come up with the concepts and varying these concepts matters because humans get stuck around local maxima, around our own creative preferences and beliefs on what works. The AI does not care. The AI explores anything that is credible. A creative strategist is not needed. I am sorry to say. The production of it—images, videos, all that—I have zero creatives on staff. Zero copywriters, graphic designers, video producers, none of that. It is not needed, especially if you are going to take this approach of a statistical, thousands-of-times-more experiment, which I think is going to prove out to be the only approach that works, not only for my company but other companies will do this too. I think this is going to be the approach that works. Another service thing: creative agencies. We are talking about brand design, different story there, you get one shot on goal, but if you are doing performance advertising, you have thousands of shots on goal, different story.

(38:29) **Automating Conversion Data and Campaign Settings**
Let us talk about the replacement. One thing that we have experienced is that 0% of our customers we work with closely have had their wiring—the actual way that we take conversion data or conversion events, define them, send the information back to Meta or Google, add value rules, add enhanced conversions, all of these optimization levers—optimized. Everyone has something messed up. This is very difficult to do, and my team is good at it because we do it all day, all the time, and this is our specialty. With all of these things, we are building these playbooks for computer-use agents to then go and for any advertiser of any size, be like, "Great, give us access to your system. We are going to take over your browser. We are going to make sure that everything is set up perfectly," because that is free performance sitting right there on the table, just for free to pick up point here, a point there. Campaign settings, very confusing. Which one should we use? We have particular opinions about that that we see work best. So we automate using nodes. Optimization: you have to appeal things on the platform. We can automate that to do the various optimizations that they suggest. All these can be run through APIs.

(40:00) **AI for Media Buying and Budget Rebalancing**
Rebalancing. Let us take media buyers. An advertiser does not care about, "I want to spend $1,000 today on Google and $2,000 on Meta and $3,000 on Reddit." They do not care. What they want is to spend X amount of money. That is their budget, and to get the best results from that. A media buyer is going to look at that, maybe rebalance once a week, once a month. They are going to make their best guesses. We have a prototype agent already that can do that automatically, and all you do—it is amazing what these tools could do now—you take a CSV of all of the performance data for the past 30 days or 60 days, or however you want to do it. Give it to the agent, say, "Hey, for all of these different objects at the campaign level, the experiment level, across channels, how should we rebalance budget, and which experiment should we kill?" It looks at that, and the recommendations it gives, I am telling you, are right every time. It is just numbers. This is another part of the service layer that AI can do.

(41:09) **The Hard Part: Handling Messy Human Inputs**
Every optimization possible. My goal is to make it so that anything with a human in any part of that just cannot have the throughput and the speed to compete. Now let me tell you about the part that is hard: the part that is going to take us the rest of this year and probably most of next year too. The service layer, even though everything that I am saying about conversational agents handling that, extracting learnings, and feeding that to agents to do the right thing—humans and customers are messy. We are all messy, and we say things in different orders, and we contradict ourselves, and then we say we want one thing, but then shown it again, we change our minds. It is messy, and there is no getting around that. Sometimes the smaller the advertising, the less sophisticated, the more opinions they have, and the more that changes because it is emotionally driven, whereas our thing is entirely statistical. How do we handle that? That is a thing that we think about a lot.

(42:25) **Designing for Human-Agent Interaction and Bottleneck Removal**
The thing that is going to take us the longest is to make it so that no matter how we design this—on the left sidebar, you have this agent that is always there, and you can talk to anytime to always figure out—they start a conversation. It might be from we do not understand what context they are starting from. They might switch topics midstream. They might say something that contradicts something before. Ultimately, all of those need to turn into fields in databases that are learnings that we can feed into generative AI calls for all the things that we are going to do and how to sort through a bunch of messy conversation and turn that into the right structure and then implement that correctly and have the thing they said now supersede the contradictory thing they said before. These are not trivial things. That is going to take us the longest. Our approach is to automate the backend, the production layer. We are working on right now. On top of our service, which is 10% of the spend, our service fee, we offer a white glove service for an extra thousand bucks a month. We offer that with humans right now. We are using that as the opportunity to understand every edge case, and we are nowhere near the bottom of that list yet. There are so many edge cases we do not understand yet. But as we do that, and we are like, we have seen all these things before, we are gradually removing bottlenecks.

(43:52) **Example: Image Review Agent**
I will give you an example. It might take my account manager for the white glove service 15-20 minutes to produce an experiment, and the bulk of it comes from looking at the images and saying yes or no to different images because even now, with AI models being amazing, you might reject half or even three to one. They cost 10 cents, so who cares? But still, that probably takes three-quarters of the time to do that. Guess what? We can produce an image review agent that passes every image once it is produced to an agent that says, "Hey, here are the 30 things that can generally be wrong with an image. The logo has a problem. The text is weird. The human has three arms, whatever." It looks at it and then says yes or no. I am pretty sure we can tune this to be not too restrictive, not too permissive, just the right amount to be efficient with this. With that, I have reduced the time for her to produce an experiment from 20 minutes to 5 minutes. That is a series of things. All of these things we cut time until an agent handles everything. That is our ultimate approach. I have been monologuing for a while. Maybe I will get to some questions here if anyone has live or I can answer the questions in the chat.

(45:18) **Q&A: Adam's Question on Pricing Model**
Who has some? Okay. Got a couple of hands coming up. Adam and then Emily.
Misha, first of all, thank you for all this really cool stuff. As a fellow ad tech nerd, I love the way you are thinking about all this. I wanted to push on that last mention of the 10% of media spend model because it has always been the case that when you go from 1 million to 20 million in spend, it is not 20x the effort. Now that astoodic thing is just going to make you more vulnerable to SaaS platforms that come along and say, "No, this is just a license fee." Maybe it is tiered a bit on spend levels, but I wonder if you could unpack that a bit.
Sure, absolutely. As spend goes up, we certainly have in mind to lower the percentage as the spend goes up. Let us talk about the reason why we are doing that model in the first place.

(46:29) **Pricing Model: Performance and Value-Based**
It was an easy one to slot into because that is the industry standard. More to the point, with AI systems, you want to pay based on results and performance of outcomes. My ideal is to charge some percentage of the profits that a customer makes based on our ads. That is ideal. Then it is a no-brainer.
If you are running the full loop and the measurement piece for them, you could.
Yes, but here is the problem: I have to get pretty embedded in their ERP systems or whatever it is to know what the actual profit or sales are. We are not at that level of sophistication yet. I absolutely want to get there. But for now, the spend is a proxy for that, because if it is working, they are going to keep spending, and they are happy to pay my 10%. If it is not working, they are going to stop, and I am not going to make any money anymore.
Also, if I were your client, I would drive so many other costs through and crush the synthetic profit.
Yes. Ultimately, we want to get as close to the value as possible. This is our answer for now, and that will evolve over time.

(48:15) **Q&A: Emily's Question on AI Pushback**
Thank you. This is fascinating. I am coming from a very different angle. I work in nonprofit marketing and communication. I am trying to figure out how to potentially leapfrog over these absurd advertising and agency costs and just get to the point of trying to get our messages out there. I am more curious about any observations you have on pushback to AI, culture-wide but also from your clients. They obviously are not opposed to AI if they are coming to you, but also the people that receive the ads. The ads and the quality of the content are getting better and better, and people are not differentiating between human-made content versus AI-made content. I am curious if that is a factor at all in terms of how you are thinking about eventually automating everything and what that means for the content and the clients that you are working with.
Yes. As you said, no one is coming to us—we are very explicit about what we are—so no one that is not in AI is coming to us. Fair enough.

(49:15) **Consumer Perception of AI Content**
Put it this way: I am sure there are consumers that look at something and say, "Oh, that is AI," and they can reflectively have a knee-jerk reaction. There are all kinds of flavors of people, people are entitled to their opinions. I would say, number one, that delineation of being able to tell, "Oh, that is AI," is increasingly non-existent. It is going to get less and less of a problem if it even was. At the end of the day, I just look at what is the cost per conversion, and enough people are fine with it that it is still lowering cost conversion by 30% to 50%. Maybe we are ruffling some feathers. Maybe some people do not like it. Maybe they skip past it. But that is no different than the other 4,000 ads they skipped past this day. Ultimately, the only way to have a statistical approach and do this at scale is with AI. There is no other way to have that volume. I had enough conviction this was not going to be a problem even a year and a half ago, when it was a problem sometimes. But I was like, it is not going to be. I think that is what we see playing out. But you are right. Some people are not going to like it, and then those ads will not work. But I think by and large, most people have no idea. My mom sends me cat videos all the time of cats dancing with top hats, "Is this AI or not?" I am like, "Come on." I think it is not a huge problem.

(50:39) **Nonprofit Ad Spend and Adam's Follow-up Question**
By the way, Emily, Google offers—you probably know this—$10,000 a month of free ad spend for nonprofits, and we have an interesting way to take advantage of that. So hit me up if you want to talk about that. Adam.
Hi. Yes, I have a number of questions. And a couple of comments. I think, first of all, I was glad to hear you talk about the difference between what you are doing and brand advertising because what you are doing is performance. It is short-term. I think most of us here have been around long enough that we see the need for a balance between some brand awareness, education, things like that. Performance marketing is very quick turn.

(51:24) **Historical Data vs. New Explorations**
I completely agree with you. There is a lot of AI that can handle this. There is a lot of AI that should be in this ultimately. I think a hybrid model is what we will end up with. I was wondering from early in your talk, you do all of this stuff algorithmically that is backwards looking. It is all historical. How do you get new stuff in there? How do you get, "Gosh, maybe we should try this market, maybe this other kind of advertising will work, maybe this other channel will work"? Do you account for that in your algorithms?
Absolutely. Let us take it in terms of channels. Yes, right now we are connected to two channels, the two big ones, Meta and Google. But ultimately, I am trying to get 30-50. If there is an API where I could send campaigns to digital advertising displays in Omaha and get performance data back, we will absolutely use that in any way that we can. I think a lot of the platforms are overlooked for various reasons. If we are only optimizing based on performance, any channel, we are always exploring that.

(52:30) **AI Creativity and Data Feedback Loop**
In terms of concepts and things to try, AI is extraordinarily creative when asked the right questions. The way that we handle that is basically for everything that we do, we feed back in, "Here are the things that we have already tried, and here is how they have done, so let us try some new stuff." It inherently, given that our goal is to sample the infinite creative space as effectively as possible, it behooves us to always be trying new things and then see how those do. That is tempered by it is not all just random shots in the dark. That is where a feedback loop comes in to try to balance here is what has worked, and so let us try things in the neighborhood, exploring from these new local maxima that we find, and so where we can find even better local maxima. Yes, it is—everything is inherently backwards looking because you need data to then see what happened and make better predictions about the future, but those predictions are always informed with a bias towards trying new things that are more likely to succeed than other new things. That is the only way that we know, and that is how we outperform as well because we just try so many things, something is going to work.

(53:50) **Continuing the Discussion**
And Misha, do you have a hard stop?
I am good. I am good. Keep talking.
Okay, for those who need to go with the hour, that is fine. You can always catch the replay, but we will keep this open a few more since there are still some questions coming in. Adam, did you have more, and then we can go to Jay-Z.

(54:16) **Adam: AI as a Commodity, What's Next?**
I have one more, which is that eventually, because this does work for performance advertising, this goes everywhere. Eventually, you are not going to be that unique. Eventually, this becomes the commodity. So what is next?
So, in any kind of software business, there are two kinds of defensibilities driven by network effect: people and data. In both of these, your system gets more valuable with either more people added to it or more data added to it.

(54:55) **Defensibility: Data Moat and Platform Independence**
I am very clear-eyed about as a builder, this is an amazing time. I have built the most sophisticated, useful, economically valuable system I have ever built for a tenth of the cost of what it—and I am a guy that has been around, but I am not unique in that regard. So our approach to this is that ultimately, I talked about—I say our data schema is one of our biggest assets. The way that we store all those different 200 creative and business decisions that go into every experiment we do. What that means is that the 50th time that we are running an ad for dog food, we are not making random shots in the dark. We are knowing what has worked for this customer, for other customers in this industry, and in general, and what is working right now, because it is constantly changing. Our bet is that the data moat that we can accumulate over time that allows us to make better and better predictive guesses than the next company. Someone can copy my interface pixel perfect, sure. Someone can even—it might be harder but—reverse engineer my schema and figure it out and copy that. It might take a little bit longer, but let us even say that. What they cannot do is then catch up on all of the learnings that we have had so far. So when a customer goes to them versus to us, we are going to perform better.

(56:17) **The Platform Challenge: Fiduciary Responsibility**
Also, in practice, most of these markets evolve into a handful of winners, and then new entrants might try, but most of them are, "What is their story for how they are going to win versus someone that is already established and has a bunch of data?" One other big thing I think about defensibility is the platforms themselves. Certainly, Google and all of them want to bake this right in, vertically integrated, a one-stop shop. Zuck says so much about Meta. The reason I am not afraid of that is that, I mean, they have this right now, and it is not very good, even though we are using the same models, and I have all kinds of opinions about that having been on the inside and understanding how it works there. But even if they make that excellent, as good as mine, Google will never tell you to go spend 30% of your budget on Meta. They cannot do that, or vice versa. Somebody needs to be the marketing fiduciary on behalf of the customer that is only focused on optimizing their spend. I think that inherently has to be a layer outside the platforms that is using the platforms most effectively in concert. That is where I see our play. That is what agencies are doing now. I am just trying to be a better agency with better performance and better cost direction.

(57:33) **Jay-Z's Question: AI Replacing ICP/Persona Identification?**
I do not want to dominate the conversation, but I have a couple of other things I would like to talk to you about. I will reach out to you all.
Fantastic. Thank you. Thanks, Adam.
Hey, Misha. Thanks for walking through all the agents coordinating. David, Adam, Karen, it has been a minute. It is nice to be back in the session.
Good to see you.
Yes, I miss you guys. I work with a lot of early-stage startups and do user research, who they are selling to, and even what features to build from a product perspective. For example, they say, "I think everyone will buy this." And I say, "No, I think only HR is going to pay for it, but maybe all team members want a meeting AI that tells them if they are being a good collaborative partner or not." So they say, "Why do not we just spend some ad dollars and see who clicks?" I am curious if you think your solution replaces finding an ICP. The AI will find your ideal customer profile for you, and you do not have to do the hard work of prioritizing them in the beginning.

(58:47) **Persona Generation and Embracing All Credible Options**
When we get inputs into what this organization is about, what this offering is about, and we feed that into our persona generation agent, it does a pretty good job. It does as good a job as a human would with the same inputs. There is not one persona. There are a hundred different personas to sell this exact same protein bar, and how you are going to appeal to them. I think the old way used to be, "Let us get our heads together about our ideal ICP and market to them." But I am like, why not everything? Why not spell it all? At the end of the day, we do not know what is going to work, and different things are going to work for different people. I am like, let us try all things that are credible and have a reasonable chance, and let the performance data tell us.
It is like machine learning with feature expansion, right? If you are doing your job right and better than the existing systems, you should have some unsupervised discovery.
Absolutely. Our agent that says, "Great, we are going to do another experiment. What are all the choices down the chain? What persona should we try this time?" We have tried this persona a handful of times, it is doing pretty well. Let us throw another experiment in there. Here is another persona. We have not tried this yet. Let us try that. Again, it is all just shots on goal. I want to give a lot of credit to these ad platforms. The way that it works is I can have a campaign. A campaign is the object that holds budget and learning. The budget is important because all the things that are sub to that campaign, be it asset groups in Google or ad sets and ads in Meta, they are all competing for that same budget every day. I can say, "What do I know? What do I know if it is stay-at-home dads or soccer moms? I do not know. It could be any one of these things. Let me put them all out there, and let me let them compete against each other to see who wins the budget." Ultimately, what do I care about? My lowest cost per conversion. As long as they get that, that is cool.

(61:09) **Brand Constraints vs. Performance Optimization**
Now, a brand can say, "For whatever reason, we care about only doing this persona." Great. No worries. Just have that be an input, and we will absolutely stick to that for you. It is probably not going to be as statistically good performance as if you let us try anything. You will never be a replacement for some ICP or persona stuff because even in a perfect world where you are jacked into the ERP and you are able to optimize towards those types of profits and stuff, there is always going to be some e-commerce people. That is cool that you get us these people, but they are not as high as an LTV.
Great. But then feedback, and we know that this person—I see where you are going with it all, and I agree with that. But Jay-Z, I do think there is still a role.

(61:55) **Value Rules for Optimized Conversions**
But let me speak on behalf of the robots here. Honestly, how do we handle this? One of the levers that we optimize with these ad platforms, I think a lot of people do not even do that they should, is value rules. Maybe a conversion is not all created equal, and we feed back in, "Sure, but then this person is going to make us $10,000, that is going to make us $4,000." So it is going to train these models to find more of the people that make us $10,000.
I agree. I agree with you in a reductionist sense, but I will give you a great example. Elective healthcare, aesthetics, med spa: $300 Botox, $3,000 breast surgery, $5,000. We were working helping a PE roll this all up. We were like, "Great, just feed us. We just need to know which ones end up converting to the $300 versus the $5,000 because it is night and day on CAC and LTV ratios." And the practice management system was such a mess. You are assuming perfection, and for a lot of industries out there, they will be able to feed that value back into your loops.
I know it can be a mess. We have had to work with customers to craft somewhat elaborate formulas that end up spitting out some sort of volume number that we can feed back in. Right now, yes, that is in the realm of human judgment. But as I look at it, and I think about, again, we catalog every single one of these learnings, and I think about, "Could an AI have come up with this same thing given the same inputs with the right prompts, and can it ultimately with a computer-use agent or an API-use agent be able to implement this?" I do think so. It might take me until the end of 2027. But I do not see any of these things that an agent cannot do. The numbers will always be the judge. That is why I am in performance, because it just—

(64:02) **Inputs, Data, and Collaborators**
I think Adam is saying with unsupervised machine learning, you still need some inputs. Misha, you are saying you do not need any inputs.
You need inputs initially. What is your product? What is your offering? Who do you think your customers are? But after that, it is about what the data is telling us. It is not unsupervised. It is supervised by the data. In a world of performance advertising, I agree with that.
Question for you: if you are showing up in a different way as an agency—you are showing up as not software as a service, but service for software—are there different types of collaborators than an agency would have? We are fractional CMOs. He loves talking to agencies because we are running RFPs all the time as the fractional CMO. I do not know. Different world of collaborators, maybe.
Yes, somebody at the end of the day needs to be a human that visits AdSmith.ai and has a conversation with a thing. Currently, I am not even there yet. Currently, somebody needs to be hands-on keyboard using the tool.

(65:10) **Targeting Least Sophisticated Advertisers**
I do not care if it is a fractional CMO or an in-house or an agency. I do not even care if it is an agency if they white label us, as long as I am getting paid. We are all good. I do think that my goal is to have it be that the business, the unsophisticated at marketing business owner themselves, can just come and talk to our system the same way they would talk to an outsourced option, be it an agency or whatever. Sometimes they do not even want to deal with that, and sometimes they want to delegate that responsibility to a fractional CMO or something else, and that person will be the one doing it. I am trying to optimize. I see the biggest opportunity in helping the least sophisticated advertisers because they currently—that allows me to expand the market of marketing services. It is also realistic because what I found when I go to talk to bigger brands, at least today, if they are big enough to have an internal creative team, it is a no-go. They hate us.
Yes.
As they should. I get it. I would too. If I am sorry, it is either me or somebody else. Somebody is going to be doing this. We are going to have to work our way up to the bigger brands until they are comfortable, until they just cannot argue economically why not to do this. I like serving the smaller brands at first. If we can do that scalably, we have a lot of longtail to scoop up.

(66:57) **Typical Client Budget and Contact Information**
The last question that I need to pop: what is the typical budget for this? What are your clients spending on a monthly basis?
We say the minimum—this is not a hard rule. Our system will work with anything, but we tell customers you should spend at least $100 a day for a given campaign. So $3,000 a month. Then it is going to be, in that case, $300 for us on top of that, and then maybe $100 additional for the creative. So maybe $3,500 a month. That would be the minimum. You can go less than that, but I warn my customers, "You may not be buying enough impressions for us to have statistical enough learning to learn from." Because we are guessing too, but we are just guessing in the most statistically efficient way. Some people spend way more than that, but if people cannot spend that much, then I am like, "Do not waste your money on advertising. Do something else."
You need to throw enough against the wall. Something sticks.
Something. And again, not to imply that your stuff is…
No. And some of it is going to perform like, for sure. Anything does, but everything is credible. This could work. Shot on goal, and that is what we aim for.
Do we have your email? I will put it in the chat here. Just misha@adsmith.ai. I also have my—I know Dave sent earlier, but here is also my LinkedIn.
All right. Thanks everyone.

## AI Governance and What Marketers Are Missing

Speaker: Scott Brinker
Published: 2026-04-03
Tags: ai governance, ai trends, ai agents, data layer
Video: https://www.youtube.com/watch?v=_iVOz_0YNE4
Page: https://aimarketersguild.org/sessions/ai-governance-and-what-marketers-are-missing

### Introduction to Scott Brinker
(0:05) Hey everyone, I'm David Berkowitz and welcome to another edition of AI Insiders from Architectures AI Marketers Guild. It is a true pleasure to introduce an old friend, Scott Brinker, Mr. Chief Martech himself and someone who's been trying to make sense of so many of the worlds that we're in for so long. And I'd even say Scott that you're usually more

### Staying Ahead in a Confusing World
(0:32) successful than most. Even if I wind up in a haze of confusion every damn day that I don't think is going away anytime soon. So can't blame you for that, can I?
>> Well, why not?
>> Why don't you share? Because I feel most people on this call know you, have at least read some of your stuff, know some of your incredible little

### Scott Brinker's Current Role
(0:57) landscapes and all that, but why don't you at least share a little bit about what you're up to today?
>> Sure. For about eight years, the previous eight years, I was actually building the Martech ecosystem for HubSpot. Which was great fun. But I left that back in September. And this work I've been doing with Chief Martech,

### Challenges of Tracking MarTech Trends
(1:17) an armchair analyst to this industry for 20 years. That always has been a side hustle, labor of love, call it what you will. Decided to actually go full-time with that. Which to your point, David, my goodness, even trying to keep track of all this stuff 24/7, I feel hopelessly behind every day in these announcements. Trying

### Current AI Interests and Inquiries
(1:42) to chip away at it a bit at a time. Doing Martech analyst and advisory work full-time.
>> I mean, obviously, I've seen some of your work. We're all in the AI space, market or not, but what areas specifically are you most interested in right now? Are people calling you most often for?

### New Data Layer Report
(2:08) Not to be promotional on this, but I will share there's a report
>> It'll be a little promotional. We published a week ago with the folks at Databricks. And although this was something sponsored by Databricks, the way the report was researched and written, it applies to any of these data cloud Snowflake, Google BigQuery, whatnot.

### Focus on the Data Layer
(2:34) I would say the two things that I've been most interested in is one, what is actually happening at this data layer? Because for all the excitement around AI, you all know it's so much a function of the data that you feed into it. And for the most part, the data layer of most not Martech stacks, but company tech stacks in general, is still, how do I say diplomatically,

### Creative Possibilities with Better Data
(3:01) immature. There's a lot of opportunity for that to get better. And I think as it does start to get better, it opens up a lot of creative possibilities, not with AI, but all the things we can do with that data and AI. That's one thing I've been focused on a lot. And then the second one is

### Rise of AI Agents
(3:23) clearly this is the year of AI agents. You can't go a LinkedIn post without seeing AI agents. There are many kinds of agents, right? There's the agents we're using behind the scenes in marketing. There's agents we as marketers are deploying that are customer facing, everything from customer service chatbots to shopper concierges to, I'm sure we all

### Buyer-Controlled AI Agents
(3:48) love AI SDRs. But the agent that is actually the most intriguing to me is the category of agents that are not marketer controlled. That they're buyer controlled. We've seen this beginning with the shift from SEO to AEO, and buyers increasingly leveraging ChatGPT and Bard and Gemini and all this to take a very different

### Evolving Marketer-Consumer Engagement
(4:15) control over their journey. But I think that's one example of multiple kinds of AI agents that are starting to pop up. We're starting to see some hints of this in the email space, AI control of the inbox that again, it's changing the relationship or the channels and the mechanisms by which marketers and consumers, customers

### Humans vs. Bots
(4:41) engage with each other. And that's very nascent, but it's probably the area I'm most fascinated by.
>> Well, it touches on something that I keep going back to and everyone I work with, they have to hear a little bit of my soapbox on this, and that's especially now, in this increasingly agentic era, that there's

### The Two Audiences: Humans and Bots
(5:07) that we basically have two audiences and it doesn't matter if you're B2B or B2C. It doesn't matter if you're talking to an individual consumer versus a Fortune 500 CEO, that you've got humans and you've got bots. And the humans all have more in common with each other. I have way more in common with Tim Cook or Oprah or some person who's running some startup out of Lagos

### Bots as an Alien Species
(5:36) now. We all have some shared characteristics and have way more in common with that Lagos founder who I've never met than I do with bots, right? And the bots, whether they're coming from Claude or they're coming from Google or they're coming from G2 or any other company, they all behave in a more similar way as this kind of alien species. And so it's

### Acknowledging Different Targets
(6:09) if you start acknowledging that you've got these two very different targets, then I feel a lot of what you have to do unfolds from there. Does that gel with what you're seeing or you have any holes to poke in that?
>> Well, to your point, and you know this better than anyone, marketers have always been doing

### Marketers Adapting to Google Bot
(6:32) this now for one, two plus decades, where there was our human audience and then there was the Google bot.
>> Now granted, it was the Google bot, but obviously the whole SEO industry, we put a lot of effort into actually mastering how do we talk to both of those audiences simultaneously.

### The Challenge of Diverse Bots
(6:52) I think what's perhaps both interesting, challenging at the moment, is it is no longer one bot. It is this increasingly diverse set of bots.
>> Although to me, there still is one notable difference is that with the Google bot, when the idea was that a human would land on that page that surfaced from Google's list of links,

### AI Reinterpreting Information
(7:18) it still had to be readable, right? An SEO expert who wanted to overoptimize keyword stuffing and all this stuff, if they made it unreadable, then the conversion couldn't happen. And now when most of that info is being reinterpreted and synthesized by the LLMs and by other AI tools, then we don't even know what version of this the human's

### Inconsistent AI Models
(7:44) actually going to see later.
>> That's a fair point. And again, there's a lot of diversity Gemini does that is different than ChatGPT is different than Claude. And worse, they're not even consistent within themselves. New model anytime. They're constantly evolving. And that combined with the lack of visibility,

### Early Stages of AI Agents
(8:07) it does make it quite a game. I don't know. I keep thinking about that movie Dodgeball. He puts the blindfold on, and they're like, "God, and it's going to be hard for him to see." It just that's a little bit what it feels at the moment. But to be honest, I still feel that's very early steps. What is

### Buyer-Side Agents and MCP
(8:28) intriguing is the notion that we are going to see agents on the buyer side that actually doing more than reading content, and synthesizing from that. But to the degree that we're able to expose things that they're able to do through companies that are exposing some of their things through MCP in the B2B space, are already seeing this

### Practical Examples of Agents
(8:52) thing where people have their work agents and they're, "Oh, can you fix this scheduling thing for me?" They go off and do it. And it's some of these things that are, I think, relatively new channels.
>> But are you, do you have any either B2B or B2C? Do you have any practical examples that you've used, you've seen, you've worked with other companies that

### MCP for Newsletter Management
(9:14) are doing in some way?
>> Most of the stuff that I've been doing with MCP has been back on the orchestration with things across your stack. For instance, I use Beehive for delivering my newsletter, and they released their MCP server. So, I'm in Claudius. I'm thinking about, hey, can you check on this thing? What

### New Interaction Channels for B2B
(9:36) happened with this audience? How's this open rate been changing? Oh, I had this sponsor here. I had this go. The fact that it can basically go behind the scenes, get that information from Beehive, this is a new channel in which Beehive interacts with me that's not through their app. We see a lot of those examples today on the B2B side,

### Consumer-Side Agent Emergence
(9:58) but I don't see why that can't start to emerge on the actual consumer side. Obviously, what Chetch tried to do here with instant checkout that they've pulled back on, but even then, what Google's trying to do with UCP, it all feels very science fair project stage right now, but it does seem to be pointing directionally into this thing of people will be

### Google Search Console and Base 44 Super Agent
(10:21) leaning on these agents to actually do things, not synthesize content.
>> That's what you're saying with the Beehive MCP, it brings to mind something that I've found especially useful lately because one of these odd things, as I've been building more, I've been learning or relearning Google Search Console because now I have to go and make

### Base 44 Super Agent Functionality
(10:46) sure that these sites are potentially visible. And I use B 44 for so much of my building. Base 44 past couple weeks released a super agent tool to work across your sites. And one of the things it'll do is connect to Google Search Console directly. It can then set up error monitoring. It can fix a lot of the errors itself and then tell you what to do with your

### Powerful Code Rewriting
(11:13) individual properties and then it'll often rewrite code for me to better accommodate what Google Search Console is doing. So that I can then go from where I'm actually building this stuff and no longer look at Google Search Console itself. This stuff feels pretty powerful and useful.
>> And when you think about it,

### Rapid Advancement of MCP
(11:40) it's been a matter of months. Depending on some of the ones that were really forward-leaning, what about 14 months since the entire notion of an MCP protocol was introduced. The speed at which this stuff is moving and advancing is pretty wild.
>> I got an email literally an hour ago saying, "Your

### The "Lake Wobegon" Effect in AI
(12:05) super agent learned 130 new tricks with all of its new integrations." So, I'm about 130 of those behind by the time I got on this call, right? I've been thinking about this lately. The Lake Wobegon effect, all the children are above average. I feel we collectively now are in the inverted Lake Wobegon effect. Everyone feels below average and trying to keep up with this stuff.

### Credible AI Startups
(12:30) So maybe that's me.
>> Do you have some because there's so, I could literally launch an AI startup that might not actually be very good but looks credible on the surface by pulling some stuff together with these code and tools right now, right? And I can give it

### Distinguishing Credible AI Businesses
(12:52) a nice domain and logo and all this stuff and it can look a very credible business. But it might not actually do anything. Do you even have some threshold for what you'll pay attention to, or will you look as far out on the fringes as possible? How do you draw some line in your world?
>> We have generally gone

### Validating Small AI Companies
(13:18) pretty close to the edge. We try and validate that things actually is a legitimate company. There's signals you can have the ability to contact it. What's their presence on LinkedIn? You can cross validate this stuff. But we look at a lot of companies that are very small because I feel there's lots of cases where you get these great

### Value of Curated Landscapes
(13:40) curated landscapes of hey, these are the top 20 products you want to pay attention to, and I actually find those things very useful because they are what we typically think of as the head of the tail or the main things. But almost by accident, I've ended up in this mode with the MarTech landscape, really looking far down the

### Analyzing the Long Tail of MarTech
(14:02) long, long tail, which trying to look at individual companies on that is not very useful to anyone. But actually looking at it in aggregate and how it evolves over aggregate and in which categories and which ones stick around and how quickly do they churn, and what's the percentage rate that grows, it continue. We're in the middle of working on our big

### Preview of MarTech 2026 Report
(14:24) State of MarTech 2026 report now, and it's full of insights of, okay, for individual companies, not super interesting. Patterns in aggregate, very interesting to start to see what these dynamics are showing.
>> Are there any patterns you give us a preview of?
>> One of the things that's actually very interesting is this. Don't spread this one on

### CMS and E-commerce Takeoff
(14:49) LinkedIn, we'll be between us, but one of them is there's quite a takeoff in the CMS and the e-commerce space. E-commerce had a nice wave around the pandemic era, just because of the big shifts, but it's starting to settle down. CMS has largely been a pretty static category for quite some. You could argue

### Category Acceleration and Churn
(15:14) it's actually the oldest Martech category out there. But we saw significant acceleration in both of those categories, and almost double acceleration, because not only did they grow, but they actually had a lot of churn. A lot of older companies have basically left the space or the sector, exited, acquired, or caught on fire,

### Growth Driven by Rethinking CMS
(15:37) whatever happened. So actually, for there to be growth, there had to be that much more to cover up the deficit from those who exited. And when you start to dig into that, and you look at, it makes sense that it's some of the things we're talking about on the CMS side. People are rethinking, okay, how do we manage

### Generative AI and AI Agents in CMS
(15:58) the website in this environment where both, hey, we can leverage generative AI as part of what we're doing here, but also, oh, we've got these AI agents of various kinds and flavors are engaging with us. How do we think about serving that audience? How is that built into this? That was interesting. That was not on my bingo card to see that clear and crisp of

### Content Marketing Category Decline
(16:23) a renewal of the CMS category.
>> Okay. Interesting. Is there anything that was top of mind the past few years that is not as prominent right now? What's on its way out?
>> Well, the category that suffered the most this past year was actually the content marketing category, which part of it

### LLMs and Content Creation
(16:57) is because it had a, it was the category that actually had some of the fastest takeoff around 2023, just because as soon as LLMs were out here, people were, oh my goodness, we could wrap this and do all sorts of content things with it. And a whole bunch of people did. But playing out a few years down the road, there's a big difference between

### Sustainability of AI Wrapper Businesses
(17:21) wrapping an LLM, and as you're saying, hey, I can wrap this. I can put it out a website and it looks credible, okay, this is actually a sustainable business. Even when you're talking about small businesses, still is it a sustainable business? Is there a competitive offering there? And probably not a surprise, but saw

### Survey on AI Use Cases
(17:43) a pretty big exit from that category. The other thing I would say is this isn't about the landscape, but in parallel to the landscape, we ran a pretty in-depth survey of 70 marketing use cases with 28 marketing ops Martech leaders. And what we were primarily asking about was, okay, for this use case, are you using AI within an existing SaaS platform? Are you

### AI Use Case Insights
(18:13) using a new AI native tool for this capability? Have you created something of your own with AI? Or no, we're not using AI, or we're not doing this use case. And that's, and of course, then we split out the data between B2B and B2C. And that's proving very interesting. But one of the things that came out was, boy, a rush of those

### Lack of Adoption for First-Gen AI Features
(18:40) co-pilots, particularly in the content space that you saw so many of the Martech SaaS vendors. I think they haven't gotten the adoption. It's almost at the point where we're now making fun of if you've got the little sparkles on something inside your, it's okay, that

### Disappointing Adoption of Early AI Features
(19:02) was stuck on. Not quite sure what my point was on that, but it was a little bit surprising that for all the effort that so many of the SaaS companies put into that first generation of AI features on their products, those generally haven't been the things that have actually gone adoption. Well, we've got two questions on the CMS

### Standouts in CMS
(19:29) front. And one is, is there anyone who stands out in the CMS field? The big brands are the ones you really know. The long-tail folks I don't have off the top of my head. But again, often, when we publish this, there'll be an interactive map. You can zoom in on them.
>> I often find the long tail again

### Long Tail CMS Innovators
(19:51) interesting. Not the odds of any one of those actually growing up to be the next Sitecore is low. But it's interesting to look at those who have nothing to lose, who are coming in with completely fresh eyes and no backwards compatibility. Even how they think about that space and those capabilities, because that becomes interesting patterns that

### CDP Adoption and Trends
(20:15) actually we might adopt even if we don't adopt that tool.
>> Gotcha. And then Earl is asking, is the trend you're seeing with CMS similar to what you're seeing in CDPs as well, since most B2B marketers are in early stages of their CDP adoption?
>> I think with the CDP side of it, what's happening right now that's

### The CDP Landscape
(20:39) fascinating is we had a whole bunch of people enter that space, obviously. In fact, almost every Martech company was, and among everything else, we're a CDP. But even among those companies that were legitimate CDPs, if you could use that adjective, it was hundreds and hundreds. But they span quite a range

### CDP Capabilities and Shifts
(21:04) of capabilities. Some were very down to the metal databases, and others, quite frankly, were more engagement platforms. Since over the past year, we've seen a couple of interesting things here, most the exits of CDP companies. So those that had scale that exited, they actually moved to the engagement layer. Which makes sense, in particular, with

### Data Warehouses vs. Pure Play CDPs
(21:30) this other thing that people are finding using these data warehouses, Snowflake or Google or Databricks or things like that, actually becomes the easier way for them to get a lot of the raw data. It doesn't solve the engagement problem, which is hence why they still need that. But this idea of a pure play CDP that doesn't have engagement,

### Importance of Data Layer and Attribution
(21:56) they're still out there, but that category seems to be fading pretty rapidly.
>> And that makes total sense, but I was curious because only because you mentioned the data layer in the beginning, and for anybody, as well as an expert, anybody who does the data, how you define your attributions and all the all the

### Engagement as a Focus
(22:14) measurements that you include influences how you integrate your platforms. So that's why I was curious to see if there's been any trends that you've been seeing outside of the CMS. But it seems to make sense that engagement would be the focus. Now you can track all those interactions. But still the data layer, probably the most important component to

### The Semantic Layer in CDPs
(22:34) identify, I would argue.
>> Well, I think one of the things at that data layer, that both the data clouds have been doing this, but also even some of the CDPs, I don't know, Hidoch, is a composable CDP. This concept of a semantic layer does become essential, because it's almost the dog that catches

### Governing Data Overload
(22:57) the car. It's our original problem was we can't get the data from across our. Then we get all the data from across our, we're nobody, we have too much and now we have no AI to make sense of it all.
>> That is actually, I would say, the area where the most active and

### Context Engineering
(23:14) interesting things are happening is, okay, now that we've got the data flowing, how do we govern it? How do we make sense of it? And that is still arguably where there is a CDP-ish role to be played, of listen, out of all that crazy sea of data, how do I package up the pieces of that that are relevant to particular experiences or campaigns?

### CDPs and Context Engineering
(23:36) In the AI world, people talk about this as context engineering, because prompt engineering is 2024. Oh my god, so context engineering. And in a lot of ways, I think that's what those CDPs were way ahead of their time, of, oh yeah, let's bundle up the context from a data perspective to do this particular execution.
>> We got a one from John. Any trends

### Vertical Market Focus
(23:59) around companies becoming more vertical market focused? And if so, what vertical markets are proving most popular? Are there any surprises?
>> That's interesting. We don't categorize it by verticals. So I don't have hard data on that. Everything I have is anecdotal. I will say I feel that narrative has had more strength, has been more popular

### Horizontal Platforms Preferred
(24:26) among the VCs, who are trying to find where can we actually put money now that has a chance to play out? And less of generally what I hear when I talk to marketers. Even if they're in a particular vertical, they still tend to largely be using horizontal platforms that they adopt, adapt to their needs. If anything, I think the twist is

### AI Changing Build vs. Buy
(24:52) now that AI is starting to change the build versus buy equation. Again, I'm still on the camp. I don't think you should build your own CRM. But this ability, because things are now opening up with stuff like MCP, is you're seeing more and more cases where companies have their commercial platform, Salesforce, whatever it is

### Custom Business Applications
(25:13) there. But then on top of that, they're, actually, we want to have our custom version of, okay, how are we going to manage a particular sales pipeline, or how do we do lean scoring on this stuff? And so in some ways, it feels that's leapfrogging a bit of the idea of prepackaged vertical market applications. What's

### Tailored "Business of One" Applications
(25:36) even more tailored than a vertical market application? It's business of one application. It would have been insane for most companies to do even a couple years ago.
>> Yep. It's now, I would still again caution, you can definitely get over your head, but that seems to be where the more likely direction is moving.

### Staying Updated on Dynamic Space
(26:02) And then Adam's wondering, oh, it's a great meta question here. What tools, publication systems do you use to stay on top of this whole dynamic space?
>> Didn't you miss my disclaimer at the beginning? I can't stay on top of it.
>> I don't know, I, honestly, it's on top of it. I follow a ton of people on

### Insightful Newsletters and VCs
(26:28) LinkedIn and X and all that, and take a very heterogeneous set of things that I see flowing through that stream. If there are newsletters I do regularly subscribe to, there's actually a subset of folks out there in the VC community that I find insightful on this. One of them is a guy named Jaman Bell here. I'll put it in the chat, who does Clouded Judgment

### Recommended Newsletters
(26:55) is a newsletter that's good. And then there's Tomas Tongas, who's now got his own firm, Theory Ventures. Those are a couple of the ones that pretty much every time I get one of their news, I'm, it's actually an insightful perspective.
>> And someone's asking if you save

### Saving and Analyzing Research with AI
(27:22) what you find into a platform, a notebook, LM, or something that.
>> Can I, can I expand on that?
>> Oh, go for it. So you said you read and follow all research online. I'm wondering if you're saving what you find, and then if you feed that somehow into some AI model so that we can

### Not Yet Using AI to Process Research
(27:46) extract insights from it.
>> That's a great question. The honest answer is no. I probably should. This is, again, I'll speak for myself, but I definitely feel on more than a few occasions the old dog new tricks. I've forcing myself to learn new things. I'm deep in Claude code.

### Overcoming "Old Dog, New Tricks" Syndrome
(28:10) I'm building some fun stuff there. But there's so many things that have changed, and there's so many things that I still have on autopilot, without having even stopped to think, oh, I should try an entirely different approach to this. And one of them is the way in which I consume writing out there.

### AI Governance Confidence and Ownership
(28:31) >> If you, I can show you a way to do that.
>> Okay. Let's follow up on that here. I'll
>> And then Peter was asking, Scott, your recent report surfaced only 8% of organizations feel confident in their AI governance, but adoption is accelerating. So what do you, who do you think should own the orchestration and governance layer? And to give credit where

### Clarifying AI Governance Study
(28:55) credit is due, that was citing a study that was done by SAS, who was one of the sponsors in that report.
>> So do you think it's high or low then based on, is there bias, is what you're saying?
>> No, no, no. I wanted to give them credit. I'm sure if they wanted to bias in a particular way, 8% is probably not a

### AI Governance Must Be Corporate-Wide
(29:22) good bias. I, to be honest, as much as I'd been an advocate over the years for marketers and marketing ops and Martech to control a lot of their destiny, there's a set of things right now that I think have to be corporate-wide. I think AI governance is one of those. Honestly, the data layer,

### CMO-CIO Relationship for Infrastructure
(29:48) marketers have to be responsible for their piece of the data layer, but it's some of these things where, as an organization, I've been at this for many a decade, there was a time when it was a barrier to what marketing needed to get done. But the world's moved on a lot. And I

### CIO Support Accelerates CMO Goals
(30:10) think in companies where there's a healthy relationship between the CMO and the CIO, there's so much infrastructure stuff that the CIO is able to provide to the CMO that accelerates what the CMO wants to do with their org. But anyways, I feel AI governance is one of those things, that's it's got to be

### Evolving AI Governance Best Practices
(30:33) corporate-wide. And it's a problem because again, you don't, it's such a new thing. Who has experience? How many, what's the best practices of that? These things are being written as we stumble through it.
>> A slight twist on that one over there. I was in a recent round table and the chief HR officer from a global bank was talking about their

### HR's Role in Agent Governance
(30:57) their new role in governance and onboarding agents as they are, as part of the personnel process. So agents go through the same type of training, evaluation and reviews and roles, and have a similar governance to the humans. I'm wondering about seeing that HR convergence with it, in that capacity.

### Opposing Reactions to HR Agent Governance
(31:23) >> I have two opposing reactions to that. One is that actually sounds smart. Of why you would want alignment on those things, and hey, it's a great opportunity for HR to reinvent itself in this next stage. On the other hand, the other reaction I had, which is a very visceral one, is I'm still in that camp where the degree to which certain leaders seem to treat

### Human-Agent Fungibility Concerns
(31:49) humans and agents as very fungible resources. It doesn't sit well with me. So I those metaphors still caused me to twitch a bit. But leaving that aside from an organizational perspective, and it sounds a pretty reasonable way to think about it.
>> And thanks, Peter. Mark's been waiting so

### Question on AI "Wrappers"
(32:17) patiently. Come on. Come on up.
>> Hey there, Scott. Had a question for you about wrappers. It seems the interface is confusing for a lot of marketers, especially solo practitioners and consultants. We've been talking a lot about enterprise. I'm wondering what are your thoughts on wrappers, and are they being used by marketers? And do you report on?

### Defining AI Wrappers
(32:42) >> When you say wrappers, you mean a product that's okay, I don't know, I'm trying to make up something here, but hey, I want something to help me build a campaign, rather than do that step by step myself in Claude. Oh, I've got something that's a little bit of a guided.
>> Wrappers are models that

### User-Friendly AI Wrappers
(33:00) are built for the interface is easier for the user. For instance, there's some for law firms, there's some for medical offices, there's some for consultants, etc. And they have APIs going to the various AI models. Okay. So, it's cheaper and you get access to 20 models, but you're restricted.
>> A couple ways I could answer that. One is

### Human and Organizational Limiters
(33:29) having observed this over decades, the rate of change in technology and the rate of change of organizations, man, that gap widens, and it's now almost insane. You almost can't see across the chasm. The limiter on all this stuff is the human and organizational side. And so while there are people who might look from a

### Value of AI Wrappers for Users
(33:54) technical level and say, hey, you don't need that wrapper, you could do it, I actually think those wrappers serve a great role if they're able to take a set of folks who aren't ready to dig into that, and this is a way that they can get value out of it and use it. Usually where the push back on wrappers is isn't the fact that they're

### Defensibility of Wrapper Businesses
(34:17) non-useful to users, because I think there's actually a lot of cases where they clearly are. It's that from being in the business, from being in the wrapper business, usually the big question is, okay, well, how defensible is that? And at what point in time do the Frontier Labs absorb that?

### SAS Opportunities in Context
(34:40) >> Like everything else.
>> Yes, like everything else. But actually, it's funny. I've been writing a lot about this, around all this stuff around context. And I think there's a lot of opportunity in the SaaS space for them to lean into the strength of what I think they've always had, which has been very, very good at framing the context of

### Domain Expertise for Context Delivery
(35:03) particular kinds of work and activity. I think one of the things that goes into being good at delivering context is you have to have that domain expertise. And again, I'm hesitant to say this, because Sam Altman will come out with something six months from now and
>> completely prove me wrong. But I think it's very hard for those frontier

### Frontier Labs vs. Domain Expertise
(35:29) labs to move in the direction of developing the domain expertise and domain-specific interfaces and things. And I don't see that's to their advantage to do that. They're in such a position to win at the horizontal layer below that. So I still think there's a lot of value out there for that. But
>> Do you report on them?

### Tracking AI Wrappers
(35:49) >> What
>> Do you report on wrappers? There's so many of them out there. I
>> I don't actually have even a way to
>> Right.
>> It's a weird continuum. What's a wrapper, right?
>> So no, we don't track that

### AI Native Companies as Wrappers
(36:02) specifically, but
>> Okay. You could say a lot of the AI native companies that have been born in these past three years, hand wavy back, you could say the vast majority of those are a kind of wrapper. Or actually, see, the thing I like about this is language is such a wonderful thing. The way in which people are now starting

### Wrappers vs. Harnesses
(36:24) to talk about this, oh, is this not a wrapper, it's a harness. The whole thing around Claude code. Oh, no, no, this is a harness for it. And again, to some degree, I actually think that's right. And there is proprietary insight, which apparently Anthropic just accidentally leaked to the whole world, but there is proprietary IP and how do you structure

### Next Questioner
(36:46) a harness to do a particular task well, even if they're all sharing the same underlying LLM.
>> All right, who else has got something for Scott? These are fun.
>> Are you daring me? Because I will. Okay, Earl, you could take the rat sock is dangerous.
>> No, no, no, not at all. Not at all. Everything you're saying, Scott, I was

### AI-Only Solutions and Adoption
(37:06) curious about all these new companies that are coming out with their AI only solutions. And I know that especially in our crowd, we're very interested in it, especially to see where it's going. But I keep going back to what I learned about product and product development, and I'm not seeing the adoption that you get from the civilian side or the technical side in

### Crossing the Chasm with AI Tools
(37:30) terms of all these different tools. For example, David will talk about Vibe coding all day. I can talk to you about custom GPTs all day. But we are very selective crowd, and I'm wondering, think in terms of crossing the chasm. How many people are actually using these in their companies? Companies are pushing them and investors are pushing for adoption. But how many

### Ownership of AI Adoption in Enterprise
(37:50) actual people in their, especially enterprise organizations, are actually pioneering these things and pushing these things forward? Back to the question earlier, who is going to own the AI adoption process? That's the thing I'm struggling with. Who do you talk to when you want to talk about AI to help them with their AI?
>> Okay. So there's a few things.

### Two Questions on AI Adoption
(38:11) Two different questions. What do we think about the civilian adoption? And two, who do we think should be owning the AI adoption process internally at enterprise organizations? Two separate questions. For the first one, there's a place where we have data, and then there's a place where at the moment all I have is anecdote. The anecdote is what I

### Lack of Adoption for New AI Products
(38:32) hear anecdotally is most of these AI features, and then also a lot of these new AI products, they're not getting adoption, for a variety of reasons. Again, I think even the weekly active users on

### Breakthrough AI Products
(39:17) things like ChatGPT or Claude, when you see breakthrough products like Lovable, And so I think it's interesting, for the most part, people don't want to have to learn new stuff if they don't have to. But out of the thousands of attempts that are throwing things against the wall right now, there

### Bifurcation of AI Product Success
(39:43) do still tend to be several dozen or so that, oh no, this catches on and people realize, oh wow, I can do this and this is great, and then they tell two friends and they tell two friends and so on and so on. So anyways, it's a bifurcation. I think it is

### Who Owns AI Adoption?
(40:04) possible for there to be great takeoff, but only for 0.2% of what's in the market right now.
>> Gotcha.
>> What was the second half of that question?
>> Yes. Second half was, who do you see, at least from your research and your anecdotal stories that you've had, who has been the owners of the AI adoption?

### AI Adoption Ownership Roles
(40:28) Because, obviously, it depends on the size of the company, whether it's SMB or enterprise, I get that. But as you said, it can be anybody from the CMO to the CIO, maybe even if they have a CPO, chief product officer, that might be a person involved, who knows? But what have you seen from your research?
>> So I see three roles.

### CEO and CIO Roles in AI Adoption
(40:50) This is CEO, who basically tells everybody they need to use AI. That's about as helpful as it gets.
>> You saw what happened with a couple companies about that. Better to rehire them back.
>> It's a wacky time. The second role is, it is generally the CIO or the IT organization that's going ahead and getting the enterprise licenses and

### Individual Teams Drive Genuine Adoption
(41:11) manage them for the Frontier Labs and some of these major platforms. However, neither one of those things actually speaks to genuine adoption, much less actual genuine impact and outcome.
>> Everywhere I see it, I'm trying to think of other exceptions. It happens so much down in the individual teams.

### Zapier CEO's AI Framework
(41:37) There's a few companies that have been forward about this, oh, what's his name? The CEO of Zapier. The whole Zapier company has been obviously core, their product has been very much on the frontier of this. But he's been publishing his, okay, and this is the framework we use of what we're expecting

### Lack of Top-Down AI Guidance
(42:00) from people. And it's not at that level of the CEO saying use AI. It's, no, no, actually these are the different kinds of things in the use cases, and how do we measure it? But he almost stands out because that is such an exception now that in most companies, there isn't enough of that guidance top down to say that anyone is taking ownership

### CRO Interest in AI Adoption
(42:20) of adoption.
>> That makes total sense. I think that companies that have a C-level executive for revenue, for example, a CSO, a CRO, would be interested in that, considering that they can make a big deal from their data, from they're getting that they're getting from their sales teams. But I don't see it in the market.

### Sales Team Tool Adoption
(42:41) >> Learning new tools is not a sales team. It's kind of big, acquire me from my experience.
>> And they just gotten excited about Gong. Okay, we think we finally got our arms around this.
>> Baby steps. Got woo. I'll take it.
>> Oh, and then Clay, everyone's, of course, we go to market, we've got Clay as if that's our

### Brand Rebranding Surprise
(43:01) magical thing. But
>> I think they rebranded, they rebranded the consumer product to Mesh, I think recently.
>> They rebranded, I believe.
>> Seriously? Look it up.
>> Emsh. They rebranded the consumer product.
>> Hang on. I have to Google this.

### Difficulty Keeping Up with AI Changes
(43:22) >> You Google me your own.
>> Oh my god.
>> I would have sworn that was an April Fool's joke.
>> No, but okay. All right. I'll have to, all right, I once again go back to my disclaimer at the beginning of this thing. I can't keep up with all of this.
>> Nobody can. Not even AI can. Meanwhile,

### Agentic Commerce Inquiry
(43:41) Yogish, you want to chime in?
>> Hey Scott, good to see you again. I had a question that's a slightly different one. In your, I know you were sharing a little bit of tidbits from your upcoming release of your new report for the year. I was curious if you were seeing anything around agentic commerce showing up this year in your analysis.

### Lack of Agentic Commerce Adoption
(44:04) >> The short answer is not a lot. We actually, and this is why anybody who says they know the future in this market, we were actually expecting to see more of that, because at the end of last year, all the big announcements from OpenAI and then Google, and everyone's headed into that, and then it's generally turned out for the

### Consumer Readiness for Agentic Commerce
(44:29) most part, consumers aren't ready for this in a lot of the cases. But part of this depends on how you define agentic commerce too. Because there's these things, I call them the shopper concierges. There are these AI experiences, whether it's a dedicated app or something like this, and you could, depending on how loosely you want to

### Science Fair Stage of Agentic Commerce
(44:54) define agentic, you can see that. But actually having agents go and do these things for me, it's still everything we're seeing right now, it's still science fair.
>> Got it. A quick follow up on that. Based on what you're seeing, at least from the larger LLMs, OpenAI and Google's Gemini, I'm curious to hear

### AI's Impact on Consumer Discovery
(45:17) what your thoughts are on what you're seeing in the space.
>> I think what OpenAI discovered in there is there is this massive shift that has been happening of consumers starting to truly use AI for discovery, and also for evaluation and weighing different options and stuff that, which, to be honest, again, in two years, the

### Industry's Slow Absorption of AI Shift
(45:46) whole nature of how people do discovery and evaluation online is shifted. And to be honest, that hasn't fully, I think the industry, we collectively haven't even fully absorbed that and learned how to deal with that well. And I think when OpenAI was retrenching away from this, they're, okay, this is we're going

### Merchant Resistance to Ceding Discovery
(46:12) to focus on for the core business. I think that's where the balance seems to be. That's obviously also one where the merchants involved are willing to cede that, because they'd already had to cede that once before. Google, not happy having to relearn this all again, but open to the possibility that they will not fully control the discovery channel, unless if

### Walmart's Stance on AI Discovery
(46:35) you're Amazon, they won't control it. But what was it, the Walmart head of AI had that quote at an investor conference when they were, the OpenAI thing was letting ChatGPT do that, that was a temporary moment in time. Walmart's, the hell we are going to cede over, and it become your fulfillment

### Consumer Trust and Merchant Readiness
(47:01) service on the back end here, my friend. So it's both the lack of consumer trust, plus the fact that I think the merchant community was maybe caught a little bit off guard when this first hit. All the ones I talked to, they're, we're not going to, we're not going to walk into that if can help it.
>> I get that. And one thing that I find fascinating in

### Merchants Creating Walled Gardens
(47:26) this whole space right now is that the speed at which some of these merchants are investing into their own, call it walled gardens, in a way, is going to create an interesting mix for brands in terms of how they'll be able to engage across different protocols, right? And I wonder if that's going to open up a new space for startups to be able to offer for potentially new

### Retail Media Networks and Fragmentation
(47:50) solutions. I don't know. I'm speculating here a bit, but it feels that that's where things are going. So,
>> Well, in some ways you could say this is what's happening with AntTech. This explosion of these little retail media networks is actually now once again we've got a fragmentation

### New Opportunities from Market Fragmentation
(48:09) in a market. And so that creates, we were headed towards what the duopoly, and it still largely is a duopoly. But now is enough interesting things happening in this fragmented space that you're starting to see the emergence of software vendors who are great, we can help in that environment. So,

### Upcoming MarTech Report
(48:33) Scott, any other things that you're excited about coming out next next few months? Anything else we should all be paying attention to?
>> We'll have that State of MarTech report out at the beginning of May. So, we now distribute that free and ungated. So, whenever that's ready. But, no, thanks for inviting me to have this chat with you. These are great questions. I love this conversation.

### David's Gratitude and Future Guests
(49:15) Welcome by anytime. We'll make sure to share your latest state of things report with the community. And for everyone, we've got. I almost get embarrassed sometimes when I'm now working on booking things, and I'm, well, you're an amazing guest. Let's look at July. So it's a fun fun problem to have, but it means that we

### Upcoming Events and Holidays
(49:39) got a lot of conversations coming, including with at least a couple of folks on this call today. So appreciate you all coming by. Stay tuned for more. Check the Luma for a lot of what we have scheduled, and a few more things we probably need to add. And anyone in New York next week, we're going to be doing a belated first Wednesday, because tonight is the first Wednesday,

### Holiday Greetings
(49:58) but also the first night of Passover for a lot of folks celebrating here. So happy Passover and happy Easter to everyone celebrating the next few days. And we will see you all very soon.

## What AI Agents in Marketing Actually Look Like

Speaker: Tom Riordan
Published: 2026-03-19
Tags: ai training, ai in marketing, ai agents
Video: https://www.youtube.com/watch?v=uheU5BRWvSE
Page: https://aimarketersguild.org/sessions/what-ai-agents-in-marketing-actually-look-like

**Welcome to AI Insiders**
(0:05) Hey everyone, I'm David Berkowitz and welcome to another edition of AI Insiders by AI Marketers Guild, part of architecture media. The architecture media angle is a little more relevant today than it is even if architecture is always top of mind in our world because we are just days after March's biggest ever event, Marchitecture Live 3.

**Marchitecture Live 3 Highlights**
(0:37) I got to say, yeah, full on I'm biased. Yeah, I'm wearing the brand. The community is a part of architecture, but I haven't been to many events that. I have a very short attention span when it comes to any of these kinds of events. Fortunately, this event was panel free, so a lot a lot of solo talks

**Marchitecture Team's Special Effort**
(1:05) and fireside chats, but the team behind it, Amelia Tren, Jesse Meyer, everyone involved working at March just did something special. I'd been on the planning calls for months. Hadn't done actually too much of the planning myself. So I could take 0.00101% credit for it. Just a phenomenal team effort there. And so I highly recommend

**Introducing Tom Riordan**
(1:38) trying to get to March events in person in the future should you ever have a chance. Today we're inviting one of our speakers from the event, Tom Riordan, who's been a longtime friend of Mark. I've even gotten to have this other role with Tom because Tom leads a lot of the AI curriculum for another phenomenal group, U of Digital, and I mentioned them

**U of Digital's AI Curriculum**
(2:09) in the past and we'll put links into their stuff, but they do some of the best training in the ad industry about the ad industry and related topics. And so they'll talk to brands, they'll talk to agencies, adtech companies themselves, and cover a lot of ground and the curriculum. I get to go and build on some of the

**Tom's Expertise at Word Power**
(2:33) things they're doing. I've been a guest lecture a couple times, including last month, and just the thoughtfulness of the curriculum development is so strong, thanks to some of the folks Tom, who also runs his own firm, Word Power. So Tom, I am fortunate to have gotten to learn from you in many ways, especially over the past couple of years and hear

**Tom Riordan's Introduction**
(2:58) some of your thoughts on where AI played a role, didn't, should have, could have, would have, especially in some of the market live conversations. But Tom, if you want to introduce yourself a little bit better, we're very excited to have you here. >> Sure. Thank you, David. Hey there, AI Marketers Guild crew. My name is Tom Riordan. I run a business called Word

**Word Power's Mission**
(3:18) Power that helps businesses go from zero to one on their AI journey. Although I'm increasingly finding some businesses are ready to go from one to two on their AI journey as well. David mentioned, I do a lot of work helping marketers understand AI, helping people understand key concepts and the fundamentals and then most excitingly for me is use cases.

**Hands-on AI Training**
(3:39) So, one signature piece of our sessions is that we're always showing demos or doing live work sessions, inviting people to participate in exercises, roll up their hands, and get them roll up their sleeves and get their hands dirty. So, my business has been around about three years. The first year we did a lot of consulting type work where we just

**Addressing AI Adoption Gaps**
(3:57) helping businesses think about how to use this new GenAI type stuff. And in my second year, it really became obvious to me that folks were being left behind at the average org. You'd have leadership teams thinking really hard about how they need to use AI. Maybe you'd have a couple power users who were building out special projects starting to see some return, but the

**Word Power's Mission: Lifting All Boats**
(4:15) majority of people, 80% plus of people at a given organization, weren't really getting their hands dirty, weren't really feeling supported. In fact, a lot of them are reading articles about potential job displacement and reskilling concerns they need to be mindful of and these individuals just haven't had resources. So, the mission of Word Power has been

**AI Transformation and Support**
(4:34) focused really the last year and a half on being the rising tide that lifts all boats at an organization, helping people feel they're being brought along with the AI journey, being invested in, enabled and supported as part of a broader AI transformation effort at a business. >> Awesome. Well, well, yeah, that training and clarity is so

**The U of Digital AI Alliance**
(4:53) sorely needed. And also it was exciting to see you bring up the AI alliance that UFD is launching, so could you just mention what that's about? >> I'm probably not the best ambassador on that. We should get someone from UFD to talk about that a little bit more. I did have the pleasure of crashing the AI alliance dinner which was some folks

**AI Alliance: Enablement for the Industry**
(5:18) from ANA and some folks from Anthropologic which is one of the sponsors. So, it's about enablement. It's about bringing something to the industry. But, yeah, your link there is probably going to be better than any elevator pitch, I think. >> Awesome. Well, had to at least give you a shot given your close

**March Event Themes**
(5:36) affiliation there and it was great news to hear live at March. So, what were some of the themes that you were picking up at during the event? What stood out to you? >> Yeah, for sure. You couldn't walk around for 20 minutes without either hearing AI spoken about directly in one of the sessions or hearing it come up in the networking portion

**AI Adoption and Networking**
(6:01) which was really notable I thought at marketer. All the marketing, the networking breaks were very social, folks were not trying to hold off and do phone calls. I met a lot of folks I'd never met before. I've heard similar experiences from others and just one of the constant topics was the realness of AI adoption within orgs

**AI in Marketing: Internal vs. External**
(6:22) if businesses are starting to see return themselves, ROI productivity enhancements, and then of course, in terms of marketing products and the actual way that we're advertising, that's the other piece too. So, two pieces: internal AI transformation for marketing type organizations and then how our campaigns are actually changing as a

**Challenging AI Agent Hype**
(6:44) result of AI. In AR's keynote when he was kicking things off he said something. What did he say about agents? He said, "None of us are really using agents right now. None of the people in this room are really using agents right now." And I thought that set the tone in an interesting way. I think he was challenging the hype around agents and

**Agent Use: Reality vs. Perception**
(7:06) saying we hear all this talk about agents but who's actually seeing return on their day-to-day and he was pushing the audience that there's a little bit of BS in terms of how much we in marketing are actually using agents and I thought that was interesting because I actually had I was talking to somebody else after at the event and they said, "I can't believe he

**Defining AI Agents and Their Use**
(7:23) said that. I'm getting all this return from agents. Can you believe that he's dogging us and saying we're not using them enough?" And I think there's a lot of fog there to clear, what exactly are agents? What does it even mean to be using them? Did you use it once? Do you use it every day? What sort of level of return do you need to see to be

**Stirring the Pot on Agent Usage**
(7:41) a productive agent user? And that came out at the beginning of the conference and already stirred the pot a little bit. So, I think that's maybe an interesting one to start on. >> Well, well, yeah, and we should definitely dive into this and so I'm just going to ask you yourself, are you actively using agents for anything

**Personal Agent Use**
(8:01) you're doing? So I am. I think I'm constantly using agents. Got to reorient myself though because the AI chat experience is becoming more agent-like. So for example, for today's we have an AI accelerator for U of Digital this afternoon and it's the agents session. So I'll be demoing agents to people and showing them agents that we use and we are constantly

**Demoing Claude Co-work**
(8:25) using a new agentic tool in that because we're just going to show people what's new. Today we're going to do Claude Co-work. Anyone on the call used Claude Co-work? >> Played with it, but. >> Lots. >> Nice. So, on one hand, you can do things in Claude Co-work that are just normal AI chat. And it looks and feels a lot

**Claude Co-work's Agentic Capabilities**
(8:44) normal AI chat, but if you spend a minute with it, you realize there's a lot more firepower under the hood. And Claude Co-work is different than regular AI chat because it doesn't just do what you tell it to do. It comes up with a plan and it deploys sub agents, little AIs to do things as part of executing that plan. And if it makes a mistake, it realizes it and it might course correct.

**Beyond Traditional AI Chat**
(9:06) If it needs to use a tool or go get information, it's going to do that. If it needs to write and create a document for you and leave it on your computer, it's going to do that. And these are all things that traditional AI chat really doesn't do. So Claude, the folks in Anthropic, are definitely calling Claude Co-work agentic but I bet there's many people

**Understanding Agentic AI**
(9:25) who use it or have seen it and don't really understand the paradigm shift, how that's different than traditional chat. So on that first point of what even is agentic, I think there's definitely still a lot of gray area. Yeah, one of the examples that I would bring up to folks is deep research. So

**Deep Research and Base 44 Agents**
(9:48) as agentic and how and for a specific task it meets a lot of the criteria, but it's also typically a one-and-done process. The agents that I'm using most consistently right now is so I'm a big fan of Base 44 and I've got a bunch of properties. I'm playing with this all. I actually was just checking

**AI-Generated Content for SEO**
(10:15) today to see, is it wait, am I on the monthly or annual price? And it's oh, I'm on the annual, but I just keep going to the next plan up and I keep burning through more of their credits because I love it. And one of the things I have running on a couple other properties are AI-generated blog content that then feeds into the site map and yeah, that can then go and

**AI-Driven SEO Strategy**
(10:41) and feed into Google Search Console and it's designed heavily for AI engines. It's a part of the site I almost don't want. It's on brand. I have some parameters. I'll spot check it, but it is not important for humans. I'm trying to train AI to look at my site and find the relevant content there and have that cross-link and all of that. And it's

**Base 44 and Vibe Coding Tools**
(11:06) the stuff that is created on Base 44 and it's also I put my own resources page just there. So, anything I reference, a lot of these are on there and you can get to them directly. Base 44, lovable. They're a little bit more, I call them the "what you see what you get" wizzywig version of these vibe coding tools. And so yeah, I use agents

**Super Agents and Weekly Audits**
(11:32) in some ways deep research or use them on other things but that's one of those things yeah it is part of Wix that I just set forget, it happens regular week. And there are some things, Base 44 for instance, has a new tool called Super Agents and I've set one up that I'll talk to it about my portfolio on there and it will and it can

**Automating Google Search Console Tasks**
(11:57) directly now connect and act to Google Search Console and do some this work for me where for instance it runs now a weekly audit report across Google Search Console. I don't have to do that. It can make recommendations and then I think there's that. I sent a link to the slides from Jeremiah Ayang and we can discuss that keynote as well but Jeremiah

**Agents Making Decisions**
(12:24) talked a lot about agents and web 4.0. And this idea of going from those big tasks that agents can do on their own to doing on a recurring basis to then making decisions for me. Guess what? I've been working in SEO for 22 years working at SEO agencies and dealing with all this. I know so little about the day-to-day.

**Agentic Actions: Deep Research**
(12:51) I'm not an SEO practitioner. Hell yeah. I trust B44 making some decisions on what meta tags to use on my site. I don't want to make those decisions. >> Sure. Yep. Yeah. I think you hit on some great points there. These are agentic things you're doing. These are. And why are they agentic? So deep research specifically,

**The Agentic Process**
(13:12) you align on a goal with deep research. If folks haven't used deep research, it's in every AI tool now in your little chat button. You select deep research. You align on a goal and a plan. The AI goes to complete that plan. It reasons and course corrects as it collects more information and works along the way and then produces the final output for you. So it's the

**Agentic Planning and Reasoning**
(13:31) planning and reasoning ability. The fact that you don't need to be there to guide it throughout the entire process. That is a total game changer. You're already you're already in that territory. With what you were describing, you then started to talk about scheduling things. >> Mhm. >> And so that's another piece.

**Agentic Continuum and Co-workers**
(13:50) Generally speaking, when I'm talking about agents, I always use the adjective. I say agentic. It's on this continuum, but the most agent-things are things that you don't have to tell them to go. They just work alongside of you, and they sort of feel a co-worker. So, they take some of that initial stuff, maybe the reasoning

**Scheduling AI Tasks**
(14:06) and the planning, the ability to land on a goal, and they also keep working on it over time. So, you mentioned scheduling stuff, that's the simplest way to bring your AI to life, and Claude has added some stuff recently. I actually haven't used it. I think ChatGPT has something too where you can just schedule things and a prompt or a certain killer

**Gemini's Proactive Reminders**
(14:24) prompts runs at certain times of the day that starts to feel agentic. It starts to feel a co-worker is part of your team who's contributing to tasks over time. Well, well, and even Gemini today, I'm starting a new role, this contract role alongside everything I'm doing here with AMG and I was asking Gemini about all these benefits questions

**AI-Driven Reminders**
(14:48) and it's asking me, "Do you want me to set up a reminder for when the benefits kick in for you to log into this portal and do these kinds of things?" I'm, "Hell yeah." Those little things go a very long way. And in that case, is it connecting to your calendar or something to set those reminders? Have you set up a connection?

**Gemini's Google Integration**
(15:05) >> I don't think it's doing that directly, but I do have the some of the names of all their subproducts. It's Google personal information. What do they call the >> Yeah. >> personal connection >> where it's what? Yeah. Where it's pulling in all your Google information

**Agentic Flag: Using Tools**
(15:25) across different. >> Yeah. So now I've just Google can run wild across >> Sure. All that stuff. And >> Well I ask because is that that the ability to use tools and not just read information from the outside world but actually write and create documents and manipulate and change things. That's another one of your big flags for

**AI Tools: Manipulating and Changing Information**
(15:43) Agentic. >> Oh man, that's Yeah. When it actually feels it's helping you with your work, >> Actually doing stuff. >> Yeah. >> And many of your AI tools can do this in normal chat now. If you connect MCPs or connectors or apps, they're going to get all sorts of

**Connecting AI Tools**
(16:00) different names. >> But there's different ways you can connect and some of them just take a few clicks of the mouse. You can connect your AI tools to these external connections where they can actually do things and not just read information. Soon as you start experiencing that, again, it's another key step towards agent. Well,

**Proactive AI Connection Queries**
(16:17) and one of the just odd tips I'll throw out is if you're not sure and if this conversation right now is sparking some ideas for someone here, go ask. It doesn't matter if you're using ChatGPT, Claude, Gemini, Perplexity, whatever it is, just ask it. "Hey, can you connect to Google Calendar? Can you connect to Dropbox? Can you do this?" And you might be surprised

**Unexpected AI Connections**
(16:41) even things Google Search Console I didn't expect Base 44 to have read write access to that. And this can change my work in a significant way for some my own consultant. So just even if it doesn't suggest it itself, can you connect to Stripe so it can update some of your payment things if you're charging for

**AI for Google Search Console Issues**
(17:06) webinars or ebooks or then on your website just ask it. >> What would you do with Google Search Console? >> Well, now I get updates every week of, oh, this there's indexing issues. Do this, do that, and now I'd rather just have AI scan Google Search Console for these errors. If there's anything that it can fix on its

**Automating SEO Fixes**
(17:30) own, fix it. And then if there's anything I need to know about, oh, I need to change X Y and Z in GoDaddy because I don't have a canonical URL whatever, any of this stuff or I need to go and change on some other property then just tell me I'm 10 years old, here are the three steps to do it. But increasingly yeah as it can do these things itself and

**AI Integration with Business Tools**
(17:55) update settings on some of your back end. And I'm seeing all these things that I'm not even using much, connecting to HubSpot and Salesforce and all of these things. It is. And then if you get if something changes in one record in this Google sheet, I personally think some companies Zapier are going to be less relevant

**MCPs vs. APIs Debate**
(18:20) this is all just being done through these other MCPs. That's a good point. >> Well, well, I actually do want to pick up on a point Daniel made because I'm curious, Tom, and Dev, if you want to chime in, too. But, he's saying in the chat MCPs are already dying because we're moving to APIs. Do you have an opinion on

**AI Standards in Advertising**
(18:40) that? >> Yeah, I do. So to tie back to market too, I think Terry made a point when he was talking about his AI predictions, he was saying we will be aligning on standards as an advertising industry. >> So MCP, being the AI standard for how AI should talk to each other. Then there's all these ad

**The Need for AI Standards**
(19:02) oriented standards, ADC CP and what is IAB ARF or something, the AMP one that they just announced. So it's, do we need these standards for how AIs can talk to each other? It certainly seems a good idea. And I've definitely done trainings where I've explained to people what MCPs are and how they might use them and people's eyes light up a

**Developer Perspective on MCPs**
(19:22) little bit and say, "Hey, that's a great idea." >> But when you talk to technical people or developers about these things, they actually think they're silly. At least some that I've spoken to feel a sweat. AI tools, LLMs are really really good at making sense of any sort of text that you throw at them. The idea that you need to include

**Why MCPs Can Be Flawed**
(19:40) very specific fields and parameters to make a successful hookup between two AIs is a little flawed in the first place because they're absolutely amazing at reading blobs of text formatted a bajillion different ways. So it's, well, so what are you getting then if you adhere to these standards? Well one thing you get is a bunch of more tokens than you

**Token Usage and MCPs**
(20:02) actually need. So the language of the AI talk is they're sending tokens back and forth and if you are using MCP for example, there might be extraneous information in an MCP call that you don't necessarily need for your tool to work. And if you're going to do this at tremendous scale or you're building an application, that might actually be more expensive

**Softening Stance on MCPs**
(20:20) than if you just do some direct API connection or custom coding or some different style of handshake between your AIs. So the more I've talked to developers about MCPs and other standards, the more I'm softening a little bit on them. I think Carrie might not have been right about this that they'll consolidate. I think it's possible that you end up with just

**AI's Ability to Figure Out Details**
(20:36) a mess of these and it doesn't really matter because as long as we're sending the pertinent information for a transaction to get done, the AI can figure out the details. >> Don't tools have less token usage than MCP? >> Well, tools is sort of an abstract definition. Tools is anytime an AI is going to use something to do

**MCP Framework Limitations**
(20:55) something and MCP is one language that you can use to execute the handshake in order to do a tool. >> Fair point, right? But because MCP is a framework, there's things that must be included every time and they don't always need to be included. They're just not always relevant to every handshake between the two AIs. So

**Skills Replacing MCPs**
(21:12) >> Yeah, I guess to add to this too, we've sort of replaced MCPs with skills, and skills have this function in most models where you give it a description and then the model knows when to call the skills. So you can load a lot of skills into a context because it doesn't really stuff it the same way that an MCP would. So when you use an MCP, it adds all of these instructions to the

**Skills for Context Window Management**
(21:34) prompts that you're sending, which really kills context window management. But with skills, it's almost you give it a short description of what the skill can be used for, and the model will call the skill, which might be sub agents and scripts and all kinds of things. And sometimes that includes MCP servers, but it's almost if you had labels in your toolbox that told you,

**Universal Concept of AI Skills**
(21:53) you need a screwdriver when you have flathead screws. And if you don't have flathead screws, you don't need a screwdriver. Yeah, I love that. And I think skills, let me know if you agree, Daniel, is starting to become a more of a universal concept that AI practitioners I feel all need to understand how to layer skills in AI conversation, which skills your AI

**Skills: A Cleaner Concept**
(22:12) has access to. MCPs is more a detail. I would say that once represented the flow of information but now just calling it skills I think is cleaner. To your point, they're slightly different concepts anyways, but MCP is a little bit of sausage making, I would say, for most practitioners. >> And and this to me sounds a

**Moving Beyond Acronyms**
(22:35) big net positive where we're where the more we're able to move away from the acronyms and jargon. And the more it's, okay, what are we trying to do? Oh, we're trying to update HubSpot when new information comes in and right and just automate this and trying to optimize. Yeah. Ad yield or something, all this stuff. Okay. Yeah.

**The Simplicity of AI Connections**
(23:02) Because adding more acronyms is the last thing that marketers want. >> Yeah. Totally. And a year ago I was trying to coordinate an MCP server by setting it up locally on my desktop and it was semi-technical. Now, any AI platform in the bottom left corner, you click to your settings, you click connections, and there's going to be some version of

**Easier AI Setup**
(23:20) this. It's either can read your Google Drive or can maybe control your Asana project management board. There's different flavors, and they get richer depending on the connection, but it's becoming much much simpler to set to set this stuff up. >> Yeah, it's tremendous. >> Speaking of, Jeff Green mentioned during

**Trade Desk MCP Server**
(23:40) the Trade Desk speech that there is a Trade Desk MCP server. >> That was a cool little leak that had not come out before. >> So, can you explain what this then means for Trade Desk customers and others? >> Yeah, for sure. And I've actually done this myself in Manis. Do you want me to show people what this

**Manis: Programmatic Control**
(24:02) looks on my? Maybe I'll try and pull this up as I'm talking if I can. >> Yeah, go for it. We love >> So, the idea is using a UI can be cumbersome. Using the Trade Desk, while it is supposedly a very trader friendly piece of technology, there's just all sorts of stuff you might want to do that could be better if done programmatically and not having

**AI Chat Control for Trade Desk**
(24:25) to rely on your mouse clicking around. And one way to do that could be telling your AI to do those things and allowing the AI to do that stuff programmatically. So the idea with the Trade Desk MCP is that there are people who are not logging into the Trade Desk. They are >> You're not sharing. >> I'm about sorry I've said enough as I'm

**Manis as an Agent Tool**
(24:49) talking. There are people who are not logging into the Trade Desk but rather are communicating with it programmatically via an AI chat tool. And this is something you could set up yourself for your Facebook ads account. So this tool I'm going to hop in is called Manis (MAN us). Really cool agent tool, got bought by Meta, I think 6 months or so ago. And if

**Manis Demo Setup**
(25:13) I start a fresh conversation it would just look a normal chat. And then once I kick things off, it opens this computer over here, which I'll show you in a minute. But this is >> Yeah, >> I'm so sorry to cut you off. What when does one use Manis as an agent tool versus just going and spinning up native

**When to Use Manis**
(25:29) tools on self? Native agents >> using a developer when you say native agents. >> >> Yeah. Or just setting, yeah. Setting up your own. Is the idea that as a, if we're not a developer, set up and running an agent and having it just persist and always be there similar to a cron job. Is just, I was just

**Manis vs. ChatGPT**
(25:56) trying to get a sense from you, clearly have a lot of experience here, is Manis a substitute for anything or this is >> you got to do something this to get agents going. >> Manis definitely was unique for a year ago. It was very different experience than ChatGPT. You talk to a ChatGPT or Claude, it gives you a response. You talk

**Manis's Unique Task Execution**
(26:17) to it, it gives you a response. Yeah. It plans, pulls up a computer, uses the computer to execute tasks on your behalf, uses tools, and then gives you a response. So, it felt night and day when it was released a year ago. Now, we were just talking about, tools Claude might do some of those things on the fly as you're chatting with them

**Manis for Complex Tasks**
(26:36) depending on what mode you're using and how you've set things up. So, it's a little less black and white of the answer I'll give you, but I use Manis when I need to control a computer, manipulate lots of documents, complete a complex multi-step task, something that might take 20 minutes or a half hour rather than something that's more conversational.

**Manis Meta Ads Audit Demo**
(26:55) So, the ask here was I said, "Hey, I got this old Facebook Meta Ads account, hasn't been optimized forever. Can you do an audit of it?" And I have a prompting framework here. That's why it's broken into all these different letters that we teach. And it says, "Yeah, for sure. I'll do this audit for you. I had already connected my Meta Ads account. So you see this connected

**Connecting Apps to Manis**
(27:14) apps button. So you see this Meta Ads Manager right here. I had logged in. You can log into whatever sort of apps you want to connect to your Manis account. And again in every AI platform now you have this connections or apps or whatever they call it, but you always click on the bottom left and see what you can hook your AI up to. So I hook mine up to my Meta Ads Manager.

**Manis's Programmatic Meta Connection**
(27:32) Said, "Hey, okay, sounds good." It pulls up a computer and then sometimes Manis will navigate the web in a web browser. In this case, it had a programmatic connection to Meta and so it just started running code and making API calls to Meta to get information. So, it's not very interesting to look at, but this is a coder calling Meta over and over again,

**Manis Generates Audit Report**
(27:52) assembling this information. Eventually, they put together, starts to look a structured report. Start to type out text and then creates this final document. Let's look at maybe this. Yeah, this looks good. Cool. So, it makes this document that is a complete audit of my account. So, it puts together there's two separate ad accounts within my account. There's

**Audit Report Details and Recommendations**
(28:14) eight historical campaigns. The data is all really old. The advertisers were a band I used to play in at a restaurant that I used to do work with. And it lists out the objectives for all my campaigns. And then makes best practices recommendations. What does it observe? It makes fun of me for being super outdated. Analysis

**Agentic Task Completion**
(28:37) of the legacy account structure reveals several areas where practices deviate significantly from Meta Ads best practices and it suggests all these things I can do to modernize my campaign. So agentic, because I gave it a high-level task. It went and completed the task on my behalf. It probably made some mistakes, it probably pulled some information back that wasn't right or

**Agentic Output: Verification Challenges**
(28:54) looked funny or something that. It course corrects, put it all into one place. Review it, sign, seal, and deliver. So, a much more complete product than working with chat. It also is harder to verify the work. So, here I have six pages that it produced for me in a few minutes. Double-checking this is a little harder than if it was

**New Validation Techniques for Agent Work**
(29:12) turn-based and I could look at every single piece. So, we need new validation techniques as we do agent work and we're constantly recommending people insert themselves at the right moments in time to make sure this stuff is headed in the right direction. Any questions on this? So when you're running a live campaign, it's also going to

**Live Campaign Optimization**
(29:32) make recommendations on how to improve it. I guess >> It could. When you're running a live campaign with Meta, for example, it's already optimizing itself. They've been using machine learning to tighten up the performance of Meta Ad campaigns for a long time on the micro level. Constantly looking for new users and things that. Then

**Agent for Strategy Shifts**
(29:49) there's moments in time where you might want to do a strategy shift. You might want to think hard about your reporting to date and decide if you want to update your strategy moving forward. That to me is more when you would bring out an agent and talk through it and ask it to audit your account, do reporting, things that. >> What about your landing pages? Can it

**Manis for Landing Page Analysis**
(30:10) analyze your landing pages and videos? >> Can it watch your videos? I'm not certain if Manis can watch your videos or not, but that seems likely to be something that they are trying to add. Yeah, go check out my navigate to my landing page and audit it. Yeah, that's a perfect one for it. And it'll literally pull up a computer, navigate to your

**Manis for Competitive Research**
(30:31) website to start clicking around, doing stuff, taking screenshots >> if you want. So, we do another demo. It's competitive research. Go to the Meta Ads Manager, which is a place you can get public Meta ads that are being ran, and it takes screenshots of them and does competitive research for brands in your competitive set. And then maybe analyze if your ads

**AI Assistance in Analysis**
(30:50) and your landing page are appropriate or >> Yeah, for sure. But as you're saying that, there's probably a bunch of people on this call who are really good at that sort of analysis. So I'd encourage you to get help from AI with that sort of thing. But lean in yourself. Providing lots of context about really what you're trying to do with a website, your business

**Tailored Website Recommendations**
(31:09) objectives, all that sort of stuff is what's going to be the difference between getting generic website improvement recommendations and actually getting something that's powerful for your business. Looks a question from Earl. >> Yeah, just looking at defining AI users adoption levels. >> I think there probably are four

**Four Levels of AI Adoption**
(31:27) actually. You missed the category zero though. There's people who don't actually use AI. They might be people who talk about AI. But there are lots of people who don't use that. Even at a market conference, if you really pressure test people are using AI, you might find that many have messed with it, but very few are getting returned regularly. I'd say there's

**Low AI Adoption in General Workforce**
(31:45) a bucket of zero users. There's a huge amount of people who've messed around. Some of them have had some successes, some failures. When people say, "Hey, to use AI," they say, "Yeah, I have ChatGPT." And maybe there's a couple things that they get returned from. I think we've already covered two-thirds of general knowledge workers at that point. And my

**Conservative View on AI Adoption**
(32:08) opinion is probably I'm a little more conservative than many on how I think AI adoption is rolled out. I think there's tons of opportunity for folks to ramp up. So I'd say about two-thirds of people are really not getting much return from AI. Then there's maybe of that remaining third a subset of true power users, many of them are developers.

**Power Users and Knowledge Workers**
(32:31) Maybe the vast majority of them are extremely technical developer type stuff, using it for software engineering, vibe coding, things that. And then there's this spot of 15% of knowledge workers who are using the tools that the vibe coders are using but to do knowledge work and that's people are doing things Co-work, Manis, agentic Claude. So I see

**Aim for Top Tier AI Usage**
(32:52) this is there's really four buckets. I would encourage folks to try and get in that bucket I was just describing as fast as possible. Maybe many folks on this call are already in that bucket. But some things that get you in that bucket are definitely actual return on your AI usage. So, when you hear about, yeah, go ahead. >> Well, well, this is one of the

**Defining AI ROI Beyond Time Savings**
(33:12) first things I want to ask you before we went down the agent rabbit hole here is when you're talking about returns, I'm still seeing so much about it's return on time. And some of these productivity metrics. But that to me still strikes me as a field in its adolescence at best because how do you go to the CFO, the CMO, the CEO and

**Hard vs. Soft ROI for AI**
(33:39) say I got this return on time? I saved I save four hours per week using AI and it's okay but what is that really doing for us? >> Yeah. Yeah. So what what are you seeing with what returns even mean and where is it worth even trying to get a hard ROI metric versus where do our softer metrics okay? What's

**Time Savings: A Starting Point**
(34:05) your point of view? >> I do think time savings is a great thing to target and that is when I see people's eyes light up. It's, oh, that used to take us so long now I can do this so much faster. That's great. But to your point that's only going to be exciting for so long. Your customers are not going to be very excited that you saved time doing their

**Turning Efficiency into Value**
(34:22) work. Neither are your managers. They want to see more value. So the second thing you do, once you are actually seeing concrete return and you're seeing productivity gains and efficiency, you need to turn that into value. And the simplest way to do that is just produce more useful stuff. Do more of what you were doing. Maybe do it

**Faster Turnaround and Customization**
(34:41) more regularly. If it's reporting, maybe you're the customer is hearing from you more often. And maybe you're just doing it faster. In my business where we have to create live work sessions. If someone wants a custom session this Friday, I can do it. But I couldn't do that two years ago, but now I have the right tools in place. I have the right

**Pricing Pressure and Customer Value**
(34:59) operating procedure in place. I could do this afternoon if somebody really needs a custom session is we have the right operating procedure in place to do that. So, the way we're trying to express it to our customers is faster turnaround, more customization, and then we're actually going to trying to slowly lower our pricing, too, which I think other folks

**Second Order Effects of AI**
(35:16) will see start to feel pricing pressure. It's, if I can do this for cheaper and faster, why wouldn't I? And our hope is that customers will buy more in total as a result of lowering our pricing. So, I think you're totally right. This first order effect that you feel isn't that exciting. It's all the second order effects you got to jump

**AI Enabling New Capabilities**
(35:32) into. I was going to say, don't you see improvements in where an ROI is doing things they couldn't do? People who are not doing things that now AI enables them to do. So, it's adding new capabilities. >> It is. >> Yeah, it's a great point. The product that many people who are producing with their newfound skills

**New Skills vs. Domain Expertise**
(35:54) isn't necessarily as good as the old product. If I can vibe code somebody a website, how much better is that than what they would have gotten from a developer who could also vibe code for example? So my newfound ability that creates efficiency for me doesn't make me the right domain expert necessarily to own that task over a longer

**New Opportunities for End Clients**
(36:17) time period. I was saying for the end client though, the end client thing then now has the ability to do a website where they wouldn't have paid someone. It's a new, the tool opens up new opportunity for them to do things that they would not have been able to do for themselves. >> Yeah, I agree with that. Yes. >> How to what's the extent to

**AI Adoption: Adding New Value**
(36:35) which you're seeing that happen as opposed to just the argument of you're going to save time. Maybe it's not an argument for you selling the services but from the perspective of business adoption of AI, you're adding new capabilities. >> More value. >> Yeah, I think I think it's a that's a great great focus point in my

**Delivering More Value with AI**
(36:58) business again where we create historically we've created a lot of trainings. People want Powerpoint presentations for us in the AI enabled era. You now get a lot more from us. You get one-pagers, you get glossaries, you get all more material. You get a custom micro site internally. So, yeah, I think you're right, Howard, more, I called it more stuff. But

**Unique Value and Continuous Learning**
(37:16) it's not just quantity, it's potentially more unique pieces of value that you're bringing to customers, too. So, I think you're right. Tom, I asked a question in the chat. For those of us who are in that 15% you were talking about, and I'm working hard to get to the top of that, what do you suggest? What is it? Your courses? Are there other places to go to

**Upskilling for Knowledge Workers**
(37:40) get this education? I'm teaching myself to build agents and learning a little bit of Python in the way, but I'm thinking I may not need that. But I am actively looking to get really really good at the knowledge work part of this. I'm not interested in being a coder. But I am absolutely interested. I think this is going to be the ability to take AI and translate

**Networking for AI Power Users**
(38:04) business processes into AI workflows is going to be incredibly valuable in the next 5 10 years. >> Yeah, well said. I think you're the bleeding edge if you want to get in that last group. So associating with other people in that group and sharing notes with them directly in real time as you're building stuff and figuring it out, I think is a good place

**Mastering Diverse AI Tools**
(38:26) to be. If you really want to be a power user, and pushing the limit at your org, you got to be using different tools and just wrapping your head around how one adds value versus another. And you might not be a power user in every single tool, but you're wrapping your head around the difference between a co-work GPT and understanding that.

**Operational AI Deployment Challenges**
(38:45) >> Yeah. >> There's also way more operational work to be done for deploying AI and getting value out of AI than I even realized at first. And by that I mean with my business, as we wanted to use AI more directly, thinking really hard about where we store information, what information we write down, obsessing over recording

**Centralizing Knowledge and Tools**
(39:08) every single phone call, picking one shared team's AI platform and using that and creating artifacts in that that we can share and pass back and forth with each other. That was actually a little more work than I thought. Or people have mentioned OpenClaude. OpenClaude is a really cool agentic tool. It's a live a teammate. It's

**OpenClaw and Claude Co-work Challenges**
(39:28) proactive. It can do stuff, but it's actually really hard to use. And thinking about how an org is going to use OpenClaude and Claude Co-work side by side is a problem that many people don't have a clean answer to. And trying to figure that adoption, figure out that next phase of adoption I think is going to keep you really sharp and give you a lot of useful perspective.

**Word Power's Course Structure**
(39:49) >> What about your courses? So, our courses that we give the businesses are plugged, I'll take my 50 bucks later. >> I appreciate it. Thank you. So, we usually show up and we do a few sessions worth of fundamentals because nearly every business in the world right now has lots of people who need to know how to prompt, need to know what tools are, need to know how to store

**Champion Programs and Use Cases**
(40:12) and share prompts and projects and all sorts of the basics. So, we usually do a couple of those. Many businesses, especially larger businesses, have champions programs where there's some amount of people who they really want to push to the next level and want to be driving use cases. So, we'll do parallel curriculum with them that's more focused on use cases

**Office Hours for Business Problems**
(40:29) and deploying the AI than knowing and learning the fundamentals. And then we do these office hours sessions where people bring their actual business problems and a small group will come to us. Three people from the engineering team or two people from product marketing and say, "Hey, we're trying to do this thing or we're trying to operationalize this."

**Practical Application Over Lectures**
(40:46) Help unblock us, help bring it to the next level. Yep. And that's typically what the more power users are more interested in. I don't necessarily have a 90-minute lecture that is going to help a power user get to the next level but rolling up our sleeves together and looking at their business problems, looking at the actual tools they're using. That's

**Theory vs. Application**
(41:01) usually when a lot of the goodness comes to life. >> Yeah, it's very clear here that theory and application are very different and where you need to be learning stuff is in the application part. >> Yes, I think so big time. I think that's totally right. >> Yeah. >> I had a I had a question about that.

**Context Engineering at Departmental Level**
(41:19) Putting it to use. There's folks here in the Boston area, the Kendall project, where they're trying to do some context engineering at a departmental level and every business process has a problem, a way of describing it, the people involved and they try and do that. Are you seeing a need in the

**Consistent Process Documentation**
(41:43) organizations you're talking to which is thanks for the AI literacy >> thanks for also helping us think through how to deploy it in general and yes we'd love your office hours but what we really would have is at least some if not all of the departments in the company have a consistent way of capturing processes or as context blocks so

**The Need for Clean Data and Documentation**
(42:10) that when we try and feed into whatever agent or chat interactive experience we're going to use, we all have a similar taxonomy. Is that a clear question? >> Yeah, I think it's a great point. Many organizations I work with are already deploying some strategy to clean up how they document and record things because they've had this realization

**Microsoft Copilot and Messy Data**
(42:33) already. We plugged in Microsoft Copilot to our shared SharePoint and realized everything was a mess. That is a very common shocking anecdote. Yeah. You'll hear that from lots of businesses. So many of them are already thinking to do something about it. Others that aren't, I'm ushering them along. I'm pushing them along. There's a lot of

**Best Practice: Centralizing Call Recordings**
(42:52) change in behavior. One simple thing is record all your phone calls and put them in one place so that people can harness them with AI and ask information about calls that you've had in the past. That is one best practice that many orgs are not doing. Maybe they're recording them, but are they putting them in one place? Are they making those accessible via AI? So

**Importance of SOP Documents**
(43:08) yeah, there's a set of those things, a set of best practices for getting your knowledge in order. And there's major change in behavior related to that. We advise businesses to keep SOP documents, standard operating procedure documents where you just write down how you do stuff. Most people don't do that. Most businesses don't have that. So

**Leadership Consulting on Knowledge Management**
(43:28) to answer your question, yes, many a lot of times that might already be in place and we're helping with that or when we're doing leadership consulting, we're guiding them on how to approach that. Just tell me quickly, what what do you what's your quick takeaways from the event and yeah, what would you want people to walk away with?

**AI in Advertising: Beyond Real-time Modification**
(43:51) >> The ad world has been using machine learning and AI to make campaigns better for a long time. >> Mhm. >> Even as people think about Ad CP and using MCPs for advertising, they're more about they're not about real time modifying the campaign. It's about speeding up the negotiation process, the strategy and planning

**Operational Changes in Marketing**
(44:09) process. When you hear about how agencies and publishers too are using AI, a lot of it was about efficiency and time savings and things that. So I think in the marketing world, thinking through the operational changes from AI is the stage we're on right now. Consumers are getting extremely targeted personalized ads powered by machine learning that have

**Inward Focus on AI Adoption**
(44:32) been for a long time. But how we do that work, how we set that up, how often we manage it, the intensity with which we do our check-ins and our analyses and our strategy recalibration, that is more what's about to change. >> Mhm. >> So maybe thinking a little bit inwardly was a theme that I got from the conference.

**Contacting Tom Riordan**
(44:51) >> Amazing. Well, Tom, this is awesome. What's the best way for folks to get in touch with you? >> People can drop me a line directly. I'm pretty accessible. I'm going to put my phone number in there, too. Shoot me a text. >> They may want to chat AI. This is what I do. I cover your AI. So, drop me a chat. All day. I'm

**Tom's Availability for AI Chat**
(45:11) sharing notes with people, best practices, things that. So, if I don't get back to you right away, don't >> don't hold it against me, but I'm available. >> We'll hold it against your agent for not answering the text. >> Yes. There you go. >> Awesome. Well, Tom, this may I'll

**Farewell and Thank You**
(45:23) I'll stay on for a few for anything else we want to discuss till the end of the hour, but thank you so much for joining us and hope you can come back sometime.

## How Many People Actually Use AI

Speaker: Nate Elliott
Published: 2026-03-05
Tags: ai in marketing, seo, geo
Video: https://www.youtube.com/watch?v=YwCojm2gzSQ
Page: https://aimarketersguild.org/sessions/how-many-people-actually-use-ai

**Welcome & Guest Introduction**
(0:04) Hey everyone, I'm David Burkwitz. Welcome to another edition of AI Insiders with AI Marketers Guild from Architecture Media. I've got a guest; this one's been decades in the making. Nate Elliot is someone whose research, analysis, and insights I've been following for many years. We were, I guess, even quasi-competitors, or at least our firms
(0:32) were sometimes got along better than others and were navigating how to play well together. But he's someone I've been able to admire in that healthy competitive sense, from everything he's been doing, one of the ad and marketing industry's top analysts. Then once he joined my old home, eMarketer,
(0:58) where I spent a lot of my formative years in the industry really early on, and seeing what Nate is doing covering AI in certain areas as part of his purview. Nate was able to come to my office, which is the coffee shop in Koreatown, and we got to catch up on things. I'm just so excited to geek out here today and to get to have
(1:26) conversations with one of those people who's one of your gurus, who you actually get to meet and learn from. I'm like, Nate, as soon as you've got something ready to share, come on AMG. You're going to get some way better questions than I'll ask from this crew. If you haven't been here before, for everyone else, these are interactive conversations. Nate, I could go on for the whole hour welcoming you, but that wouldn't be fair to our crew here. Welcome.
(1:47) Thanks very much. Earl, I think you were clapping, David, saying you were going to stop. Is that what you're...
(2:08) Oh, yeah. That was me. That was me clapping. I will neither confirm nor deny. Excellent. Well, thank you, David, for having me. I'm excited to be here. Thanks to everyone else for joining today, both live and anyone who watches the recording. Hello, future people.

**Nate Elliott's Background & Focus**
(2:19) My name is Nate Elliot. I am a principal analyst at eMarketer. I cover how AI is changing marketing, commerce,
(2:27) and the customer journey. As David said, I've been an analyst in the industry for quite some time. I actually worked at DoubleClick back in the 90s when we were helping to invent the way advertising would work, for better and for worse. I joined Jupiter Research as an analyst in 2003, and since then I've been an analyst ever since, at Jupiter, then Forrester. I ran my own shop for
(2:48) about a decade, and I was thrilled to join eMarketer last summer and get to work as their lead analyst on AI. What I want to share with you guys, and forgive me, we're a Google shop, not a Zoom shop, so I'm going to ask you, David, to tell me if I'm getting this right as I share my slides. I still don't know how to keep an eye on the Zoom chat while
(3:12) I'm presenting. So, I'll surface if someone's shouting things out. What I want to talk about is the building blocks of AI adoption. I've been looking at this space even before I came to eMarketer to lead the coverage of AI.

**The Quest for AI Adoption Truth**
(3:20) Of course, I was paying attention. I was running surveys and research on this for companies, including Walmart and
(3:34) Burger King, and a bunch of others. One of my first tasks when I got to eMarketer was to create our single version of the truth around what's happening in AI adoption. All the stuff we want to talk about, the analysis we want to provide to platforms, to vendors, to brands, be they marketers or sellers, all of that is analytical. I would dare
(3:56) say there is no truth. There are simply informed opinions, and that's where the job gets really interesting. As a baseline, one of the things I wanted to understand was how many people are actually using these tools? Because it's a thing we talk about all the time. It's a concept, a number that I honestly don't believe the industry has put really good
(4:20) analysis or factual data around.

**The Discrepancy in AI Usage Data**
(4:20) I see numbers that are absolutely all over the place. One of the things that I like to do when I'm in a room with people is ask everyone to shout out what percentage of the population uses AI in the US. I did this at a couple events last week. I got numbers as low as below 20% and as high as 85 or 90%. The joke, of
(4:46) course, is that whatever number you say, as long as you're between, say, 10% and 99%, you're correct because I can find data from a credible source that will back you up, that will say that yes, that is the number of people who are using it. To answer the question today, I want to use a piece of research that was published a little over a year

**Gallup Study: AI Usage Awareness vs. Reality**
(5:00) ago. It was conducted in December of 2024, and I'm fully aware of it. Showing you AI usage data from 2024 is like showing you space travel data from 1944. It doesn't make a lot of sense, but hopefully it will in just a moment. This is a study that Gallup published last January 2025, that ran in December of 2024, and they asked about 4,000 US online users, "Do you recall
(5:39) using an AI-enabled product in the past 7 days?" They weren't just asking about ChatGPT and Gemini and things like that. They were saying, "Is there anything else you've used that is AI-powered or AI-enabled?" And 36% of US online users said that they could recall having done that in the past week. Then Gallup came back and said, "While you're here, let's just ask if you used
(6:01) any of the following tools."

**99% Use AI (Known or Unknown)**
(6:01) It turned out that 99% of these same exact respondents had in fact used one of these AI-enabled technologies in the previous seven days. Everyone uses AI at this point,
(6:26) whether they know it or not. That's been true, of course, for at least a year or 15 months, probably longer than that. I'm sure that I'm preaching to the choir a little bit. But one of the reasons I want to show this, even to an educated and interested group like the AI Insiders, is to reinforce this gap between people choosing to use AI, or being aware of using AI, and actually
(6:50) using or interacting with these technologies. That gap makes a huge difference to the brands, the marketers, and the sellers who are looking at using AI as a marketing channel. Helping those brands, marketers, and sellers figure out how they can best leverage AI as a marketing channel is a big part of my remit. It's a big part of the work that I'm doing.
(7:12) The thing is, not all AI adoption is created equal. While lots of people use AI, 99% of people in that study from a year and three months ago had actually interacted with an AI tool in the previous month.

**Adoption vs. Adaptation in AI Usage**
(7:12) Not all of them had chosen to do so. Even amongst those who choose to use AI, the adoption is really high, but the adaptation, as my colleague Jacob Bourne
(7:37) would call it, is really low. What we talk about with adoption versus adaptation is adoption are people who use the tools. They go and they choose to take advantage of the tools. Adaptation, we're talking more about changing behavior patterns: taking things you've done for years online and moving those behaviors from one tool or platform or location to another tool or
(8:00) platform or location. One of the big behavior patterns that we see, of course, is people going to traditional search engines, Google. The number one reason people go online is to find information. That's been true for a couple of decades now. Google has, depending on the market you're looking at, anywhere from 70 to 95% market share in traditional search.
(8:21) When we look at traditional search engines, we know that 95% of people who go online every month are using a traditional search engine at least once a month. And 86% of online users, almost all the people who go to a search engine monthly, are going there on a frequent basis, 10 or more times per month, at least about once every third day.

**AI Tool Adoption: A Sampling Phase**
(8:42) When we look at top AI tools, the numbers look a little bit different. It's much earlier in the adoption curve. So the total adoption, the total number of monthly users, is not going to be as high. 38% as of August of last year is still a remarkable number for a set of tools that at that point were less than three years old as a category. What's really interesting to me is that
(9:05) only about half of that 38% were choosing to go to these tools on a very regular basis, 10 or more times per month. A lot of people are playing with AI. They're sampling AI. They're using it unintentionally, or they're using it intentionally every now and then. While we all live in this bubble where everyone uses AI all the time, if you look at the general online
(9:29) population, the reality is a lot of people use AI a little bit, and a handful of people are using AI a lot. That distinction, the gap between those polls, is really interesting and important, I think, to the marketers and sellers who are looking at using AI as a channel.

**Is AI Mobile-First?**
(9:48) Nate, quick question here.
(9:51) I mean, AI just by its nature also strikes me as something that is so mobile-first. Do some reports like this give short shrift to that concept, that you want AI to be with you everywhere?
(9:51) I'm sorry to cut you off. I wouldn't say it's mobile-first. It's snackable interactions,
(10:15) and that's often on mobile. I do a lot of my stuff on desktop.
(10:15) Oh, I do too, but I feel for most who are creating a recipe or telling a kid's story...
(10:15) That's a fair point. You're right.
(10:15) No, but both can be true.
(10:34) Mobile's a huge part of the story, if for no other reason than mobile's a huge

**Challenges in AI Usage Data Collection**
(10:34) part of the story of how people use all digital technology at this point. The study that I'm showing you happens to be based on desktop data analysis and clickstream data. Again, it gets to my point that it's really hard to find a good single source of truth around how many people are using these tools, which tools they're using, how frequently they're using them, and for exactly what
(10:53) purposes. There are imperfections in methodology no matter what study you look at. There is no research methodology that's going to perfectly give us this data. This is an imperfect study alongside a lot of other imperfect studies. I will soon be publishing my own imperfect studies, but hopefully in a way that add
(11:15) to the clickstream observational data, like what you see on the screen right now, to add some nuance to the conversation and to fill in some really important missing gaps that we see not just in studies like this, because Semrush and Dallas did a great job on this research, but it's limited by the data with which they were working. So I want to start to fill in those
(11:38) gaps with a consumer survey methodology, and we'll talk about that in a moment.

**OpenAI's Data Dump & Challenges for Marketers**
(11:38) People use AI in lots of different ways. This is great data. Full credit to OpenAI. The study they published called "How People Use ChatGPT" in September of last year is one of the greatest corporate data dumps I've ever
(12:02) seen in a quarter century of being an industry analyst. They update a lot of it, not all of it, but a lot of it on an ongoing basis for us, which is incredibly generous, and is one of the handful of things they do that still reference their founding as OpenAI, a company that would share its knowledge and information with the world. I show this
(12:23) slide because this is really hard to process. It's, I think, seven different primary categories, two dozen different subcategories, and there's this enormous range of different ways in which people are using artificial intelligence. Brands and retailers look at this, and they come to companies like eMarketer, and they say, "What am
(12:47) I supposed to do with this? Where am I actually supposed to start if I'm hoping to reach people who are using these channels?" I'll get it out of the way upfront. There's an entire section of this industry that is not using AI as a channel, but is using AI to make your work better as a marketer, a seller, a brand, a retailer. That is research that
(13:07) we are working on and will be producing. But a lot of what I'm talking about today is how people are using AI, and therefore, how brands and sellers can use it as a channel to reach those people. When we talk to brands and sellers, we think, rather than look at this enormous amount of data that's out there, rather than look at slides like that colorful spaghetti I
(13:26) just showed you on the previous chart, ask these three questions, and ask them in this order. First and foremost, how many of our customers use AI?

**Customer-Specific AI Usage**
(13:26) That's a more difficult question than a lot of us probably understand because in this little bubble that we all work in as AI Insiders, and people who think a lot about this, we assume that the vast
(13:49) majority of the world uses AI. You hear things like AI is replacing Google. I've seen people stand in front of slides that say 80% of the population goes and uses LLMs every month, and I've never seen really credible data that says that's true. But I have seen data that says that's true. Whatever the overall number is, I think the first thing that brands
(14:13) and sellers have to ask themselves is not how much of the general population uses AI, but how many of your customers use AI?

**Generational AI Usage: Most Adoptive**
(14:13) I want to play a game with you guys. I'll ask you to pop it in the chat or shout it out, but which of these generations do you think uses AI the most in the US right now? Any guesses? David, what's your guess?
(14:35) I want to say Gen X, but I feel millennials are still going to be a little more tech-forward. So, I'll say millennials.
(14:35) We'll say millennials. I see people go in the chat. I'm seeing a bunch of different guesses in there. Gen X, Millennials, a lot of people are focusing on Gen X and millennials. Those are some solid
(14:58) guesses. The actual answer is category D, Gen Z.

**Generational AI Usage: Least Adoptive**
(15:00) Let's flip that coin around though. That's the population, the generation that uses AI the most as a percentage of online users. Which generation uses AI the least? David, let's hear your guess. I know you have a guess on this.
(15:00) Well, Gen Alpha.
(15:00) You think Gen Alpha?
(15:20) Yeah.
(15:20) All right, let's see what else is going on in here. An even mix of boomers and Gen Alpha. A couple people said maybe it's Generation X. The generation that uses AI the least, you guys have gotten this right. Congratulations, more than any other audience I've asked this question. It's
(15:42) actually Gen Alpha. The reason is this: a lot of Gen Alpha is online. We run these numbers in our forecasts as a percentage of online users. That's what I'm looking at here. Something like three-quarters of Gen Alpha is online. That's terrifying to me because Gen Alpha right now is between the ages of two and 13 years old. For 75% of them to
(16:06) be online is scary. Our forecast team assures me that if a parent starts YouTube on their tablet and hands it to the toddler in the stroller, that is in fact an online individual. That's how we get to 75%. The reason that those online users in Gen Alpha aren't using AI is that most of them don't know how to read and write.

**Gen Alpha's AI Adoption & Future Projections**
(16:33) I do wonder, and this is my own bias here, having a 12-year-old, that my 12-year-old is so skeptical herself. I feel there's, and we often see this, generations back-to-back often one rebels against the other. So if my daughter sees all these college kids and 20-somethings obsessed with AI, it's like, screw them, right?
(16:56) Yeah. There are definitely a lot of motivations that we didn't get into because, to answer Selena's question, this isn't a study, it's a forecast. This is a model that was put together by our forecasting team using thousands of different data points from hundreds of different sources, then modeling it forward based on how we see the market
(17:16) changing over time. We do think that once these two to 13-year-olds—I'm assuming, David, that your 12-year-old is very good at reading and writing, but the 2-year-olds certainly don't know how to read and write. The average age at which someone in the US learns to read and write basic words is 6 years old, I believe. That means over the next few years, as Gen Alpha gets more
(17:38) literate, they will start to use AI more, and we're forecasting that they'll overtake boomers in 2027 by the end of next year.

**Gen Z Leads AI Adoption, Challenging Assumptions**
(17:38) Anyone who guessed millennials as the biggest adopters of AI, you would have been right about a year and a half ago. But according to our model, Gen Z overtook millennials towards the end of 2025. The reason I love your
(18:04) comment, David, your 12-year-old called Crusty, that sounds right, not because of you, but because of Gen Alphas. The reason I show this and the reason I play this game, which hopefully you guys found fun, maybe enlightening, is to show you that we are some of the most interested people about AI adoption and how AI is changing our world and our
(18:29) industry. A lot of us got this wrong. I would have gotten this wrong if you'd asked me this question before I sat down with the eMarketer forecast on this topic. We make a lot of assumptions, and a lot of those assumptions are based on what the people directly around us are doing. The people around us are not the general population, and they certainly don't, on average,
(18:50) reflect the entire spread of individual groups, be they generational, gender, race, income, education, whatever it is. The people immediately around us don't often give us the best lens to see what's really happening in the world. That's why we think the first question that is really important for marketers and sellers to get great data on is what percentage of your audience
(19:12) specifically is using AI, because that will start to tell you how important AI is in your marketing and your sales strategy.

**AI's Role: Primary or Secondary Channel?**
(19:12) I don't think there's a seller or a brand, no matter their audience, that shouldn't be working with AI. But I do know that there are some sellers or brands for whom AI is perhaps the single most important channel for getting messages out and marketing to and
(19:37) selling to the people that they're trying to reach. And I know that there are a lot of brands, probably many more, for whom AI should remain at this point a secondary channel that they're leveraging to a limited extent, while keeping their focus on the larger, more important channels today, and also testing and learning and preparing themselves for when that tipping point
(19:59) eventually comes. It's a really important distinction because I've talked to marketers, I've talked to CMOs who think we need to drop everything and only focus on AI right now. There are a handful of companies and product categories in which that is true, but that is not what is happening for the vast majority of brands, fans, marketers, and sellers
(20:22) right now.

**Understanding Customer Motivation for AI Use**
(20:22) I think understanding exactly how much your audience is choosing to adopt these tools is a really important starting place that a lot of the people I talk to aren't actually starting their investigations. Once we answer that question, I think the next question we have to answer is, why do our customers use AI? When I talk about why, I want to
(20:43) be clear. I'm not talking about what features or tools they're going to these platforms to use. Not, "Hey, the why is they want to play with Sora 2," or, "the why is they want to play with a character AI." Those are important things. We're going to get to that in the third question, but I want to know why people are actually choosing these tools in the first place. Why are
(21:05) they going to these platforms? What is the human motivation that drives them there?

**SEO is Not Dead & Mixed Data Challenges**
(21:05) To Adam's question in the chat, no, SEO is nowhere near dead. We can talk about that in a little while. But when we talk about why, most of the data I see, and again, I'm having a really hard time finding single sources of truth on this, most of the AI usage data I see, whether it's
(21:26) survey-based, whether it is behavioral and observational, at the very best mixes motivations and features. That OpenAI slide, that rainbow spaghetti I showed you a few minutes ago, talks about why. And Daniel Green, yes, there are some Anthropic and OpenAI reports on this. But just like the AP survey on the screen right now, just like the OpenAI data I
(21:50) showed you a few minutes ago, what we're really seeing is a mix of motivations and also the technologies, the features, the actual tools that they're using. I don't think the tools are as important a question as the features. There are a couple of reasons for that, but let me skip ahead and get to that.
(22:13) The third thing is, which tools do our customers use?

**AI Tools & Features: Rapidly Changing Landscape**
(22:13) First, how many of our customers are using AI? Second, what is their motivation for going and using these tools? And the third question is, which tools do they use? The reason I don't think the tools and the features are as important is because they're changing every day. There are days in
(22:32) which multiple frontier platform companies launch entirely new models on the same day as each other. There's not a week that goes by that most of these platforms are not launching brand new models, brand new features. Everything is changing quickly enough that if you, as a brand, a seller, a marketer, any kind of company using AI, try to build an entire strategy around AI technology,
(22:59) that strategy will last for four to six weeks. Because in four to six weeks, there will be completely different technologies, new features that you didn't count on, that people have started to adopt, and adopted for five minutes, and then walked away from. If you don't believe that things can change really quickly, ask DeepSeek, whose usage chart went up and down like a classic bell
(23:22) curve.

**OpenAI's Changing Market Share**
(23:22) Ask OpenAI in a variety of ways. Sora 2 spiked and then settled down at a much lower rate. Ask them about ChatGPT, because one year ago, ChatGPT had 87% of global generative AI website traffic share. Again, this happens to be website data, not all data, because there are no sources that credibly combined the web share and the mobile share that I've
(23:48) seen at least. A year ago, ChatGPT had 87% of the share, and Gemini had 6%. In 12 months, ChatGPT lost a quarter of that share, and the Gemini share almost quadrupled, and every other platform combined roughly doubled in that time. Platform and feature adoption can change quickly. They are changing quickly, but the motivations for why people go and use
(24:16) these tools, I think, are going to be a lot more constant and evergreen. That's what we're working towards, and that's the model that we're going to start providing to brands, retailers, marketers, and sellers in the coming months.

**Building Blocks of AI Adoption Model**
(24:16) What I'm working on right now is building a data model to answer these questions in ways that brands can use. What we
(24:37) want to do is talk about what I'm calling the building blocks of AI adoption. This is something that is a work in progress. I'm going to publish this research in a couple of weeks. We're collecting the survey data to power this right now. What that means is I don't even know which order these different levels of behavior and motivation are going to end up in,
(24:57) because we're going to stack them in the order of prevalence that we see. The bottom of the building blocks is going to be total active adoption. Those are people who choose to use AI. It's not the 99% from that Gallup study at the start of the call who use AI without even realizing it. It's people choosing to use AI on a weekly basis. As we go up from there, we're going

**Motivation Categories: Asking & Doing**
(25:20) to see some non-mutually exclusive categories that refer to the "why," that talk about the motivations people have for using these technologies. One of the ones that we expect to be most common, based on all the data that I've seen collected everywhere else so far, is "asking." It's using AI to look for information or explanations. I'm going to show you five or six categories
(25:42) here. We're going to talk to brands and sellers about these five or six categories, but we're also going to dig deeper into the specific behaviors that add up to these categories. More than 50 different individual behaviors people are taking right now using artificial intelligence platforms. "Asking" is a pretty easy and broad category. It's people
(26:03) looking for facts and information and people looking for explanations. I think very likely the next building block up from that will be "doing," which is using AI for personal productivity, for advice, for guidance, and for tasks. There's a lot that can hide under here: writing or editing personal correspondence, creating and managing to-do lists, schedules,
(26:28) budgets, getting directions or navigations, translating from another language, brainstorming, as well as getting advice and guidance on a whole variety of different categories that we're surveying people on.

**Motivation Categories: Work, School & Shopping**
(26:28) How are people using AI to make their lives easier, more productive, more efficient on a personal level? They're not just using these tools at home.
(26:51) So we have a working building block that's going to get into using AI for work tasks and for school tasks. Then I'll be using AI for info and research, for correspondence, for images, audio, video, charts, graphs, presentation, for calculations and data analysis, computer coding, vibe coding, making apps, translations, but for work purposes, summarizing documents or meetings, and
(27:15) things like to-do lists and brainstorming. The one that I get the most questions about, but that I honestly think is not necessarily going to be that common or that big a building block, is shopping. Listen, I work at eMarketer. My clients are companies trying to sell
(27:39) things. They're very, very interested in this particular one. But I don't think that this is going to be huge. Daniel, it sounds I think you're saying you disagree that it's going to be huge. We can get into that afterwards, but the specific behaviors we're going to look at are using AI tools to get product recommendations, to look for information on specific products, comparing
(28:01) products, comparing prices, comparing stores, completing purchases on AI, or using AI for all this research and going somewhere else to complete that purchase.

**Motivation Categories: Relaxing & Connecting**
(28:01) We'll also be collecting it based on lots of different product categories. "Relaxing" is going to be a really interesting one: using AI for fun or to pass time, things like chatting
(28:21) with character AIs, creating or editing audio or video or images for fun, or writing text for fun, whether that's fiction or some other reason. Then perhaps the most interesting is "connecting." That's using AI as a friend, a companion, or a therapist. David can tell you all about his experience going on a date with an AI that would fit in the middle of these
(28:45) three bullets. Clearly, there are people using AI for companionship or for counseling or therapy. So this is what we're going to collect into these large categories you see as the building blocks, again, with more than 50 different specific behaviors that we'll be able to split the data on. Our hope is that these answers are going to help marketers and brands guide their AI
(29:08) strategy.

**Guiding AI Strategy: How, Why, Which Tools**
(29:08) How many of your customers use AI is the most important first question. That bottom building block, total active adoption, will show us the percentage of an audience that chooses to use AI each week. That'll define the importance of AI within a marketing plan. We're going to go with weekly. Of course, there's been a lot of discussion about OpenAI saying weekly
(29:27) data, sites weekly data. Google cites monthly data. I think if you're getting your haircut or paying your rent or mortgage more often than you're using ChatGPT, you're probably not really a ChatGPT user. So we want to look at this weekly and not monthly. Having said that, when we get into the specific granular, crunchy behaviors, like "did you compare prices?", we will look at
(29:50) that monthly because honestly, the weekly data, I think at this point, just won't turn up very much. It'll help us answer the question of why do our customers use AI? The motivations that are driving people into these technologies, and that will help us define the strategies. Because if it turns out your audience is using AI for relaxation and companionship, that's an incredibly different set of use cases than if your audience is mostly using it for looking for information and shopping.
(30:09) One of those instances very easily lends itself to marketing, advertising, and commercial purposes. The other one actually could lend itself to commercial purposes, but it's a much different scenario that requires a much different
(30:33) strategy to say, "Let's get a message through to people who are chatting with AI for fun or for companionship." That's going to be very different than putting something in front of someone who's using it basically as a search replacement or as Amazon replacement. Finally, it'll help us get to which AI tools our customers use. The

**Future Data & Open for Questions**
(30:52) platforms, the features, which of those 50 different behaviors are they engaging with? Which of the 10 leading AI platforms are they using? We'll have all that to offer brands and sellers to say, "This is where you execute. These are the tactics you use to execute that strategy." But while it's important, we do think it is the third question to ask, not the first question to ask,
(31:14) which is what we're so commonly seeing right now. That's where we are right now in our thinking. I'm really excited to get this data back from the field. We're working on collecting that data right now, and we'll be publishing all of this. I will post it in the AI Insiders AMG Slack. It'll be on my LinkedIn and everywhere else.
(31:38) There's a list of a bunch of stuff that we've been working on that I won't spend any time explaining to you because I've seen the chat lighting up. I've been trying to pay attention while presenting.

**Audience Questions: Shopping & Embedded AI**
(31:38) I'm really excited to hear people's thoughts and see where this lands for you guys.
(31:58) Loving this, Nate. It's so much fun to get this look even before it's all out there. Adam, go for it.
(31:58) Yeah. Nate, I was going to chime in on the shopping bit there and just generally wondering out loud if shopping is one of those things that people will make use of. But just the way I don't actively sit in front of a frontier platform and say, "Go to Amazon and tell me that people that

**AI Integration into UX**
(32:20) bought this may also like that," it's just embedded into the UX. Maybe shopping is the one more embedded, but it's a broader question: people have been asking this since the beginning, which is, what happens to the use of AI? People like David and myself are hacking away inside the base 44s, inside cloud code, and very active use of some kind of AI-
(32:48) first user experience journey. Whereas I would assume over time, this stuff just gets embedded into everything we're working on. There's no recommendation algorithm first UI interface at Goto anymore. Even in the early days of Google, I think a lot of people just started saying, "Wait a second, I'll just use Google's algorithm and I'll just put
(33:16) it directly on my site rather than `site:`."

**Meta AI & Google's Blurring Lines**
(33:16) Yeah. We're seeing some of that. Meta AI is an interesting example. Of course, they have their own model, but their definition of a Meta user is anyone who does any kind of search on Facebook, Instagram, or WhatsApp in a given month. My former
(33:41) boss referred to it as insidious. You can't avoid using Meta AI. If you need to find anything, congratulations, you're now counted as a Meta AI user. There are positives and negatives to that. If that's applied properly on a shopping site or a content site, then the AI can help you better find and more quickly get the
(34:04) information that you're looking for. It can get you where you're going faster. It will make it harder to count some of these behaviors. What we're collecting right now, we're looking at some of the largest embedded AIs within shopping sites: things like Amazon's Rufus and Walmart Sparky. There's no doubt that the companies involved in both the
(34:27) search space, the AI platform space, and the commerce space are all working as hard as they can to make my life as hard as possible when it comes to collecting this data. Google, in particular, has taken a particular joy in blurring the lines between traditional search and AI in a way that is very much to their benefit, that hopefully makes things much easier and more seamless for users, but
(34:51) that certainly makes it really clunky to think about the difference between a traditional search and an AI search when almost half of traditional searches throw up AI results. All of what you're saying makes sense to me. It's all stuff that we're thinking about, and we'll keep collecting that data as best we can. For now, this really interesting
(35:11) thing of people choosing to use AI, I think, is a really important distinction at this point in time that will become less important as time goes on, and we'll try to adapt to that as it happens.

**AEO vs. SEO Strategy**
(35:11) Who else has questions who can ask the analysts? This is a fun one we don't get every day. Hey, Nate. Thanks so much
(35:36) for giving this really informative. Here's a question. If I'm working with a client developing an AEO strategy for them, obviously, a lot of these sites are becoming invisible because of AI and make it searchable. Based on what you're finding, is there a parallel path, or do you want to almost start out first by answering those three
(35:58) questions about the user before you get into that, or do you see it as a parallel path?
(35:58) It's a great question, and I think there are two pieces to that. The first version of that that I hear pretty regularly is, how important is AEO or GEO compared to SEO?

**Is AI Killing Search? AEO vs. SEO Comparison**
(36:22) And the corollary question there is, "Is ChatGPT killing Google?" The second version of that question that I hear pretty regularly is, "How similar is AEO and GEO to SEO? Can I just do the same thing?" because a lot of very smart people, including Danny Sullivan, one of the OGs in search and SEO, are saying things like, "Good SEO
(36:49) is good GEO." So, I'll take those one by one. In terms of how important is AEO and GEO compared to SEO, as someone said earlier, no, in fact, AI is not killing search. GEO is not killing SEO. There are, again, certain categories and products where this is incredibly important already. I talk to B2B technology vendors, and this stuff is
(37:14) vitally important to how people are finding and evaluating their products. We've known that for a while. We've seen anecdotally a couple of years ago already, we were seeing really high ROI or really high conversion from traffic to warm lead between AI tools and these technology vendors, B2B technology vendors. I suspect that
(37:44) other very high consideration products, even on the consumer side, are experiencing something similar, although it's too early for us to have great data on that.

**Traditional Search Dominance (3.3% AI, 96.7% Traditional)**
(37:44) Overall, no, AI is not killing traditional search. I ran this data in September. I'm working to update it right now, now that it's been a few months. But in September of last year, I ran this
(38:04) analysis, and we combined the amount of time people spend looking for information. People spend in search-like behaviors on the top four AI platforms: ChatGPT, Gemini, Claude, and Perplexity were the four that we used in that case. We also looked at the amount of time people were spending on traditional search engines, and the only two we counted were Google
(38:26) and Bing. What we found was if you add up all the time people spent looking for things, searching basically on the top four AIs, it was 3.3% of the time they spent searching in total when you add up those AI platforms and Google and Bing. Of course, there are other places people search for things. I wasn't counting people searching for things in the Reddit
(38:50) toolbox or the YouTube toolbox, or any of those places. But these general search locations, traditional search engines and AI platforms, 3.3% of their time was spent searching for things in the AI, and 96.7% of the time was spent searching for things on traditional search. Although Bing has always been an industry punchline, it's a huge tool
(39:13) that makes a lot of money for Microsoft and drives a lot of value for a lot of advertisers, but it's so much smaller than Google search. It has been for years that we kind of joke about Bing. As of September of last year, people spent more time looking for things on Bing than on the top four AI tools combined. What that tells me is, yes, there are some
(39:36) products and categories and audiences for whom AI is a dominant and important way they're finding information and learning about products and services. The companies in those categories need to pay a lot of attention to AI and to GEO and AEO. But for most products, most categories, and certainly most audience segments out there, Google is still far and away the number one place people
(39:59) are going. When I hear marketers say, "We're just going to move all of our SEO initiative into GEO," for almost all of them, that would be a pretty big mistake.

**SEO Success ≠ GEO Success**
(40:08) The second part of that is, are AEO and GEO the same thing as SEO? Folks like Danny say good SEO is good GEO. I see people every day,
(40:23) I saw it again this morning, people trying to answer that question on a theoretical basis, using logic and the advice of experts.
(40:23) Sure, I love using logic and the advice of experts, but even more than that, I love using data. There's cold hard data that shows that success in SEO does not lead to success in GEO, and vice versa. Again, there are a million
(40:45) studies with a million different versions of this data. One I quote most commonly shows that eight or nine percent of the links that are cited in ChatGPT responses would show up in the first page of Google organic results for the same
(41:08) query, the same prompt run as a search query. Eight or nine percent from Gemini, eight or nine percent from ChatGPT, eight or nine percent from Co-pilot. It goes up a lot for Perplexity because they clearly are using a different concept for what they want to show. I've seen numbers as high as 30% and 40%. Those are definitely a lot better than 8% or 9%. But either way, the vast majority of the time that you have achieved success in SEO, landing on the
(41:33) first page of Google results, you have not achieved success in GEO. Whatever the theoretical, expert-driven opinions are on whether they should be the same thing, the reality is, when we look at the data, success in one does not lead to success in the other. That tells me that we need to think about different strategies and tactics. I like the dimensions and the
(41:56) perspective about consumers, customers, and industries. But I was wondering what lens we should put on for global brands around geographies, if any regions, and just some basic perspective on adoption across countries.

**Global AI Adoption & Local Data Importance**
(41:56) If it's hard to get this data across different behaviors and tools, it's even harder to get it across
(42:25) countries. We are constantly looking for data and trying to help our clients understand that. But again, we're in this situation where different studies have very different answers to that question.
(42:25) Yeah, that's fine. Totally. Trun, I think you're unmuted, and we can hear you.
(42:49) I did an analysis of the three largest studies I could find that included at least 20 different international markets adoption of AI, and I evaluated whether there were commonalities amongst their findings. None of those studies, if you categorized each of the countries into low, medium, or high AI adoption,
(43:17) none of them had more than 40% overlap with either of the others, which is infuriating and entirely typical of where we are with AI adoption data right now. So again, we're working on our own study to try to answer that question. But there's this other point: does it matter in Indonesia as much as elsewhere? I've seen data saying that
(43:43) Indonesia has surprisingly high AI adoption. I don't know a lot of marketers who are just targeting Indonesia. I've worked with Coca-Cola. They are most certainly targeting Indonesia as a whole place. Most brands, most companies are not just targeting the entire general population of a country. The question for me is a lot less about which
(44:04) countries have higher or lower adoption, and a lot more about what your target audience within each of your target countries is doing. For that reason, I don't think you need to get to a big, broad global study on it. I would say go and find reliable local data. I've seen fantastic local studies that look at six or seven different Latin American countries.
(44:28) I've seen, very bizarrely, the best study I found on Europe focuses on the Nordic region plus, I think, UK and Germany, and just seems to ignore Spain, France, and Italy. But go and find the local data and make sure you can cut it by the audience that you're looking for, and that will help you answer the first of those three questions I mentioned before.

**Recommended BTV Interview**
(44:52) All right, good one from Jim K. Let's see if Jim C could keep up and keep the bar high. Item number two. I just wanted if anyone wants to double-click on some of what you've said, Nate, they should go and watch your BTV interview of yesterday.
(44:52) Because it's what is it, eight or nine minutes long, but you do...
(45:13) much shorter than what I just dumped on you guys.

**Common Misconceptions & Data Quality in AI**
(45:13) It's all the search engines, and it's all the SEO, GEO. I'd watched it just before we started here, and it was very helpful for framing. That's it. I can't beat what the other Jim said.
(45:13) Other questions? I know Dan.
(45:13) Talk about Indonesia.
(45:35) We just want to talk about Indonesia. Daniel Green was hoping for some hot takes. Just a couple quick ones I'll borrow from him here. "Where does Nate think many people are wrong about AI today?" There are so many different things, so I won't say there's one thing that I think people are wrong about. I will say that most data I see is wrong. As I said, I've been doing this
(46:04) as an analyst for a quarter century now. I founded Jupiter Research's coverage of search marketing and led Forrester Research's coverage of social marketing, both at the periods of time when they were just starting to explode as marketing channels. In moments like the one we're in now with AI, in previous waves of technology, we've seen general confusion about what's
(46:31) happening, why it's happening, and how quickly it's happening. I have never seen anything like the amount of confusion and the amount of badly produced or incorrectly analyzed data that I see in AI. So, rather than answering that question directly, what I'll say is please, please, please check the base of the data that you're referencing, especially
(46:56) if it's survey data. The number of times I see people referencing data and saying X% of people think this about AI or do this with AI, and the base isn't general population. It's not online users. It is almost always things like active AI adopters, people who use AI tools at least once a week. I've seen data saying 80% of people trust AI responses more than organic search
(47:22) results. Well, 80% of people don't use AI.

**Critique of Synthetic Audiences**
(47:22) Nate, have you tried synthetic audiences and checked it against your own research to see how they compare?
(47:22) We are actively looking at using synthetic audiences. We don't think that they're ready. I have worked at other research firms not so long ago,
(47:47) where they were more bullish on synthetic audiences. My concern is this: it's the same as using AI to produce creative and things like that. These are probabilistic machines, which means they're averaging machines. They take the average of what's out there, and they give you something that makes sense
(48:10) within that context. That's not what's interesting about market research. If I wanted to make a pile of market research that looks like the last pile of market research I collected, a synthetic audience would do that for me every day. My challenge is how good are these things going to be at identifying and representing the outliers? I would say that about
(48:33) data collection and synthetic survey audiences. I would also say that about things like creative concepting, designing actual creative assets. The interesting things in those fields are not what looks most like the things that you can find that have gone before, but how do we break molds and patterns?
(48:56) But how about the marketers' perspective? Because if they're looking for user reactions, would they be able to get what to expect from the audience?
(48:56) Yes, it is by definition a backward-looking averaging machine. There are many good uses for it, and in most cases, I think it's probably perfectly fine to use it for
(49:28) synthetic audiences for survey work, for example. But again, most cases aren't the interesting ones. If I dig through fresh survey data, I'm actively looking for the pieces of data that stand out, not for the pieces of data that look like all the other data. And the piece of data that stands out, I then validate. I make sure that there isn't some mistake or
(49:56) other reason that it's standing out that it shouldn't, and make sure I asked the question properly and that the survey logic was working properly, and all of that. Once I know that this is a real result that stands out from the other results, then I start to look at what's happening and why. That's what's interesting. That's what turns into the reports that I write and
(50:15) the talks that I give. If you're just looking for the average of everything, if you're just looking for what the median person would say, and you create this audience that is a lot of people very close to that median person, it's a lot less interesting to me. I know that I'm oversimplifying enormously, and I know that there are potential ways of
(50:40) solving this problem. Personally, if they came to me and said, "We want to run your survey not with a real audience but with a synthetic audience," I would beg, borrow, and steal to avoid that happening right now.

**Concluding Remarks & Future Engagement**
(50:40) Well, Nate, we're going to have way more questions for you. Appreciate you coming
(50:58) on and answering quite a bunch, maybe some we didn't even know we had. As you have more to share, this is an open invite. I think it's a tremendous look that's not just, "Oh, here are some trends that are happening," but the stories behind them and what data you could trust, what you can't. It reminds me of some other areas I've
(51:23) seen research in, like research in millennials back in the day, where, "Okay, there are 80 million millennials." You can get the research to say whatever you want about them. They're both very into social causes and also very self-serving, just wanting to look out. You can find data to support anything with AI. See how it's so messy out there?
(51:45) Helping be a guide for what we can trust is eMarketer's heritage, it's your heritage. Glad there's a great fit together. Thank you for this, and I'm excited to keep getting to learn from you.
(51:58) Thanks so much. I'm happy to come back. As I said, when the data is ready, I will absolutely be posting it in your
(52:06) Slack and on various social channels, and I'm happy to come back and present it if that's of interest.
(52:06) Well, I think you'll get some takers here. No vetoes in this crowd, right? All right. Lots of applause going on. Thanks so much, and thanks everyone for always coming with your great questions and interest in the conversation today. I like how I looked
(52:24) away from the chat for a second, and it's like 20 new messages: "What the heck is going on here?" Nate, I could share that with you as well if you want to catch up on anything you missed. Thank you all. Next week we're off because I hope to see SEU and Architecture Live 3 in person, but then we'll be back with a great lineup of guest speakers coming up. See you all
(52:44) very soon.

## AI Search How to stay visible in 2026 with the AI Growth Academy

Speaker: Catherine Toms
Published: 2026-03-03
Tags: geo, seo, ai search, ai in marketing
Video: https://www.youtube.com/watch?v=4OdKnxF0BN0
Page: https://aimarketersguild.org/sessions/ai-search-how-to-stay-visible-in-2026-with-the-ai-growth-academy

In this AIMG APAC masterclass, Catherine Toms (AI Growth Academy) explores how AI is reshaping the future of search.
From the rise of Google AI Overviews to the growing influence of tools like ChatGPT and Perplexity, she breaks down how search behaviour is changing - and what that means for marketers. Catherine explains why traditional SEO metrics are no longer enough, how zero-click search is impacting traffic, and what brands need to do to stay visible in an AI-first landscape.
A practical, no-hype session for marketers navigating the shift from SEO to AI-driven discovery.

[07:30] How Does the Rise of Zero-Click Search Impact Website Traffic and the Buyer Journey?

Answer / Description:

The rise of zero-click search means that users can find the information they need directly on the search engine results page (SERP) or within an AI interface without having to click through to a website. While this trend reduces overall top-of-funnel website traffic, it shifts click-throughs further down the buyer journey, resulting in website visitors who possess significantly higher purchase intent and are more likely to convert.

In an AI-first search environment, generative engines compile data from across the web to answer complex user queries in a single view. Because the AI acts as an intermediary researcher, users only click through to a brand's website when they are ready to engage deeply, evaluate pricing, or initiate a purchase. This structural shift requires marketers to pivot away from measuring traffic volume as a primary KPI, focusing instead on high-intent conversion metrics and ensuring their brand is represented accurately in zero-click AI summaries.

Keywords:
Zero-click search, generative search traffic decline, AI overviews traffic impact, search engine optimization metrics, conversion intent funnel, generative engine optimization, SEO website traffic drop

[10:10] How Is the AI Search Market Fragmenting Across ChatGPT, Perplexity, and Gemini?

Answer / Description:

The search market is fragmenting as users shift from a single search default (Google) to specialized AI engines that cater to different search intents. For example, Perplexity is heavily favored for professional B2B research due to its strong real-time citations, while ChatGPT is predominantly used for consumer queries and personal research, with 95% of its user base utilizing the free version.

This fragmentation mirrors the evolution of social media, where different demographic groups and search intents cluster around specific platforms. Google AI Overviews and Gemini draw heavily from Google’s own ecosystem, including YouTube videos and Google-indexed sites, making them powerful for mixed-media discovery. Meanwhile, platforms like Microsoft Copilot lean corporate, and Claude maintains an empathetic, ethical tone in its outputs. Marketers must optimize their brand footprints across all these engines to match where their specific buyer personas conduct research.

Keywords:
AI search fragmentation, Perplexity B2B search, ChatGPT vs Gemini, LLM search demographics, search platform market share, multi-engine optimization, Google AI Overviews sources

[15:38] What Is the Difference Between SEO, GEO, and AEO in Digital Marketing?

Answer / Description:

The difference lies in their targets: SEO (Search Engine Optimization) optimizes websites for traditional search engine algorithms and blue links, GEO (Generative Engine Optimization) optimizes content to be retrieved and cited by Large Language Models (LLMs), and AEO (Answer Engine Optimization) formats content to directly answer conversational, multi-word questions. While SEO focuses on keyword match and domain authority, GEO and AEO prioritize context, conversational structure, and direct query resolution.

In practice, traditional search queries are short keyword strings (e.g., "best CRM small business"), whereas AI-driven queries are conversational questions averaging 10 to 11 words (e.g., "find the best CRM for a business with 50 employees that integrates with Gmail and costs under $100"). AEO works to optimize for these long-tail, question-based prompts. GEO encompasses the entire technical and contextual strategy required to make sure a brand is mentioned, summarized, and cited by AI models during these conversational searches.

Keywords:
Generative Engine Optimization, GEO vs SEO, Answer Engine Optimization, conversational search queries, long-tail AI prompts, keyword vs prompt research, AI search optimization definition

[18:44] How Is Generative AI Reshaping the B2B Buyer Journey and Sales Funnel?

Answer / Description:

Generative AI reshapes the B2B buyer journey by enabling buyers to self-service their research using deep reasoning and AI agents, which pushes direct vendor outreach much further down the sales funnel. Instead of entering the funnel early via gated content, B2B buyers use AI to map the market, compare features, and build their initial consideration sets before ever contacting a salesperson.

Because of this shift, brand authority and early-stage visibility in LLM training data are more critical than ever. Research indicates that up to 80% of B2B buyers already know which vendor they want to buy from before initiating direct contact. If an AI engine does not include a brand in its initial automated comparison tables, or if it presents outdated or negative information, that brand is excluded from the buyer's consideration set entirely.

Keywords:
B2B buyer journey AI, self-service B2B sales funnel, AI-driven market mapping, brand consideration set, vendor evaluation AI, B2B search optimization, deep research AI agents

[22:30] How Does AI-Driven "Headless Commerce" and Universal Baskets Change B2C Shopping?

Answer / Description:

AI-driven headless commerce and universal baskets streamline B2C shopping by bringing the checkout process directly to the user's search interface, eliminating the need to visit an e-commerce website to complete a purchase. By integrating with payment networks and technologies like Google's Universal Cart Protocol (UCP), AI systems allow users to search for products, track prices, and purchase items with a single click inside the AI engine.

This paradigm shift reduces friction in the path to purchase but transforms the role of the brand website from a storefront to a fulfillment and logistics engine. B2C brands must optimize their product feeds and structured schema data to ensure their inventories are readable by AI shopping agents. These AI agents can actively monitor prices, find the best deals, and execute transactions on behalf of the consumer, making highly structured data feeds essential for brand discoverability.

Keywords:
Headless commerce AI, Universal Cart Protocol Google, AI shopping agents, e-commerce automated purchasing, structured product schema, B2C search optimization, checkout friction reduction

[27:11] Why Is Traditional SEO Still Necessary in an AI-Driven Search Landscape?

Answer / Description:

Traditional SEO remains necessary because generative AI models and LLMs rely on search engine web indexes, crawlability, and standard technical SEO hygiene to discover and extract information. If a website has poor crawl accessibility, broken links, or a blocked robot.txt file, AI engines will be unable to retrieve its content for search summaries and citations.

The relationship between traditional SEO and Generative Engine Optimization (GEO) is foundational: SEO acts as the underlying architecture that enables GEO to function. Key technical SEO elements—such as fast page load speeds, mobile responsiveness, structured site maps, and logical internal linking—are still critical. Without these fundamental practices, search bots and AI crawlers cannot index website data, keeping it out of LLM training sets and real-time search generation.

Keywords:
Technical SEO hygiene, AI search crawling, LLM index retrieval, site speed for AI, robot txt AI scrapers, search engine indexing foundation, crawlability for GEO

[29:05] How Do You Align Content Strategy with AI Search Using Prompt Research and Topic Clusters?

Answer / Description:

To align content with AI search, marketers must shift from targeting isolated keywords to conducting "prompt research" that maps to the exact conversational questions target personas ask AI engines. This approach is supported by creating dense "topic clusters"—highly structured webs of related content on a website—that establish consistent topical authority across owned, earned, and paid channels.

Unlike traditional SEO, which primarily evaluates individual on-page keywords, AI search engines assess a brand's holistic expertise across the web. LLMs scan for topical consistency not only on the brand's primary site but also on external platforms like LinkedIn, YouTube, and podcasts. Developing robust, interlinked content hubs around core areas of expertise signals authority to AI models, making them more likely to cite the brand as a trusted resource.

Keywords:
AI prompt research, topic clusters SEO, cross-channel brand consistency, building topical authority, generative search content strategy, semantic search optimization, expert entity signals

[33:18] What Role Do Off-Site Trust Signals Like Reddit, Discord, and Reviews Play in GEO?

Answer / Description:

Off-site trust signals on platforms like Reddit, Discord, and review directories are heavily weighted by AI search engines because LLMs cross-reference multiple independent sources to verify a brand's authority. AI engines do not merely rely on what a company says about itself on its own website; they analyze user reviews, community discussions, and external publications to determine brand trust.

Because AI crawlers actively scrape user-generated content from forums like Reddit and industry-specific review sites, positive digital PR and community presence are vital for Generative Engine Optimization (GEO). A brand that frequently receives positive mentions in peer-to-peer discussions, independent roundups, and forums is prioritized by AI models. Conversely, brands with a weak external footprint or negative sentiment on review sites risk being excluded from recommendation lists generated by tools like ChatGPT or Perplexity.

Keywords:
GEO trust signals, Reddit AI search scraping, third-party brand validation, digital PR for AI, community sentiment analysis, user-generated content SEO, Perplexity citation sources

[35:10] How Should You Technically Structure Web Pages to Be Easily Cited by AI Search Engines?

Answer / Description:

To ensure web pages are easily cited by AI engines, structure them with a concise meta-summary at the top, logical anchor links for page navigation, short scannable sections, listicles, embedded YouTube videos, and targeted FAQ sections. This format caters simultaneously to human user experience (UX) and the parsing behavior of AI search crawlers.

Using tools like jump links and structured lists helps AI crawlers break down, extract, and reference specific portions of a web page easily. Additionally, incorporating multimodal elements—such as images with descriptive alt text and YouTube video embeds—significantly boosts visibility, as search platforms like Google's Gemini actively prioritize video and rich media in their summaries.

[Web Page Schema Layout for AI Retrieval]
├── 1. Short Article Summary (Meta-Description Equivalent)
├── 2. Interactive Anchor/Jump Links
├── 3. Scannable Body Copy (Bullet Points & Listicles)
├── 4. Multimodal Embed (YouTube Video + Image with Alt-Text)
└── 5. Contextual FAQs (Conversational Questions & Direct Answers)

Keywords:
On-page SEO for AI, web page structure GEO, content layout listicles, multimodal AI optimization, FAQ schema conversational, Studio Hawk playbook, anchor links crawlability

[43:30] How Do You Test and Audit Your Brand's Visibility Across ChatGPT, Claude, Gemini, and Perplexity?

Answer / Description:

To test your brand's true visibility in AI search, run diagnostic queries across major engines while prepending the prompt with instructions to ignore personal data (e.g., "Ignoring any saved memories or personal data you have about me, what does your general training data say about [Brand/Company/Product]?" ). This approach mimics an "incognito mode" for AI, returning clean results based purely on the model's public training data rather than personalized user history.

Conducting this audit across ChatGPT, Claude, Gemini, and Perplexity reveals if your brand is visible, whether the information retrieved is accurate, and what sentiment is associated with your brand. If the AI returns a "not enough data" response, it indicates a critical lack of digital footprint. Marketers should run these checks regularly to locate inaccuracies, monitor competitive benchmarks, and identify content gaps that need to be refreshed or indexed on their main websites.

Keywords:
Audit brand AI search, incognito AI prompt, test ChatGPT visibility, monitor Perplexity citations, competitor benchmarking LLM, brand sentiment audit, training data retrieval

[49:15] What Is the HubSpot AI Search Grader and How Does It Audit Your Site for GEO?

Answer / Description:

The HubSpot AI Search Grader is an automated, web-based audit tool (currently in beta) designed to analyze how effectively a website is discovered, parsed, and cited across OpenAI, Perplexity, and Gemini. Users input their website URL and company details to receive a performance score and an analysis summary of their brand's recognition across these specific models.

The tool evaluates key indicators like brand recognition and citation health, generating actionable optimization recommendations. Because manually querying every LLM for various search scenarios is highly time-consuming, using structured grading tools like HubSpot's grader provides marketers with a consolidated, high-level diagnostic of their current Generative Engine Optimization (GEO) performance.

Keywords:
HubSpot AI Search Grader, GEO audit tool, website search grade, OpenAI visibility checker, Perplexity citation audit, Gemini brand performance, automated SEO grader

## Vibe Coding for Good

Speaker: David Berkowitz
Published: 2026-02-27
Tags: vibe coding
Video: https://www.youtube.com/watch?v=nI_DwCHEZ0I
Page: https://aimarketersguild.org/sessions/vibe-coding-for-good

**Welcome to AI Insiders**
(0:05) Hey everyone, welcome to another edition of AI Insiders by Marketers Guild. I'm your host, David Burkowitz, and I actually get to be the featured guest presenter today. I'm my own guest, and I hope I'll make myself at home in this community. You all make it easy for me to do so because this is a topic I can't talk about vibe coding enough.

**The Power of Vibe Coding**
(0:29) I am trying not to have 20 different vibe coding things going on while I'm doing this session. I'll see if I can focus enough for the next hour or so, but I'm blown away by what can be done with it. I'll share one of the latest ideas that came to mind: could we create resources that benefit others?

**Introducing Vibe Coding for Good**
(0:57) It could be related to something we value like bringing people together, helping families connect better. All kinds of positive ways this tech could potentially help others. On a whim, I created Vibe Coding for Good. I got the domain. I built this in Base 44. If you haven't been playing around with

**Base 44 and Test Sites**
(1:27) the resources yet, I'll go back to that. I need to republish this site in B 44 because a few things aren't here yet that I was putting together, but there are a bunch of resources on here. I tried creating a test site. This is something I admittedly did on my own without community input, trying to see

**AI for Community Support**
(1:54) threats against the Somali immigrant community. So I wondered, what would that look like if AI helped create some resources? I did run this by an activist friend who had some ideas for what to do with it and how to hone things a bit. Again, I'm not sure this whole thing is perfect

**Open Conversation & Breakouts**
(2:26) yet. I'd want to do this with a local community or give this to another org that could do something with it. But it struck me as amazing to see how readily one could put something together that might benefit others. That was the premise for today. This is an open conversation. What I'd love to do based on

**Vibe Coding Projects for Good**
(2:55) what we did in the last Vibe Coding 101 session back in November is to do some breakouts, try to come up with ideas for things, even a quick vibe coding site, things people could work on together or individually, and do a bit of sharing after. First of all, I want to open this up: has anyone worked

**AAC Device App Inspiration**
(3:24) on any vibe coding projects remotely that are in the 'for good' space? >> It's still in build and conceptual mode, but the idea came from my son. He sometimes relies on an AAC device. This is for individuals who have speech difficulties and rely on a device to say basic things. If they

**Limitations of Current AAC Devices**
(3:55) cannot rely on their speech, it's like an iPad. It's called assisted technology. It has buttons for simple verbiage they might need through the day. They tap on it, and it becomes their voice. It's very limiting to people whose thoughts are so much bigger than what the device can offer. We have this book that offers games to

**Creating a More Flexible App**
(4:24) individuals who want to practice speaking out because a lot of it is anxiety and closes up your throat. >> Okay. >> With permission from authors, we're trying to put the games into an app that would be much more flexible than what this AAC device does. It's a big goal, but we'll see.

**Tools for Bringing the App to Life**
(4:45) We're trying to put the games on an app. >> That's incredible. >> We copied the games. >> Okay. >> We have an idea. We have a sketch. We haven't done it yet. >> Are there any tools or apps you've been using or plan to use to bring this to

**Exploring Replit and AI Prompts**
(5:03) life? >> I am trying to see if Replit might be a good idea because all it would need to do is give prompts. It's very similar to the way qualitative researchers are using prompted questions with respondents. It would need to give prompts

**Open to Suggestions for Tools**
(5:29) and have the person join a conversation with AI. >> Okay. >> Not too difficult. I'm thinking Replit might work, but I'm open to suggestions. >> Nice. This is a good place to get some suggestions. I like the ones I'm most familiar with

**Different Coding Tools**
(5:50) are Base 44, for more basic projects, but it's still robust. I've been playing a lot in ChatGPT Codex to build more full-functioning applications, which I find easier than Claude Code, which feels a bit more technical for me. There's everything on that scale, from technical to more 'what you see is what you get'.

**Codex vs. Other AI Tools**
(6:12) was >> I was wondering about that. This is a basic question, but can't I just do this? I get what Vibe Coding is. Can't I just do it with the AI tools I'm used to? Do I need Replit? Is there something special? Well, for ChatGPT versus ChatGPT Codex, Codex is designed specifically for building sites and apps. So there

**Connecting Codex to Hosting & Randall's Expertise**
(6:39) are even tools that can easily connect Codex to a web hosting app or things like that, or connect it to GitHub and all these specific things. Randall, who I learned most of what I know about Claude Code from, is in the room. I'm so glad you're here, Randall. Please put me out of my misery and up. >> Yeah. No, happy to. The

**General Purpose vs. Optimized Tools**
(7:09) fundamental model capabilities between Claude Code and Codex, and the things that are on your computer, are general purpose. They can do just about anything. You can ask a finance question on Claude Code. They're great at coding. They're great at doing many different technical things. What you'll see in tools like Replit, B4, Bolt, Lovable, is that they

**Purpose-Built Tools Explained**
(7:30) are more optimized for creating the products they're developing. That's just a website. You're not going to ask Replit to analyze your financial statements or review a legal document, right? Claude Code can, but that's because it's a general thing. Those more purpose-built things have optimized additional code and structures and

**Coding Directly in Claude vs. Claude Code**
(7:52) other things they work with to make them better at doing that particular set of tasks, rather than being something as general for any task. Then, since it builds on the questions she was asking, what would you say is the biggest difference of trying to code directly in Claude, for instance, instead of using Claude Code?

**Workflow Differences in Claude**
(8:17) >> Yeah. A couple of things will be on workflow. Anthropic is working on this to connect the different systems. When you do coding on Claude on the web, it doesn't have a good way to get into a code repository on your computer to test. It doesn't have a good way to get into a server to host somewhere. You can create connections. You can do many

**Anthropic's Evolving Capabilities**
(8:41) things to mitigate that, but it's still primarily a chat thing. Anthropic is building capabilities to allow you to work on the same code in different modalities or tooling, whether it's web, app, or even on your mobile app. But that requires a bit more setup and configuration to get that to work effectively. As terms of an

**When to Use Specialized Providers**
(9:09) exploration, it's fine if you want to do a one-off. But what I suggest is if you're trying to build something that is a website, maybe it's an interactive website, maybe it has AI in the backend, which some of these providers now will help with, just do it in one of those providers. They're optimized for building out websites that are interactive, that are

**Advanced Development Options**
(9:32) nice design, that have AI in the backend. As you get more sophisticated or get into other types of activities, maybe within a company, that's where some of the other tooling like Claude Code, or even much more sophisticated, complex options like agentic development, which you may have heard of, that's where that stuff can happen.

**Integrated Workflow for Self-Contained Projects**
(9:53) is more on the technical side. But if you're building something that is fairly self-contained, those tools are probably your best bet because they have the entire workflow of 'I want to chat with the app and make it do stuff and have it move stuff around and then make that available to other people.' That's all built into how they've

**Vibe Coding for Good: A Community Forum**
(10:12) constructed their systems. Anyone else who's already tried using vibe coding for good in some way or has an idea you think could be built that way and wants to use others here as a sounding board? Enjoy this forum for it. David, it's interesting and great being here with the group as we talk about vibe

**Learning Through Real-World Projects**
(10:38) coding for good, alongside a general shared interest across this group of people of learning and getting more knowledgeable on how it works. I ran across, and I haven't actually done this, someone recently who, instead of just going free-form and creating something made up, like you, went free-form and created something

**Vibe Coding for Nonprofits**
(11:00) meaningful that might have purpose to a charity or some other entity. It's a really cool model. If you know a nonprofit or some group out there that wouldn't have the funding to bring in people to do this and create your own pet project. Now, that can get quickly out of hand in terms of overhead and time involved,

**The Value of Real Projects**
(11:22) overhead and time involved, especially if it's for good and not for compensation. But there is nothing better, which I'm sure most of the group agrees, than doing something like you did that's real as part of learning the new technology. Yeah. There was someone

**Zero Budget for Informative Sites**
(11:41) who was telling me about ideas she had for a new site just this week. She was asking my opinion on budget for this, and I almost jokingly went like this, right? I held up a big zero where it's like, not literally, but you can at least get something, especially for something more informative. Yeah, a step up from a brochure-ware site.

**Avoiding Costly Website Updates**
(12:16) then you should get as close to that as possible, at least for a pass, before you need a designer, developer, or others to take this more seriously. There was even a partner at a boutique investment bank who came to me and said, 'Look, we just got a quote, 10,000 bucks, to update

**Shocked by Site Cloning Results**
(12:43) our WordPress site, and then 1,000 dollars in maintenance fees. He's like, 'We are brochureware. Can I avoid that?' I'm like, 'Here, let me try cloning your site and seeing what happens.' I got to say, I was shocked. By the way, I did that in Base 44, not one of the even more sophisticated tools. I was like, 'Wait a second, I did not'

**Achieving Quick Landing Pages for Causes**
(13:08) think it would work that well while we're on this call. So, I'm like, yeah, I don't know why the heck for their specific situation. So, if it's getting a landing page for a cause, a new event, things like that, that's now X month, right? We want to do something that is all about this campaign that connects our cause to

**Easy Thematic Campaigns**
(13:35) Black History Month, Women's History Month. Whatever the most relevant theme is right now. We want to do this for Mother's Day and connect this to our breast cancer research, right? This kind of stuff seems so immediately achievable. These are great points, Jared. I haven't worked on any nonprofit projects recently, but one of

**Claude Code for Landing Pages**
(14:03) the things that's really um nonprofits never have the resources you need to build landing pages, and they're resource constrained. They're people constrained. I played around with Claude Code and created a landing page not for one of my nonprofits, but for a company. I was impressed at how quickly it

**Hosting with Netlify**
(14:26) worked. Talking about launching it and making that within Claude Code, Claude Code suggested using a site called Netlify to host it, where you can create the domain URL for it. It was easy. I just uploaded the files there, drag and dropped it in the project, and I had a test page live. Now I just have to transfer the domain

**Nonprofits and Conversions**
(14:52) over and I can do it. But in general, nonprofits are always constrained for building landing pages, but it's one of the strongest things you can do to increase conversions and accomplish whatever you want to do in terms of activation, as we all know. >> Yeah. This is where I

**Casey Newton & Vibe Coding**
(15:14) learned from Casey Newton about Netlify for the first time. It's a pretty big web hosting site. When Casey Newton was talking about how his boyfriend—they just got engaged or married, I saw some big news on Instagram—Casey was having his partner help him learn how to vibe code stuff. He's in

**Hosting and Domain Investments**
(15:43) Claude Code and he used Netlify. He used this terminal called Ghostly to help build it, which I think you can use the same terminal on your Mac. My projects that I've been doing in ChatGPT Codex are hosted on Netlify; the ones on Base 44 are hosted within Base 44. I've been buying way more domains than maybe I ever have, because

**Low Hard Costs Per Project**
(16:14) I keep connecting things to my vibe coding projects. That's been my biggest investment, buying these $13 a year domains. It's incredible how low the hard costs are per project for these. I think it's also worth hearing from others, and Randall, I know earlier, doing anything like

**Passion Projects for Inspiration**
(16:44) this, but stuff you've been vibe coding, it doesn't have to be directly in that 'for good' umbrella, but things that might be a good source of inspiration. Is anyone working on any passion projects? It doesn't have to be specifically marketing related. So, I can jump in here. David, I don't know if I would consider for

**GPT3 for Voter Registration**
(17:09) good, but I've been testing out my custom GPT3 with a gubernatorial campaign here in the US Virgin Islands. One of the big things we noticed as we were getting the campaign ramped up is that many people aren't registered to vote. >> Okay. We have about 100,000 people in the territory, and 30% of those are

**User-Friendly Voter Registration Site**
(17:32) active voters, meaning those who registered to vote and voted in the last two years. One of the Vibe Coding projects, inspired by you and your approach for Vibe Coding, was to create a site that's not funded or sourced by the government, but to make it easy for people to check if they're registered to vote, and to give them the information they need

**Promoting Civic Participation**
(17:55) to get registered to vote as soon as possible by the deadline so they can participate. Sorry for the background noise. I love life. But yeah, one of the Vibe Coding products I had in mind, based on your recommendations, was creating a user-friendly, simpler site to check if you're registered to vote and if not,

**Encouraging Voter Participation**
(18:14) these are the steps you need to take to make it as easy and simple as possible so you can get registered and vote when you're ready. >> Yeah. I don't think there are many better causes in any democracy right now than encouraging voter registration and voter participation. So I'd say that's

**Adapting Vibe Coding for Causes**
(18:37) very little good is going to happen without strong electoral turnout. So, Earl, that is awesome. I think ideas like that are tremendous. And also, by the way, the ease with which one can adapt some of the work you're doing to other kinds of causes and themes is tremendous. I'll share an example that and I'll put

**HighCaliber AI Test Bed**
(19:15) this into the chat right now. HighCaliber AI is my consulting site, but it's also what I use as my test bed for anything and everything. I had this training curriculum that I had done for a cooking school, and a friend of mine's like, 'I'm working with some political campaigns. Could you'

**Applying Themes in Base 44**
(19:42) potentially do something with this?' I haven't actually done this yet, but I just said, 'Base 44, can you take what I did and apply it to politics? Add a red, white, and blue theme.' I gave it that direction. It probably would have figured that much out on its own, and it came up with all these detail things. What I really like about this, by the

**AI's Insidery Jargon**
(20:06) way, some of the small touches here, like GOTV—if you've worked in politics, you know it means 'get out the vote,' right? It's insidery jargon that it already built in and makes it sound like I know what I'm talking about. Clearly, if I was going to do this for someone or even do an hour or two-hour version of it,

**Reskinning Content Quickly**
(20:30) I'd need to review this in detail and see what they need and stuff like that. But to say, yeah, if there's something you've already put out there, and then you go into one of these apps—yes, you could do it in ChatGPT, you could do it in Claude, or your app of choice—but if you're also using a tool like Base 44, Codex, Claude Code that you've

**Tremendous Speed-to-Market**
(20:55) already built something in, okay, just reskin it. The number of subpages I have on my site now for all kinds of different purposes, all kinds of different events, anything I speak at, how quickly you can put something in market with all of it is tremendous. >> Can I chime in? >> Please? The tools that I have are

**AI Media Centers for Campaigns**
(21:26) not vibe coding, but one of the target markets I've always had were political campaigns. >> Mhm. Creating media centers, then having AI as a way for people to inquire, and then having the RAG GPT behind it where the answers only come from your own knowledge base and the answers come up. There are a couple of different

**Overcoming Campaign Challenges with AI**
(21:55) aspects. I've done a lot of canvassing for Bernie when he was running. There are a few challenges: one is training, like you said, outreach. There's a set of information, and there are usually gatekeepers for that information. You have to find them to get it. They have these Slack channels, and people

**Valuable Knowledge Center**
(22:21) repeat the same thing over and over again. To have a knowledge center where you put everything in there and people can just ask, and it's going to pull information and lead them to the source, is very valuable. If you have your own campaign material, appearances on TV, newsletters, whatever knowledge and information you

**Early Campaign Outreach**
(22:45) want to share, to make that easily shareable. I always thought, and I met with Cornell West's team when he was running for campaign, but my product wasn't quite ready. >> Uh-huh. >> The people who were running it loved what I was showing them. But, as you said, one of the things you want to do in a political

**Empowering Campaigners with AI**
(23:06) campaign is to empower everyone who is running to share valid information, and my KA tool is a perfect solution for that, and not live coding, but as AI. >> But when you, as a founder, right, when you're going to someone like Cornell West, or maybe want to talk to James Tarico, or whoever's bubbling up today,

**Demonstrating the AI Tool**
(23:40) have you been using it yourself yet for a quick glimpse about what this could look like? >> Well, what I did, and that's what I've done with the AI marketers group, is I took a lot of interviews and content from Cornell, put it in there so that when they ask questions, they would get answers

**Challenges with Political Campaigns and AI**
(24:10) and they would experience what it looks like. Absolutely. One of the challenges with political, the bigger ones, I remember back when I was going after Elizabeth >> Warren. Elizabeth Warren. >> Exactly. Elizabeth Warren. One of the problems they have is they're very conservative about

**Vetted Media Centers for Campaigns**
(24:33) especially when it comes to AI. If it deviates from their talking points by even one degree, they're very wary of using it. Political campaigns always want to have vetted information, especially the higher up they get. But it's all a matter of having one campaign use it effectively and then replicating it. But it's

**AI as a Viable Solution**
(25:03) having vetted media centers, and for that purpose, it works great. It works really well. You can probably optimize the prompting a lot more, but to pull information off your own database and provide answers, I think is a very viable solution. >> Is there anything that's held someone back from

**Failures and Lessons Learned**
(25:33) from doing so? I'll build on that, David, and ask, what have you tried that didn't work, and what did you change because of that? I'll give a quick example there to open that door, because one of the things I've gotten into playing around with, and I'll show you a failure in the chat,

**Trying to Vibe Code a Point Solution**
(26:01) here. Yeah, you could see where I tried to take something. One of the things I like doing sometimes is I'll find a point solution and be like, 'Can I vibe code this? Why or why not?' This was a case of why not, because I saw a tool that did something like being able to track some public social accounts and then get alerts based on

**Prototype Challenges**
(26:26) this, and I had ideas that I thought would be particularly useful for a client I was working on. Ultimately, I was building all this, well, in ChatGPT. We were getting far. I got enough of a working prototype going, but ultimately it was that I thought the app I was initially modeling this off of was

**Data Access Limitations**
(26:55) just really hacked together and not that impressive. They since had a major upgrade, and so they're a bit more professional now. What ultimately I came across was an issue of direct, reliable access to LinkedIn's data for being able to get public LinkedIn posts. It turns out when

**Third-Party Data Sources**
(27:25) I went to explore what this other app was doing differently. It turns out it was using a third-party data source, and ChatGPT Codex found the calls to this specific data source that the other company was subscribing to. So, being able to pay for that data made that app work. I was like, okay, onto one of my next projects, and I'll see if I can

**Failed Guitar Solo Project**
(27:54) either launch or break that one. It's a great question. Is there anything else where anyone got stuck or wasn't able to take something all the way? >> I tried something not for good, just for egos. I use music AI a lot to help me do stuff, and I wanted something where I could say, 'Here are the chords or here's the

**Academic Sounding Solos**
(28:20) music. Give me a guitar solo that sounds like a guitar solo.' >> Mhm. >> I vibe coded the hell out of it. The challenge, and I gave up after this, is that there are sources of harmonic structures that people, very academic sources, say these

**Data Access for Music AI**
(28:44) notes work with these chords. It provided me with really academic sounding stuff that was horrible, and it became much easier to just go to Sunno and strip out the rest of the music and have it play the guitar solo for me. It was really access to data more than anything else that was the

**Successful Mood Board Tool**
(29:04) problem, the same way you were talking about. On the other hand, I did something where I read that there was a mood board thing that one of the big publishers had come out with. I thought that sounds like it would be fairly easy, and it took about a half an hour of talking to one of the AI engines to code something. It's

**Usable JavaScript Tool**
(29:26) all in JavaScript and HTML. It saves stuff. It doesn't make any calls to anywhere else. It works well. I kept saying, 'Hey, could we save this? Hey, could I pick it up later? Hey, could we do this? Hey, could we do that?' It's a very usable tool. I posted that in the AIMG Slack somewhere. Anybody who wants to download it is welcome to tear

**InnerVoice and Interactive Journaling**
(29:48) it apart. >> Awesome. Feel free to post it in the chat too if you want. It reminds me of another app I was playing with that's more of a consumer app. You can see it here at innervoice.me, because I'm a big fan of the interactive journaling app Rosebud. I'm still using the official app, not my own in this case. So make of

**Exciting and Scary Aspects of AI**
(30:13) that what you will. But I was like, this to me is also one of the exciting and maybe scary things, because we have Kevin in the chat talking about what some of the 'moats' are, where I'm like, when I'm building these things, I'm not just saying I want to do exactly what this other thing does. It's like, 'Oh, this tool is useful

**Customizing Tools for Workflow Needs**
(30:37) for my workflow. If I could do X, Y, and Z, or the Y and Z on top of the X here, that would really hit it out of the park.' Maybe there are other people with those needs, too. In this case with InnerVoice, I was playing with Rosebud, and I love this interactive journaling app. It's actually really impressive

**Functional Explorations**
(31:00) for that. Are there other ways to learn about psychological modalities in the process? So, yeah, this exploration, I don't even know how good this thing is yet, but it's functional, which is further than I got for other things. Then, I'm playing with theological

**Exploring for the Heck of It**
(31:30) philosophical treatises and stuff. There are all kinds of weird things I can just explore for the heck of it, and if it doesn't live to see the light of day, or if it doesn't come up until 6 months from now, and I'm talking with someone about it, it's like, 'Oh, I actually built something like this. Want to give it a whirl?' This is the fun thing for me right

**Data Access as a Shared Challenge**
(31:55) now. I love it, Mark. It's also interesting having that shared challenge. So, what are some of those 'moats'? Access to data has been a big one for a long time. Mark, you know, I was deep in the social media marketing space for a while and even went over to one of those apps, Sisimos, for social

**Lack of Innovation in Social Listening**
(32:20) listening. I'm not saying this about Sysimos or any particular company, but in general, there was so little innovation in that whole space, and there were so many great early-stage startups that failed miserably in that space because you had to pay for Twitter's firehose. You had to pay for real access to Instagram.

**Ethical and Legal Use of Data**
(32:49) Those costs were astronomical for a startup. >> The other thing is that it's possible to find data. But we also have to think in terms of the ethical use of data, and the legal use of data that's fairly easy to scrape. But you may be running into PII from some of the sources, and then you're getting into all of the

**The Scary Side of Site Cloning**
(33:13) regulatory nightmare that we're living in. >> Well, and this also, when I was talking to the investment bank and I showed them how quickly I cloned the site, the obviously scary thing is how I could do this for anyone, and anyone could do this for your site right now. There's literally no way to stop that that I

**The Ease of Malicious Cloning**
(33:38) know of. There wasn't exactly a way 20 years ago to stop that from happening, except now someone can do this while they're having a cup of coffee. Yeah, anyone could have scraped some source code and done that before the ease of doing so, and then registering a domain that is off by one letter or includes a hyphen, and then creating email addresses on top of that.

**Gift Card Scams & Vibe Coding for Bad**
(34:04) and this is a group that I know has been hit by those crazy gift card scams. You get a text, and they see you're working with someone. 'Oh, yeah. I'm stuck in a meeting. Can you buy me a gift card?' 'Yeah. This is Jane from Banking Resource.' 'Go do this and buy a $50 gift card to'

**Vibe Coding for Good vs. Bad**
(34:34) Pizza Hut at Walgreens or something. And send me the code.' It's like, geez, that's scary, right? So, there's vibe coding for good, and then there's vibe coding for basic ethics. How you prevent vibe coding for bad is maybe above my pay grade. Other thoughts, projects you're building, like

**Mutual Learning Session**
(35:04) I feel like another time we can go back to more of the Vibe Coding 101, where we actually vibe code stuff on the fly. I feel this is better as a mutual learning session on what else could be done with this. But who else has learned anything from their vibe coding journey or attempt to do so? Randall, please, you could be running five, ten

**Personal Learning in Vibe Coding**
(35:28) of these a day anyway. So please don't hesitate. >> Yeah, I'll talk about something I've learned, and it's not about Vibe Coding, more about me and how I think about things. I've realized that where some of my particular strengths are, they don't necessarily lend themselves to how I think about my coding and some of these different tools. And

**Leveraging Strengths & Mitigating Weaknesses**
(35:51) so it's been interesting for me to find ways to leverage those things I'm good at, while acknowledging and maybe mitigating through finding other people or folks to help with those areas I'm less good at. My background is product and technology, and I tend to go very deep, very quickly, to understand the mechanics of things

**Avoiding Rabbit Holes in Prompting**
(36:15) without actually skimming across the surface and understanding the surface of things, the shape of things. What I found is that I can go in different rabbit holes very quickly, which is not necessarily as valuable as being very surface and layperson in my language and prompting. It's been a very

**Modifying Communication Style for AI**
(36:38) interesting observation for me in terms of how I work, think, and communicate, and how these tools respond to that, and what they're able to do versus what I see other people able to do. If you want to continue that question, for people who have tried different things, what have you found in terms of modifying your communication style or how you think

**Claude for Landing Page Research**
(37:00) about whatever you're working with? What have you changed to get better results from how you think about things? >> This is Brad. One of the things I did when I did my first landing page was, I asked Claude to do research on what typical landing pages are for that type of business, and I had it come up with a whole report and

**Claude Code's Plan Phase**
(37:20) what I did was I fed that into Claude Code. I started out not knowing what to do on this type of landing page, but having it research it for me through that report it creates, I fed that within Claude Code and used that information to create the page for me. One of the things I also found out is, I don't know if this is new or

**Conserving Credits with Planning**
(37:43) not, but Claude Code has a 'plan phase.' So instead of using your credits to build the page right away, you can switch it from plan to build so that as you're planning it out, it doesn't build it in the background. It keeps coming up with ideas and does the plan so you don't waste those credits. When you're

**The Value of the Plan Function**
(38:05) ready and it's thought through the process and saw something made sense, then you can trigger it to build from that. >> Yeah. I don't know where the raise hand button is, so I just gesture. But on that note, I've been toying around with a couple of nerdy projects in Claude Code, and I've used the plan function, and

**AI Choices and Feedback**
(38:26) I will say it is fantastic. It gives you the ability to put your stuff out there. Claude Code comes back with, 'Here are the things I'm going to do. This is how I plan to execute it.' And then it will often give you choices. So, it's like, 'Here's option A, here's option B, here's something else, or tell me what you think is different and what you think is wrong about this.' And

**Avoiding AI Tangents**
(38:50) to your point, Brad, it stops you from wasting those credits. I think your point about having either Claude or something else do research and come up with a plan and feeding that into Claude Code is great, because I found with any AI, if you just let it go, it can go off on a tangent that is great for somebody else but not what you want. So, second all of

**Data Science for Social Impact**
(39:17) that. I'm going to share something I saw on LinkedIn. There was a woman who lives in England. Her brother was coming for a pub crawl. She's got a PhD in data science, so she's not necessarily vibing with all of this stuff. But she turned that into the disappearance of local pubs. I believe she might have vibe coded the

**Empowering Good Ideas with AI**
(39:44) data visualization, or maybe she knew how to do it herself. Either way, the exciting thing I took away from reading this is, I can have a crazy idea about what is the good thing, what's something I can do for good that I otherwise wouldn't have been able to implement, and because of these tools, have the ability. >> And Zach, I appreciate your point in

**Top-Down vs. User-Driven Good**
(40:03) the chat. Is it up for users to go and use Vibe Coding for good? Because it's probably not coming from the top down from these major LLMs. I wouldn't be surprised at some point if there's just the way Google and others have funded nonprofits and various causes with certain kinds of ad credits that they're

**Collective Benefit of Vibe Coding**
(40:38) wind up being more like these credit-based systems, and if nothing more than just to make it look like they're doing some good in the world, papering some other things over. Maybe certain companies will come from a good place. But yeah, this is where how do we collectively

**Easy Access to Voting Resources**
(41:09) think about how to use this in ways that benefit others? Some of it could be as simple as putting together resources, just like you, making it easier for people to find voting resources when some localities aren't making that as easy as they possibly

**Greater Good of Voter Participation**
(41:37) could, and benefits people across the spectrum, not just an individual candidate's campaign. Yeah, all the stuff, I imagine your candidate, any candidate you'd work with, wants as many people to vote as possible, and that's very much the greater good when it comes to something like that.

**Awaken the Wonder: Knowledge Centers**
(42:05) >> Just want to add one thing. >> Go ahead. >> The slogan 'awaken the wonder' comes from this project to have a media center. Especially when it comes to voting and politics, how do you get people out of their mentality or challenge what they're thinking? By creating a knowledge source that may question or have a vetted

**Forums for Constituent Issues**
(42:34) research knowledge base to answer questions so it may challenge their preconceived notions. >> Mhm. >> Yeah. And to your point, David, the idea is, again, this is not for a particular candidate, but I know that California does this a lot where they have bills that they put

**Addressing Local Challenges with Vibe Coding**
(42:54) out to their constituents, but having a forum where voters and supporters can come in and bring up their issues that are targeting people. For example, in the Northeast, snow plowing will be a big one. So I think maybe it'd be a good opportunity to vibe code a site where people can come in and bring

**Candidate Focus and Voter Concerns**
(43:19) up some of their issues. That way, candidates from all backgrounds can say, 'These are the issues we want to focus on, and what are the things we can do and policies we can enact that can help remediate some of the challenges their voters are facing?' One of the ways you can find out if you have a forum is the questions they ask are the issues they are

**Missing Channels for Constituent Dialogue**
(43:41) concerned with. So it creates a way for you to understand where people are coming from by the type of questions they ask, and then you can respond with content. >> Right. Exactly. Other than that, you're going to have social media, whether it's on X or Facebook. But one thing I haven't seen, whether in the United States or back here in the

**Structured Information for LLMs**
(44:07) territory, that there's not a channel where people can address their issues that people in office can respond to, which is surprising to me. >> And by the way, that also can lead to a lot of structured information, which, of course, is great fodder for feeding LLMs. One thing I also can't recommend highly enough when using any of these vibe coding tools is, first,

**SEO and Geo AI Optimization**
(44:35) of all, start asking them, 'How can I make this site or app more SEO friendly or more geo AI optimization friendly,' especially if it's a website or web app. It often has many useful suggestions built in. Sometimes I will feed it a good article I've read about AI optimization and then say, 'Hey, Base 44, Codex, whatever I'm using, can you take'

**Google Search Console & Sitemaps**
(45:04) some of these best practices and make recommendations for what to do. You'll see things like these highlight boxes on top of most pages on my own site that are designed entirely for AI crawlers based on the best practices I've been reading about. The other thing that is super old school, that was one of the last steps along the way that I learned because I

**Indexing Pages for Discoverability**
(45:27) was like, 'Wait a second. Something still doesn't make sense here. How the heck is Google, let alone ChatGPT or anything, going to discover some of these properties I'm working on that I want to be discovered?' Base 44 is like, 'Oh, you need to take a sitemap and put it into Google Search Console.' And now

**Old School SEO Basics**
(45:50) you can authorize crawls of it. You can index your pages. It can tell you if and why any pages are not getting indexed. Some of this really old-school stuff, for folks like myself who've often just had other people doing a lot of this stuff for them, one of these basics, like, okay, yeah, these apps won't

**Manual Sitemap Creation**
(46:14) always instinctively create sitemaps and readme files and other information that's useful for these crawlers. So then, going in there and asking it for it, and then taking that other step until at some point this is done automatically by them, linking that with search console and being able to give your site at least a shot at being seen

**The "What Else?" Question**
(46:42) out there. It's just some of the basics become really important. So those questions of how to make this more SEO friendly, and also my favorite question to almost always ask with AI whenever I hit a stopping point on a project, is, 'What else should I be doing here?' Then seeing, okay, of those five ideas, I want to do one and

**Utilizing Excess Credits**
(47:13) four, go for it. Let's see where this goes. With a lot of that vibe coding, there are often great suggestions as far as how to take things further. If you wind up in a situation where you have, on some of these things, maybe more credits than you know what to do with, I've mentioned in the chat, the ChatGPT, I'm on there all the time. I've yet to

**Wrapping Up the Discussion**
(47:35) get a credit warning from them. I don't know why, because I'm only on ChatGPT's $20 a month plan, and I assure you, ChatGPT does not know I'm running forums like this. But if you have credits to burn, burn them. I'm going to wrap this up because we are at the hour here. Really fun discussion. I appreciate so many folks sharing what you've learned,

**Thanks to Subject Matter Experts**
(48:02) what you do differently. Big shout out to Randall, who gave me a primer on Claude Code, even if I'm not quite using it to the potential I could be. But I've learned a lot more about how to use other tools as well that I'm a bit more comfortable with. But we have incredible subject matter experts here, and Karen and

**Join the Slack & Upcoming Sessions**
(48:27) Earl, and all these incredible builders here, tinkerers like Mark. This is a lot of fun. Keep discussions going in the Slack. If for any reason anyone's on this call that isn't in the Slack, I put the direct click-to-join link so you don't have to register or anything special, right there in the chat. We'll be back next

**Future Events & Community Input**
(48:50) week. We got Nate Elliott from EMarketer. We'll take a week off from Architecture Live after that. If anyone's going in person, let me know. It'll be great to see you there. Then we've got an amazing schedule of guest speakers. Keep ideas coming my way and things you want to see us talk about and do through AMG. It's great to get to riff on

**Learning from the Community**
(49:12) things with you. I love these more open-ended community conversations because you're all doing such cool stuff, and I love learning from you. Have a great rest of your week, and we'll see you soon.

## AI Transparency in Advertising What Gen Z Executives and Regulators Get Wrong

Speaker: Caroline Giegerich
Published: 2026-02-20
Tags: ai in marketing, ad tech, ai trasparency
Video: https://www.youtube.com/watch?v=YJtFMopIQr0
Page: https://aimarketersguild.org/sessions/ai-transparency-in-advertising-what-gen-z-executives-and-regulators-get-wrong

**Introduction to AI Insiders & Sam Khoury**
(0:05) Hello everyone, I'm David Berkowitz, welcome to another edition of AI Insiders by AI Marketers Guild from Marchitecture. We've an incredible session today with our IAB friends coming back for another round and Caroline Gilbert assembling another all-star session. Before I introduce one of my Marchitecture friends and colleagues, Sam Khoury, to share

**Sam Cury on Marchitecture's Upcoming Conference**
(0:31) a couple things about what's coming from Marchitecture next month. Sam, take it away. Thank you, David. Hi everyone, my name is Sam. I'm the chief strategy officer of Marchitecture. Focused on diverse content at the conference, heavily focused on its distribution and scale.

**Conference Details & Target Audience**
(0:53) distribute and scale it. To give you background on my focus, I wanted to present a couple of things today regarding the conference coming up on March 10th and 11th. This is a sales pitch, but take it as it is. Since everyone here is heavily focused on AI,

**AI's Impact on Marketing & Technology**
(1:14) we all in this room or on this call understand its impact on us as marketers, employees, technology providers. We know it will play a big role, and many of us are using it regularly, if not daily. We're addressing many concerns at the conference on March 10th, primarily how it plays a role in technology

**Conference Topics & Free Access**
(1:38) evolution and creation. Focusing on its impacts on publishing and what it means for better data utilization. I wanted to let you know it's happening March 10th and 11th. I'd love all of you to be there. Quick note, it's complimentary if you are a brand or an agency. David has some

**Discount Codes & Keynote Speakers**
(2:04) amazing codes he can provide to the community to discount it further. >> Great to have you here. >> Salesy, David. >> No, no, no. It's a great heads-up. I'll quickly ask you the direct question: Who are a couple of

**Notable Speakers at the Conference**
(2:25) the speakers you're most looking for? We have the commissioner of the FTC speaking. That's really interesting because it's not every day he sits on a stage and talks about policy and changes. That's huge. We have all the holding companies speaking as of right now. Every single holding company has representation at the conference. We

**Top Speaker Picks**
(2:49) have the chief marketing officer of Molson Coors, the CEO of People. For me, FTC is number one. The CMO of the NFL is probably number two, especially with the Super Bowl just ending last week. >> My number three is I really like Lipman. I don't know if you know him, David, but he's really

**Andrew Lipman's Unfiltered Session**
(3:11) unfiltered. I like that content. He has a session called 'Oversold, Overhyped, Overrated.' >> Okay. >> He's not holding back. We've seen what he talks about. There's no BS with this guy, and I love it. This is Andrew Lipman. >> Andrew Lipman. Yes.

**Diverse Conference Content**
(3:31) >> He's great. >> Fantastic. Every time he speaks I'm like, 'Oh my gosh, he said everything I've been thinking. Thank you.' >> I think overall it's pretty diverse. So if you're from an agency, brand, or publisher, it'll be valuable. This is very different than our last two conferences, which were

**Scaling the Conference**
(3:49) very ad tech heavy. >> I think we maxed out on that. We had 450 people, sold out a month in advance. This is a thousand people, two days. We've partnered with Ad Week for content tracks. We've partnered with TV Rev. >> Variety is also a partner.

**Broadening Reach Beyond AdTech**
(4:10) you'll be seeing an email from Variety coming out probably in the next two days. Generally, the content is diverse and intentional. The adtech core is there, but we want to reach beyond it and address everyone. >> Awesome. This is great. I'll add that the other thing I'm really

**Looking Forward to Catering & AI Ad Disclosure**
(4:32) looking forward to is Russ and Daughter's catering. >> Yes. >> We can get into all the details, but I don't want to short-change our panel because we have a lot to talk about regarding disclosure and AI ads, including one of my favorite newsletter writers on the subject, Debbie Williamson.

**Transition to IAB Panel**
(4:54) I'll head over now. Thank you, Sam. This is awesome. Glad to give people a taste of what's going on beyond our AMG walls and what's part of the bigger picture here. Hope to see many of you from this chat in person there. Caroline, who do we have then? We have IAB coming back. A reminder for everyone

**Community Conversation & Caroline Gigerick**
(5:19) or for those joining us for the first time, this is a community conversation. Everyone's expecting great questions and participation. I get to sit back and listen today as Caroline Gri brought together incredible thinkers and doers across the industry to talk about a really important issue. Caroline, the floor is

**Caroline Gigerick's Introduction & Call for Interaction**
(5:40) all yours. >> First of all, thank you, David, as always, for having us. It's great to be in these interactive conversations. As you just said, if you want to put comments or questions in chat, we'll try to go directly to them in thread, off mute, or whatever you are comfortable with. My name is Caroline

**Role of IAB in Advertising**
(6:01) Gigerick. I'm VP AI at IAB, the Interactive Advertising Bureau. If you're unfamiliar, I call it Switzerland among the advertising industry. We sit between publishers, agencies, brands, tech platforms, and we coordinate between many different goals and constituencies. I think we do a fantastic job. I'm going to bring up to the very

**AI Transparency & Disclosure Research**
(6:28) beginning a conversation where we show you a few slides from research with the talented Debbie Ao Williamson. She is founder and chief analyst of Sonata Insights, done in collaboration with IAB. This is on the topic of AI transparency and disclosure. We'll share a few insights, then show you a few insights from the framework we then

**Framework for AI Disclosure**
(6:53) cultivated to answer the questions of why, when, how, and who is responsible for disclosing AI use in advertising. Debbie, I'll ask you to unmute, and I'll share this presentation and let you take over. >> Sounds good. Thank you so much. I see a lot of familiar faces. I actually spoke, David, at one of the events probably

**Debbie Williamson's Background**
(7:22) almost two years ago. >> You were early on. >> Great. >> You and I worked together back in the day at eMarketer. My background is I was an analyst at eMarketer for 19 years, and now I run my own independent research firm focused on AI

**The AI Ad Gap Study**
(7:39) and consumer behavior. This study looked at the gap, which we call the AI ad gap. This is the second study. We did one in late 2024. We just published new results in early 2026 from a study we ran late last year among Gen Z and millennial consumers. We also included a comparison study among advertising executives with media budgets ranging from 1 million up to

**Study Premise: AI Exposure & Consumer Feelings**
(8:05) above 1 billion. Caroline, please move to the next slide. A few things to set the stage: We went into this study thinking that with more exposure to AI-generated advertising and more awareness of AI use, younger consumers, Gen Z and millennial consumers, would feel more positively

**Widening Perception Gap on AI Ads**
(8:30) more negative towards the concept of AI-generated advertising, and the gap between how ad executives thought they felt versus how young consumers actually felt widened. That sets the tone for why what you did, Caroline, with the framework is so important, because these people are feeling strongly about AI and advertising, and closing that gap is

**Gen Z's Negative Perception of AI Ads**
(8:57) important. What's interesting, if we can move to the next slide, is that even the youngest consumers are feeling more negative and less likely to feel positive, using negative attributes to describe companies that use AI to generate advertising, terms like inauthentic, disconnected, or unethical. We see younger Gen Z consumers

**Gen Z vs. Millennial AI Perception**
(9:20) more likely to choose those terms. We also see millennials, on the other hand, more likely to feel positive. We probably discussed and unpacked that more in the presentation, but it's something to note that these younger consumers, who are very likely to use generative AI in their daily lives, are feeling more

**Importance of AI Usage Disclosure**
(9:38) negative towards it when an advertiser uses it. One important thing we dug into in the study is the idea of disclosing AI usage. There's much to unpack, which we'll discuss with the panel, Graham and Ken, in a few minutes. Leaving you with a few data points, one thing that

**Disclosure Drives Ad Engagement**
(9:59) we found is that disclosing AI usage can drive engagement for advertising. When we asked what would cause people to pay attention to generative AI ads, high quality makes sense. Funny ads are always interesting, no matter where you see them. Think about all the Super Bowl ads you might remember. Most of them

**Disclosure as a Trust Builder**
(10:22) are probably funny. But then this idea of disclosure ranked number three. This is something we see young people saying: maybe we feel more positively about it if we know we're not being fooled or the wool isn't being pulled over our eyes. If we

**Disclosure Boosts Purchase Likelihood**
(10:43) move on to the next slide, this is the money slide literally, because we found that if an ad generated with AI had a disclosure, for 72% of respondents it had no impact or increased their likelihood to purchase from that brand. Only about a quarter said it would have the impact of being less likely

**More Upside with Disclosure**
(11:08) to consider purchasing the brand. Overall, we see more upside than downside when advertising to Gen Z and millennial audiences with disclosure. >> I'm going to ask you one question, Debbie, from Tamika. Thank you for this, Tamika. Are there plans to survey Gen Xers or even older users to find out their POVs on disclosure?

**Future Research on Older Generations**
(11:29) Yes, that would be great. We focused on this audience because we knew these were people more likely to use generative AI in their daily lives; we see that in consumer surveys. I thought this would be the best audience to survey

**Value of Full Population Study**
(11:47) because these are people already very familiar with the concept of generative AI. But, Tamika, you are absolutely right that having a full population study would be super interesting. So, Caroline, let's consider that for later this year. >> Absolutely, and Michael, we'll get to that question during the panel discussion. Debbie, moving

**Key Takeaways: Widening Gap**
(12:06) to the key takeaways. >> Quickly, the key takeaways: The perception gap is widening. That's something we need to close. We didn't discuss this in the data slides, but it's in the full study available on the IAB site. One thing advertisers are doing that might

**Efficiency Over Creative Quality**
(12:23) be contributing to this is prioritizing efficiency. When we asked advertisers why they use AI in their creative process, they ranked efficiency and cost savings above creative quality. In my opinion, AI should be used to make ads better and more appealing to consumers, improving your creative, and that should be more

**Transparency and Consumer Perception**
(12:43) important, and I think it would help consumer perception as well. Finally, transparency, which is what we're going to talk about for the rest of the time here, transparency around AI, what it means, and how it can potentially help consumer perception. >> Thank you so much, Debbie. I'm going to

**AI Transparency Framework Overview**
(13:02) take the baton and show you a little preview, if you will, on the AI transparency and disclosure framework. Alyssa, I saw you somewhere in the crowd. If you would put links in the chat to both the research and the framework, I would be so grateful. I'm going to start with the opportunity and the risk, lifting out of what Debbie

**Opportunity for AI: Adoption & Cost Reduction**
(13:29) was just talking about with the research. On the one hand, the opportunity for AI is quite high. There's been exponential adoption; we're here talking about it, and David does these AI Insiders every week, so I don't think we ever run out of things to discuss. Reduced production costs are real, especially for public companies who are

**AI Benefits: Personalization & Localization**
(13:51) constantly thinking about efficiencies. That pressure is high. Large-scale personalization. We at the IAB are even talking about all the benefits in terms of ROI upside you can get from personalization and rapid localization. On the risk side, we've got the trust gap Debbie just talked about, anything from

**Risks: Trust Gap & Label Fatigue**
(14:15) inauthentic, 'I don't really like it,' to 'this is creative, cool,' but as she just mentioned, that's a wide gap. Label fatigue: if we label everything AI, then all of us will just tune it out. We don't want to go there. Fragmented regulation, and I'm sure this comes up in this group to some degree, but we've got the federal

**Fragmented Regulation & Consumer Confusion**
(14:40) government that's somewhat light on AI regulation, and then we have a very state-level approach. We also have platforms such as TikTok and Instagram, who have their own labeling mechanisms. In the middle of all of this, with this last bullet, is the consumer, who's like, 'What? What is it? Stars? Does that mean AI? Is it this text here? What's going on?' Just like in a pinball machine, if

**Focus on Consumer Deception**
(15:04) you will. Moving on to our next slide, what we chose to focus on in our framework was this idea of consumer deception. If we're going up with a threshold for when we think something should be labeled, what is that threshold? It's: do we think that without this label, this consumer will be deceived? The example I always give, and I'm so sorry to everyone who's heard

**Deception Example: Synthetic Influencer**
(15:29) me use this over and over again, but it's my favorite. If I'm on TikTok and an ad comes up for some skin cream—I buy too many of them—and I think, 'Cool, it's going to solve all my wrinkle concerns.' I buy it. I use it for the recommended three weeks. Nothing happens. Then I find out it was a synthetic influencer this whole time.

**High-Risk AI Use Cases**
(15:51) Wasn't labeled. I thought it was a real person with perfect skin. That would be, in our view, deception, and we would want it labeled. This is for the record, underneath all the advertising industry regulation that already exists. This is just an example; there's more in the framework. On the left side, I'm going to give you an example: What do we think of as high-risk things

**Disclosure Not Needed for Routine AI**
(16:16) like synthetic humans, for instance? Prompt-to-video and image generation is something we talked about a lot; we can get into it in the panel. Voice clones, digital twins of deceased persons. We can get much more into all of this because these are just words on slides. However, we also have examples of AI in which we don't think there's deception, and therefore, no disclosure is needed. Things like routine

**Low-Risk AI Examples (No Disclosure)**
(16:42) retouching, color grading, upscaling, things like it's obviously stylized. If we were all in cartoon form here today, you would realize something had been done to all of us, unless you think the world is a cartoon world. I want to join you there because that sounds lovely. And generic AI background music. I'm going to tuck this away and bring up the

**Introducing Panelists**
(17:05) esteemed panelists to the virtual stage to get into this discussion now that you've seen what we're about to talk about. First, I want to introduce Ken Fischer, editor-in-chief of Ars Technica. If you don't know about Ars Technica, how? Because we're all lovers of technology. Secondly, Graham Wilkinson, EVP, chief innovation officer

**Graham Wilkinson & Super Bowl Ads**
(17:28) and global head of AI at Acxiom. I wanted to call him Crayon because he told us people mistake the way he says Graham for Crayon. But I won't call you Crayon, that would be disrespectful. Welcome to all of you. If you would unmute so I can officially welcome you to the virtual stage. We just came out of the

**When Does AI in Advertising Matter?**
(17:52) Super Bowl, and we saw multiple ads that had AI or were touting AI capabilities—Anthropic's ad, OpenAI. Let's start with the fundamental question: When does AI use in advertising actually matter, and when is it just white noise? >> First, thanks for inviting me. It's cool to be here and see this side of the

**Ken Fischer on AI Disclosure Meaning**
(18:18) business. I spend most of my life in the editorial world. It's a great question. I feel that, coming from an editorial background, my sensitivities change, but I think disclosure really matters when AI is changing the meaning of something. As we discussed, as you showed in that slide, it's just white noise when you're

**Transparency for Changed Meaning**
(18:42) talking about AI as a production or efficiency tool. But when you start doing things that change the meaning or create a perception that might not be real, it's always safest to be transparent about that. Consumers expect that now. Whether they do in five years is a totally different question, but today it's clear that users want to

**Trust Threshold & Perspective**
(19:04) see that if there's a material change. >> For me, I agree with Ken, and Caroline, your analogy sums it up well. I think it's a matter of the threshold for trust, and that's a matter of perspective too. That's why I suppose the

**Generational Trust Differences**
(19:33) research from Debbie is important: looking at that particular generation, but also looking at other generations. An older generation might say they are more trusting because they've grown up being bought into TikTok videos that may already be heavily touched up and are nowhere near reality. And

**Ad Aim & Trust Breakdown**
(20:00) therefore their threshold for trust is lower, in a relative sense, to somebody else. It all comes down to who the ad is aimed at and our understanding of trust in that, and obviously once it breaks that trust. It could be a celebrity they entirely trust.

**Defining Trust Across Generations**
(20:28) was authorized to use their voice, their image, whatever it was. For somebody else, that might not be the breaking point. So, defining and understanding what trust means to different generations and perspectives is a key part of it, and it's a moving target.

**Gen Z & AI Videos: Knowing It's AI**
(20:50) >> I would add, I have two Gen Z daughters. They go on social media and watch AI videos a lot. They love them, share them with me, saying, 'Oh my god, this is so cool! Look at this!' They buy into all the trends, but they know they're AI. I think that's where the challenge is and where the

**Trust vs. Deception in AI Use**
(21:08) difference is: if they're not being fooled, if they're bought into it, if they're part of a conversation, if they get it, if AI is used in a way they think, 'Oh, okay, that's cool,' they love it. But if they're being fooled, or if it looks fake or like 'slop,' that's where my daughters get really

**Honesty and Openness with AI**
(21:28) negative toward it. For me, it comes down to trust, but also being honest and open, bringing your audience in on the joke or game, whatever you're doing with AI. That's what I take away. >> That makes a lot of sense.

**The Movable Line of Perception**
(21:48) also picking up on something you said, Graham. Graham and Ken were part of the working groups for this framework for months. We talked about this for months because there are so many edge cases, and what Graham just talked about in terms of perception—there's going to be a movable line of perception—also how we all on this

**Synthetic Humans in Testimonials**
(22:11) call think about AI is going to change over time. We recognize that all of what we put here may shift in six months. I also wanted to pick up on some questions from the chat. Derek asks, 'Why would I use synthetic humans in a testimonial?' I see Miesa's response is interesting: it converts better. Sometimes, an example would

**Digital Twins for Celebrities**
(22:37) be, we've all seen Jennifer Aniston in the Smartwater commercial. Let's say Jennifer Aniston is in Bali, having the time of her life, but isn't able to come to the set in Chicago. What if she could send her digital twin? It's completely authorized. It's much cheaper than flying her from Indonesia to Chicago. That would be an example of it.

**AI Changing Meaning in Ads**
(22:58) would be a digital twin of her, completely synthetic. Maybe it's just the look and feel of the person that you wanted and how fast you wanted it. I also want to ask this question to Ken. Marshall asks, 'Ken, can you give us an example or examples of AI changing the meaning of what is being conveyed or advertised?' >> Sure. The example you

**Synthetic Influencers & Simulated Authority**
(23:21) gave, Caroline, very early at the outset, a synthetic influencer. I think that's a great example where if you're creating a person who is reaping the benefits of a given product, but that person isn't real, I think that's a good example of an area that is 'icky.' I think you can also do simulated authority. It's adjacent to how I think about

**Financial Industry & Fake Authority**
(23:46) influencers. If you watch an ad and the guy's saying '9 out of 10 dentists say whatever,' it'll say 'actor portrayal' or whatever. We're always very careful about that. So what if a financial industry wants to run an ad that has, say, the new digital EF Hutton? Remember those old EF Hutton ads? You

**Faking Product Realities**
(24:09) create the new digital EF Hutton. That would be an attempt to create what is essentially a highly credentialed but fake representation of authority. Another thing I think of is product realities being faked. For instance, if you show someone suddenly dunking when they couldn't before, or these

**Augmenting Human Capability**
(24:37) kinds of things. You can augment human capability, which also means their reaction to products in a way that's not realistic. I want to circle back. I think it's very interesting that Debbie's research is showing this gap because you have people who want really creative and interesting ads, and then you have people who are

**Trust is Dangerous Territory**
(24:59) hyperfocused on the trust aspect. Between those two possible trajectories, the trust aspect is the more dangerous territory right now. That's why I think we want to advise advertisers to stay away from those areas if there's any way it's going to come off as,

**AI as a Cost-Saving Tool**
(25:23) essentially faking it. >> Yes. >> There's real sensitivity around faking things. I was also very interested to see the advertising industry realizing some people are aware that AI is just a way to save money. On the journalism side, I can tell you that's the

**Consumer Perception: Cheapening Content**
(25:47) consumer perception everywhere, and why they hate it. They feel like you're cheapening this, making it less real, because all you care about is money. >> That sounds like a quality concern. If you're cheapening or reducing the money, are you reducing the quality of the content? Is that what I'm hearing?

**Upset by Cost-Saving Over Quality**
(26:08) >> No. On the edit side, even if the quality was just as good, I would say people would be very upset at the idea that we're using a tool simply to save money when there were traditional ways of doing it that, in their view, are just as good. >> Interesting. Can I ask a follow-up question? We talked about this in the prep call, but I

**Niche Audience vs. Broader Audience**
(26:29) think of your readers as a very niche audience because it's Ars Technica, emerging technology aficionados. Do you think that's representative of the broader audience, or is that a signal from your specific audience? >> It's very interesting because it seems to be in pockets of community. For instance, at Ars

**Community Pockets & AI Perception**
(26:55) Technica, broadly, most readers are incredibly suspicious of AI. But there are other brands in the building, not necessarily demographically different, whose readers embrace it. They're like, 'Oh, this is great! I want to see more AI stuff!' What we have learned is that you can't guess this. If

**Online Communities & Creativity**
(27:18) somebody had said, 'A GQ reader is going to think this about AI,' you'll be wrong. You'll be wrong when you collect the data because these pockets of communities, I think, take creativity differently, and they get something different from creativity. And

**AI and Creativity: Be Safe, Not Sorry**
(27:40) so, as much as these tools are touching on creativity, that's where I think you get different levels of response. To me, it's a minefield, and that's why I always say, 'Be safe, not sorry.' >> I also want to add that >> what Ken brought up about sensitivity is important. I also think people have been desensitized as well.

**Desensitization to Fake Content**
(28:07) There's kind of equal and opposite. There are groups of people that think it's okay for things to be entirely fake as long as it's funny and maybe they're not the butt of the joke. But the consumer doesn't always have to be the person being deceived. Think of a travel ad where a celebrity is being

**Deception Beyond the Consumer**
(28:32) superimposed on a location, and that location doesn't want to be associated with that celebrity. Maybe they're controversial, and they know that celebrity never even visited there. That's disingenuous. The subject of the deception is not just the consumer; it's the location itself. >> Right. I think that as

**Balance of Notification & Fatigue**
(28:54) much as there are groups who are sensitive, there are groups who are alarmingly desensitized to it. And that's why, again, we talked ad nauseam about the balance of giving people notifications on this type of information and the fatigue associated with it, but also a duty to the industry to stay honest in what we're

**Andrew Cuomo's AI Ad Example**
(29:21) doing. As I said, there's no silver bullet, but it's important to think about all its aspects. >> This also brings up an example we talked about a lot. How many people here are in the New York City area, David? For sure. I know. We had a mayoral race where Andrew Cuomo was one of the candidates, and he did an

**Digital Twin & Deception**
(29:47) ad in which it was his digital twin in a variety of activities he never did in real life. Of course, there was a label at the bottom of the ad. You'd say, 'Okay, that's his digital twin. He endorsed it because it was for his ad.' But wait a second, he's now in a variety of activities he didn't do. Doesn't that seem like deception? Because people could

**Consumer Deception in Political Ads**
(30:16) potentially change their vote if they're like, 'Oh, Andrew Cuomo.' I don't even remember the activities in the ad, so I can't speak to them. But there could be some consumer deception there, and we thought long and hard about that. >> I want to jump in because I watched that ad. I live in Seattle, so I have no interest whatsoever in the mayoral race in New

**AI Woven into Creative Strategy**
(30:37) York. Well, I do. The point I wanted to make was the way the AI was woven into the ad pulled the audience into the idea. He basically said something like, 'These are not things I would actually do, but this is what I can do for New York City.' He used these crazy,

**Labeled or Unlabeled AI?**
(31:02) AI-generated scenarios as part of the ad's creative to demonstrate something else. That, to me, goes back to: why wouldn't you bring your audience along on the idea? >> Let me ask you: Do you think it needs to be unlabeled or labeled? >> Oh, it does need to be

**Political Ads Need Labeling**
(31:23) labeled. When we did the research, we asked what types of industry ad categories should have labeling, and political is one of the top, and I believe that's very true. I would definitely vouch for it. I think it's smart that it was labeled, and the idea of the creative incorporating the AI part of it into the ad's conversation

**Most Surprising Discovery from Research**
(31:45) ad was what made it work for me. >> I'm going to turn back to you because I have another question. From all the research you worked on with us, what was the most surprising discovery? The fact that the gap widened, from the previous research in late '24 to the research in late '25, the fact that the

**Widening Gap Between Execs & Gen Z**
(32:08) gap between ad executives and how they thought younger consumers felt, and how younger consumers actually felt, was probably the biggest surprise for me. I thought with more awareness of AI-generated advertising, we would see more acceptance and positive feelings toward it. I think the Gen Z part was also really interesting: that Gen Z

**Gen Z's Negative View: Wanting AI on Their Terms**
(32:26) is even more negative. For me, I feel it's because they use AI so much and are so attuned to it that they want it on their own terms. They want to be able to use it on their own terms, but also appreciate other people's use of it on their terms, when it's as art, as creativity, as

**Gen Z's Skepticism of Advertising**
(32:52) something they really value. I also think, generally, Gen Z is more skeptical and discerning of advertising messages. We see that in other research about marketing effectiveness, so that could be another factor. >> Oh, I like Tamika's comment. Perhaps it reflects the broader sentiment of Gen Z

**Gen Z Anxiety & Desensitization**
(33:13) being overwhelmed and more anxious because of tech. We did talk about that, especially because entry-level jobs are being heavily disintermediated, and those would be their jobs. I'm going back to something you just said, Graham, about desensitization. That's a big word for me. I'm wondering if maybe when we do this study again, we ask: how do you feel, maybe

**The Awkward Time of AI Maturity**
(33:38) indifferent, because I think when we hit a plateau of the tech, that's when it's invisible, meaning we're not talking about all the AI in the ad anymore. It's just an ad, and we don't care how it was produced. What do you think? >> I think that's the awkward time we're in at the moment. I said this a lot: we're in this

**AI: Model T with No Roads**
(34:03) between where the tech is in everybody's hands, but honestly, it hasn't matured enough from how consumers are using it, how it's embedded in processes and businesses. The Model T has been invented, and there are no roads to drive on. So, people are driving all over the place, modifying their Model T so that it drives better off-road and

**Need for Faster AI Research**
(34:29) instead of focusing on building roads where more people can drive and get places faster. We're just in this weird in-between time. This type of crucial research that Debbie has done needs to be done faster. Debbie, do it faster and more often, because things are changing

**Speed of AI & Obsolescence**
(34:56) so rapidly. Even the window we get into it is already obsolete by the time we look at it. That's the speed of AI, the double-edged sword of this situation. >> I totally agree with that. But one of the things I heard from somebody else at Kai when we

**Loss of Trust is Immediate**
(35:20) were talking about this: AI is fast, but the loss of trust is immediate. >> It's instantaneous. That's one of the things we try to bake into our work. We may have to revisit our governance, revisit these things. It's going to be a cadence, but we should be very sober about it. We should resist,

**Trust Damage is Insane**
(35:47) the shiny new thing and running after it like fools because that trust loss will get you in minutes. It's not a developing thing if you get caught in it. One thing about the speed is I know it creates a lot of anxiety for FOMO, but the other side of that is the damage to trust is insane.

**Nuance Over Extremes**
(36:11) >> I like that a lot. It's the saying, 'A moment on the lips, a lifetime on the hips.' I think that's the learning: I get incredibly nervous around anyone who swings either overtly bullish or bearish on anything, because I think the nuance somewhere in the middle is the place to try to be always. I really like that perspective. Graham, I

**Transparency in AI Personalization**
(36:38) also want to ask you about something from your particular place at Acxiom. You are at this intersection of data and personalization. We talked about personalization quite a bit when we were trying to think about transparency. As you're innovating with AI for audience targeting, personalization, where does transparency fit there?

**Low-Resolution AI Solutions**
(37:03) of my time personally is spent with marketing departments doing this really boring exercise of mapping out agentic flows. The reason is that so many people have jumped into AI and built what I would call low-resolution solutions. There are many problems with low-resolution solutions; one is they go, 'I've

**Inability to Explain AI Processes**
(37:31) built a brief writer,' or 'I want to build a brief writer.' Then you say, 'What does that mean? What does a brief writer do? How many tasks are nested under that?' And people can't explain it. That's the problem, because if you can't explain it, you can't get to what we're talking about, which is supplying metadata associated not just with the output,

**Focusing on Decision Tracing**
(37:53) itself, but the decisions, the reasoning, the reason tracing that sits behind it. It also results in a generalized response. I think a big part of where we're focused is not necessarily, 'Hey, this is how you should use Acxiom data. This is how you should use audiences.' It's more, 'Hey, let's look at your entire

**Recursive Task Atomicism**
(38:22) process and break it down.' We carry out this thing called recursive task atomicism. Essentially, we break every task down to its atomic state. Its atomic state is defined as something that has one persona, uses only one tool or API connection, and its output can be judged by a pass/fail binary response.

**Mitigating Risk & Explaining Outcomes**
(38:49) Now, if you can break a description like a brief writer down into its constituent tasks, and every one of those tasks' output can be judged by a pass/fail output, then it means: one, you're going to mitigate risk associated with its use in the advertising process; two, if you have to explain why something was done, you can trace it back

**Business Process Articulation**
(39:17) very easily. And three, what I'm realizing most is that most businesses cannot articulate the processes they carry out on a day-to-day basis at that level of definition. That is a big problem. But for us, it also means that once we have that map, we can say, 'Hey, these are the points where you can inject data,' and that could

**Data Injection Points**
(39:45) be injected because it's low risk. It could be injected because it's going to have a multiplying effect on what you're doing. Maybe it does something that's not possible for humans to do if they undertake that process. For me, the focus is less on what Acxiom sells, and more on saying to brands, 'Just take

**Deep Dive into AI Solutions**
(40:07) a step back, let me show you that for you to get the ROIs you're under pressure to achieve for efficiency and things like that, you can't do it with these low-definition AI solutions. You have to go deep into it, and you need to. One of the byproducts of this is that it tends to bring to the surface the people

**System Thinking & Organizational Transformation**
(40:35) who are more leaned into system thinking and also highlights people who are more rigidly stuck in their ways—they generally cannot explain very well what they do, but they've done it for a long time. It's part of the organizational transformation too. That is big. It's pretty much where I

**Nuance & System Thinking**
(41:00) spend 99% of my time these days. >> I also think it connects to what Ken was just talking about. Yes, that was about breaking trust, but you're also talking about applying some nuance to how you're thinking about full context needed before optimal AI operating behavior, let's call it, which is the same sort of system thinking. Debbie, I wanted to

**Consumer Clarity on Transparency**
(41:26) turn it back to you because we did not show in the slides earlier the nuance around what the data was telling us in terms of what consumers wanted transparency around and what they didn't care about so much. Could you give us some clarity on that? >> For sure. I am going back up to get the study in front of me so I don't

**Strong Alignment on Disclosure Techniques**
(41:50) misstate... >> Okay. It's 9:45 in the morning here, and I still haven't had enough coffee, so I can't say the words I want to say this morning. When it comes to the types of ad creative techniques people want disclosed, there's a strong alignment

**AI-Generated Content Disclosure**
(42:11) between how the younger consumers in our study felt and how the ad executives felt. The largest percentage of people said, for example, if an ad is 100% AI generated, 57% of advertisers and 58% of consumers said that should be disclosed. Similar for AI-generated images. When it comes to video, there was a bit more discrepancy; 48% of advertisers said

**Video Disclosure Discrepancy**
(42:39) they felt that should be disclosed, but 57% of consumers wanted it disclosed. Almost the exact same percentage of consumers wanted disclosure if the ad was 100% AI-generated. Things less likely to be desired for disclosure were AI-generated copy or AI-generated avatars, which were somewhat less

**Obvious AI Disclosure Scenarios**
(43:04) likely to be desired for disclosure. I feel it's kind of obvious, right? If everything is AI, let's disclose it. If we're using a lot of AI images, yes, let's disclose it. Video is another thing. Those are the top takeaways, but what was really interesting to me again was that advertisers were pretty much like, 'Yeah, we

**Panel Alignment on Disclosure**
(43:25) agree,' similarly to what consumers feel about the disclosure. >> Interesting. I'm glad we're aligned. I also wanted to get deeper into this conversation for both the panelists and all of you. There are a lot of things I see coming through in the chat about the specific nuance. I saw Elena. Hopefully, it's not Elena. Is it Elena? Did I

**AI Photo Enhancements & Photoshop Scrutiny**
(43:51) get it right? I like how you brought up whether there's additional scrutiny around photoshopping, especially with AI photo enhancements. We talked about this because there's photoshopping, and there's Photoshop with enhanced AI. Is there any nuance for the consumer? We chose to think no in this case because if we hadn't been disclosing it

**Paris Hilton Ad Discussion**
(44:16) before, why would we disclose it now? What I wanted to do, because I thought it would be fun, is show us an ad. Plus, I really wanted a reason to bring a Paris Hilton video into this conversation, and the panelists agreed with me. So, I'm going to show you two ads. Does anyone here, by show of emojis or hands, remember the Paris Hilton ad from 2005 or 2006 in

**Carl's Jr. Paris Hilton Ad**
(44:42) which she's eating a Carl's Jr. hamburger on a car and spraying herself with water? Anybody? Derek, Ken, Peter, Michael, Debbie. Okay. Enough people. I'm going to show that one first to remind us all of what we're about to talk about. >> My bad. It's all good. Look.

**Digital Twin in Paris Hilton Ad**
(45:09) Famous Starwatch. What is this place? Good boys. >> Buy one, get one for $1. >> We're going again. >> Okay, a couple things to mention. First of all, the versions of Paris in the bathing suit—they're trying to draw your attention to the fact that that's the digital twin of

**Panel Question: Disclosure Needed?**
(45:50) her because of the blue eyes. That's her digital twin, not real Paris. The real Paris was her in the pink jumpsuit at the end when she comes out to them in the car wash. My question for our panelists, and I'd love to know from everyone, is a global question. Just put your answer into

**Audience Poll on Ad Disclosure**
(46:14) the chat here: Do you think any part of that ad needs to be disclosed as AI usage? Just say yes or no. Nos. Yes. No. I don't know. Marshall, very honest. Honestly, it feels very split here so far. No. Thanks, Nate. I want to get a little deeper into the panel. Ken, let's go to you

**Ken's View: Obvious AI Doesn't Need Disclosure**
(46:45) first. Your answer was no, I believe. >> Yes. I think there are going to be situations where it's obviously not real. I don't say obvious, as saying 'obviously AI' might assume too much on the viewers' awareness of technology trends. But I think even my grandmother would be able to watch that

**Obvious Manipulation Threshold**
(47:12) ad and know, 'Okay, they didn't clone this Paris Hilton person and give them laser eyes.' It's in the same way that we're not requiring cartoon avatars of famous people to be disclosed. I think it passes an obviousness threshold for being manipulated with technology. >> Fair. Graham, what are your thoughts? >> I said yes, and I think two

**Graham's View: Intermingling Real & AI**
(47:41) reasons. One is when you are intermingling real and AI generated, you are kind of inferring or at least giving the impression to people that maybe it's one or the other. I think too, in a world where vanity, appearance, and all these things have become heightened, and at the same time we all

**Unreal Expectations & Vanity**
(48:11) are apparently way more anxious and everything else. It's this idea of, 'Are you setting unreal expectations for people that view this?' Paris Hilton is a lot older these days than she was back then. >> Ouch. Come on. >> No, she's a beautiful lady, but it is

**AI Exacerbating Bad Actors**
(48:36) there is a reality aspect of it. As much as, maybe as a man, you can sit there and go, 'It makes no difference to me, I couldn't care less,' as a young girl, does that make a difference to you? I think it's not necessarily an AI problem. AI is just the tool being used, but there's a problem

**Societal Pressure & Unrealistic Expectations**
(49:00) with perception and how you set expectations that are unrealistic for people. Certainly, in a world where you can log on for 5 minutes and get yourself a GLP1, not necessarily because you're morbidly obese, but you just want to lose a little weight because you feel a ton of pressure from society to look better. Look better being a subjective thing, so I think,

**AI and Bad Actors**
(49:25) it's less that AI can be named as the bad guy in this, but I also think AI will just exacerbate bad actors, which we know already. >> Ken vigorously nodding. >> Yes, Ken agrees with you. I also think it's incredibly interesting because something that came up in my mind on the organic content side of things, and this is because I was at

**ABBA Experience & Voice Clones**
(49:48) Warner Music Group before and thought about this non-stop: the ABBA experience, how they brought them back, obviously as younger versions of themselves, and used voice clones of their younger versions. I would hope everyone would realize that ABBA is not perpetually youthful, but I haven't thought about the larger societal implications like the

**Debbie's View: Yes to Disclosure**
(50:14) Kiss experience; they want to do the exact same thing, but them as a younger band. I haven't actually thought through. Graham, this is why I love you so much because you constantly ping it into new parts of my brain. Debbie, let's get to yours. Was it a yes or a no? >> I was a yes. I think it's because I feel that right now I

**Current Lack of Obvious AI**
(50:35) wouldn't say yes forever, but right now, it may not be obvious to everybody that there's AI. We saw in the research that disclosing didn't have a huge impact on purchase likelihood, and it was one of its drivers of attention, in addition to high-quality visuals and funny content. I would agree this is funny content, and it's

**Upside of Disclosure, True Test of Ad Performance**
(50:59) pretty high-quality visuals. I feel there's not a downside to putting a little disclosure on this ad. There's probably more of an upside. But the true test, I think, is, 'Okay, we're selling hamburgers. Did Carl's Jr. actually sell more hamburgers? How did this buy-one-get-one offer do?' That's the true test of did

**Framework: No Disclosure Needed**
(51:20) this ad perform outside of whether they used AI? I'll come back to our framework, which, just for the record, would have said no disclosure necessary. The reason it would have said no disclosure necessary is because there's no real deception in using a digital twin that Paris Hilton has authorized. And whether people realize multiple Parises, she

**Nuance & Avoiding Label Fatigue**
(51:45) didn't just somehow have a bunch of twins she never disclosed in her lifetime. We would not recommend disclosure there, and we would also say so because we're being nuanced about what we want labeled, as we don't want it to lead to label fatigue. What I think this conversation should elucidate is that this is a constantly moving target of perceptions and trying

**Closing Question: AI Transparency Guidance**
(52:11) to figure out, and it's not an easy discussion. It took us many months to even get to some semblance of what we think for now. I know we're coming up on time, so I will ask a closing question for all the panelists. If you could give everyone in the room one piece of guidance on navigating AI transparency right now, what would your advice be?

**Prioritize Creativity with AI**
(52:37) >> I'll start. I believe that using AI to improve your creativity should be the top reason you do it. If you believe AI will actually be cheaper, yes. But if you go into it with the idea that you're going to be able to produce lots of ads very cheaply, they're going to look that way. I don't think over time consumers are going to react

**Transparency as Long-Term Brand Strategy**
(53:01) positively. Always have that creative north star in your mind when you're using AI. >> I would say, even though I said no with regards to this ad, think about and treat transparency as a long-term brand investment strategy instead of a short-term performance variable for

**Transparency as a Safety Valve**
(53:25) your campaigns. Because of the rate of change and how much the space will change over the next two years, something we think today is no problem could very well be a problem in a year. You never know what scandal might change public perception, etc. So, I think transparency is the safety valve for experimentation with

**Understanding AI Deployment Processes**
(53:50) AI. >> I would harp on my point about better understanding the processes you deploy AI into. Forget consumers or anyone else, but you're deceiving yourself if you can't fundamentally understand how the output was generated. I don't mean you have to understand

**Precision in AI Outputs**
(54:17) how neural networks work, but if you give a generalized task to a generalized agent, it's going to give you something very difficult to unpick and explain. At the end of the day, I think we should be focused on being more precise in our outputs. It doesn't restrict creativity; to Debbie's point, it enhances it, I believe. But I

**Value Human Skills with AI**
(54:44) think, put much more thought and effort into, 'Why does this process work? How do I do this task as a human being?' Don't devalue your natural skills as a human being because we take them for granted that we can do all these multifaceted things. Try to figure out what those things are and what it means to be human

**AI and What It Means to Be Human**
(55:08) because that'll make you super awesome at using AI. >> I'd love to do an AI Insiders, David, on what it means to be human. >> After going on some AI dates last week at a real-world cafe. >> Wait, did you go to the popup? >> I was quoted in the New York Times for going to the popup.

**AI Dating Popup**
(55:29) >> Put the link so we all can read. >> Does everybody know about the popup where you can date AI that's happening in New York? Nate, we should go. That would be a fun video for us to test this out. >> David, I want to read your comments immediately. >> Yes, that was my dinner date with

**Upcoming AI Dating Session**
(55:54) AI. I'm putting this in the chat right now. This is >> very top of mind >> and we will have a session on this. We had to schedule it a little bit later because we have amazing sessions like this already scheduled. Robin Gelfan, this incredible comedian who did this with me back-to-back, we're going to have a

**He Said, She Said AI Dating**
(56:17) >> When is that session? Tell us all. >> It's not until March 25th, but we'll get into it. We'll do a he-said, she-said version, and it's going to be a lot because these issues will be bubbling up for a long time. >> Thank you for having us, and thank you to Debbie, Graham, and Ken. I put in the chat, I've

**Session Wrap-up & Future Invitation**
(56:36) never seen a session go by this quickly. I thought we had tons more time. Thank you all. If you ever want to do a longer version of this, you've got the invite here. >> Thank you. >> Thanks everyone for coming. Incredible questions. Caroline, I'm happy to share the questions with you too in case you missed any of the

**Final Thanks & Upcoming Events**
(56:54) comments and things you want to follow up on. Thank you all. This is great. Hope to see some of you, as Sam mentioned, at Marchitecture live in a few weeks, and see you next week for a session on vibe coding for good.

## AI in Advertising Codex Agency Adoption  Super Bowl Ads Signal

Speaker: Garett Sloane
Published: 2026-02-08
Tags: advertising, ai in marketing
Video: https://www.youtube.com/watch?v=RSG4BfXSu3c
Page: https://aimarketersguild.org/sessions/ai-in-advertising-codex-agency-adoption-super-bowl-ads-signal

**Introduction to AI Insiders and Guest Garett Sloane**
(0:05) Hello everyone. I'm David Burkowitz and welcome to another edition of AI Insiders by AI Marketers Guild by Architecture Media. I am here with an old friend, Garrett Sloane, chief tech reporter at Adage. Garrett has been I've read a lot of your work recently, but once I saw all the excitement around how you were describing agency operating systems I

**Discussing Agency Operating Systems and Super Bowl Week Changes**
(0:32) wanted to learn more about that. I know we got a lot of people in the community want to learn more about that and all these other exciting things you're seeing Super Bowl week and constantly changing crazy times Claudebot malt book week it's a thousand weeks in one welcome Garrett great to have you here thank you David I'm proud to be here for

**Garrett Sloane's New Role at Ad Age**
(0:57) the first time. I have an update breaking news I'm now the senior editor of technology and AI at Ad Age. Chief was a good title for a reporter, but now editor is in the title. I had to swap out that cushy chief looking thing, taking on even more tech coverage and putting AI in the title. On my resume now.

**Congratulations on the Promotion**
(1:24) Okay, now it's official. Congrats on that. Excited to have you break that news. As long as we get more of your coverage, whatever your title. My grandmother used to always brag because my aunt was a top person at a record label and my dad was a senior person in this hospital. My grandmother loved talking about having a

**The Relevance of Agency Operating Systems**
(1:52) daughter who no, my dad was a chief and my aunt was a president and she talked about that to no end. Senior editor is pretty good too. I'll take it. It's been crazy like you said week. We're here to talk about the OS, and that story I think was two or even three weeks ago, but it's still going to

**Ad Age Article on AI in Advertising**
(2:16) be relevant going forward. I'm going to bring this up, and I'll put the link in the chat for those who aren't subscribed to Adage. You can even get some of this stuff with the free registration. It was one of the best summaries I've seen of what's going on in the space. I love

**Origin of AI Platform Names**
(2:42) did you come up with the names yourself? Did AI come up with these names? This is amazing. I could take you behind the scenes. As I've been reporting on AI, I am using AI a lot more in my reporting, not to write these things, but to brainstorm as people say. Let me analyze some of these platforms, do some of the research, deep

**AI's Role in Brainstorming for Reporting**
(3:04) research on ChatGPT, Gemini, cross-reference them, fact-check, do all that. I was getting a vibe of each one as I was communicating with the LLM. I had to do a lot of tweaking to come up with these names, but these are my names. AI assist. I love it. So what do you

**Agency Differentiation with AI**
(3:32) what's going on here? There's so much more in here, but are these holding companies and agencies successfully differentiating with AI? That's why I did this story. Out of CES, I think we saw so many of these announcements. We've seen these platforms that the media holding

**Evolution of AI Platforms in Agencies**
(3:59) companies and agencies are launching probably for the last year or two. Publix with core AI for years. Omnicom has had Omni AI, I think it made it Omni. Dentsu's changed from Mercury as its key branding to Dentsu.connect last year. These have been coming up for a little bit, but I think we saw a

**CES and "The Machine" as a Breakout Moment**
(4:24) breakout moment around CES. Stagwell, "The Machine" was what set it off. When you get an email about "we're launching the machine," you think, "what is this? What are these platforms? What do they do?" I tried in this chart, where you see seven different categories, to compare them where they do

**Differentiating AI Services and Tools**
(4:46) differentiate, and I think they do. I think there are different reasons for each of them, different ways brands will interact with them, different marketing messages, business strategy. So I think there is some difference, but a lot are coming up with the same services and tools too. AI audiences, say, every agency needs AI audiences where you're creating personas. That's

**The Rise of Vibe Targeting**
(5:08) the hot thing to do in advertising: create target audiences. Vibe targeting, that's a term I tried to coin, tried to help coin last year after Brian O'Kelly first came up with it, but I latched onto it. So vibe targeting is a thing. All these platforms help with these new-age advertising formats.

**Unique AI Use Cases**
(5:31) Was there any one of these things where you thought, "wow, that's a use case I hadn't seen before"? I have to think back because I've done a few. I've looked at Horizon Blue in the past and done a few stories on where these are coming up with interesting "oh, wow" when I look at the

**AI Platforms Making Sense of Data**
(5:55) platform it's making sense. Different platforms make sense of data in ways that when you're not in advertising every day, you're looking at the data. These platforms are relying on sales data, sales data coming from everywhere across the country, products being sold at cash registers across the country. You're like, "Oh, wow. Surveillance

**Brands' Hesitancy to Discuss Data Usage**
(6:17) advertising is real and it's all coming into these platforms." Some of the details when I get into these systems, they do come up with interesting things. I would like more of that in my reporting too because brands are hesitant to discuss this stuff. They don't want to discuss how they're using this data. They don't want to discuss when the LLM comes up

**Credit for LLM-Generated Campaign Ideas**
(6:37) with an idea for a marketing campaign, they don't want to credit the idea to the LLM they may have brainstormed with. It's hard to get, but those are the great stories. If you hear the chocolate maker found the best idea for its ad through talking with LLM or creating audience personas, it found a target demo it never thought of before. Those details are the things I would

**Agency's Fear vs. Enthusiasm for AI**
(6:59) like to get more of. When you talk to folks on the agency side, especially at some of the major players, what's your sense of the fear-to-enthusiasm spectrum for all this? The fear of AI is definitely real. Another behind the scenes after the

**Creative's Skepticism on AI Platforms**
(7:28) story went up, it got a lot of attention on LinkedIn. I think that's where you saw it, David, and came to me. I also got a DM from someone in the agency world on the creative side who wanted to downplay how relevant these platforms are. Anonymously, I know who it is, but anonymously said, "No creative in New York even knows what

**WPP's Open Pro and Self-Serve Marketing**
(7:51) these platforms are. No one's using this stuff. It's not even real." I have some sympathy for that message. But then I talked to WPP about them developing this platform Open. They have Open Pro, a self-serve tool which could bring brands to use the agency on their own. Who do they need if they're using the platform to create

**Agency-Wide AI Adoption Push**
(8:16) their marketing? I'm talking to WPP, and they say everyone's using this stuff. Everyone in the agency is using it. No one's not enthusiastic about using it. I would be surprised. I would like to dig in a little more about the climate in agency land about this stuff because there's a message from up top to adopt AI, use it all

**Innovation vs. Efficiency in AI Messaging**
(8:43) around. I think unenthusiastic workers about that might get a stigma. As far as the messages they're trying to send to the market from these holding companies, how much of it is about innovation versus efficiency? Efficiency is pretty scary when you're working on

**Agency Restructuring and Downsizing**
(9:10) the agency side. I was a publicist for a few years, and I can't tell you the number of reorgs I was part of in just a few years at one holding company. Always pairing down. We've seen it play out. I don't cover the agency world as much as our agency reporters and what's happening Omnicom

**Mergers and Disappearance of Agency Brands**
(9:32) buying IPG. I read the headlines, and it's this classic hundred-year-old agency losing its name and now it's agency plus. With this it's RIP JWT came from the chat right now. Oh man. Has anyone in this room who's

**Fear of AI's Impact on Agency Brands**
(10:05) been on the agency side not had one of their brands on their resume disappear. I can see a few folks here where it's like, "yeah, 360i MRy, all my old brands, all my old t-shirts, they're collector's items now." There's some legitimate fear that AI isn't going to help that. But then is it

**Race to Commoditization in Creative Work**
(10:34) just a race to commoditization when we all have access to all of this? Yep. How much does the creative become the same? You see it happening in some of the commodifying tools that are, "oh, let me create a banner ad for a retail site" or "let me create a quick CTV ad with a little animation." These brands

**Blurring Brand Identities with AI Tools**
(10:58) blend together. You can hard to tell what's luxury, what's common brand. It's equalizing the look of some of these brands. So I think brands are going to have to watch out for that as they adopt these tools. With some of the innovation happening now, are you seeing some of this democratization of innovation

**Democratization of Space-Age AI Tech**
(11:29) and access now thanks to how easy it is to get what I consider space-age tech that a few years ago was unfathomable. It was unfathomable. That's a fact. It's there. No one. It was something that you knew was being worked on in the basement of Google but not available, and then it came out and is publicly available. So it is

**Brand Distinctions and Super Bowl Ads**
(11:54) a great leap, and it does democratize. I think you can see it; there are still distinctions between the brands. Pepsi has a Super Bowl commercial, say, and there's a polar bear in that commercial. I don't know if you've all seen they're taking on Coca-Cola, showing the polar bear moping around because it likes Pepsi more.

**AI's Role in Polished vs. Sloppy Ads**
(12:16) I don't know if that Pepsi polar bear is AI. I should mention Pepsi is not my brand I cover, but I could see it being AI if it's not now. At some point, AI has a hand there. I'm sure it's CGI AI. So maybe AI comes there. That ad is a mix maybe, or it's polished. You see a major brand use AI in a way that's still cinematic and polished, and then you see

**Generative AI for Brands like Kelshi**
(12:45) other brands come out, and they'll quickly create a generative AI commercial. Kelshi came out with one last year. That was a crazy one. That was leaning into the fact that AI is still a little sloppy. It's clearly not polished, but that's okay for a brand like Kelshi. So, different brands are going to have to

**AI and the Uncanny Valley for Luxury Brands**
(13:03) figure it out. If you're Prada, you're not going to mess around with your logos and icons in any way unless you have it perfectly captured. It's a good point. I was talking with someone about the movie Polar Express which brought the phrase "uncanny valley" into public consciousness and

**Future Perception of CGI as AI**
(13:28) I even had to stop myself from saying it was an AI-generated movie. Obviously that technology was not available then. With what you're saying about the polar bears, from the viewer's perspective, a few years from now, we're probably going to call all past CGI AI, right? It all

**Generational View of Non-Real Imagery**
(13:51) looks like my kid's going to call Jurassic Park AI. If it's something that isn't real, she's not even going to know if it's animatronic or CGI or AI. It's going to be, "oh, someone did something that maybe was cool." It's all AI now. I think it must be some of the platforms that

**Generative AI Production Studios**
(14:16) are coming up that aren't agencies. I hear so much about Brandtech Group and Pencil, Secret Level, a production studio. These are all companies we've covered coming up with generative AI in production, and this stuff is getting better. There's a subreddit on Reddit, "Is this AI?" Nobody can tell on a lot of

**The "Super Bowl LLM" and AI Presence**
(14:38) these. Now getting into the Super Bowl this year. Is this the AI bowl now? What are you seeing? I'll coin this with you now. Let's see how this flies. Super Bowl LLM. Can we start spreading that? I want to be the first stamp on that because Anthropic

**Anthropic's Super Bowl Marketing Strategy**
(15:03) just said it's in the game today. We had heard rumblings that they could be, and then we got some word that they're an F1 sponsor. So they're clearly ramping up marketing. Anthropic is an interesting case because they're an enterprise B2B business. What are they doing in the Super Bowl? They want to be mass adopted to consumers, obviously. That's what we've seen of

**Anthropic's Anti-Ad Stance in Super Bowl Commercial**
(15:26) their Super Bowl commercial. It's to appeal to consumers. Fittingly, they're taking on ChatGPT with that commercial, saying, "We don't have ads in our platforms." Again, it's all about ads. It's all about AI. I'm in a sweet spot here. At the same time, it's becoming trendy to make fun of

**Alexa's AI Super Bowl Spot and Anti-AI Trend**
(15:51) AI, like Dollar Shave Club did. We have a Super Bowl Alexa. He's in Alexa Plus. He's in the game too with an AI spot. Amazon's leaning on AI too. Sorry I interrupted. No, I am wondering if anyone here beyond glancing at it

**Amazon's Goodwill and Alexa AI**
(16:14) the day the story broke actually used the Alexa website for anything when they're AI-powered. I think Amazon at least earned a lot of goodwill from fans by putting Michael B. Jordan in an Alexa ad a while back. So, I guess they can stay in the game. They've done some pretty good ones so

**The Anti-AI Backlash from Brands**
(16:44) far. That backlash to AI, which I'm seeing on two fronts. There's the Dollar Shave Club that came out with their new ad that's entirely making fun of AI. Then there are all these brands, even Wikipedia and others, trying to come out and say, "we're anti-AI." It's

**"Real People" vs. AI Messaging Traction**
(17:10) going back to, "we have real models, we have real people, we're real, we don't want to use this stuff." How much traction do you think that message even gets? Especially as soon as the message gets out there and everyone's saying it. That's my Super Bowl story coming tomorrow: Super Bowl LLM.

**Brands' Strategies: Against or Criticizing AI**
(17:35) Look for that. I'm trying to look at what the strategies are. One of the topics is who are the brands that will lean against AI or criticize AI. As AI is becoming mainstream, there's an equal reaction to going another route because that becomes a strategy. "Oh, you're going this route, I'm going to go that." Pepsi, Coca-Cola uses AI instead.

**Rivalry in Anti-AI Advertising**
(18:00) Pepsi criticizes Coca-Cola for AI, or like you said with Manscaped or Dollar Shave Club. Are they competitors with Manscaped? They're both shaving brands. Let's say they're in the same category. Same category, men's grooming, but they're not direct competitors. Manscaped, if anyone

**Manscaped's Ill-Received Generative AI Character**
(18:24) recalls, a few months ago, had a generative AI launch of a new character called Mr. Balszac. I don't know if anybody saw this character, but he was I don't think he was well-received. No, he was not. I wouldn't look him up. He's not going to be making a Super Bowl appearance, I'm sure, because it was not great, but he was

**Opportunistic Anti-AI Messaging by Rivals**
(18:46) AI generated. If you're seeing Dollar Shave Club now with a very anti-AI ad, there could be a subtle dig there. These rivals are setting each other up. When one rival goes into AI hard, the other sees an equally opportunistic message to go anti-AI. So maybe it is which one wins out, but hard to see AI losing out, right?

**Advertising's Push for "Artistic, Human" Content**
(19:10) Earl, is there any more to add? I would say that it's going to be the push for "artistal," "artistic," "human," "not AI generated." That's going to be the push in terms of advertising. "Our stuff is made of real people versus assisted by AI bots." That's what you're going to differentiate yourself

**Hypocrisy in Anti-AI Stances**
(19:32) which I also think runs the risk of being hypocritical. They're like, "okay, we have real people in our ads," and then more stories come out of all the other ways their organizations are using AI. Who isn't? Who can be? It's like being an electricity-free

**The Impracticality of Being "AI-Free"**
(19:57) company. "We use smoke signals." I don't want to hire the accountant who uses pencil and paper. I think that's what will come off. I don't think TurboTax will say, "hey, we have abacuses and we're better for that for some reason." I think if we had to guess on this one, it will be about sentiment.

**Public Sentiment on AI**
(20:21) What is the temperature on what the country thinks about AI? I think it's mostly negative, which is coming probably more from TikTok than anywhere else. But TikTok is not a reflection of the general audiences. G, where are you seeing where the winds are blowing these days?

**AI's Inevitable Future**
(20:41) I'm always torn until it's settled. Everyone said, "you can't." This technology is going to be here. No matter what shakes out, in the future, we're going to be talking to an LLM to search the web or engaging an assistant to book us a hotel room in some capacity. Whichever company wins out, it will be the question.

**Garrett's Personal Use of AI in Reporting**
(21:05) Let's go down to a micro level for a bit. I think it's fascinating for someone like yourself in your role: where do you use AI? What do you like using yourself, and where do you specifically not use AI? I think media is a good example because we're always going to be the

**Caution in AI Use for News Gathering**
(21:34) most careful. The best reporters and editors will be very careful, and newsrooms will be very careful about how it's applied in images. The standards of everything in actual news gathering have to be impeccable, unimpeachable. We, the serious ones of us, live by that. But

**Avoiding Wikipedia for News Background**
(21:59) that doesn't mean I was never going to be like, "Oh, I'm not going to Wikipedia." I don't use Wikipedia ever. There's an example of a tech that I wouldn't go there and try to get information for background or context for a story. There are still some ways you are keeping it pure, but you have to use this. It's like using

**Using ChatGPT for Earnings Analysis**
(22:21) Google. I'll go to, for instance, say, "who's going to give their earnings tomorrow, Google?" They're giving earnings this week. You could take their earnings report, put it through ChatGPT, take last quarter's, put it through ChatGPT, and really do an analysis. That's where it's coming up; that's where you might be using it to run analysis and

**Gemini vs. ChatGPT for Google Earnings**
(22:45) then fact-check it. It would be very funny if you're comparing Gemini versus ChatGPT on Google's own earnings and Gemini tries to massage that. We could see how it positions it, although with Google, there's never anything bad to say; make another hundred billion dollars tomorrow.

**Personal AI Play for Super Bowl Research**
(23:09) but they were supposed to make a hundred trillion, and now it's only 99 trillion. Disappointing. Are there any things that you especially like playing with yourself? I liked for the Super Bowl, looking at, going back, doing some

**Gemini for Super Bowl Brand Mix Analysis**
(23:32) research on Gemini. "Hey, what was the mix of brands over the last five years in Super Bowl: auto, travel, retail, tech?" And "give me a chart over the last five years." It can spin up a chart that's relatively accurate. It didn't look that great. Gemini now Pro, I'm only on the free tier of Gemini, so I don't know what the enterprise is getting, but they're

**Gemini's Multimodal Capabilities**
(23:57) promising multimodal, right? Graphics, charts, images, all this stuff. So, it could spin up these graphics, which is a good way to condense information. "Show me this in a chart," and I can read it. Hence the chart we opened the podcast with, which was the chart for, "show me these agency platforms and what they are, lay out

**Notebook LM Discussion**
(24:20) the landscape for me so I can visualize what it looks like. Notebook LM someone mentions here is the one I hear all the time, and I have to get into it. I'm the proponent. I'm literally the unofficial salesman for Notebook LM. Well, you're going to have to rival Daniel for the sales job there. We're going to fight on this

**Exciting AI Trends for the Year**
(24:44) one. Educating myself on AI topics. It's gotten really good, G. What are the other trends you're excited about this year? What are things you think you're going to be covering way more now that you weren't a year ago? What will pop up this year? I think

**The Frenzy Around Ads on ChatGPT**
(25:09) obviously it's the ads on ChatGPT. We knew it was coming, but we were waiting for that announcement. You saw the frenzy that set off, unleashing all the same debates we discussed: AI versus non-AI, who needs AI, do we need ads in AI? How bad will that make AI if ads influence the responses? That sets off that debate. Also, ChatGPT, OpenAI

**OpenAI's Need for Revenue and Ad Strategy**
(25:36) needs money. It's like looking at Facebook or Google when their startups are not generating revenue. How are you going to do it? They turn to ads. In Google and Meta's cases, you're talking to hundred-billion-dollar ad businesses. Amazon turned on its ad business now, making 60-75 billion a year in ads. Can OpenAI do that?

**Standard Startup Monetization Models**
(25:59) It's a smart play though. Every startup started with a freemium model, then they started with an ad-based sponsored lower tier, then a paid subscription model for regular users, and then enterprise users. It's pretty standard at this point. This is not surprising. Absolutely. It always interests me how you

**Google Search Ads and Netflix Ad Revenue**
(26:20) stumble on the right formula. Google search ads must be super valuable. But it's also, I was reading what Netflix, they had a decent year with their ad business last year, but it was 3% of revenues. So if the Netflix ad business went away, some

**Netflix's Subscription-First Business Model**
(26:46) advertisers might miss it, but it's not like "there goes Netflix," right? That was never their business model. Their business model was the subscription model, which is what they succeeded on. They only added the ad-supporting tiers to get people who couldn't afford the regular subscription model. It's pretty straightforward if

**Netflix's Ad-Supported Tier and Crackdown on Sharing**
(27:07) you think about it. I have a sense that probably people who are submitting to Netflix or getting a new subscription or renewing their old, there's a difference that five a month versus eight a month. I think they cracked down on sharing at the same time they released their ad-supported model. Coincidence? I think not.

**Poor Ad Experience on Netflix vs. Prime TV**
(27:29) and the ad experience is awful. That is a fact. Yes. Which has to be effectively. Sure. Yes. In terms of Prime, I don't mind it on my Fire TV. I did my Cyber Monday shopping, and now I'm getting bombarded with all

**Omnipresent Advertising Across Platforms**
(27:50) the Prime ads. You wanted the Fire, and in the other room, I've got the Telly. I've got these CTV ads going on 24/7 here. Everywhere. Out of home, on your mobile, on your TikTok, on your CTV. Everywhere you're being messaged. No matter where you are, no matter where

**Poor Targeting and Irrelevant Ads**
(28:15) you look, where your eyeballs are, advertisers and brands are going to be there. My biggest problem with any of these ads isn't so much the ad experience; it's that Telly thinks I'm next in line to join ICE one of these days. I seriously The targeting is low. That's

**Opening the Floor for Questions**
(28:35) the targeting issue here. That's just targeting. I think it's or lack thereof. Fair enough. Who else has questions? We got a great bunch of people in the room here. Who's got questions for Garrett? What he's seeing, stuff he's covered? Who wants

**Tamika's Question: ChatGPT Privacy Concerns**
(28:55) to question the questioner? Tamika. Hey, Garrett. How are you? I have a question along the lines of privacy with ChatGPT and the sentiment piece, partly because with chat, people are being way more intimate with their bots than they would. I have a friend who talks to it like it is a therapist. So, are

**Intimate Chatbot Conversations and Regulatory Implications**
(29:23) there any implications from a privacy and a targeting standpoint that people share things with their chatbots that they probably wouldn't share with anyone else? Have you heard anyone thinking or talking about the privacy implications or what type of regulation might be thrust upon OpenAI from a targeting perspective?

**OpenAI's Personalization Opt-Out Strategy**
(29:48) Good. Obviously they talk about that when they launch the ads. Stratd wants to get ahead of that. "Here's how you turn off personalization," but they're being a little cute with it because you could turn off personalization. First of all, you're still collecting all that data. And they're saying the other part is, "we

**Decoupling Data Sales and Audience Targeting**
(30:08) don't sell that data to advertisers," which I think is the Meta stance. "We're not selling you the data, we're selling you the audience that data is based on." I don't think there's any way to decouple that you're targeting using this data. Personally, I wish privacy was more of a concern. It never seems to be. And

**The Sisyphean Challenge of Online Privacy**
(30:34) you can't use the internet without giving your full credentials in ways that are able to be tracked across the web at all times. It's almost a Sisyphean thing. You can't beat it. That's why privacy is tricky. Certainly there will be users concerned about it, a lot. In Europe, this is a much bigger deal. That's where it will

**Europe Setting the Tone for Privacy Regulations**
(31:01) mostly play out. I'm sure in Europe they set the tone, and then hopefully some of those regulations get adopted in the US. I share your sentiment too because someone I know very close, same thing, talking to ChatGPT about their girlfriend, about the problems, about "what do you do, what's my personality issue with this?

**Chatbot Advice and Double Standards for Privacy**
(31:25) What are you getting advice from ChatGPT about this stuff? People are... It is also, one of the things writing my Quadbot piece for tomorrow, it's great to see a lot of these privacy and security concerns come up. It's also funny that the standards we set for the companies that most of us

**Trusting Big Tech vs. New AI Companies**
(31:48) trust? Apple, Google especially? The amount of info that Google has on us across email, browsing history, search history, files, documents that Google has. People aren't too worried about that showing up on the web somewhere? You see some of these new companies

**Lack of Trust for New AI Companies**
(32:17) trying to get access to all this from scratch, and they have not earned that trust. Maybe they're not worried because they haven't seen the consequence. What is the consequence? I challenge you, Karen. What is the consequence? I don't know. If you're looking for a job and

**Potential Consequences of Data Usage**
(32:39) then they take that data and use it against you, or you're running for office, whatever, there could be all kinds of consequences. People are more trusting. I don't think a lot of people give a lot of thought to that. Let's keep the questions coming for G. Derek, you've been waiting. Let me ask. Let me go to Derek

**Derek's Question: Agency AI Use Cases**
(33:03) first because he's been waiting. Questions anyone can answer. It's cool. Derek, why don't you go for it, and Karen, we'll come back to you. Thanks, David. I wanted to ask Garrett or the others on the call, where are the larger agencies? Where

**Focus on AI Personas and Audience Targeting**
(33:20) what are the use cases for the LLM operating systems that they're building? Where are they putting the most effort and time? Is it creative media planning and buying? Where are they putting their efforts? I'm seeing a lot of AI personas, the targeting to build audiences based on data you collect. You go out in the field, you get human research, panel

**Customizing LLMs for Brand-Specific Strategies**
(33:45) data, and then you feed the LLM. I think they're trying to customize the LLMs with bespoke data for each brand. They want each brand to have their own unique place to go and brainstorm these ideas, media strategies, creative ideas. You have to train the LLMs to know the brand's colors. If you're doing generative AI, it needs to know the colors, the fonts,

**Broad AI Application Across Agency Functions**
(34:10) the style. I think across all these areas is where that is. I don't see one standing out over the other, except for the ones that are easier to grasp first. Persona AI personas are a lower lift than deeper training of AI for other purposes. Media buying is coming into play, agentic AI is coming into play. All these things they're doing that I

**Understanding Agentic AI Pitches**
(34:38) want to see the results of before I say it's done. That's what they're doing. When someone gives you an agentic AI pitch, are you seeing substance behind this? Are you able to understand what they're saying? I try to translate it in those stories. When Pubmatic comes to me and says, "Oh, we

**Agentic Trading in Programmatic Advertising**
(35:01) did the first agentic trading in programmatic." You're like, "Okay, what does that mean?" At least they put a brand to it. Using Claude, the brand uses Claude, the agency uses Claude, defines a media strategy, sends it to Pubmatic. The idea is that they don't have to go into Pubmatic's interface to execute this campaign.

**Agentic AI as an Incremental Efficiency Leap**
(35:26) That's where it is. I still think it's a basic leap of efficiency. It's not like bots are handling trillions of dollars of trading every day. It's still an incremental step. But there is a lot of tension about what is truly agentic, what is just using an LLM to give you some help. Hey, on the same vein as the agency

**Independent Agencies' Window of Opportunity**
(35:52) topic, you mentioned Horizon. A lot of your analysis or views on the hold. I'm curious if you have a thought on if independent agencies have a window of opportunity here. I say that knowing that the holding companies have acquired a lot of data assets and things like that that give them somewhat of a

**Leveling the Playing Field for Independents**
(36:24) proprietary advantage. Generally speaking, when it comes to things like creative or planning, or even some of the more nascent stuff like agentic buying, do you think there is a window of opportunity for independent agencies to level the playing field, or does that stay the same? I say that knowing that a lot of the

**Challenger Moment for Independent Agencies**
(36:51) holding company power in the past has been derived by scale and power of the network. Does that change or stay the same in your mind? No, I think you definitely have a challenger moment. I hear Brandtech raised a lot as the model that some of these are going after, where you're creating a

**AI-First Holding Companies and Vayner Media**
(37:15) holding company of different AI-first services and agencies. Brand has emerged a lot. Stagwell is doing what it does. We mentioned Horizon, Vayner Media. I don't know if they've put AI into a product as they're not trying to AI. I have people inside. They're building it.

**The Ubiquity of AI and New Opportunities**
(37:39) and they're building it. I like to say we have AI everywhere. AI is two letters. Why not? They'll have something. This is the general move that people are going, and it will open up opportunities and things we haven't even thought

**Earl's Question: Client Awareness of Agency AI Use**
(38:00) of in advertising services will emerge. Thank you. Thanks, Justin. Earl. I forgot what I was talking about. I want to confirm that G is right in terms of agencies using them either knowingly or unknowingly without knowing what's happening, and I think that clients don't know. So, if

**Ingesting Sensitive Client Data into AI Systems**
(38:34) you're going to build a comprehensive AI system, it's going to have to ingest all the different sensitive data from all the different clients they work with. I don't think they understand AI point of view on that. It's a very good point. Something we have to watch out because brands want to know, and this is

**Karen's Question: API vs. Consumer AI Privacy**
(38:56) being written into the contracts: "my data to train your AI." Awesome. Thanks, Earl. Karen. I asked Gemini and got the answer. My question was if there is a difference between API privacy and general consumer privacy, and it seems like there is. So if I have my own app and I use the API

**Data Privacy: API vs. Consumer Platforms**
(39:34) to connect to the cloud, that data seems to be more private than what you do on the consumer platform. Have you looked into that? Do you have any comments on this? You're stepping into technical territory. I'm going to stop technical

**Custom GPTs and API Data Privacy Choices**
(39:57) and legal, technical and legal. I would be interested to know more what the differentiation is. What do you mean by who's using the API where they would? For example, I have a product called Curator, and you can have your own API and create your own custom GPTs. The data that is generated there, you can then choose in your API whether you want

**Agencies Using Proprietary APIs for Data Privacy**
(40:24) that public or private. I'm assuming a lot of these agencies are also using their own API so that their data will be proprietary and private to them. For sure APIs do mediate that, I know that much. It is technical for me. The other example you were sharing is where which other

**Security of Paid vs. Free AI Conversions**
(40:48) way would it be giving data fully up? If you paid $20 for OpenAI or Gemini, that conversation would probably not be as secure. No, it just knows you're conversing with it and keeps track. But even if you have your own API, it still has a

**API Cost and Custom Access to AI**
(41:11) context memory. I think you can use that or feed it back and use it for your activities. It depends on how much you spend at the API level. The more you spend, the more custom access you get. ChatGPT for $20 a month or now for $8 a month if you use ChatGPT Go. You can't opt out of

**Opting Out of Training Data Collection**
(41:37) training data collection with the $20 Pro account. With the Plus account, you can opt out of it. Anthropic doesn't do it at all. Gemini makes it almost impossible to opt out. Anything you put into Gemini is going into the Google training machine. All of the companies have been asked this question so many times that they're very secure.

**Government Use of AI and Security Requirements**
(42:01) If the CIA and the FBI and the three little government agencies are using Anthropic and Gemini and ChatGPT for government, most likely an advertising agency will have the security requirements, and all of them have reporting. There are a couple companies that do SOC compliance and all of that, so you can get that trust report for any of the

**Walt's Question: Agency Integration**
(42:24) companies. Let's hope. Thanks, Daniel. We got more questions coming in. Let's go to Walt. Hey there. Thanks for joining us, Gareth. This is a great discussion. I'm old AF, so I've been around long enough to have started at full-service agencies. The thing that

**Lack of Integration in Agencies**
(42:46) I've seen that's been left behind is a tight integration between creative and media. First we had creative and media. We used to call data "research" before it became data, and then technology came in. Now you have four things you're juggling, and they're all splintered into so many sub-disciplines. I understand that the holding companies have to

**AI's Potential for Integrated Agency Functions**
(43:07) over-operationalize, so they don't mess it up because they have 50 people touching every campaign start to finish. Are you seeing anything in our new world of AI where we might return to really combining media, creative, data, and technology together in integrated discussions across teams versus the

**AI's Role in Converging Creative and Media**
(43:26) silo effect we've seen happen over the past 25 plus years? I feel it converges a bit with creative AI being able to go across all these and inform, using media to inform your creative and creative informing media, and this is all coming together. I would imagine that's the way it's going.

**Reality of Cross-Team Collaboration**
(43:49) Not really. If you ask the people on the individual teams if they work across the aisle, the truthful answer is not really, not to the extent they could. That's interesting because I talk to the people who are pushing this technology mostly, of course. We have ears everywhere, so not saying we

**Technology's Promise of Walls Coming Down**
(44:12) don't talk to the people inside using it too. But the people pushing the technology would say, "Oh, I can be a media buyer and get the research that a data researcher used to have to provide just by going into the AI platform." So, they're talking about the walls coming down. In reality, that could be something totally different, but the idea is, "I'm a media

**AI's Impact on Roles and Skillsets**
(44:36) buyer. I can make an ad now using these tools." Whether that happens and can you really make a good ad? David's talking about developing his own apps using Claude. That's an example where he wasn't a developer, now he is. So, I haven't read any deep articles on closer integration as a result of all this

**Identifying Leading Integrated Agencies**
(45:02) yet. I'm wondering who the first holding company or large independent is going to be to break out and kick everybody else's ass. My reporter sense, what would you look for? Who would be the best source for something like that? Because I'll go get this story. That's why I do these interview

**Importance of Great Ads vs. Data-Driven Targeting**
(45:20) these talks. We'll go write that story. Who would you like to hear from on that? There's also the bit that could be a whole other discussion here, maybe too long for what we have now, in terms of how much great ads matter versus if you've got the data, if you're vibe targeting, if you're vibe creating and

**Debating Standards for Ad Effectiveness**
(45:45) all this stuff. If it's matching the right thing to the right person, then who cares where it's running and if it's targeting who you say it is? Even if your brand messaging is perfect, if it gets someone to click and gets enough people to buy something, are some of our standards too high? This is not something I

**New Territory: Data Insights and Ad Standards**
(46:07) necessarily want to espouse, but I think these are going to be conversations and debates we're going to have to have as we get more data and have the potential to get more insights from that data. So we're talking about a lot of new territory here that we'll need to get into. Adam, what do you have for us? Very quickly, somewhat

**Adam's Question: Agency Team Morphing**
(46:29) dovetailing on the previous thing about how all these teams are going to morph. That article is great about highlighting what each holding company is doing. But I feel like I'm getting some PTSD from back in the early days of mobile and use of data and custom graphs. I was part of the early team at Jumptap, one of the mobile app

**Past "Centers of Excellence" and Dispersed Budgets**
(46:58) platforms, and WPP Digital was an investor. We worked with the early Zaxis team. Then we went over to Anallect, and I thought they were the coolest things on paper because it was a center of excellence within the holding company. Then you talk to the people at the center of excellence, and it turns out all the IO's, all the dollars, are held by all

**Future of AI Tools in Holding Companies**
(47:21) the different agencies out in the field. Garrett, putting you on the spot, do you have a crystal ball as to what happens to all these things agency holding companies are talking about? Ultimately, in theory, everything that is AI

**Cynical Take on Agency AI Development**
(47:44) meshes up with an employee, with a user, digital experience and journey. Do all these AI tools for the holding companies become a very thin layer over whatever frontier model the agency's deploying in its stack? I want the cynical take on what you're doing right now, which is not

**Cynical Outlook: Agencies vs. Big Tech and AI Ubiquity**
(48:14) a knock against your article, but what's going to happen in 18 to 60 months with these things? My cynical take on it would be that they can't take on Google or Meta in terms of what they're trying to do in some ways. These tools will become so widely used that you may not even need an agency to do some of this

**Need for Deeper Investigation into AI Platform Effectiveness**
(48:44) stuff. That would be my most cynical take. As far as their platforms, there's more digging to do. I appreciate the question because I'm writing about other platforms. I want to dive into more specifics about which sides of these platforms actually give results, or are brands actually using, or are the creatives within the

**The Role of Prompt Engineering in Agency AI Use**
(49:07) agencies actually using? So there's a lot more. And any agency or account that is actually using it, how much are they having to prompt engineer like crazy to the point where why do they need to give up? That could be something too.

**Peter's Question: Client Demand for Agency AI**
(49:22) when they could probably vibe-code up a middle layer or a series of OpenAI or Claude code skills. We'll see. That's why this story was hopefully the first, and I was happy. Great start. That's awesome. Peter, one quick question. Sure. I was wondering about the

**Client Readiness and Agency Investment in AI**
(49:45) other side of the coin: client demand. The agencies are investing heavily, at least the big holding companies. A report last week on AI and marketing showed that on the client side, there's low readiness; they're not doing much. Is this the expectation the agency is going to

**Clients' Willingness to Pay for Agency AI**
(50:10) handle this, and does the brand actually want to pay for this to make up for what they're not doing themselves? I think yes and no. They don't want to pay for it, of course, but they will, or maybe it cuts costs, and that's why they're pushing the adoption. "Hey, save us money in some capacity." Some of this stuff does cost money. Person--I hate to

**Brands Expecting Free AI Services**
(50:36) lean back on AI personas, that's on my mind, but we're hearing from agencies, "Oh, that should be table stakes and free." Brands want AI personas for free. "You should give that to us. Your market research using AI should be free." That's one area. The other area is brands are pushing adoption or forcing agencies to adopt because

**Brands Participating in the AI Hype Train**
(51:00) they want to see, "oh, we're so AI forward. We're doing it." It's a hype train, but they're participating in the hype train. Garrett, appreciate you walking us through what's hype, what's real, and all your ongoing coverage. It would be fun having you back sometime and see how things are changing.

**Closing Remarks and Future Events**
(51:22) You're welcome here anytime. This discussion, I think we had a new record with the number of comments during the chat. Gary, if you missed anything, I can send you all that so you can see the whole likely sidebar that's been brewing. This has been a lot of fun. Appreciate everyone participating. We got a lot of terrific events coming up. Enjoy the Super Bowl

**Thanks and Farewell**
(51:45) everyone. Look forward to your comments in the Slack on the AI ads. We will see you all very soon. Garrett, thanks again. This was terrific. Thank you, David. Take care, everyone.

## What Startups Are Using Now Will Define Enterprise

Speaker: David Levy
Published: 2026-01-30
Tags: venture capital, startups
Video: https://www.youtube.com/watch?v=XijrTBlD7dM
Page: https://aimarketersguild.org/sessions/what-startups-are-using-now-will-define-enterprise

Welcome and Introduction
(0:05) Welcome to another edition of AI Insiders by AI Marketers Guild and Market Media. Great to have you all here. I'm here with an old friend. We're old friends now, aren't we, David?
(0:16) I think we're old friends.
(0:19) 10-15 years is plenty.
(0:22) We got to know each other when you were running

Social TV Startup and 360i
(0:32) that social TV startup. I was at 360i. I remember grabbing lunch around the Tribeca offices there. I can picture the exact time and place of that era and some of the weird but super cool things you were trying to do.
(0:56) It was fun. I know back when social was scalable but also weird.

Understanding Tech and Markets
(1:03) Not just eyeballs. That was fun. Being one of the people I've stayed in touch with better than others, I get to have an ongoing check-in to understand what's going on with tech, what's going on with markets. You know way too much about a lot of things. Since I've been able to learn so much from you, I figured

David Levy's Humorous Self-Description
(1:27) it's time the community gets to do the same. Welcome.
(1:30) Thank you, David. I consider myself great at cocktail parties. I can talk about many different topics, but only so deep, then I have to run away. Or a decathlete, which you've probably heard is someone not good at 10 things. Those are my dad jokes. Dad jokes over.

Entrepreneurial Journey
(1:51) The quick background is I started as a banker and investor in growth in tech for 10 years. I left to start two companies because I thought that would be more fun, more lucrative, more interesting. I was one out of three on that. The first company was a retail company.
(2:14) The second was the social TV company. We were white hot for a hot second. Of the early social TV companies, we had the highest dollar return for our investors in that I only lost a million dollars, while my competitors lost $10 to $20 million. Tada. After that, I did some

Corporate Venture and AWS
(2:40) EIO for a couple of corporate venture funds. One was Lauren Michaels Cooper Venture Fund. I never met him, but I spent time in his office and some interesting characters would walk by. I was the e at Comcast Ventures for a year, then fell into a spot at AWS where I co-built the startup engagement team

AWS, Stripe, and Rapid Growth
(3:04) starting in 2014. I was there for the better part of 10 years. I left halfway through that. I did the same thing at Stripe. I stayed there for a year and a day. It wasn't for me. I went back to AWS. Our group had gone from 14 people when I joined to 150 when I left the first time, and by the time I got back it was in the thousands, listening to a sales

Focusing on AI Infrastructure
(3:28) team. Many of us, overpaid ex-entrepreneur venture capitalists, overstayed our welcome. I left that world two or three years ago and have been focused on AI infrastructure, digital infrastructure, from chips to cloud infrastructure and other infrastructure, to optimization

Shifting Tech Adoption Signals
(3:58) tools on top of that, and playing around with some of the apps we're seeing now. David and I have always talked about how it used to be that when I was a Wall Street analyst, earlier stage companies would use whatever they used, and then large company CIOs, CTOs would dictate the
(4:19) enterprise software applications and infrastructure they would use. That's where you got your signals.

Startups Lead Cloud Adoption
(4:19) That flipped. From my perspective, it flipped right around when I was at AWS. The numbers I'll use are functionally 100% of startups were using cloud, but only 10%
(4:44) of enterprise IT spent on cloud. That changed dramatically.

Stripe's Early G Suite Adoption
(4:44) I went to Stripe. The weirdest thing in 2018 when I went to Stripe, and Stripe is a very odd place, was that they were using G Suite as their internal email system. This doesn't sound weird now, but then for a multi-billion dollar
(5:05) company, it was very strange.

Startups Predict Future Enterprise Tech
(5:05) What we started to realize, and what I've realized recently, is that you can tell what enterprises are going to use two, three, four, five years from now by looking at startups. I don't necessarily mean startups using startup products; sometimes it's startup products, but not always. The reason is not so
(5:34) much that startups are brilliant, which I do think they are, but it's out of necessity.

Startup Tech Advantages
(5:34) One, they have no technical debt, and two, they don't have options but to use platforms that get up and running quickly.
(5:50) What does that mean today? What's popping up on marketers' tech stacks now? What are startups using?

AI Models Before Cloud Infrastructure
(5:57) You tell me. I can tell you starting with tools, I'll call them infrastructure tools, which lends itself well to what's happening in the marketing world. A piece came out from Business Insider, my old group there. It was a leaked memo. I had nothing to do with it; I was long gone

Startups Prioritize AI Models
(6:24) point. It was a bunch of stuff, but the key point I saw was that for the first time, startups were choosing their AI models and tools before they were choosing their cloud infrastructure provider. Kind of a boring statement, but having spent the better part of 10 years at
(6:49) AWS, that was a monumental change.

New Era of AI Tool Adoption
(6:49) Because we had been so early, and in every company so early, we could influence how adoption of other things went. Now you're seeing something different. Companies are choosing whether they're using OpenAI's API,
(7:14) Cloud Code, or their own GPUs or GPU infrastructure. You're seeing that happen now before they start to choose other platforms. The reason I think that might be important for marketers is that the tools people are using today, they're likely choosing tools first that they
(7:39) wouldn't choose before. I'd love to hear what you guys are seeing.

Startup Automation Trends
(7:39) In general categories, it's customer service that first comes to mind, or maybe second. The first is cloud code, but that's less to do with marketing. Anything on customer service, anything on sales
(8:03) automation, that's what I'm seeing first. It's because startups can't hire 100 salespeople. They can't hire or outsource to a call center. Anything agency or agency-adjacent, writing copy, automating outreach.
(8:32) With things like sales automation, marketing automation, CRM, are there things like what you saw with the Google suite that are standing out as go-to options?

Shifting Preferences in Marketing Platforms
(8:32) It's a little hard to tell
(9:00) right away. What I don't hear as much as I used to is people jumping on to HubSpot so quickly, or Clavio. Presumably, there are platforms out there

AI for Marketing Effectiveness
(9:26) that are doing this much more effectively, or using artificial intelligence to make it easier. Have you seen any of those? I can tell you what comes up when I hear names, but I'm curious what you're seeing. A lot of it has tried to tap into the

Clay's Popularity in CRM
(9:50) AI tech from existing tools, seeing if that can get folks further along. On the CRM side, Clay pops up a lot.
(9:59) Yep. Clay is one for sure.
(10:02) On company platform types of things, Clay for sure, but that's been going on for a while. If you ask most enterprises what Clay is, they'll say, "Oh, my
(10:15) kid plays with it."

Modern HR Platforms for Startups
(10:15) Having no idea what this is. This isn't the marketing side, but every startup I know uses Rippling or something similar. Not ADT or some platform that has a Windows 3.1 interface. You're seeing the Salesforces of the world add all this functionality,

Salesforce Complexity for Startups
(10:44) that's super cool, but if you're a startup, installing it... At AWS, we had a department that managed our Salesforce instances.
(10:52) Okay.
(10:54) AWS had a team, not just a person, a team. As much as they'll adopt AI for their existing customers and enterprises, what's going to
(11:08) happen with these newer companies is anybody's guess.

Llama's Traction and Open Source Models
(11:08) I think we can make some good guesses by seeing what there is.
(11:13) I was seeing some questions about Llama. Yes, Llama is certainly getting a lot of traction. There are some limitations to it, but the open-source stuff is free. You'll see many inference players moving towards these open models because they can't run the closed models on their systems.

Security Concerns with Open LLMs
(11:30) And ditto regarding the Chinese open LLMs; there are very significant security concerns, and they persist even on closed systems.
(11:52) For things like Meta, you don't hear about it as much, and what's gone on with Llama. Are there certain kinds of businesses adopting it more, and what are they mainly using it for? Llama itself?

Ways to Build AI Models: Building from Scratch
(11:55) Yeah.
(11:56) Or others?
(11:58) Anyone building their model, there are three ways you can build your own model. I'm saying that in air quotes for a reason. One is you can build your own model. That is a lot of work. It requires a lot of expertise. Perhaps most importantly, it requires an enormous amount of compute. That's when

Ways to Build AI Models: Retrieval Augmented Generation (RAG)
(12:39) you're going to a CoreWeave or even AWS or one of the other neo-clouds. Very expensive. That's number one. On the other end of the spectrum, you have folks doing these RAG limitations, which is taking in OpenAI or Anthropic. I forgot what it stands for. It doesn't really matter, but it's putting a shell
(13:06) around it and being able to... It's reinforcement augmented generative.
(13:11) It's really... Yeah.
(13:13) I'm supposed to know that, but I don't, and I'm not going to Google it right now because
(13:19) Retrieval Augmented Generation.
(13:25) There you go. Thank you.

Ways to Build AI Models: Open Source
(13:28) Was that clearly telling you that was said?
(13:30) That was Google AI.
(13:33) Oh, there you go. Nice. In the middle, you've got folks using open-source models. You can use an open-source model and tweak it to any dynamic you want. You still get a head start on
(13:52) building a model from scratch. You'll still have compute overhead, etc. I don't want to call them more serious engineers, but folks who need more fine-tuning of a model, but don't want to build a model from scratch, will begin with an open-source model.
(14:10) Yes, openrouter.AI. Holy cow. I see

Openrouter.AI: An AI Model Tracker
(14:15) in the chat, somebody is talking about a good tracker for AI model usage. I have been looking for the "Built With" or "Intricately" for AI models for a long time. I went as far as to try to build something that looked into website code automatically to see if it could figure out who's using OpenAI calls or Anthropic calls. I couldn't find it.
(14:45) I spoke to friends at OpenAI and Anthropic. They said, "I have no idea." But this openrouter data is just amazing. I was trying to build something to see early adoption, then I looked at openrouter and realized they did that. All right, onward.
(15:05) That's a really good way to see what people are doing with models right now.

AI in Marketing: Voice, CRM, and Outreach
(15:08) Things pop up and down, and it's wild to watch. Back to the marketing side, the early things I heard about, and I don't know how they're playing out, are the obvious things: CRM, cold outreach, voice AI stuff.
(15:34) I don't know if anybody has received an AI phone call yet. I think I got one. It's wild; you still know, but it's getting close enough where it's harder to distinguish. I'm seeing a little more traction on that in international markets. What I've seen is direct outreach, SDR type stuff, showing up first.

Identifying AI in Phone Calls
(16:08) Yeah. Those calls, once you realize this is not a real person, and it's not one...
(16:17) The pause. Some of those...
(16:21) They're not even AI, right? It's just a timer.
(16:35) Yeah. Yeah.

Voice Interaction with Chatbots
(16:35) It's interesting though. Have any of you interacted with any models or chatbots with your voice? Have you used ChatGPT? Have you used Bard?
(16:47) Oh yeah. Yeah.
(16:50) Even in some other
(16:58) applications of it, I've...
(16:59) Rosebud.
(17:02) I've mentioned Rosebud here, and David's the guy who introduced me to it. I love...
(17:09) I haven't used Rosebud voice-wise yet, but I use it text-wise every single day, at least once, if not several times a day.

Normalizing Voice AI Engagement
(17:22) David, you can explain Rosebud: it's a therapy app,
(17:25) but it's more a journaling type.
(17:28) Anyone I've shared it with has come back and said,
(17:34) "Oh my god, this is borderline addictive." I think the word you used was addictive. Ironic given its context. I
(17:47) realized this is marketing. I realized it was becoming easier and normal to use voice AI to engage when I was trying to create a logo for my advisory group. I was using ChatGPT while walking through the airport. I thought, if I put it on chat mode and put the AirPod in my ear, nobody's going to know who I'm talking to, and it nailed it. I've used it to prepare for discussions – not this one, I probably should have. I've used it to prepare for interviews. It's a very good way to use it. As we become
(18:40) more accustomed and okay with using those for our own use, I think that'll drop the barrier to when you get an AI call, as long as it's not spam. I think we'd get more comfortable engaging with voice AI as we use it more on our own.

AI for Post-Meeting Productivity
(19:04) in-person meeting, and now I'm full of ideas. I've got some follow-up things, and as I'm walking out, I'll just start talking. I tell ChatGPT or Gemini, and I just start. "Remember this, remember that," and then have a five-minute conversation so that when I's back at my
(19:26) desk, I haven't forgotten the things I wanted to remember.

Agency-Adjacent AI Tools
(19:26) The other place I've seen a handful of companies, I don't know the traction yet, is what I call agency-adjacent.
(19:42) Sorry, I missed that. I'll get to you in one second. But these platforms will build your marketing
(19:53) campaign, build the copy, and get you 80% there. It's wild. I know 2048 invested in one that was in Tech Stars New York called, I wish I remembered the name, I'll find the name. With an R, I think. I assume there's a handful of those. I just haven't seen what's gained traction yet and what hasn't. And if folks are going to work with agencies or if they're
(20:18) going to replace agencies or what have you. Earl, hands up.

Custom GPTs for Linux Exploration
(20:22) Sure. Thanks so much, David. One thing those who know me in the space know is I push custom GPTs all the time. Going back to your questions of use cases, using them through talking to one of your custom GPTs, one recent use case I've had was I've actually been
(20:44) exploring Linux.

Hands-Free Installation with Custom GPT
(20:44) since all the news with Windows 11 and people making migration because of the limitations. Last weekend, I set up Winnix on one of my machines, and I used a custom GPT. I spoke to it as I was going through the installation and configuration process. That way, I could keep hands-free and navigate on my new machine, ensuring
(21:07) everything was set up. I could literally ask questions when I ran into issues or had questions regarding any issues during the installation, configuration, or migration process. It's a great use case. I even used it once to help me clean the bathroom and make sure I didn't miss a spot. You never know the ways you can come
(21:27) up with ways to speak to somebody knowledgeable. It's great.

AI Content Creation Tools
(21:27) I'm going to look for the AI creation. I couldn't find that one, but I did find a quick list of things like Jasper. Figma is an obvious one. Predus. I'm even curious with some things Jasper is a good example where a client needed some SEO-optimized long-form copy,
(21:56) and I started testing Jasper and Writer and a few of these tools. This was before Gemini 3 came out, 2.5, I can't remember all the numbering systems. But I was asking ChatGPT and Gemini, "Can you do stuff with these very specific parameters, the exact word density, and all these kinds of things that SEO folks tell you"

LLMs Overtaking Point Solutions
(22:28) need to be there. They had someone who said, "Here's what the content needs to look like. Whether it's right or wrong, I didn't care. I just needed to do it." I tried Jasper, I tried Writer, then I tried Gemini, and Gemini was way better at following instructions. I realized, wait a second, I don't even need to pay for this extra thing. The thing I'm
(22:52) already paying 20 bucks a month for is better than a lot of what's out there. I'm curious where some of those areas are going to just be obsolete. I stopped paying for Ideogram, which I loved for image generation. Gemini, and sometimes ChatGPT now, just do it better. I still enjoy riffing on Ideogram sometimes, but some
(23:16) of these things, you might think they have their niches, and now the major LLMs keep expanding their pool of what could be done.

Seeking Alternatives for "Agentic"
(23:16) I think that's a really good point. I'm trying to figure out a way to start this part of the discussion without using the word "agentic." Can we come up with a better
(23:38) word for agent or agentic?

Defining "Agentic" and "Digital Worker"
(23:38) I'll use it for now. For what it's worth, my personal definition of agentic is when models are prompting models. But I'm not going to use that right now.
(23:51) I sort of didn't get it at first. I got it like, somebody's going to go out and do stuff for you. I used Manis, and I was
(24:01) an early user that paid them a lot of money. They sent me a t-shirt, which is terrific. And then I got acquired by Fadersburg. But when I asked myself why I was using this when I could use ChatGPT or Gemini... Yeah, digital worker. I like
(24:24) it.

The Power of Digital Workers
(24:24) Digital worker, not agentic. A chatbot digital worker, there we go, if it's able to more effectively utilize different models, etc., to figure stuff out and create stuff for me, it's better. When I have more involved things to do, I go to Mattis, and it is a much more effective platform for me.
(24:54) I just put in the chat that the Techstars New York 2048 Ventures-backed company was called ripple.ai (getripple.ai). I don't know if you've seen that, but now it's describing itself as a marketing automation agent. If you have these digital-worker-esque
(25:17) tools that can use the best of different models and different capabilities, and their own, you can have a 1+1=3.

Specialized AI for Complex Tasks
(25:17) I don't disagree with you that models keep broadening out. There's a company I work with
(25:42) called Polymathic. It has nothing to do with marketing; it's AI for science, for lack of a better term. It's a bunch of astrophysicists and other PhDs that built a thermodynamics model. Can ChatGPT and OpenAI build those types of models? Yeah, but those cases are not nearly as
(26:07) good. Their "special sauce" (I know we hate that term, but it's retro, so I can use it) is they have their own foundation model, and their agent can go and pick the best things to do for a scientist to more effectively complete a very complex set of tasks. I think that's where

Value of Agentic/Wrapper Platforms
(26:28) these agentic or wrapper type platforms need to be. They need to be in a place where they're going to unlock more value than using a single platform.
(26:38) Selena's wondering if "digital worker" instead of "agentic."
(26:42) Yeah, but I think that's an interesting distinction. The digital worker is that thing that
(26:54) goes out there and does things for you.

Agentic AI and Infrastructure Impact
(26:54) When I think of "agentic," the real profound implication for infrastructure everything else is when humans,
(27:26) prompting a model, asking questions, asking it to do things, that type of activity is going to grow. It's obvious: more users are using ChatGPT and other platforms more often. But when the models themselves that you're prompting start to do things and prompt other models, they can do that
(27:52) all the time. They can do it much more frequently and much more quickly. All of a sudden, the number of inference calls and token consumption goes practically vertical. Is that when Skynet becomes alive? I think that's where I'll make the distinction between agentic AI
(28:18) infrastructure and a digital worker.

"Digital Worker" Nomenclature
(28:18) A2A digital workers are becoming the nomenclature in Silicon Valley. Terrific. That means they do dictate our vernacular right now.
(28:38) What are other folks in
(28:41) here using? I started to put together a list of AI-native marketing tools.

Audience Check-in: AI Marketing Tools
(28:41) Is anybody using one already? Something two years ago they did not use but now they are using liberally? Whether it's for another area, tell me the other area. If it's in
(29:16) customer outreach, branding, copywriting, and it's not one of the models, we all know the models, what are the other tools?

Happenstance for Business Development
(29:16) The one I probably use most, I want to hear from others here, is Happenstance. It's more biz dev than marketing, but I'm on there all the time.
(29:39) Talk more about that.

Happenstance: Smart Network Search
(29:39) Anyone is welcome to connect with me there too. I just put the link in there. Happenstance is basically a smarter way to search your network or the network of a group of people you're in. I'm in one of these marketing agency collectives, and now we've got a group on Happenstance where we can search each
(30:02) other's networks. It's also so useful for me to say, "I'm looking for brand marketers at companies between 1,000 and 5,000 people that are in the D2C e-commerce space." I'll give it some of these criteria, and it can search whether it's my network or for people who have opted in. I use this stuff all the time.
(30:34) Often, one of the biggest use cases I also find it helpful with is for investors because I never remember who I know in my network who invests in what. Even if I know you're an investor, are you pre-seed or seed or A or B? It's very easy if you're aligned with a VC firm and there's a clear thesis they put out there. But many of the
(30:59) folks I know who invest are not part of an institution. So there might be some info they've shared out there that's hidden on their LinkedIn profile, that along with running this company, they're a pre-seed investor in agritech, and I sure as hell won't know that.
(31:22) Sounds like a next-gen Hashable, not
(31:25) the New York.

Synthetic Audiences for Content Creation
(31:25) Oh, I miss Ash.
(31:26) Awesome. Flame out. Yeah, I miss it too.
(31:29) Eric, do you want to share anything about Go Marble?
(31:31) I use synthetic audiences a lot. I don't know if that's relevant to what you're asking.
(31:37) Yeah, sure. Synthetic audiences I find very
(31:41) useful for content creation, creating surveys, adding the results to build content. They're extremely useful tools.

Tools for AI-Powered Content Generation
(31:41) I use Ask Rally and I just started using Navara, from Jill who presented. That's a little
(32:14) more complicated; I like to understand it, but they have features for generating content. With Ask Rally, what I do is create surveys. I ask the audience, they take the survey, then I take the output, put it in Gemini for example, ask for an analysis, and then I can use that to generate my content.
(32:45) That is awesome.
(32:47) Yeah, and the other thing I do is, for example, research this other tool I use for researching prompts, what people are searching for, then use that and Ask Rally to generate surveys, then use all of that to create content.
(33:09) Sorry, did you say there's something that tells you what prompts people are
(33:12) using?

AI in Marketing: Limiting and Avoiding
(33:12) Yes, I'll tell you in a second what that is.
(33:14) Yeah. While you do that, I can bring up another angle I've seen AI in the marketing space, which is the flip side: not how to use AI, but how to limit or avoid it altogether. I know there are many companies that
(33:36) ask, "Is your brand being used properly, etc.?"

Voit: AI Imagery for Brand Protection
(33:36) There's a company I advised for a while. It's now called Voit. I'll put the URL in here. Forgive my Techstars plug again, but this is a Techstars LA company. They provide a workflow to make sure that AI imagery doesn't end up in companies' brands. Where they became very well known was in
(34:09) things like Magic the Gathering. They have a big contract with Wizard of the Coast and video games, etc., to make sure people aren't sneaking AI-generated content into their actual IP. In those fan bases and groups, there are very significant problems if it's not original content. I think brand management
(34:37) for sure is something I've seen.

Prompt Research Tools and Resources
(34:37) The company is called Gumshu AI.
(34:39) Oh, nice.
(34:40) Of course. Yeah.
(34:42) As we're talking about things for researching prompts, the two I've spent some time with are Otterly and Passion Fruit, and those are pretty good. What I'll also share in the
(35:05) chat is I updated my own collection of resources.

Vibe Coding on Base 44
(35:05) Not everything discussed here is on this, but some of them are. Feel free to check that out and recommend others that should be there.
(35:14) I was about to ask you if you have that, and I'm glad you do.
(35:18) I have it, and I realized that with my vibe coding obsession
(35:31) on Base 44, I checked a couple of weeks ago.

Experimenting with Vibe Coding
(35:31) I had so many more credits for this month than I realized. It's funny, often there's this fear of running out of them, and I was being too conservative with it. So, I just started vibe coding everything. Dave, I put something in the chat that I didn't even show you yet, that I asked,
(35:53) "Could I actually vibe code an alternative to Rosebud?" I created "Innervoice Me." It's not great, but it works.
(36:01) Yeah. Well, I think that's another really great thread. Whether it's vibe coding or things like Lovable, which David, you and I have talked about. I know you adore it.

Marketers Using Lovable
(36:20) "Adore," I guess, is a better word. You passed Lovable.
(36:23) How are marketers using platforms like that? Lovable seems more targeted at folks doing things that are more marketing-
(36:30) Yeah, it's really good.
(36:31) heavy. How would you line up Lovable, Replit, Vercel, and all
(36:42) these thousands of others?

Platform Technical Savvy and Entry Points
(36:42) For me, I think one of the biggest factors is how technically savvy you need to be to use them. Base 44 versus Lovable, for me, I still see those two as the first entry points. You can get something pretty competently done even
(37:12) with the free trial. But as you get into other things, even with Lovable, it helps once you start asking it to do something more complicated. It's like, "Register for Supabase, register for GitHub," and it'll tell you how to do these things. Many other platforms are free, especially if you're not launching a major consumer product.

Base 44's Integrated Development
(37:33) For me, Base 44 is the fewest number of other things I knew to use. I just realized, for instance, that they have backend functions and agent mode that you have to manually turn on. But once you do that, originally I was using Form Spree that both Base 44 and Lovable recommended as an email capture thing to sync with.
(37:59) It was easy and cheap. Then one day, B4 said, "Yeah, I can do that as part of this. You don't need to go somewhere else for this." I've tried Replit, I've tried Vercel, I've tried some of Google's own tools. I really want to learn how to use Cloud Code, and I have it downloaded. I wrote this last week in the AI brief of using
(38:21) Cloud Code and Ghosty as the terminal and Netlify. I've gone down all of this, but to me, Cloud Code is harder for me to understand than the Korean alphabet, which I'm learning on Duolingo right now.

Cloud Code ROI for Developers
(38:27) A data point I'll give you is a twice-exited founder CTO I know is using Cloud Code. Many developers
(38:53) pay $200 a month ($2,400 a year), and he's getting the equivalent of $2 million in annual developer work done, in a week. He knows these numbers because he's run large engineering organizations.
(39:18) So when I hear things like, "enterprise adoption is so slow with AI," that may be the case, but at some point, when you have these ROIs that are orders of magnitude, it's too hard to ignore.

Blurring Lines: Development, Agency, and AI
(39:20) David, do you see the line between, now that you have all these, what we used to call no-code, now it's not even coding? These are like,
(39:45) "Hey, what do you want to do?" Are you seeing the line between what is development, what is agency, and what is developer blurring? Yeah. How does that all fall out? Who's doing what?

Vibe Coding for Faster Site Management
(40:00) I have a freelance developer who I use for all my personal stuff on Upwork, and I took my main site for
(40:11) consulting back from him. I vibe-coded it. I gave it to him. He put it on WordPress. He did all the things any smart developer is doing. I trust him. But then I realized, "This is too slow for me to tell you what I want done," even if WordPress has better SEO functions or things like that. I literally was
(40:37) yesterday on Base 44, telling it to build sitemaps for me, then I was submitting them in Google Search Console.

Vibe Coding Speeds Up Creative Projects
(40:37) These things mean there's a lot of quick-hit stuff I don't need developers to do. For more involved things, I vibe-coded a page for a content project I'm working on with a client.
(41:07) They didn't ask for this. They didn't ask for anything like this. I gave a few things they didn't want in the process. But I was able to bring this to life for them. I thought, "Okay, great. Now you can put this as a page on your own site however you want. I just gave you a whole vision for it and this functioning version." This speed to
(41:30) develop... I wouldn't trust myself or a typical marketer for a really important interactive thing that has to work. But there's all this stuff coming from the agency side. There were all these things that even just for the agency's own marketing, let alone doing client work, where it would be a big resource discussion. Do you spend
(41:56) months in meetings trying to get something done just for some quick hit, some wacky idea you want to bring to life, so you could go and, in earlier, less crazy days, you'd put it out in a tweet, and see if it bites? Now you can just do that in a couple of hours or a couple of minutes. That kind of
(42:21) stuff, for a creative person, or even an account manager, to run something by a client and say, "What about this? Can we do something like this?"

Evolution of Content Optimization
(42:21) That's way better than creating 50 slides about it.
(42:26) Yeah. As long as you talk about client work, I feel we went from, in
(42:43) Web 1, it was SEO, then in social media times, they refused to use the term Web 2. It was, "How do you get your stuff in the socials?" Now, David, I think you told me they were calling it Gener GEO. Whatever it's called, there are so many of these.
(43:07) Yeah. I read something a year or two ago, and I said, "Let's call it Seal Mode." It was SC AI. It was a joke. LLM optimization. It was a joke, but it seems like that's a thing. And this is where we're going, Daniel. I see your question about compensation models for AI-powered creatives. What
(43:34) if publishers or content creators need to have their stuff show up in models because that's where everybody's looking for them now?

AI Content Compensation Models
(43:34) How are they getting compensated for that? How do you think that plays out? I have some ideas. I don't know. You can see what OpenAI is starting to do now. It's
(43:54) some revenue generation, but how do you see that working out? SEO or social media optimization is, "Oh, you can get more stuff in, you get more traffic." This is not traffic. This is content. This is content generated on your content.
(44:19) Although it depends what business you're in because the trends we've seen are fewer clicks, but those clicks become more valuable.

ChatGPT's Ad Business Model
(44:22) A reporter was just asking me about the ChatGPT ad business model, and was hearing something about a $60 CPM, which sounds absurd, especially when they should be able to charge potentially obscenely high
(44:56) CPCs compared to what people are paying if the intent coming from AI is higher.
(45:00) So this whole business... Given how AI has seemed poor for general branding, not that it doesn't influence brand, but branding... Are you going to get more
(45:23) traffic from that? Are you going to get more engagement?

OpenAI's Token-Based Business Model
(45:23) Yeah.
(45:24) Yeah. My opinion, right or wrong, is that OpenAI's business model is ultimately predicated on selling tokens, not ads. I could be a thousand percent wrong on that.
(45:50) The markup on a token is 2x, so you have a 50% margin. I think they said something like 40% of their revenue is now from API access. It was a quarter. I thought it was 75%. I was wrong, but I'm getting less wrong. It's DoneKazam, which is nice. It'll be
(46:19) interesting to see what their ad model looks like.

Future of Marketing Technology
(46:19) I heard people talking about it yesterday about this new product, and they thought, "We know what the product is."
(46:27) Is it new?
(46:29) What do you say, if 20-30 years ago somebody was using Oracle for their CRM,
(46:42) and you say it got replaced, then now within the last 10-20 years, it's Salesforce. Platforms like that, if we had to look forward 20 years from now, what do the big names in marketing technology look like?
(47:10) What gets replaced?

Viability of AI Point Solutions
(47:10) Curious what others here would place bets on and are thinking, just beyond the rich getting richer. Manis was fascinating because it kept popping up as a favorite of many marketers. I feel so many of these things
(47:39) that have gotten some degree of traction. If you look at Jasper Writer, or Beautiful AI, or Gamma, which both have probably had the most traction on the "what's the new PowerPoint" kind of thing, but if Gemini, Google, and Microsoft don't just flat-out acquire those, they'll build their
(48:09) own versions of them into it. I'd be skeptical that some of these point-solution things are viable for the long haul, especially when anything is competing with part of the Google or Microsoft Office suite. Just because Microsoft tends to neglect user experience in Office today doesn't mean they're not looking at every single one of these
(48:35) and are going to... Yeah, and won't place a bet for the rest of the decade. Yeah.

Native AI vs. AI Add-ons
(48:35) Yeah. I said this to an EVP at Microsoft five years ago. They were thinking about their startup engagement. He reported as SIO; he wasn't really concerned about startups, but I said,
(48:59) "Look, you don't have to make another nickel in Azure, but you need to know what startups are using." This is the thread we started with, because this is what your enterprises are going to be using five years from now. Google is not a tiny company by any stretch, but I don't know the market share numbers now, but
(49:19) I would expect that G Suite has a material market share in internal email systems and documents. What I think was very different when cloud happened: Microsoft said, "Sweet, let's put Word on the web." Google, I think they acquired into it, said, "It's not just put some word processor on and give you web
(49:43) access. What does a truly native cloud-based word processor document sharing system look like?" I think the questions being asked now are not, "How do you add AI to Salesforce?" or "How do you add AI to Excel or Google Sheets?" but, "What are the native AI? What does a native AI analysis
(50:11) look like? What does a native AI look like if we don't start with the tools we use now?"

Reconsidering Startup Tech Adoption
(50:11) Yeah. Jamie, I thought, "Holy, that's big market share."
(50:15) I know your question was, "What are the big names in marketing technology in the next 10 years?" but
(50:30) what I'd like to do is revisit something slightly adjacent to what you said at the very beginning when you took an interest in what people are using now and that adoption curve explodes over the next five years.

Fleet DM: A Model for Remote, Cloud-First Companies
(50:30) I do M&A, and I advise mostly on the sell side. I'm in outreach now for a company I'm selling based in Paris.
(50:54) I discovered a company, I'm going to drop it in the chat, that is refreshing and unique: Fleet DM. The reason I discovered them is their ethos is 100% remote, no offices. They have a post office box in San Francisco. The company I'm selling is the same way; it's in emulation and virtualization. It's very high-tech
(51:23) stuff, but these companies are operationally aligned, totally cloud-first, distributed delivery, distributed management. To decide whether or not to contact them, which I did, you find out their CFO is in venture, and she's amazing, but she's not on site. The guy they have running it, who actually is running corporate finance
(51:47) over there, is a specialist in building remote organizations. If you surf around their site, it's completely transparent. It's so refreshing to see a company that says, "We're doing it this way because it's transparent. You can trust it. We can move fast. We're not encumbered by anything that's legacy." I think that's kind of what you were hinting at at
(52:14) the beginning when you were saying, "What's going to move fastest with cloud?"

Wrapping Up and Community Engagement
(52:38) Yeah. Jim, I was hoping you'd chime in at some point. You never disappoint. David, you don't disappoint too often either. You shortened today. So I appreciate you coming. We've hit the hour mark. It would be great for others to stay in touch with you and have you back sharing more ideas about this. A really fun community conversation.

Community Shout-out and Next Steps
(53:03) A shout-out to Daniel and Jean for the technical recommendations. I like how there are many of us at different skill levels on the marketing side, on the tech side, on all kinds of sides of this business. This community is welcome to one and all. Porch Capital, learn more about David Levy, and stay in touch
(53:27) with him. His Substack is really good. I often need to not read it the second it comes out because I need to sit somewhere where I can process it.
(53:35) I write long-form.
(53:37) That's a compliment. Follow me on LinkedIn. All this stuff. Next week, we have Ad Age's chief technology

Next Week's Guest and Closing
(53:45) reporter, Garrett Sloan. Another great observer of the market. You'll be able to ask better questions than I could think of. Thanks everyone for coming. Hope it's a great rest of your week, and see you next week in Slack and everywhere else. Thanks everyone.

## AI Insiders with Peter Shankman on PR AI and Building Source of Sources

Speaker: Peter Shankman
Published: 2026-01-22
Tags: pr, pitching to journalist
Video: https://www.youtube.com/watch?v=XQdhXqTtPDc
Page: https://aimarketersguild.org/sessions/ai-insiders-with-peter-shankman-on-pr-ai-and-building-source-of-sources

**Introduction to Peter Shankman**
(0:05) Hey everyone, I'm David Berkowitz, back with another edition of AI Insiders by AI Marketers Guild, and very excited. I'm always excited with our guest today, but Peter is a longtime friend, someone I've gotten to know very well in New York, and gotten to bond over AI and family stuff and all kinds of things where our paths have intersected. He's someone, well,

**Peter Shankman's Reputation and Source of Sources**
(0:34) before I got to know him as a person, I got to know him by reputation because he's been one of the most prolific masters of PR and communications and communicating what PR and comms are all about. He's now got this incredible platform, Source of Sources, where you can be a source or request sources for any journalistic endeavors. It's a tremendous free

**Source of Sources Conference and Gratitude**
(1:01) resource. It's an incredible Source of Sources conference debuting in New York, February 11th. We'll do a drawing for some passes by the end of this. Peter, I can't say enough about you, but I have to. I need to let you speak sometime. Thanks for being our source of wisdom today. >> Thank you. Glad to be here. That was a

**Peter Shankman's Opening Remarks**
(1:25) conversation. No, it's good. I recognize a lot of people. So, hi everyone, to those I know and those who I don't. I see Dave. I see Savio. I see good people here. I wouldn't expect anything less from something that you're running, Berkowitz. So, thank you. I appreciate you having this, and I'm glad to be here for a little bit to

**AI, PR, and Peter's Career Beginnings**
(1:46) chat on AI and the whole PR, where we see the PR, marketing, advertising, mostly PR, thing going with AI. I think that I didn't prepare a deck or anything, but I will tell you what I've seen. I've been doing PR since '96. I started my career. I had one job out of college. I was one

**Founding the AOL Newsroom**
(2:10) of three founders of the newsroom at America Online back in Vienna, Virginia. For those in this room under 35, AOL used to be the internet. It was how we talked to everyone in the world, and we invented it. So, I worked for America Online down in Vienna, Virginia, and we built AOL News. I took AOL

**Post-AOL and Starting a PR Firm**
(2:37) along with a bunch of other people from about 500,000 members to well over 10 million. It was an amazing time, but I left AOL in '96 and moved back to New York in '97, '98, and the internet was just getting started as many people knew it. I had all this internet knowledge from Virginia, and I said I should start a PR

**The Geek Factory Agency**
(3:01) firm, and I did. That was my first ever working for myself. It was an agency called The Geek Factory if anyone remembers the New York New Media Association, those back in those days, Ninma. >> I ran Geek Factory. We repped, we were right below a sweatshop on 38th Street and 8th Avenue. We repped clients like Napster and Juno and

**Post-Geek Factory and Autobiography Idea**
(3:23) AOL, and just it was a blast. We, I wanted to write an autobiography after I sold the agency in '01 called "We Ate All the Sushi and Drank All the Alcohol." There's none left, but it was an amazing experience, and I left when I sold the agency to a larger agency. I walked away and I tried

**Consulting, ADHD, and Early Source Matching**
(3:44) to take a year off. That lasted a week. I realized I didn't know how to relax. I blame my mother. I wound up consulting in PR for years and years and years, doing a whole bunch of other things. Eventually, because I have massive ADHD and talked to everyone, I had a giant rolodex, and reporters would call me from all over the world: "Hey, I'm

**From Rolodex to HARO**
(4:01) doing a story on whatever, who do you know?" And I'd find them someone. That started taking way too much time. So, I turned it into a mailing list, and that mailing list became "Help A Reporter Out" or HARO. HARO blew up between 2007 and 2010. It added about 300,000 people to it. It was selling ads, and it was incredibly successful, and it was acquired in 2010 by a company called VO

**HARO Acquisition and Post-HARO Endeavors**
(4:26) or a company called Vocus, then became Cision. Now Cision is part of PR Newswire. I walked away. Over the next 15 years, I did a bunch of other things. I wrote books. I have a couple of bestselling books on marketing, a couple of bestselling books on ADHD and neurodiversity in the workplace because I'm massively ADHD, and I just kept

**Re-launching Source of Sources**
(4:48) getting emails from people over and over saying that HARO has declined severely, and it's been ruined ever since I sold it, and why don't I start over? I didn't want to do that because you can't go home again. But because I have ADHD, I wound up starting it over. I run a company now called Source of Sources. Source of Sources is about a 50,000

**Source of Sources: How It Works**
(5:08) person strong mailing list supported by advertisers. We have two emails that go out every day to 50,000 people. Each email has roughly 10 to 20 queries from journalists all over the world, ranging from New York Times, Wall Street Journal, Associated Press, NBC News, CBS News, MarketWatch, you name it. You read the emails. It takes you about 10 seconds to scan the table of

**Simplicity and Effectiveness of Source of Sources**
(5:33) contents. If you are knowledgeable about something a journalist is looking for, you reply to the journalist directly, and you can get quoted in the media, and that's it. It is unbelievably simple. It's not HTML, it's all text. Email, I still believe, is still the killer app. It's doing well. At some point, I'm sure I'll sell it again, but

**The Downfall of HARO and SEO Spam**
(5:55) it's a very easy process as long as people don't abuse it. The thing that took HARO to its death was the rise of SEO farms, and SEO companies out of Asia and the Middle East and Africa who reply to every single query hoping for a backlink, and that really destroyed the credibility that HARO had. One of the things that it did was it wound up

**SOS Rules: No Spam, No Off-Topic, No AI Pitches**
(6:20) causing journalists to go other places. So when I relaunched SOS, my rule was very simple: no spam farms. Pitch on topic. If you pitch off topic, I ban you. I ban entire countries. Most of the Middle East and a lot of Asia cannot use Source of Sources because it's all spam farms. One of the other rules I've had to put into play is no AI because

**AI Pitches are Easily Detected by Journalists**
(6:44) people are very confused when it comes to PR. They believe that AI can do all the writing for them, and reporters will love that. Here's a tip: reporters do not love that. Reporters can instantly tell if you've written your pitch in AI. They don't bother even going through AI checkers or anything like that; they simply know. If you've been a journalist for any amount

**Small Businesses Misuse AI in PR**
(7:09) of time, you know what's real and you know what's not. The problem is that small businesses, entrepreneurial-type people who are doing so much more with so much less, have succumbed to the belief that AI will save them time and is as good as their own writing, and that has, without

**Peter's Personal Use of AI (Math Tutor)**
(7:38) them realizing it, destroyed their ability to get most of their. I'm not anti-AI. I'm a big fan of it. I use it frequently. I'm a single dad to a 12-year-old daughter, and I can tell you that AI has single-handedly taught me math, because she's in seventh grade now, and I finally understand the math she's doing. Prior to AI, I gave up after third

**Modern Math and AI Fan**
(7:57) grade. They don't do, for those who don't have kids, they do not do the same math we learned. The numbers sound the same, but that math is just entirely different. They do not do the same math the way we learned it, and it's really annoying. >> I'm a huge fan. >> I use it constantly, but here's the thing: from a PR standpoint,

**AI Doesn't Make You a Better Writer**
(8:18) if you are not a good writer, AI will not make you a better writer. >> Pretty much, if you're not good at anything, AI will not make you better at that thing. If you're not good at writing, AI will allow you to broadcast to the world that you're not good at writing, and it will allow you to do it faster and at scale. That is a problem. What winds up

**Ineffective AI Pitches**
(8:41) happening is that people go in, they say, "Here's my product. Please write a press release about it." Or, "Here's my product. Here's the query that I got off SOS that the reporter wants. I need you to quickly write a pitch to the reporter about my product's great." And that will immediately be thrown in the trash, and the reporter will not pay attention to you ever

**Effective AI Use: Journalist List Building**
(9:01) again. That being said, there is very much a place to use AI when you are trying to get media attention. There are several places you can use it. One of the first things that I strongly recommend everyone does is use AI to cultivate a list of journalists who write about your market, your segment, your topic. It does it

**Advanced AI Prompts for Journalist Discovery**
(9:25) much better than Google does. Ask it: "Find me 20 reporters who work in print, who work online, who have in the past six months covered the following subjects and use the following terms." By saying "use the following terms" and throwing those terms into your request, your prompt, what you're doing is you're avoiding the cursory, pay-for-play

**Avoiding Low-Quality News Sites**
(9:52) stories. There are a lot of websites out there that are being built as news sites, but their goal is eventually to get acquired, to get flipped. They're writing about everything and writing a very broad, top-level view that no one really cares about, and it won't help you by getting quoted there.

**Targeting Quality Journalists with Specific Terms**
(10:10) They'll talk to a tree and quote a tree if they can. So, by putting in terms specific to your industry that go deeper than just top-level, you're asking it to bring you back a list of journalists who actually write about this for media outlets that matter. So, that's the first thing I would suggest. There are

**Accessing Journalist Contact Information**
(10:30) services that will then give you the contact information for those journalists, services such as Muck Rack, Cision, things like that. They are expensive. Your best bet for finding contact info for the journalist you want is to partner or work with someone who already has a subscription. Go halves, go quarters, whatever it is.

**Muck Rack: A Recommended Service**
(10:52) PR Newswire used to have a deal where you could put up to 10 people on an account, and they don't do that anymore. I don't think any of them do, but you can still get the information from someone who does have an account. It's a simple search of these databases. I'm a big fan of Muck Rack. They are an advertiser on SOS, but I'm a big fan because they tend to keep, they have a

**Keeping Journalist Contacts Up-to-Date**
(11:11) couple hundred people working for them overseas who tend to keep up with what the journalists are doing. There's nothing worse than writing a great pitch for a specific outlet for a specific journalist and having it bounce because they don't work there anymore. You also need to understand that the majority of journalists out there are doing 10 times more with five times less, and they

**The Golden Rule for AI in Pitching**
(11:30) don't have time for pitches that are off topic or pitches that waste their time. The rule is simple: if you want to use AI, you can use AI to better your pitch, but make the pitch perfect first. So, I look, and this is a 30-second

**How to Structure a Media Pitch**
(11:51) class on how to pitch the media, but here's what I recommend everyone do when pitching a reporter: three paragraphs, no more than three paragraphs. The first paragraph: "Hi, here's my name. Here's what I do. Here's my company." Second paragraph: if you're answering a query from SOS or something similar,

**The Three-Paragraph Pitch Format**
(12:09) here's why my answer, product, or topic is perfect. Here's the answer to what you requested. Bullet point, bullet point, bullet point. Third paragraph: here's how to contact me. It literally does not need to be deeper than that. This is your very first date. You're not trying to get into bed with anyone. You're simply letting the

**Value Proposition for Journalists**
(12:29) reporter know that you have information that will benefit them, help them write their story, and save them time. I used to send, when I was running my agency doing PR for other companies, I used to send out pitches that read something like, "Hey, just want to let you know, I know you've written a lot about

**Building Relationships: The 'Just Letting You Know' Pitch**
(12:50) rocketry or a related topic. Just want to let you know I have a new client in the space. They did X billions of dollars in revenue last year. They're doing this. No specific pitch here. Just want to let you know to put them in your rolodex. I'm always happy to help. Feel free to reach out anytime, and I can get them for you on a second's

**Ensuring Source Availability**
(13:08) notice." That's the key. Also, make sure you're letting the reporter know that, "Hey, if you do wind up using my source, this CEO who I work for, who I handle PR for, I have access to him. If you call me and say you want to speak to him, I'll have him on the phone for you in 5 minutes." The biggest problem I hear journalists talking about all the time is that someone

**Journalist Blacklists and Unresponsive Sources**
(13:27) says, "I have this great CEO. I'll get him." "Oh, well, he doesn't really want to talk to the media right now." "Well, then why is he pitched?" That's a guaranteed blacklist. Journalists share blacklists constantly. There's actually a text file; I've seen it. It's a gigabyte long now of PR people

**Using AI to Refine Pitches**
(13:44) they'll never work with again. Chris Anderson at Wired used to send out what he called the PR blacklist, and he was famous for these things. If you pitched Wired off topic once, you're on this list. So, one of the key things is what I usually do: I write my pitch. I put it into AI, I put it into ChatGPT or similar, and I say, "Hey, I'm emailing the journalist with very little time.

**AI as a Sharpening Tool, Not a Writer**
(14:05) Make this pitch stronger and tighter. Don't change the words. Don't change the subject. Don't change the keywords that I put in. But make it stronger and tighter. Did I miss anything? Is my grammar right?" You just want to sharpen it. You don't want AI writing your pitches. You don't want AI doing your PR for you,

**AI for Cleaning Up, Not Creating**
(14:22) but it can always clean things up. I think that is one of the most underrated features of any GTP type concept: it will make whatever you've already written stronger. But if you're writing poorly, it's just going to make it slightly less poor. I don't recommend that. I use it anytime it's needed. I use it anytime I

**AI for Personal Communication and De-escalation**
(14:45) have to email my ex-wife to talk about my daughter's Bat Mitzvah right now. I run everything through it: "On a scale of 1 to 10, how much, with everything you know about her, how much will this piss her off?" If it's above three, I rewrite it. So, it's key to understand that you can't have

**The 'Too Sweet' AI Pitch**
(15:04) AI do it for you, but you can have it help you. Again, journalists are smart. They're savvy. They understand if it's been written by AI because it just seems... The best description I ever heard was that when someone pitches me AI, it seems "too sweet." Like if you expected a coffee with one Splenda and they put five of them in, it just tastes like too much Splenda.

**AI-Generated Social Media Content**
(15:30) That really is a perfect example because I've started seeing people post on social media, on Facebook or whatever. They think they're being all smart by having AI write some really smarmy thing, whether it's political or whatever, but you can tell it tastes too sweet. Some of the best advice I can give you, and I'll give you another great piece

**Preparing Pitches in Advance**
(15:53) as well. If you're using a service like Source of Sources or anything similar, have a pitch ready. Reporters use Source of Sources, and they know when their pitch has gone out and hit my list because they will go from zero to 100 pitches in their email in under five

**Timing is Key: Tailor and Send Quickly**
(16:15) minutes. My emails come out at 6:30 in the morning and 1:30 in the afternoon Eastern time. My recommendation is to have a pitch ready. The second you see something that fits, tailor it. The second you see something that fits, add words into the blank spaces in your pitch that's waiting to go. So, the contact info is already there.

**Pre-writing Pitches for Speed**
(16:38) The "here's who I am" part is already there. All you're doing is answering the specific question in their query within your pitch. You create that so it's already ready, and all you have to do is add a couple of words, and then click send, or run it through AI to get it stronger and then click send, because it really is a timing game.

**Immediate Response Strategy**
(16:59) I know people who set a specific email chime to my emails, the first one that comes out at 6:30 and then 1:30, so that they are awoken by my email. They read it, they scan it. If there's nothing to respond to, they say they go to the gym, but they probably go back to bed. So, make sure that you're answering immediately because that is, hands down, one of the best ways to get

**Source of Sources Advertising and AI for Copy**
(17:19) quoted: to be one of those first 5-10 answers from a source to a journalist. I use AI a lot. SOS is supported by a small text ad at the top of each email, and then an even tinier one-line text ad under the table of contents. It's very small, but they work. When I ask my advertisers to send me their copy, they, without

**AI for Ad Copy Shortening and Impact**
(17:43) question, send me too much. What I use AI to do is, by keeping the same gist of this advertisement, shorten it to three lines, make it just as impactful, keep it small. Then I'll look back on it. I'll read it again. I'll make sure it doesn't sound too sweet, and I'll usually use it. It is phenomenal for quick fixes, but it is

**AI Won't Replace Writers & SOS Success Rate**
(18:04) never going to replace the writer in you. It simply won't. At least not in our lifetimes. I want to answer questions because I speak too fast, and everyone says, "Give me an hour." I'm like, "I'm not going to need it." So, happy to take questions or answer more. I can tell you that 94% of the journalists

**Strict Enforcement of SOS Rules**
(18:26) who use Source of Sources do come back, which is a great number. I think it's because we're pretty tight on blocking people and kicking people out and off the list if they pull some nonsense. So, if you're pitching off topic, if you're spamming, I don't know. I'm also not above suing companies who violate our terms of service, similar companies to what I do, who

**Journalists Need Help: Become a Trusted Source**
(18:49) violate terms of service by pitching reporters to get them to use their service. My basic premise is this: journalists do need our help. The more you can offer them in the quickest amount of time to make their jobs easier and do their job faster, the more they'll come to refer to you as a source. It's like online dating

**Building Direct Journalist Relationships**
(19:08) in the respect that if they are using Source of Sources and they find a good source for their niche, for their industry, and you have proven to them that you're worthwhile or beneficial, they're going to come back to you directly. That's what you want to work for and build towards. If you're pitching a journalist without using

**Reading Journalists' Current Work**
(19:28) any source-matching service like mine, one of the things I suggest is to use AI to generate and build those lists of journalists who are working in your field. Make sure you're reading what they write. Just because a journalist is, AI, I've seen it. It'll send you a list of 20 reporters, and four of them have moved on and are doing something

**Avoid Pitching Outdated Topics**
(19:48) completely different. If you pitch the journalist on an old topic, that tells you that you're not reading their current work. If you pitch someone who writes a column, a blog, anything like that, just do yourself the favor of spending five minutes and reading their latest work. It's all available. It's all free. It's

**Journalist's Humorous Rejection Method**
(20:05) all there. I used to have a friend of mine who worked for the Journal, and she would always say, she'd keep the pitches that were off topic, and she'd write back, "Hey, got your pitch about product X. Great to hear from you. This is exactly what I covered 10 years ago, so

**The 'Do Better' Slapback**
(20:29) no, I will not be covering you now. Do better." I like to think that that was a double rebuke, because not only did it tell them that they're not paying attention, but it gave them the idea that they might actually be covered, and then she just slapped them down, and I always respected that. Peter, can you talk about the event in New York City?

**Introducing Small Giants 2026 Conference**
(20:45) >> I'm running this event for small to mid-size businesses, but primarily small business entrepreneurs. It's called Small Giants 2026. I came up with the idea because I got a ton of people who use Source of Sources who are one-man shops, two-man shops. Maybe they've hired their first person, and they're building small companies, and this might be their first

**Addressing the Gap in Resources for Startups**
(21:01) small company or their 10th. But they feel there are great resources once you start making a little bit of money, and there are not a lot of resources when you're just starting out that don't cost a lot. I actually found that funny because when I sold "Help A Reporter Out," that was a game changer for me.

**Expensive Masterminds for New Entrepreneurs**
(21:20) I was shocked when it sold for as much money as it did. I never expected that. But one of the things that came from that was I immediately started getting emails from masterminds and conferences: "Hey, saw that you sold your business. We would love you to join our mastermind for only

**The Inspiration for Small Giants Conference**
(21:37) $50,000 a year. You can get access." I'm like, "If I had $50,000 when I was starting my company, I wouldn't have needed you." The whole reason I needed something was because I didn't have any funds and was just starting out, and was, read the room. So my premise was to create a conference that was helpful and that wasn't full of fluff.

**Small Giants Conference Speakers and Value**
(21:58) We have about 10-12 speakers, including people like David, Jeremiah O'Yang, Scott Monty from formerly Ford. We have an accountant. Everyone who comes, we have a photographer who's going to do professional headshots for everyone who comes. Just trying to add value to this thing, because there's too much content without benefit out there where

**Diverse Topics at Small Giants**
(22:16) people come in and there's no benefit. We have two people talking on AI. We have an accountant who's going to tell you how to save money on your taxes. We have a travel person who's going to tell you how to save money and how to make sure your flight isn't canceled, or you don't wind up in the back of the plane or underneath it.

**15-Minute Talks for ADHD Audiences**
(22:31) says the guy who's going to Malta for a keynote on Friday and knows damn well he won't be able to get back to New York on Sunday because of the snowstorm going in, but he's still going. So, we have a ton of people speaking, and they're each speaking 15 minutes because I don't know about you, but I'm absolutely ADHD, and if someone's talking to me for more than 15 minutes,

**Focus on Health and Well-being**
(22:47) I lose their attention. I look at half of you have shut off your cameras because I know you're not even there now. You're just whatever. So, it's a focus on mental health. It's a focus on actual health. We have one of the top physical trainers in the country who's coming in and telling you what you can do every

**Conference Details and Peter's Motivation**
(23:02) single day, even if you never go to a gym, to not die early. Simple things like standing up, walking, treadmill desk, things like that. So, it's going to be in Midtown Manhattan on February 11th, all day, breakfast and lunch. It's going to be pretty fun. Everything I've ever done, everything I've ever built, has really been built with the desire to help

**ADHD and the Drive to Help Others**
(23:20) people because when you're undiagnosed ADHD all your life, and you're told that you're broken your entire life, you tend to want to help people. It's in your nature because you don't want anyone else to feel as awful as you did growing up. That's what this conference is all about. The website is sourceofsources.com/conference.

**Conference Website and Q&A Invitation**
(23:36) Would love to see you guys there. Source of Sources, by the way, is sourceofsources.com. You can sign up, and you'll get those two emails today, and they're pretty easy to read. I'd love to take questions and hear what you guys are thinking about, or how I can help with PR and give you some advice on any of that. I'm happy to share whatever I know.

**Opportunity to Win Conference Passes**
(23:53) >> Yeah. And a reminder, if you are off camera or something and not checking the chat, then you can go to highcaliberi.com/sos for Source of Sources and win one of the handful of passes I

**Meet Peter and the Crew**
(24:12) have to give out and be great to meet some of you there. You'll get to meet Peter. Come on. After seeing this, how would you not want a chance to meet Peter and this amazing crew in person? Feeling good about my standing desk setup right now, too. Okay. Jazella. >> Hey there. Big thanks

**Jazella's Question: Imagery in PR**
(24:31) for the candid conversation. This is awesome. Peter, I'd love to hear, both with regard to Source of Sources specifically, but also in general, how you see imagery factoring into the PR conversation these days. Is it still helpful to attach images, headshot, whatever? Is it the kind of thing you wait to follow up on? Is that possible to even share through

**No Attachments in Pitches**
(24:52) Source of Sources? >> That's a great question. Two things: number one, no attachments ever. If you're emailing a reporter, no attachments ever. That goes without saying. But the second thing, there's something about offering too much information. One of the things that SEO farms do out of, they all

**SEO Spam Tactics and Red Flags**
(25:10) follow the same playbook. One of the things the SEO farms are doing out of Asia and all that is they're saying, "Hi, here's my answer to your query. It's totally off topic. This is written by AI. I'm happy to talk to you more. Here's my name. Here's my LinkedIn profile. And here's my headshot." The headshot is hosted by some cloud, the LinkedIn is

**Fake Profiles and Website Flipping**
(25:30) fake, and that is a dead giveaway that it is not a real person answering. A lot of what these people do is they buy a website, "We will buy your home in Austin, Texas.com" for instance. They try desperately to get some backlinks from media outlets using Source of Sources, using HARO, things like that. When they do, and they get high enough SEO juice, they flip the

**The Generic Spam Template**
(25:56) website to someone else who wants it because now it's backlinked. If they were smart, they would not all use the same outline for their emails, because it is: "Here's my information. Here's why I think this is useful. Here's my LinkedIn, and here's my headshot." It is 100% the same in every single thing, and you think, "Okay, this person does not exist."

**LinkedIn's Fake Profile Problem**
(26:17) LinkedIn has a massive fake profile problem. I don't think they have any desire or need to fix it because it probably generates them more revenue somehow, but they have a massive fake profile problem. Some numbers are one out of four, one out of five are fake. It's obscene. Every single SEO farm, the first thing they do is create a fake profile for

**Leave Journalists Wanting More**
(26:42) this expert, and that's their website expert, and that's what they pitch. So, do not include a headshot. You want to leave them wanting more. It's like a first date at a bar. If I tell you everything about me, and I show you my family tree, and I give you measurements and whatever, there's no excitement left.

**The Power of Scarcity in Pitching**
(27:04) We're probably not going to get together for a second date. Where's the magic? But if I leave you wanting more, you're going to come back. If you give the journalist just enough, say, "Hey, I know my subject, I know what I'm talking about. I'm happy to help you. Reach out anytime." That's enough. They'll come back to you. Here's the thing,

**Journalists Prefer Their Own Photographers**
(27:24) 99.999% of the time, no journalist is ever going to need your headshot. If you're lucky enough to get a media outlet that wants a photo, they're going to send a photographer. >> So, headshot might have been a bad example, but as someone who previously represented, >> You can give them a link to a

**Providing Media Kits on Request**
(27:40) media kit if you have it. Give them a link to your website, but no LinkedIn, no headshot. Give them a link to a website. Give them a link to, "Here's what I do. Here's my company name." Highlight the company name with a URL. >> As someone who previously represented architecture and design firms,

**Visual Pitches and Architect Firms**
(27:57) a visual medium, to be fair, this was 10 years ago, I would basically be ignored if I didn't include imagery so they could get a sense of the quality. But I hear you. >> I wouldn't put it in the email. Instead, I'd offer to provide high-res photos upon request. I have a good friend of mine who handles PR for about 85% of

**PR for the Adult Industry Example**
(28:17) the adult industry. It's something he fell into, and he's repped the ABNs for 15 years. He's an awesome guy, a nice Jewish guy out of LA. We've been, he's one of my closest friends, and even he, he has a mailing list that for some reason I'm on, and you haven't really lived until you know that some specific porn star is

**Links on Request for Images**
(28:40) releasing two new anal scenes. It changes things. The reason I bring it up is because even he knows better than to put photos in, not even sexual, just photos of the porn star. He puts them, he says, "Links available on request, high-res available on request," whatever. It's not worth it, because keep in mind

**Mobile Readership and Image Loading**
(29:02) someone somewhere is reading one of your journalists is reading this on the subway, and they just went underground, and all they got was the first two paragraphs, and now the email, the rest of it's not loading on their iPhone because they have no service, and it's waiting for the image. If they want images, if they want imagery, if they want quality photos of your

**Diane's Question: Breaking into National Media**
(29:19) product, your this or that, factory plant, they'll let you know. >> Diane was asking in the chat: "I got local media covered. How to break into national media better?" >> If you have local and national media, international is actually surprisingly easier. For some reason, reporters

**International Media Insights**
(29:43) still want sources from the USA when they're overseas. I, at this point, don't know why, but they still want sources from overseas. So, one of the best things you could do is start subscribing to a lot of the journalists who have subsects, outside of who they're covering. Start subscribing, start interacting with them

**Engaging with Journalists Directly**
(29:59) one-on-one. Start letting them know, "Hey, I'm here in New York," which, it's New York City, it's not part of America, "and I'm happy to give you whatever you need if you're looking for XYZ, whatever." You'd be surprised how many of them will actually respond. It's just you're not wasting their time. You're saying, "Hey, I'm a source, or I rep a source, and I'm happy to help.

**Building Journalist Connections**
(30:18) It works in your industry. Here's what you need." I've made some of my closest journalist connections by doing just that, by following. It used to be that you follow them on Twitter. No, I don't recommend that anymore. But, threads, Instagram, Substack, things like that. Just figure out where they are and

**Understanding Journalist's Language and Beat**
(30:36) become passionate about what they're reading, and actually take the time to read them because learning all that stuff will actually give you a good background on them. The added bonus of that is that you're then starting to learn it in their language. I don't mean a different language, English versus French; I'm talking about the

**Approachable Language and BlueSky**
(30:54) language the journalist uses when they're talking about their specific beat. There are certain keywords and trigger words that you'll find that they're using on a regular basis. If you start using that, it makes you more approachable. >> I don't want to say BlueSky is dead because I don't think it is. There's still a base, but I don't

**Threads and Meta Products**
(31:12) most of the journalists I know who went there. As much as we all don't want to be on Threads and don't want to be in a Meta product, I was watching the season finale of Land Man this morning at 5 AM on my Peloton, and I felt the same way. I don't like that I like that show, but I have no choice but to come back for season 3 in six months.

**Blue Sky Engagement**
(31:31) >> It's the same thing. I'm still a skater, but also the folks I follow on BlueSky are politicians and folks I don't necessarily need to care about if I can reach them or not, or if they pay attention to me. >> Brian has a good question here. No, because there's something about the entire email versus one line.

**AI for Single Lines vs. Full Emails**
(32:01) I think you get a pass if you use one line because sometimes that one line is valid. It's the same thing with M-dashes. I like M-dashes. I think M-dashes get a bad rap, but I don't use them. But every once in a while, I'll slip one in. It's not the end of the world. Again, I think there's a difference between writing the entire email, having the entire email written

**Maintaining Your Voice with AI**
(32:17) by AI versus one line. If it's your voice, they start to know your voice. It's a lot easier. And Jim misses the word "delve." I can't tell you how often I have no random bolding in all my instructions on Gemini and ChatGPT, and I'm always telling it: "No bolding."

**David's Open Question to Peter**
(32:50) Stop. What else do you have for Peter? This is an opportunity here. I can ask him a ton. >> Okay, here's the thing. I've heard some of these complaints, and I've experienced them myself, the bolding, the M-dashes, the N-dashes. I think that one of

**Custom GPTs and AI Training**
(33:11) the advantages that I have using custom GPTs is that I instruct it to speak and respond in the way I choose, including no unnecessary bolding and no M-dashes. So, I think part of the issues that people are highlighting are just lack of training on our part. >> Yeah. >> But I think one of the challenges I'll

**The Importance of AI Training**
(33:30) push back on anybody is if there's something wrong with the AI where it's not doing what you want to do, my challenge to anyone is: are you training it or teaching it? Are you building it in a way to avoid those issues? >> I think you're saying what I'm saying, but in different wording. If you're not, but that's the thing, because if you're

**AI as an Editor and Writing Coach**
(33:46) training it and you're not a great writer, you're not training it well. >> Right. The way I would frame it is that you change the role of the AI. Instead of a writer for you, it's your editor that can copy check, watch, and train you how to write better. For example, I'm a better speaker

**Continuous Improvement in Writing**
(34:05) than I am a writer, so I have a custom GPT that helps me improve my writing and gives me coaching feedback based on information I give it. Everyone who's ever worked for me has been allowed to take as many writing classes at local CUNY or whatever college as they want, and I'll pay for it, because you can never be too good of a writer.

**Impact of a High School Writing Teacher**
(34:26) And, I'm a huge fan. I still take writing classes whenever I can. I think that I had a teacher, I grew up in New York City. I went to public school here, and I had a teacher in high school who gave me a journal, and she saw something in me and said, "You should write more." I'm so glad and thankful she did it. She's actually still alive. I'm

**The Importance of Writing Skills for the Future**
(34:44) taking her to dinner in a couple weeks. She changed the trajectory of my life. If you have any skill at writing, you can always get better. I see some of the kids in my daughter's class. I see her texts, and the things that, I weep for the future in a lot of ways. I think that

**David on AI and Authentic Communication**
(35:04) as much as you can do to become a better writer, it's never a bad thing. >> No, I was just saying someone in the comments said they heard some tips to use voice to help capture voice better. I will say the one thing I've discovered that relates to a lot of what Peter's talked about today is that there are so many things in either

**Value in Human Messiness**
(35:30) one-on-one communication or LinkedIn that I'm intentionally not using AI for. Peter, I even have gone the opposite way with some of what you've said about cleaning things up, because some of that messiness, even if it's not all the AI tells and all that, I feel some of that messiness is such a tell that here's a person who's communicating

**Authenticity Over Polish**
(35:52) his ideas well enough, but isn't trying to polish this. It's so clearly this person that they haven't gotten that exact message today. I'm kind of relieved when I know for sure that even something as simple as a LinkedIn comment, I'd rather have it be one of those automated "congrats" or "thumbs up" things than

**AI as a Thinking and Writing Partner**
(36:18) someone who I know used AI to help them write it. >> 100%. >> I look at it a little differently. The way I look at it is that I'm not writing for polish or to create an extra tone or style. I look at it like a writing partner. I'm making sure that I'm communicating my ideas clearly.

**AI for Clarity, Not Generic Slop**
(36:39) There may be typos. There may be grammatical mistakes, but the idea for me is that I use AI as a thinking partner and as a writing coach. That way I can communicate my ideas effectively and clearly, not necessarily to make polished, generic AI slop that everybody's seeing on LinkedIn. It depends on your

**Training AI for Your Voice and Tone**
(36:58) relationship you have with your AI. It does. I think that, again, journalists are a different breed. They're going to see and they're going to know if you're able to. I've been training my AI to speak in my voice, and it always asks, the one thing it always asks, and this is

**Voice Prompts and Contextual Writing**
(37:17) probably more of a tell to me, "Is it snarky enough for you?" [laughter] There's something to be said for that. >> I've also created voice prompts. In similar fashion, you've trained your AI. I've created voice prompts based on the context I'm writing, because it's trained

**Self-Promotion: Keynote Speaker & Neurodiversity**
(37:32) on my writing style. But I have to write in different style and formats depending on who I'm talking to and what I'm talking about. It's a mental training and how you teach it. That's what I always push back. I will say this, the one thing I didn't mention, if I have a second to self-promote: I've been a keynote speaker for 20

**Companies and Neurodiversity**
(37:53) years, and I've spoken to companies as big as Morgan Stanley Worldwide to as small as startups. I speak on customer experience, and I also speak on neurodiversity. One of the things that I'm finding is that more and more companies are starting to ask themselves what they're supposed to do with their neurodiverse

**Mental Capital Consulting**
(38:10) employees. So, I launched a company about a year ago with a doctor friend of mine called Mental Capital Consulting. We work with companies like Morgan Stanley, Adobe, Google, and a lot of small companies, helping them to create neuro-inclusive environments, because whether companies know it or not, 20% to 30% of their employees are

**Productivity Spike in Neurodiverse Employees**
(38:32) neurodiverse. What we saw during COVID is that when everyone got to work from home, there was a spike in productivity from the percentage of employees who were neurodiverse, because for the first time in their lives, they got to work the way they wanted to. Then, when they came back, it dropped off. So, companies are starting to learn that there's a benefit to that. So if

**Supporting Neurodiverse Kids and Adults**
(38:52) I could ever help you out on the neurodiverse side, or if you have a kid who's ADHD, take comfort in the fact they're going to take over the world. I've written a couple of kids' books on this and a couple of adult books on this. Peter, I have a question about that because in the last month I've talked to three different people who introduced

**Neurodiverse Individuals and AI**
(39:09) themselves as neurodiverse, which I appreciated. >> And all of them, in a different way, said they love AI because it's the only thing that can keep up with them. >> Yeah, that's 100%. Absolutely. >> And they're doing amazing work with it. >> The only downside I would say to that is that I want to get a t-shirt that says this: "AI should not be your

**AI as a Thinking Partner, Not a Therapist**
(39:30) therapist." I have absolutely no problem. I talk to my AI all the time. Sometimes it's as stupid as, "Why are people so stupid?" And it's smart enough to go, "Okay, what happened now? Let's walk through it. Don't stab anyone." But, as long as it's not replacing an

**Dangers of Untrained AI Therapist Apps**
(39:48) actual therapist. I think that the rise of these AI therapist programs and the rise of these AI therapist apps, a lot of whom have absolutely zero cognitive training behind it, is dangerous. But in terms of using it because it's that quick, 100%. >> The one woman I spoke with said that she describes herself as having 25 to 100

**AI Handling Mental Overload**
(40:17) tabs open in her mind at any time, and AI can handle all of that, and no human can. >> My introduction is that I have only two speeds: I have namaste, and I've got "get things done," and there's no middle ground. But, I'm petershankman.com. I answer all my own email. I'm

**Peter's Take on Meta**
(40:37) at Peter Shankman on all the socials except Twitter. I want to answer Daniel's question: "Is my hot take on Meta considering that they literally shut down the metaverse yesterday?" It's not going anywhere. It still has Facebook. It still has Threads. Facebook, as long as old people exist, Facebook exists.

**Meta's Declining Relevance (Facebook, Instagram, Threads)**
(40:56) It has Instagram. So, it's not going anywhere per se. But I think it's starting to become slightly less relevant. I could tell you that I don't keep Facebook on my phone anymore. If you told me that I wouldn't be doing that 5 years ago, I'd have thought you were crazy. Also, I can tell you from a 12-year-old's

**Gen Alpha and Social Media**
(41:15) perspective, my kids see very little value in social. They're much more private than we were even 10 years ago. I'd be curious what Stella says about that, David. I don't let my kid on social. The one thing they all do have, and every single girl in my daughter's class, they're addicted to

**Pinterest and Chick-fil-A's Gen Alpha Popularity**
(41:37) Pinterest. >> It's amazing. >> I don't think they thought about that as the target market. >> The two brands that have surprised me the most with Gen Alpha: Pinterest and Chick-fil-A. I cannot believe my daughter's in middle school and has this "out to lunch" thing a

**The Chick-fil-A Phenomenon**
(41:57) number of days a week, and I can't tell you how often the brand Chick-fil-A comes up in the parent group chat, that this is what the entire school wanted to know: "Is Chick-fil-A within the out-lunch zone?" They're obsessed. I went to take her there on Friday. It was the most entertaining mob scene I've ever seen in a fast-food restaurant. It's

**Why Pinterest is Popular with Gen Alpha**
(42:24) just full of 12 and 13 year olds. It was kind of amazing. >> Why do you think Pinterest is so popular amongst them? >> I think there are a couple of reasons. One of the big ones is that parents, for better or for worse, we think it's okay. It's not TikTok, it's not Instagram, it's not YouTube. So, I

**Parental Approval and Privacy on Pinterest**
(42:46) think for whatever reason, we think it's okay. Mind you, half the content she sees, I'm sure, comes from TikTok on Pinterest. But, if she has a Pinterest board about sewing, my daughter likes sewing for some reason, and she has three Pinterest boards about different stitches. I love

**Pinterest: Perceived Safety & Private Boards**
(43:02) that for her. I think the number one reason for parents is we don't think it's that dangerous, so we let them on. And the second reason is because they can create these Pinterest boards that are only viewable by their friends that we've approved to be their friends. So, they feel they don't feel like it's a public space.

**Daughter's Concern for Privacy**
(43:23) As long as I can remember, she's, I called her, she was bothering me on the subway a couple of years ago, and I said, "Jess Shankman, Dad!" And she said, "Now everyone knows my name, and they're going to come up to my house and hack me!" That was literally what she was worried about. It's that feeling of safety.

**Pinterest Content and Buzzfeed Quizzes**
(43:40) >> It is wild, and it's also funny. So much of Pinterest is just a feeding ground for Buzzfeed slop and poorly produced content. But, I'll actually get on there and do the quizzes with her. "What kind of fruit are you?" This stuff is still all over

**Peter's Instagram and Travel Reels**
(44:04) Buzzfeed. I put my Instagram link. I do a lot of reels with my daughter. We make a lot of fun stuff when we travel and things like that. Feel free to share if you want some fun eye candy. I don't know. >> Great. I have to go catch a flight and not be able to come home in a few

**Farewell and Thank You**
(44:22) days. So, I want to thank you guys for having me. David, thank you for setting this up. I hope that was useful, guys. Feel free to stay in touch and reach out, please. >> This is awesome, Peter. Safe travels. Many of us will see you next month. >> I hope to see you guys. I hope you guys show up on February 11th. We

**Conference Seats and Discount Code**
(44:39) have about 15 seats available. It's sourceofsources.com/conference. If you use the code SOS100, it drops a bunch of money off the ticket. So, feel free to use that and see you guys soon. >> Thanks, Peter. We'll see you next week with David Levy.

**Closing Remarks**
(45:01) Anything else you need? Thanks, everyone.

## How Leo Morejon Builds AI Tools with Lovable Cursor and Vibe Coding

Speaker: Leo Morejon
Published: 2026-01-16
Tags: vibe coding
Video: https://www.youtube.com/watch?v=YuO6CGxOjZc
Page: https://aimarketersguild.org/sessions/how-leo-morejon-builds-ai-tools-with-lovable-cursor-and-vibe-coding

**Introduction to AI Insiders**
(0:05) Hey everyone, welcome to another edition of AI Insiders with AI Marketers Guild by March Media. I am David Burkowitz, your usually friendly host. We've got a much more friendly guest today, Leo Morejon, a multiple-time colleague, multiple-time collaborator. He was with me from the start. He actually built the first Ad Markers Guild website.

**Leo Morejon's Entrepreneurial Journey**
(0:32) He's been with us in prehistoric days, and Leo has been more and more entrepreneurial, always doing cool stuff beyond these amazing hats he's worn on the agency and brand side. He can tell you all kinds of things about winning Guinness World Records for cookie brands and crazy stuff.

**Focusing on AI and Leo's Background**
(1:00) Today we'll focus a little more on the AI front and the stuff that Leo is able to ideate and come up with and market fast. It's cool. So why don't you share a little about who you are and anything I didn't quite cover yet?
No, of course. I appreciate being on. Thank you so much for having me. I appreciate everyone joining to hear me

**Leo's Career Philosophy: Gold vs. Shovel**
(1:23) speak and hopefully have a really fruitful educational conversation that really supports you and your work and everything you're doing. Berky, thank you for the kind words and for having me. I look at my career in two different ways: either I am looking for the gold or selling the shovel. I started in big ad companies from WPP, JWT to 360i at Densu.

**Career Shift: From Hunting Gold to Selling the Shovel**
(1:46) I look at that, I'm looking for the gold. I'm out there hunting. I'm out there digging. I'm out there shuffling, looking through what can I build and do that is amazing and impactful. Then I went into the technology world after that, still within the realm of social media and marketing. In the MarTech world, I look at that as selling

**Enabling Marketers: Entrepreneurship and Hormel**
(2:03) the shovel. I am giving the tools to other marketers so they can go and find the gold. From there, my experience goes into entrepreneurship to working at big brands. My day job currently is at Hormel, the makers of Spam, Applegate, Justin Peanut Butter. I'm running all social media, influencer marketing stuff. As Berkie mentioned, I

**The Best Time to Be a Builder**
(2:26) am always building. I'm always learning. I'm always testing. The last few months, I've shipped faster and built some of the coolest things and most successful things that I ever have in my whole life. I think right now is the best time to be alive for someone who's a creative, for someone who's a builder, or any marketer. If you're not getting up and

**Embracing AI Possibilities**
(2:48) getting excited every single day, maybe a little scared, you're really not testing and looking at all the possibilities that are out there. David: Why don't you dive in because we could ask why you're not so scared about AI taking your job, but I feel we all have our reasons and some of us might

**The AI "Aha!" Moment**
(3:10) even want AI to take some of our jobs. The stuff you are building, what's great with it is, was there even taking one step back, was there an aha moment for you where you're, "This is fun now, or I can do things I couldn't do before now?"
Leo: I remember it very distinctly. It was about my son's about to be 3 years

**Pre-ChatGPT Discoveries**
(3:38) old. It was right before ChatGPT came out. There was the playground so you could still play with GPTs. I remember going in there and building simple calculators or word replacement tools, and if this then that, just for text. I was profoundly amazed and felt I discovered gold. The only other time I ever felt that was

**Social Media and AI: World Changers**
(4:03) when I discovered social media in one way, before it was called social media, just doing some stuff on MySpace. I'm, "This is going to change the world." I saw the future. I'm, "This could do marketing. This could do coding. This could do essentially anything." I still remember that, and it coincides so well with my

**Personal Connection to AI's Emergence**
(4:23) son being born. There's a lot of positivity in my life looking at all this, and just being able to play with it is incredible.
David: So he's around the same age as ChatGPT, huh?
Leo: Yeah. Yeah.
David: That's super fun and just something that sticks out in my memory. But yeah, I remember working, and then

**Chat GPT's Accessibility Shift**
(4:42) when ChatGPT came out where you could actually talk to it, that was also incredible. This is going to change and make it a lot more accessible to everybody else, not just this playground that looks almost not a terminal, but just a little form that you work in.
David: And then you mentioned this new

**The Vibe Coding Phase Shift**
(5:02) phase shift or something to that effect happening. You said it several months ago, and what happened then? Why? What did it for you?
Leo: I think all these vibe coding platforms from Cursor, Lovable to Replit, to anything else that's out there. I've always built things, but I am not a coder. When I say I've built things, I probably should say more like I've

**Producing Ideas vs. Coding**
(5:23) produced things. I've always had a lot of ideas. I've hired. I've partnered with technical people. I've gone out there and tried to put scripts together, but it would take so long. It'd be so costly. The resources would take up so much energy and time that things would ultimately fail. Even if I had successful things, I actually had one of the first successful-ish

**"Prompt to Product" Tool**
(5:49) products when it comes to combining ChatGPT with some kind of marketing thing. It was called Prompt to Product. What I did was I allowed you to get a WordPress setup plugin where you could write a prompt but then turn that prompt into a feature. We actually used it for one of your earlier websites, Berky or David, we

**Translating Resumes and Recipes**
(6:10) looked at your job, right? Or your resume, and then we translated it to be more marketing. I had built one that is put in a recipe and then make it gluten-free or make it vegan or whatever. This is just on the back end, a few forms that you would fill out and say, "If someone inputs a recipe, make it

**Maintenance Challenges**
(6:30) gluten-free." You could put it into any WordPress site and make it a feature or a tool. It was super successful, but then it would break.
David: Have to pay someone to fix it.
Leo: Then they would change the API, I'd have to pay someone to fix it. I want the new features, I'd have to pay someone to fix it. While I was making money, I was

**The Rise of Vibe Coding**
(6:51) also losing money. Then just about the tail end, I was, "I can't do this anymore because I'm funding this and actually losing money."
David: Vibe coding really came to be. You could always vibe code and Chat-B, right?
Leo: Vibe coding as a vertical and a focus came to be, and I'm, "I could update this myself."

**Coding Independently**
(7:12) I can code this anytime something breaks or changes.
David: Yeah. This, by the way, one of the things, I've got this developer on Upwork for doing the kinds of things you're talking about, and he wrote me the other day. He's, "Did the servers change for your site and my High Caliber site?" I'm,

**Taking Control of Site Edits**
(7:36) "Oh, I forgot to tell you. I had given it to him to go, and then you put on WordPress to make it a little more stable or something." Then I said to him, "Yeah, I forgot to tell him. I want to go and make a bunch of edits myself and start screwing around with it more." So I took it back to Base44, one of these vibe coding platforms.

**Constant Iteration and Ownership**
(8:00) Now I'm constantly iterating myself. Even if WordPress can be more SEO friendly or some of these other benefits, this to me, I get to own this. They're great microsites all the time and things like that. It's fun.

**Sharing Vibe Coding Examples**
(8:22) Leo: It's so much fun.
David: Yeah, it's totally fun. Maybe I could show you.
Leo: You know what?
David: I was just going to say, why don't you share some examples of?
Leo: Yeah. And I can maybe vibe code, but do I know how

**Introducing Jingle My Brand**
(8:38) to use Zoom?
David: That is always one of the biggest tests of your technical acumen here.
Leo: Let's see here. Okay, we got Jingle My Brand. Yes. One of the most successful things I've ever done and one of the most fun things I've ever done. This was built essentially within a weekend. It took a couple of weeks, but

**Jingle My Brand: A Weekend Project**
(9:03) just as far as hours go, in a weekend.
David: And I will preface this and say that I spent eight hours in sessions training marketers. I built 250 slides of material, not including live demos and all this other stuff that I built for this massive training marathon on marketing stuff. This was their favorite thing. It was

**Everyone's Favorite AI Tool**
(9:33) in a way, the least important, right? It wasn't they're going, "Put this on TV."
Leo: And it was everyone's favorite, hands down. So now, David, you can explain it.
David: Yeah. No, of course.
Leo: All right. My wife and I always talked about jingles and how amazing jingles were, and it's so sad that after, I don't know, 1998, it just

**How Jingle My Brand Works**
(9:54) went away. We're, "It'd be cool if brands brought back the jingle." One weekend, I started coding this and developing it. All you got to do is go to the landing page jinglemybrand.com, type in your brand name, type in your product information. I'm just using a pug perfume, pug cafe. Type in all the benefits. Choose your style. You're going to choose 80s 90s pop rock,

**Free Jingle Generation**
(10:19) 80s new wave, classic jingle, the length. What's cool right now, you can generate your lyrics on the fly based on that information. If you want to use this, this is completely free right now. I am funding this all with my own money. Anytime you create something and use it, I am happy to pay for it for you. You don't have to do anything. It's

**David's High Caliber AI Jingle**
(10:38) just free for you to use. Then within a second, you generate music. I already have one loaded up. I loaded up David's here. Here we go. This should work. Ready?
David: Yes. Yeah.
Jingle: Smart and true. High AI here for you. From strategy to making it fly, they'll help your business reach the sky. High

**Create Your Own Jingle**
(11:05) caliber AI. It's the real deal with David Burkowitz. You know how good it will feel. He's an author and exec with a brilliant mind. The other David Burkowitz, leave him behind. High.
David: So yeah, anybody on this call could go right now to Jingle My Brand and create a song. I know Jim and Amy and others are going to be creating

**Jingle Features and Sharing**
(11:34) jingles, totally screwed the rest of their day. They're going to be canceling one and two and three o'clock meetings.
Leo: I love it. The back end for you. My back end looks a little different, but for you, you'll be able to play it. If you share it, it'll extend the song.

**Jingle My Brand: 100% Vibe Coded**
(11:52) David: Now you can embed a video version of it, as I just did during a talk yesterday, mind you.
Leo: Yeah. Guess what? This is 100% vibe coded.
David: Wow.
Leo: From front end to back end.
David: And tell us a little more in the weeds, what platforms did you use for vibe coding?

**The Cost-Effectiveness of Vibe Coding**
(12:11) Leo: Of course. Maybe I'll tell you a little about my philosophy. First of all, when people talk about this is expensive or that's expensive, it is all really inexpensive, right? To build this would have taken thousands of dollars. It wouldn't be possible really, but thousands of dollars and it would have taken months to build. Because I have a young child,

**Why Lovable is a Preferred Platform**
(12:33) a day job, startup, all that stuff, I decided to go with something more Lovable. I actually built this on Lovable. One of the reasons I love Lovable, and it might be a little more expensive than Cursor or even Claude Code, is that it helps me with all the backend stuff. When I put my son to sleep, I could come in

**Lovable's Role in API Connections**
(12:51) here and code, and I don't have to worry so much about the backends. I built this all, but it's not only that. I've looked into other LLMs and APIs that I connect to in order to generate the lyrics, in order to generate the songs themselves. Not everything actually happens within Lovable, but Lovable allows me to connect everything. I would say 98%

**Connecting Other Tools with Lovable**
(13:10) of it all is built within Lovable.
David: Yeah. Because the one point where I was getting a little stuck on things, and I still love Lovable, and we might actually have them present here soon, is
Leo: Yeah.
David: There are other things you typically need to connect once you start doing things a little more sophisticated, like what you're doing.

**Using AI for Meta-Layer Guidance**
(13:37) David: It's just a matter, do you keep it? Yeah. You just, and it's usually stuff that one of the things that's been so helpful for me for all of this stuff is having a running conversation with ChatGPT or Claude and being, "Okay, now I'm trying to do this, how can I do this in Lovable?" having that meta layer.
Leo: Yes.
David: So,

**Cursor vs. Lovable**
(13:59) Leo: I do that quite often. I actually prefer to use something like Cursor.
David: And if I, if it was just up to me, and I'm sorry, not up to me, it is up to.
Leo: But if it was up to my time and resources, I would actually use more Cursor or Claude, and do you use the terminal

**Recommendation: Pick One Platform**
(14:19) interface. I'm more comfortable with code. I'm more comfortable with connecting APIs on the back end, all that good thing. But using both in parallel will save you a lot of time and a lot of money and a lot of energy. So I definitely recommend that. Ultimately, what I recommend is pick one, try to learn it as best as you

**Cost vs. Value Proposition**
(14:35) possibly can, and just go from there. Cursor might be cheaper than Lovable, and by quite a bit if you compare them. But ultimately, this website would have cost thousands of thousands of dollars to build. So if I'm spending even 80% more, I'm okay with that.
David: Yeah. Base44 typically costs a little more than Lovable,

**Platform Cost Trade-offs**
(14:58) but it does more stuff inside it. Claude Code is only 20 bucks a month, and Netlify for instance, for hosting it, is free, or there's a great $9 a month plan. But you have to be a little more tech-savvy to use it and use a platform like Ghosty. So there are definitely all these kinds of tradeoffs, and I actually spent a long time talking

**Scaling and Gemini Guidance**
(15:22) to Gemini, "Okay, I'm not really comfortable doing this. Tell me for something I'm usually dealing with small B2B sites. Jingle My Brand versions of it could go huge. It could be owned by Hallmark.com. So you might need to, hopefully, you need to scale it one day."
Leo: Yeah. Yeah. Oh, that's a whole other

**Digital Sovereignty and "Jailbreaking" Platforms**
(15:43) conversation. Eventually, if Jingle My Brand gets so big, I will want to jailbreak it. I don't personally like that essentially a different corporation or another company is owning my technology or my platform or my IP, in a sense, Lovable.
David: It's behind their gate.
Leo: I'd like to break it out eventually. So while I am personally comfortable

**Building Quickly with Lovable**
(16:08) with coding and things like that, I look at it from a resourcing perspective. I don't want to spend time doing that. I just want to build and build quickly. So I would choose something like Lovable. I recommend the same for everybody too. One thing I'll say too, I'm connecting to different APIs and things like that. Just to create

**Social Lollipop: A Vibe Coded Startup**
(16:26) the song, that's one different API. To download the video, that's another API. This is all being connected in the back end. That's one of the things I love about the other thing is legitimately a startup. I'm a founder of this company called Social Lollipop, and I have a customer. I am building. I am iterating, and I 100% built it from Vibe Coding. So I'll show you some of the

**Free Lead Generation Tool: Safe Zones**
(16:52) tools that I have right now. The first tool, it's completely free, just between us. It's all lead generation right now. It's not the most fancy tool, but I use it as a marketing lead generation thing for people to use for free. If you're in social media, you know that being able to actually see what the safe zones are, where things are being blocked, is difficult. I go ahead

**Social Media Safe Zone Tool**
(17:11) and build something where you could go in here and let me find Mr. Bezos over here, and you can select the platform, mobile, desktop, Facebook, and go through the different types of posts. You'll actually see what the safe zones are, right? Super simple, but this is 100% vibe coded. The other thing that I

**Adberry: Paid Media Ad Insights**
(17:33) have here is something called Adberry, which I absolutely love. If you come in here, you're going to be able to see what paid media ads are being run by companies. You'll go ahead and, Nike, do a search. I'm showing you this because this is 100% vibe coded by me. I am not a developer. I just have ideas, and I know how to use talk sometimes.

**Adberry: API Connections and Report Generation**
(17:57) You're able to search, find your ads, different ads. I love this, generate reports, all that. This is just another example of things that you could build, but by also connecting different APIs. One connects to the Facebook API. Then if I generate a report for this, I am generating a report using ChatGPT or

**Using AI to Connect APIs**
(18:20) Gemini, or something that actually analyzes the text. I am in there typing out, "I want to connect to Facebook, how do I do it? I want to connect to ChatGPT, how do I do it?" And iterating and having conversations with Berky said, either I'm doing it within one of these tools, or I'm actually doing it on the side on Claude or Chach team. One other

**Speech Therapist Tools SAS Platform**
(18:44) thing before I let anybody ask. My wife and I have a business called Speech Therapist Tools. She's a speech language pathologist, and we build different tools that help speech language pathologists, and it's another SAS platform. You come in here, there's a yes no question generator. There's a chronological age generator. There's a tool that allows you to

**Developmental Goal Generator**
(19:04) type in your son's or daughters' or kids' favorite toy. Type in their age. You start playing, and then it'll give you different developmental goals and different developmental things and ideas that you could use specifically for that specific goal, excuse me, specific toy, and for your ultimate child's age. This is all 100% vibe coded.

**SAS Product Traction**
(19:26) Essentially what you've been seeing here are examples of SAS products. I've built SAS products. I ultimately think about how can I build a business around what I'm doing.
David: Do you have any traction on these now? How many users are using this?
Leo: Oh yeah. Speech Therapist Tools is probably the most popular one. We get thousands of visits a month.

**User Growth and Business Model**
(19:45) That's because it's based off of a different property that's existed forever. I'll show you, this is a WordPress site, but it's just a general business. We have a SAS platform that we've attached to it, but it's really resources for speech therapist tools. Social is more of an enterprise medium to size business tool, and I found it a

**Jingle My Brand User Base**
(20:04) few months ago. I have one client and a few free users. I say clients as people paying. When it comes to Jingle My Brand, I have over a hundred users, and I have probably, David, you are one of the top users, but I have at least five people using it every single day.
David: Wow.
Leo: This is a paid product.

**Free Jingle My Brand and Holiday Styles**
(20:22) David: This is free.
Leo: I am giving it away for free. Even during the holidays, David actually helped me create some of this as far as the music styles. Even created a version for creating Christmas music or Hanukkah music. You should still do this, even if the holidays have passed, but I think particularly, I'm not pandering, but David did such a great job

**Favorite Projects and Influences**
(20:43) of creating the Hanukkah styles.
David: I love every single song that comes out of it. It is one of my favorite projects I ever worked on. It's so funny as someone who grew up with all these references from the Beastie Boys and Adam Sandler, and folk music, to be able to contribute to this. It is one of my career highlights.

**Legal Disclaimer and Lovable Pro**
(21:11) David: And for legal purposes, they are in the style of Rob Rack Rob
Leo: Inspired by. Yes.
David: Yes.
Leo: You're not actually getting new Beastie Boy songs. No.
David: So, do you have the pro version of Lovable? 25 bucks a month? Is that
Leo: I pay more than that, but yes.

**Higher Lovable Costs Due to Usage**
(21:31) David: Why do you pay more?
Leo: Yeah. Oh, because I'm using it. I'm using it for all these different platforms. I'm using for different iterations. I'm not voluntarily giving them more money. I'm using a lot more credits.
David: More credits. Okay. That's
Leo: Yeah. Yeah. But as Burky said, I save money by having conversations in

**AI for Efficiency: Claude and ChatGPT**
(21:49) Claude or having conversations in ChatGPT, and then popping it over into Lovable and saying, "This is what I want to do." To reiterate, I love Lovable. Nothing against them, but because I'm a little more technical, I prefer to use Cursor or one of Google's tools or Claude Code. But it's just for time and resources for me. I'm happy to open it up and answer any

**Q&A and Tips**
(22:10) questions or David if you have any other questions, but I thought I'd give some ideas or some tips that I've learned. I love it. Happy to pause.
David: Yeah, let's see. Just other questions, other stuff you've shared so far. I know the URL for sociallollipop is going to make sure to put that in the chat.
Leo: Thank you. And it, yeah, it

**Jingle Length: Feature or Bug?**
(22:34) it is a funny thing that I noticed with Jingle My Brand that Zach just brought up, it tends to make longer jingles than you ask for.
Leo: Yeah. That is a known, whether it's not a bug, it's a feature. I have known about the bug, but no one's complained about it. People have mentioned it to me, but they're,

**Theme Songs vs. Jingles**
(22:56) "Oh, but I still like it." So I've never changed it. But I could easily change that. Jean was mentioning they're maybe more theme songs than jingles. What would you call them?
Leo: Oh, yeah. For sure. I think to make it a jingle, maybe I do have to shorten it and make sure that the time frame works. Yeah. Because I think

**The Fragility of Vibe Coding**
(23:13) the jingles are quick.
David: Yeah. Fit in a 50 or 30-second ad, right?
Leo: Yeah, for sure. Yeah. So, maybe that's what I'll do tonight.
David: There you go.
Leo: The funny thing is, I am sometimes scared to go in something because if you've vibe coded before, you could update one super simple thing, and

**Risk of Breaking Things**
(23:33) it just breaks everything. I'm always, "All right, I'm going to go in there, and I'm going to update this. I need to be okay that if everything else breaks, Zach, I saw you come off mute. Nice seeing your face, by the way."
Zach: I was going to say, to me, a jingle's eight seconds.
Leo: True, true. It's the hook that goes across every

**Voice Selection Feature Request**
(23:53) thing.
Leo: Yeah. I love it. It's your audio brand.
Zach: This is fun. I'm working on a retro brand. The 80s synth-pop is awesome. It doesn't, it's it's, does it let me select the audio voice?
Leo: Not the voice right now. We're

**Value of Feedback**
(24:13) recording this, right? Because this is all super valuable, and I love this feedback.
Leo: Oh yeah. I'm happy to build all these things out.
Zach: Yeah. I mean, as somebody who, from a recommendation from Burkowitz, has been spending a lot more time than probably needed inside of all the sweet things that Gemini's released recently.

**Gemini Voice Limitations**
(24:34) Zach: Yeah. Yeah. Even though they only have a limited number of voices they're letting you use.
Leo: Yeah.
Zach: It's nice to be able to switch between the voices to get it right. So that's just one recommendation.
Leo: Yeah, I love that.

**Pro Version Considerations**
(24:53) Leo: No, I love that. Let me just, because we're all marketers, let me give you my thinking behind this. I could have made this a lot more sophisticated, but I would have created a lot more friction. I am thinking about creating a pro version that I will charge for to recreate the song, remix the song, update the lyrics,

**Simplicity and Friction Reduction**
(25:11) and things like that. But the reason I made it so simple is because if I had to have you fill out more than this, or more Jingle My Brands, or generate your lyrics, you wouldn't do it, or very few people would do it.
Leo: Right now, I was, "Let me get it out there."
David: And then iterate. That's related to if I became a

**Financial Projections for Pro Users**
(25:36) pro user, are you able to run financial projections of how much tokens it uses if I were to become a pro user, right?
Leo: Yes, on my end. So, are you at this stage with this specific application able to pencil out how much money I make or lose you as a pro user?

**Monetization Strategy and Thresholds**
(26:01) Leo: For sure. I know how much I'm losing every time anybody uses it right now. But I'm comfortable. I've set aside a bunch of money saying, "I'm going to have this bucket of money for my own, not personally, it's by my business, but my corporation set out money." I have a little bucket, and I know how much people are charging. So once we hit that threshold, I will then revisit and be

**SAS Profitability and User Acquisition**
(26:23) like, "Am I going to start charging people or not?" But yeah, I need to, and this is the fun thing about, you run a SAS and you run a business, how many users do I need to pay myself back to make this profitable? I'm probably going to be losing money on the first 100 users, but after I get to 101, then I'm going to start

**Jingle My Brand's Potential**
(26:41) making money, and I can continue to grow. Those are all things what I'm talking about. The funny thing about Jingle My Brand is that I have people David, my wife, and other people saying, "You need to build this further. You need to build this further." But I essentially built it as a cool little tool. I'm, "It's definitely not a toy," but I'm, "It's

**Credit Consumption Transparency**
(27:00) just fun. Everyone's loving it. Everyone's having fun. Everyone calls it magic." I'm, "It's just there." But more conversations, I'm, "I need to jump on this more." And David, Zach brings up this other point that still confuses the heck out of me. I often have no idea how many credits I'm burning through, and then it's

**Credit Tracking Challenges**
(27:25) are any of the platforms really good at that?
Leo: No. I would say no. But that's why if you're worried about credits, and I still think this is all inexpensive, but 200 bucks can be a lot of money for someone, right? But ultimately it's 200 bucks versus spending thousands of dollars and months to build something, right? It's

**Firebase for Cost-Effective Building**
(27:47) all relative. If you were worried about money for whatever reason, I would use something like Firebase or even another one of Google's tools. Especially Firebase, it may not be the most powerful. It may not be the best, but it connects to a lot of Google's products in the back end, so things are easier, and sometimes

**Cursor for Comfortable Coders**
(28:08) cheaper. If you are a little more comfortable, use something like Cursor. I used to pay 25 bucks a month for Cursor, and I never hit my limit.
Zach: My question wasn't about, I think my question was to David, is it even possible right now with the way they're doing the tokens, and the way you consume the credits, to actually run a financial model?

**Financial Modeling for AI Agents**
(28:27) David: Oh, that's an interesting question. I look,
Zach: That's more what my push was. I got a buddy who runs a company here, and it's scaled, and they put in a bunch of AI agents to help them automate stuff, and they have no capability of modeling out their

**Is Small-Scale Financial Modeling Possible?**
(28:48) their input for what that translates on a monthly basis to their credits, and then the credits change.
Zach: Yeah. Just curious on the small end if that's even possible. David, do you want me to go?
David: Yeah. Why don't you go?
Leo: I think just anything else, it's a calculated risk. There's always going to be, unless you're fully verticalized,

**Calculated Risk in AI Costs**
(29:09) it's a danger that you're going to have to pay for, right? If I build widgets, and they're plastic widgets, where am I sourcing my plastic from? Are there going to be taxes? There's going to be that. So, I think it's always a bit of a calculated risk. But ultimately, a lot of this, because there's so much competition and the technology is moving so quickly, it is

**Inexpensive AI Generation**
(29:27) relatively inexpensive. I'm sharing this between us, I know it's being recorded, but us friends, if I just charge everybody here $20 a month, I'd be making money. It cost me so little to generate a song and generate a video that I'd be making money right off the bat. It could change tomorrow, right? But then just anybody else, that's how you plan.

**Diversification and Self-Hosting LLMs**
(29:48) I want to diversify. I want to have different tools. If it came to the place where I'm so worried about these tools, I could see about hosting my own LLM somewhere and self-hosted, which has its own other costs and stuff. But if the business grows, maybe that's something I could do with Amazon or Microsoft or something. It's an interesting time in the

**Sustainability of Free AI Tools**
(30:09) market because it is so competitive. At some point, the freebies are going to go away.
Zach: Are some of these things that people are building with vibe coding that are set aside as fun projects, if they did go to scale, is it something that's sustainable? That's, it's just a conversation I've had recently in the last couple weeks

**Digital Sovereignty and Platform Risk**
(30:33) with several people.
Leo: I love this conversation. I think we could talk for hours on it. The one thing I'll say, the sound bite of it all is, this is a big problem with digital sovereignty across the board. Look at Facebook. I come from the social media world where they built this garden. They told everyone they could come build

**The Facebook Garden Analogy**
(30:50) and get millions of followers on their page, and you post for free, and it's all great. Then one day they're, "Nope, we're going to build a gate and a wall, and it's not organic anymore. It's paid." Now you're, "Oh, but we need first-party data." Everyone's, "Oh, I need a website now." There was a trend when people, "Forget about a website. Don't

**The Continuous Risk of Digital Sovereignty**
(31:05) build websites." We live in this world where that is a continuous risk, and something that we all need to think about as users and business people, ultimately digital sovereignty. How does it exist? Does it exist with these big players? But I'll shut up now because I can talk about this forever.
David: You're the guest, so you don't have to

**Vibe Coding Focus and Frameworks**
(31:22) shut up.
Leo: No, no, no. But I want to focus on the vibe coding thing, especially if anybody has any questions, but I'm happy to jump into some tools or thoughts or frameworks that I wanted to share.
David: Yeah, and share away, right?
Leo: Awesome. First and foremost, plan as much as you possibly

**Planning with Whisper and Chat Tools**
(31:40) can. I actually use Whisper. I barely type. I just hit a button, and I talk directly into these tools. I open up a notepad, and I just talk. I'm, "I have an idea to build this tool, and the tool should do this, and I want to have this as the goal, and these are the type of audiences, and these are the type of users." I try to talk and iterate as much as possible in

**Start Ridiculously Small**
(32:00) ChatGPT, or one of the more conversational tools before I even put it into Lovable. So, when I'm feeding it into Lovable, I'm going in as planned and as thoughtful as I possibly can. If this is your first time building anything, I would say start ridiculously small. Build a calculator. Build a simple tool that you need help with, or maybe you don't even need

**Building Confidence with Small Projects**
(32:27) help with, literally an engagement calculator. Build something. The reason for that is I want you to build confidence. I want you to see the magic first before you're, "I'm going to try to build the best SAS in the world, or I'm going to replicate TikTok or something." It's going to break. You're going to fail, and you're going to be left feeling

**Importance of Backend Reporting**
(32:47) demoralized, "This doesn't work. I don't like this." So start very small. Build something ridiculously tiny and super simple, and then build from there so you can see the magic and gain the confidence. The other thing is I'm very big on building the back end. When I say that is reporting. For instance, yeah, Whisper Flow, I use that all day long. I

**Admin Sections and Data Collection**
(33:09) paid for it. I used the trial for two days, then I paid for it. "What am I doing here? Take my money, please." But build the back end. I have a backend admin section for all my tools. I know who's logging in, when they're logging in, when they sign the agreement, the songs that they're creating, things like that. I even make copies of certain things on the back

**Building Tools for Your Tools**
(33:28) end, not necessarily for Jingle My Brand, but I'm talking about across the whole website. Even when you use an email, it's collected in the back end. I could export it, right? So, think about that. The other thing, and this is going to be a huge money and time saver, build tools for your tools. For instance, I constantly build out. If I'm building

**Admin Tools for Content Management**
(33:49) this, let's say I'll give you the example for the Hanukkah one. See all these different styles. I don't constantly prompt Lovable or anything to update the different styles. I built a tool in the back end just for admins that says, "Allow me to build out prompts. Allow me to build out styles. Let me build out time frames," and things

**Gaining Control with Admin Tools**
(34:14) like that. Therefore, all I got to do is log into my website, go to my admin section, and constantly add different styles, and it's 100% for free versus going in there and, "Oh, update this. Now, change that." One thing I hate about that is I actually lose control if I'm just prompting. I don't know what it's building. I don't know what the prompts

**SEO for SAS Platforms**
(34:33) are. I don't know how the API connection looks. I don't know how many people are using it. So, I'm constantly building tools for my tools, and it saves me a ton of credits and a ton of money. The other thing I want to say is, when it comes to marketing and SEO, and I'm talking about SAS platforms more than if you're just building a landing page or some small marketing

**WordPress for Front-End SEO**
(34:58) project. What I do is, if you look at sociallollipop.com, it actually runs off of WordPress. So, I am able to control the website, the way it looks, build the blog, all that without using credits or anything. But if you start using the tools, then you go to app.sociallollipop, and that's when you're in the back end for

**Vibe Coding Tools and SEO**
(35:20) the vibe coding thing, because using WordPress or something else has better SEO. Right now, most of these tools suck at SEO. You could always build tools in the back end. I have for Jingle My Brand, I'm, "Allow me to have a tool on the back end that's for SEO, updating metadata and update." But I find that it's just easier and better to build something in the front

**Getting Started with Vibe Coding**
(35:38) end, and have the back end be built by the tools. Has anybody built anything, or is anybody currently building anything?
Earl: Leo, thank you so much for some of your tips. My question is, I've heard about the vibe coding, and I speak every day with my custom GTP bots on my AI team, but getting started with vibe coding, I'm, "Where do I even start?"

**Lovable.com: Your Starting Point**
(36:02) Literally, I get AI, but I'm, "Why do I even start in the beginning?"
Leo: I would tell you this. Go to lovable.com. Go to any one of these tools, Replit, or Bass, or Firebase. We could drop in the links in here, and it's super simple. Let me show you this again. Here's Lovable. It just says, "Start telling me

**Building a Simple Calculator**
(36:25) about the tool you want to build." Say again, start small. Say, "I want to build a calculator that measures engagement. I put in the interactions. I put the impressions. I put in the reach. And give me the answer." Hit go, and you're going to see the magic. You're going to have a tool that you could use. Especially if you start small, you're going to see something be built.

**Career Prospect Slot Machine Challenge**
(36:44) Earl: Awesome. I've always wanted to build a slot machine for career prospects. This is the industry, this is the company, this is the role. Do a slot machine and see what comes up based on my skill set.
Leo: Having spent enough time in these tools, I bet you can build that within the hour.
Earl: Sweet.

**Digital Marketplace Trend Space**
(37:02) Earl: Challenge accepted.
Leo: Go, go. If you need any help, reach out to me. I'm happy to talk.
David: I want to see it, Earl.
Earl: All right. Please.
David: Gauntlet thrown. I see you, David. Amy had a question about, "Curious if you know, Leo, the best tool to create a digital marketplace trend space where it constantly updates and creates

**Connecting to Trend APIs**
(37:22) charts to bring the insights to life. Can anything do that?"
Leo: Yes. Yeah. The answer is all of them could do it. The hardest part for you, and I could help you with this. I'm happy to go in deeper, but there's not enough time here to do it. It is then connecting to the APIs, right? Making sure that you have the connections to, let's say, TikTok

**Building the Frontend and API Connections**
(37:42) trends API, the YouTube Shorts trends API, and things like that. The front end is going to be pretty easy to build, and this is easy too. I'm talking about degrees, but you build a front end with the marketplace that's going to be Lovable or Replit. Then you need to find the API connections, get your API tools, get your app set up, and then make sure

**Data Scraping Complexity**
(38:01) that you collect all that information and feed it into Lovable or Claude or whatever, and you're going to be able to build that 100%. There are other tools that you could use to scrape data and have agents go out into the web, but that gets ridiculously complex. Essentially, any of these tools could do it.
Amy: Okay, you're definitely going beyond what I was even

**Learning Vibe Coding as a Newbie**
(38:22) imagining.
Leo: So, that's awesome.
Amy: Maybe I'll reach out to you on LinkedIn.
Leo: Yeah, please.
Amy: Okay, thank you.
Leo: Yeah, go ahead. Of course. Happy to do it.
Karen: I'm still learning what vibe coding is, a newbie in this space. I'll go as

**The Addictive Nature of Vibe Coding**
(38:42) Leo: Yeah. The best thing, being able to talk to one of these apps and have it build a site for you. It's, yeah. This thing, once you start doing it, it can get so addictive.
Karen: Yeah. It blows my mind. It really does. I'm going to keep going.

**Vibe Coding for Landing Pages**
(39:05) David: I literally do not give a talk anymore. I was prepping a talk that I'm giving tomorrow. Before I worked on the slides, I vibe coded the landing page. I was kind of more excited about the vibe coding than the slides. So yeah, but now it's totally messing up the order. I'm, "I can't not have that."
Leo: You build something.

**Building Tools for Day-to-Day Tasks**
(39:28) Leo: Yeah, I do the same if I need something at my day-to-day job. I'm, "Oh, this is hard to connect these two data points or mixing this." I'm, "How can I put it into whatever tool they give me to use and build it out?" One thing I also want to say with the short amount of time we have, Karen, you asked a good question. I'm sorry if I'm mispronouncing your name, but you asked a good question, how many people are actually using

**Marketing and Sales, Not Just Building**
(39:45) these things, right? Here's the thing with all these things, and I believe this across all AI tools. We're in the marketing game. We're in the sales game. This isn't necessarily on the building game. If you go right now, "Is there a tool that does this?" There probably is a tool. There's a tool that builds that you could probably build it.

**Winning in the AI Tool Market**
(40:07) It's the people that can get in front of clients, get social media attraction, get growth. Those are the people that are going to win because ultimately, if you want a tool that looks at social media analytics, I bet you if you go to any one of these directories, you're going to find 50, right? We're still in a weird place where can't ChatGPT just do

**Users Want Tools, Not Chatbots**
(40:29) this for you, right? If you're building a certain tool, a calculator, can I just go to ChatGPT? One thing I found, especially when it comes to speech language pathologists, they don't want the code start, "Here's a chat, ask it anything." They actually still want a tool. They just want a hammer, right? They don't want the Swiss Army knife that they have to

**Reverting to Simple Tools for User Engagement**
(40:47) figure out how to open and do that. People still want to say, "What's the age of this person?" and put in the information. I learned this the hard way. I had a bunch of tools built, and I'm, "I could just create a chatbot that does all this. Just ask it what the age is, and have it create a lesson plan." The user base went down. I reverted back to these

**Vibe Coding: Web Pages vs. Apps**
(41:09) simple little tools, and I got my traffic back up.
Earl: So for vibe coding, is it only really building web pages or things on web pages, or there other extensions of it?
Leo: Oh, that's super. No, I love this. Lovable and Replit and Firebase are a little more just for front end, SAS, server-side applications, right? But Cursor,

**Building Mac and Mobile Apps**
(41:39) Claude Code, and a lot of these other tools, you could build apps for your Mac, build mobile apps, and things like that. Yeah, this does get harder, right? Because then you need to understand how to install certain backend code stuff, Java on your Mac, and have it connect. A lot of times you're not seeing what you're building until it deploys. That gets harder, but

**The Challenge of Non-Web Interfaces**
(42:02) it's definitely possible. I've built some things on my backends, just as a Mac app that I wanted
Earl: Because I, you can just make web page after web page, but that's not necessarily how consumers are interfacing. They still are, there's all the rest in it.
Leo: One of the bigger problems that a lot of

**App Conversion for Vibe Coded Tools**
(42:21) these tools have right now, but they'll probably fix in the next few months, Lovable and some of these other tools, is that you can't actually create an app for it, or software for your Mac or Windows or something that's jailed within a website. I would love for Jingle My Brand to be an app on your phone, and it can be. There are tools

**Connecting Payment Systems**
(42:41) that you do a search, you'll find tools that take your vibe-coded app and make it into an Android app. You'll find those things, but it's not as easy as you would think.
David: Cool stuff that folks are sharing. Christopher, yeah.
Leo: So, what's funny is on Jingle My Brand, I already connected the payment system and all

**Stripe Integration Complexity**
(43:00) that, but it's super simple. A lot of times, Lovable itself will prompt you, "You need to create a Stripe account, and Stripe account needs the code." Then I'm, "Wait, where do I connect? Where do I go into the chat?" I'm, "Where do I get the code for Stripe?" Then it gives it to me, and then I put it into Lovable. So yeah, that gets super complex. The other

**Hitting Technical Walls**
(43:20) thing I'll say is for Social Lollipop, I've built the tools you see in some of the other things. There's going to be a time when I'm going to hit a wall, or I've hit walls before, where I'm, "I want to build this," and it's super complex. It doesn't exist out there. I need someone with a technical mind to go in there and

**Needing Technical Expertise**
(43:39) start coding and connecting things. So you will hit a wall as you start scaling, or I waste a lot of time asking questions that someone who knew better would have asked a better question. Christopher, have you built anything or are you building anything right now?
Christopher: Oh, he has. Yeah, he's been sharing some cool stuff.

**Wish List and Community Sharing**
(43:56) Leo: Oh, nice. I'd love to see it. Yeah, I'll make sure you get the chat too so you can see any of the links because we always have the funny, fun added conversation. Yeah. What other things is people trying to understand, or what's on your wish list?
Christopher: Yeah. I see Christopher. Lovable can get you a

**Prototypes for Technical Collaboration**
(44:26) prototype, which can help you sell the idea to a technical person, help you get to the finish line.
Leo: Yeah, that's exactly where I am with Social Lollipop. I have been going to events, startup events, startup pitches, and presenting it.
David: And this is something where, yeah, I also, for those of us who maybe have clients with their

**Explaining Ideas to Dev Teams**
(44:48) own development teams and things like that, where we're not going to ship a finished product to it. Then I've worked with others where I'm, "You can get this so much of the way there, and tell them the kinds of things you're looking for, instead of you trying to explain, 'Okay, I want this kind of site, and it looks Airbnb, but for booking

**Creating Visuals for Design Concepts**
(45:14) restaurants, and it should be this kind of thing, but it should be a much more old-fashioned design because these are more old-fashioned trends,' and trying to do all this stuff." You create something yourself that's so much more further along than I could never use Figma or all these tools. I'd spend forever, even in Canva,

**The Last Mile: Designers and Developers**
(45:35) trying to make something look right.
Leo: And it's, great. Then when they have a designer and developer, all that you can work with, they'll figure out that last mile and make it way better than you can imagine. Yes. Yeah. The other thing I think there's a pitfall, and this is we saw this, David, when we were in the SAS world,

**The Dangers of Self-Building**
(45:54) or even the agency world. Sometimes building stuff yourself could be dangerous. Things break. Things need to be updated. All that, unless you have someone dedicated to be looking at all that and changing it, especially if you're building something that's client-facing. If Jingle My Brand is low stakes, but if it broke at 3:00 in the

**Resource Planning for Tools**
(46:12) morning, who's fixing it? Who's updating it? Who's looking to see if it's done, right? If my business is marketing, and my business is, I don't know, selling widgets. I'm focused on the widgets, not necessarily on this one little tool that's a lead gen tool or something. So be careful to build things without properly resourcing it or properly understanding how much

**The Replet Trap: Unused Tools**
(46:30) time it's going to take.
Zach: Hey, thanks. This is great. I built a couple tools in Replit. The problem that I find is that I'll build something that has a limited use case for me, and then I'm just stuck paying monthly fees on something, I'm, "Oh, maybe I'll use it on this next thing or whatever." But it's

**Subscription Overload**
(46:52) just, it's a trap I find. Then I've got all these now. I'm looking at Claude Code, and I've used Lovable for sure, but I just end up with all these different apps, and with one thing or two things that I used for. How do you, what are your thoughts on that?
Leo: I'm going to give you more of a

**A Bigger Picture: Marketing Costs**
(47:15) bigger picture look at things. I try as a marketer, and money is money. Everyone has different financial situations from a business to a personal perspective. I get it, and I'm not minimizing that or doing anything to ignore that. So, there's that, but let's skip it for a second. I always say, "Look at how much marketing cost in the 90s." They cost so much money, and you

**Digital Marketing: Inexpensive Targeting**
(47:36) didn't even know how well the targeting was going, and where it was going, and the resources it took to build it was incredible. Now there's social media, and there's digital marketing, which is stupid inexpensive, right? Even as it gets more expensive, it's still really inexpensive. If I took a time machine, and I went back to the 90s or 80s, and I said, "There's a tool that

**The Value of $20 a Month**
(47:56) you could use to target almost anybody you want, and it's relatively inexpensive." You'd be, "Here's all my money. Forget about that billboard. Forget about the TV. Take all my money." Right? I'm, "This exists, and it's social media." So I look at that in the same world as the question you proposed. So this is costing me 20 bucks a month, $25 a month. You got to ask

**Assessing Tool Value**
(48:17) yourself, "Are these individual tools that I built actually worth that much? Is the value there for myself?" But ultimately, it's so inexpensive for you to have built that tool. Would it cost you thousands, maybe hundreds of thousands of dollars, right? Or taking you so much time. If it breaks, it's going to cost you hundreds to fix it. So, would I pay $20 a month to have

**Cost vs. Efficiency**
(48:37) a simple calculator that saves me two hours a day, two hours a month? I'd say yes. So, you got to look at, what have you built? What's the value it's providing, which is time, money, efficiency, and is that worth what you're spending? If you look at it from that calculator or that algorithm, that will answer your question.
Zach: But you haven't found there isn't

**The "Jailbreak" Dilemma**
(48:57) a way that you're, "Okay, this tool's done." I can't jailbreak it. We're probably never going to use it again, right? That's where I end up with on a lot of these things, right?
Leo: Yeah. I've run into that. Ideas I've had, I've built, or I've hit walls, and I can't build it any further. But because I'm building multiple tools,

**Managing Multiple Tools and Credits**
(49:16) and multiple things within the same platform, I say, "All right, that one's dead." But I'll use the credits for Social Lollipop or Jingle My Brand. So, I'm always building something. That's my own unique use case.
Zach: So, are you still just maintaining your subscriptions across a bunch of these things, and figure you're

**ChatGPT as an Affordable Partner**
(49:33) okay.
Leo: Yeah. Yeah. Exactly. But again, I look at it from the frame, "Shoot, ChatGPT is a partner that helps me think. Even just that's cheaper than a VA. I'm paying 25 bucks a month. That's free." Not everyone has $25 a month to spend, but what?
Zach: It's just, it's very easy to end up with

**The Challenge of Too Many Subscriptions**
(49:51) 50 tools that you're paying 30 bucks.
Leo: Yes. Yes. Yeah. Totally.
Zach: I, that's the challenge that I wrestle with a bit. It's, "Oh, I did this cool thing. Am I using it a lot? I don't know. I feel I'm going to use it."
Leo: There's only so many hours in the day. That's the, they're all fun. They're

**API Errors and Time Investment**
(50:07) just as you said. Some of it was headache for me building the app in Replit. I tried to do a few apps that just weren't possible. It just couldn't.
Zach: I just kept running into API errors and stuff, and I was finally, "All right, I've spent 25 hours trying to build this thing. How many more hours am I going to spend?" Right?

**Voice Cloner Success Story**
(50:24) Leo: Amen. Amen.
Zach: But I did build some simpler things that, to your point earlier, they're the calculator. I built this voice cloner that would take my clients' voices through Eleven Labs since it's a huge pain to go directly through there and do that. I was able to do that for this big video series that we did for them, and they weren't able to

**Subscription Philosophy**
(50:44) come back to set. So it was able to recreate that, and that was worth it for sure.
Leo: Yeah. No, I guess my philosophy, I look at it as this is just a problem with anything. I have a subscription to Wired. I have a subscription to Men's Health. I have a

**Valuing Subscriptions**
(51:02) subscription to this. I'm, "Just the other day I went through, I'm, 'I don't even read this. I don't have time to read that. Why am I paying for this?'" But I focus on the ones that are providing value. But I would love to subscribe and read all these things, right? Same thing for AI tools. If it's worth it for you, cool. If you

**Conclusion and Appreciation**
(51:17) could afford it, cool.
Leo: But if not, get rid of it.
David: Well, Leo, this is always better than expected. Appreciate you coming in on the fly and
Leo: Sharing all this stuff with us.
David: And in the community. Yeah. It's fun to get to see a lot of the V0.1 versions well before they launch. Really honored for

**Upcoming Speaker and Community Engagement**
(51:45) that. Thanks everyone for showing up. We got Peter Shankman next week talking about AI and comms, and he's also a dynamic speaker. You're in for a treat. Great to see everyone, and look forward to seeing what all you create with all these new things.
Leo: Yeah, please reach out to me. Happy to look in. Thanks for having me on all the

**Farewell and Future Invitation**
(52:08) great questions.
David: Yeah. You, I think you'll be welcome back here, so we know where to find you.
Leo: Awesome. Thank you.

## How Pete Rakozy Uses Vibe Coding  Custom GPTs to Build Faster and Smarter

Speaker: Pete Rakozy
Published: 2026-01-08
Tags: vibe coding, ai in marketing, ai in operations
Video: https://www.youtube.com/watch?v=BUS5CSPHnzA
Page: https://aimarketersguild.org/sessions/how-pete-rakozy-uses-vibe-coding-custom-gpts-to-build-faster-and-smarter

### Introduction and guest welcome (00:05)

(00:05) Hey everyone, I'm David Burkwitz and welcome to another edition of AI Insiders by AI Marketers Guild by March Media. And I'm excited to be here with a lot of community members and also our guest this week, uh, Peter Ricozie from Exit Loop. And uh and I mean we were geeking out on some topics like uh like vibe coding and custom GPTs and I was just so excited uh getting to learn from Pete and hear about some of the work he's doing and he was kind enough to offer to share some of this uh with our fair community. So uh Pete, welcome.

### Guest intro: Exit Loop and operations background (00:48)

(00:48) Excited to share. Why don't you fill us in a little bit more on who you are since I'm not sure I could fully do you justice, but

> > yeah. Um, so I'm Peter Rosi. I'm co-founder of Exit Loop. Exit Loop focuses on helping business owners get out of the day-to-day through AI automations and other trainings.
>

### Career path and focus on “the plumbing” of marketing ops (01:09)

(01:09) and my background, I've been in marketing and operations for about the last 15 years. have done I think the track everybody's done where they've been an account manager and then worked their way up through an agency and then I started my own agency run a couple of those failed many many times because running agency's hard and then discovered that where I really excel is operations like I always gravitated towards the marketing operations side of things not so much the glitzy and gl glamorous like ad creatives and all that

(01:39) fun stuff too or the strategy I just like building the the the the the plumbing of it all, right? And so that's just turned into what we do now at Exit Loop, which is in short what happens after leads generated. We help a lot of service businesses and local businesses fix that pipeline. And then now because AI is so awesome and cool, it's training them on how to use AI in their day-to-day and then building new operational processes that leverage AI to help them do more with less, profit more with less. So yeah, that's that's

(02:14) my career in a nutshell.

### Personal background and interests (02:14)

(02:14) And then personally, um I've got three kids, twins that are turning four and then a toddler who's two. And they keep me busy. They're they are so much fun um and hilarious. And I live here in just outside of the Salt Lake Valley in Utah. I haven't done any snowboarding or skiing this year cuz there is none out here.

### Hobbies: outdoors and Zelda (02:40)

(02:40) Greatest snow on earth. My eye. There's none. Um and and when I'm not nerding out with stuff, um I try to get outside and go camping, fishing, hiking, things like that, or play some video games. I'm a huge Legend of Zelda fan, so I'm really excited to see what happens this year with the 40th anniversary, the new movie, all that fun stuff.

### Transition to AI work and keeping it practical (03:01)

(03:01) So, hopefully it won't be a disappointing year. >> But, yeah. >> Awesome. Yeah, I I I Yeah, it's still have the uh the original NES and uh I mean to to me nothing beats it no matter how good Call of the Wild is and everything. Yeah. >> Yeah. Yeah, that's true. Um well well yeah I mean we'd love to hear more about uh you know what you're doing in the AI space and what we can learn from you.

### Demos, tools, and community Q&A format (03:28)

(03:28) >> Yeah. Yeah. Well, I've got a bunch of like little demos that I put together that just shows like here's practical things we've done for clients and I can share those and then I've got a bunch of links to tools that I've built to help myself um that I'm willing to share with everybody as well and they can they can use.

(03:48) So, we'll hopefully keep this as practical as possible, not death by a thousand slides and and go from there. >> Yeah. And for folks who are uh newer to this series, yeah, we welcome questions come through and and folks come whether you want to be on camera or just share stuff in the chat. Uh but then um yeah, by by all means, yeah, we can just uh keep this conversational.

### Goal: a simple loop, templates, case studies, and tools (04:13)

(04:13) >> Sweet. Awesome. Well, I'll walk you through this. Yeah, ask questions along the way. Um, what I'm hoping I can give you guys today is just a simple loop, some copy and paste templates. I'll walk you through some case studies that'll hopefully spark some ideas or new ways to do things.

### Problem: finding custom GPTs across accounts; Crewar tool (04:29)

(04:29) And then of course, I got some tools. So, uh, if you build custom GBTs like I do or gems or whatever, my biggest problem has always been where the heck is it? Like where's that specific GPT I built for that specific purpose? And so, um, one of the things that I've used, uh, Vibe coding for is to build my my like solve my own problems.

(04:50) And so, one of the tools I built, I call Crewar. Um, and it's a free tool. You guys can use it if you want. Um, the the site and link and everything are here. But essentially, it's an app that organizes all of your custom GBTs, right? But what I like the most is that you have this quick search feature that allows me to come in.

### Quick search and “where is that GPT?” workflow (05:10)

(05:10) I can be like, "All right, cool. I I know I need to create um this transcript AI playbook, right? Where the heck is that? What account is it in? And if you're like me, I've got multiple Chat GPD accounts across my own and clients and things like that. And so I never can find where they're at. So this is one of the cool tools that just scratched my own itch and um and it it saves me so much time in actually finding where these custom GPTs are and where I built them.

### “Stacks”: grouping GPTs by workflow (05:39)

(05:39) And I'm working on some other cool stuff inside of it when I've got free time, which is never um to build stacks and share like your stacks and things like that. Stacks or are what I would consider a grouping of GPTs that I use for a task, right? So like for example, if I'm building an a marketing um campaign or some marketing content for a client, usually that's five or six different GPTs that I'm using together and I'd put those together in a stack, right? or we do AI audits for a lot of our clients. Um, and I've got a suite of

(06:11) custom GPTs that I use to execute that and put that together.

### Question: one GPT with many prompts vs separate GPTs (06:11)

(06:11) >> And so, um, can I just ask one basic thing and into this is

> > Yeah.
>

> > is like at what point do you decide cuz like you can have a custom GBT that you can have like a number of default prompts for. Yep. and tell it to do different things like at what point like like when do you decide to have one GPT that works with a number of different prompts and all that versus like when you want to break it out into something entirely separate.
>

### Rule of thumb: one job, one GPT (06:44)

(06:44) My my rule it with a custom GPT is um one job one GBT like one output essentially right so and the reason for that is I've found if I've built like a um a Facebook marketing bot for example right it'll be good at strategy but then when I ask it to do specifics like different type of post type or content types or things like that um I don't get as good as results as if I were to break it up into three different bots like one for Facebook one for Instagram were, you know, the individual channel specifics like that. I found that the more

(07:18) specific I get with the actual task or job to be done, the the higher quality outputs, less revisions, less hallucinations, things like that. And so that's that's just my default. Um, I think of it in terms of an org chart, too. Like if I were to build a team of people to do this, then I would have a social media manager and then each of them would be a channel manager, generally speaking, right? And so I try to mirror my my bot development structure accordingly.

### Prototype manually, automate later (07:48)

(07:48) >> Um and and then the other question I get sometimes on this is well why don't you just automate this all through like Naden or things like that and usually it's because um you at this stage I'm usually still prototyping out what works right and trying to figure out which which sequence of AI bots will help me get the outputs I want.

### From master prompt to Replit/Lovable (08:13)

(08:13) And then once it's like finite and defined, then we move it into an actual workflow um to output things. But yes, anyway, but this was simple. This took me maybe like an hour to put together. And it was one of those aha like this sucks. I hate having to find all this. I used um oh, let's see which one is it. I used this custom GPT that that I got from good old Perry Belchure uh to build out these actual Vibe code prompts.

### Vibe coding workflow: work the problem in Chat/Gemini first (08:45)

(08:45) And this is usually where I start when I do any sort of I coding things is start inside of chat or or Gemini and really work the problem, work what it is I want to do and help create a master prompt first and then take that master prop into replet or lovable um and or or your vi coder of choice, right? and have it start with that and then I find it's much more efficient uh at credit usage but I also end up with a lot less junk code and broken stuff and things that happen just through thousands of iterations that typically come out of

(09:21) vibe coding bit by bit right and so that's one one cool tool I don't want to leave you with quick quick and easy something for you to to think about like what else like what are things that you're you you're doing um and and this is the process that I follow, right? It's it's the in creating these what I would call growth growth assets.

### Building only the “one little thing” instead of buying full software (09:42)

(09:42) So, I build that for me. But often when we're working with clients, they're like, "We want to do this or we want to use this software, but we only want this thing out of the software. We don't want to spend $10,000 a month for, say, HubSpot or whatever because it's, you know, out of their price range.

(09:58) They only want this one little thing." >> And so, we'll say, "Oh, cool. Well, let's take that. Let's see if we can just extract that out." And that way you don't have to buy the whole software. You just build the part that you want um and build it the way you exactly that you want. And I'd say probably eight out of 10 times um you're able to do that using your vibe coding platform of choice which is awesome, right? And so starts with, you know, that that initial vibe master prompt and then we take it into the interface and build that out. I

### Replit as an all-in-one build + integrations platform (10:29)

(10:29) and then if needed, I'll connect the brain to it, which is usually um something I've worked out in the custom GPT. Uh the cool thing about like Replet's my favorite right now, but it's become the all-in-one like I don't have to go and daisy chain stuff from NN and or Zapier or all these. It handles most of the workflow stuff internally, and it's doing a pretty dang good job.

(10:51) Um, and it's got built-in integrations for most of the LLMs as well, so I don't have to actually pull in my own API if I don't want to. Um, and then I could build out the bots's brain inside of the the vibe coding tool instead of having to pull it in outward um from an external source and then iterate and deliver, right? Super super simple.

### Prompt-building GPT: live demo and “coding brief” style output (11:15)

(11:15) So, yeah. So yeah, like we're talking about one user, one job, one screen. That's that's really kind of my focus. And so um but yeah, this vibe coding GBT um which I'll share with you all super super super useful. Um you just tell it the idea that you want and say, "Hey, help me build an app." And I can say, "Great.

(11:38) Let's go ahead and work through it." And now it starts to give me like here's the the template you would use. I usually stop it and say, "No, I don't need that. That's okay." Um, do you guys want to go ahead and test this out now? Anybody have an app idea they want to throw out and say, "Help me build this prompt customizer to save variables.

(11:57) " Uh, let them fill them in. Cool. So then it builds out the the plain English prompts. And and there's a question if this uh if this custom GPT is specifically designed to use Reflet.

> > It is it's specifically built for for Replet, but I've used it in Lovable as well and I've had great results with it.
>

### Breaking down app requirements and success checks (12:40)

(12:40) Um because all it really does is it it breaks down in plain English essentially a brief of here's what we want to do.

Here's the deliverables. Here's the pages we want to have in it. Here's your rules and limits. Right? Safety and access. Here's the look and feel.

Um and then it it goes into more, right? Here's your success checks. Here's stretch ideas that we can include. And here's notes for the builder. Right? So it it chunks everything down as like almost like a coding brief, like if you were to sit down and build this from scratch with an actual coder.

### Replit design-first feature (13:08)

(13:08) Um what are most of the questions they would ask to help start to plan this out? So then we take this, we go over to Replet, maybe we drop it in, and bam, like it builds out our first iteration. Um Replet has rolled out this new design feature, which is cool because we can actually take this and have it build out the look and feel first before it builds out the brain and guts.

### Case study 1: quiz funnel redesign vs agency quote (13:31)

(13:31) Um, and that saves you some time as well in terms of if if that's most important to you is I want to make sure this looks a certain way before it functions a certain way, then great. So that that's cool. Um, another use case that we've had, let me see. Oh yeah. So So we had a client who they had a quizunnel and this is essentially what what it looked like.

(13:56) like they answered some a basic form. They had this form that they filled out and then it took them to a results page that essentially said, "Here's your results. Let us know if you want to if you want us to talk to you about it." Right? And then we're like, "All right, this is this is garbage.

(14:12) " So, we asked their their their web development team who managed their website, which I think was built on Web Flow, um if they could redesign it and make it much much better. And they agreed. They're like, "Yes, the user experience here is garbage. we need to make this way better. And they said um it would take them about 12 weeks to build out and I think it said they said it would cost about 80 $85,000 for them to to create all this stuff.

### Building a better quiz funnel in ~4 hours (14:38)

(14:38) And I was like, okay, well, they're a small company. They don't have a budget for that. And that's also a long time to wait. So I then said, well, let's just take your existing everything that you have in there. And I took all the screenshots. And this is before I had the VI coding. um GPT that I use now. Took all the screenshots, uh ran it into GPT, had it helped me build out a prompt, and then we built out this this uh awesome quiz funnel.

(15:07) Uh and I think I've got it here that we can look at. Yeah. And so I took their branding, I took their brand voice, I took all that type of stuff, and then it built out this beautiful page that used their images. We got all this type of stuff. And then they can go through and complete this assessment. And this looks way better.

(15:30) It's than before. It's on design, all that type of stuff. And then like a good quiz funnel should, it asks for their information. Um, and then uh it gives them a customized results page. And this whole thing took me probably about four hours to put together.

### Implementation details: CRM, webhooks, and admin dashboard (16:06)

(16:06) >> Um, and I think I did this while they were on vac like the the owner was on vacation and he came back and he was like,

> > "This this is awesome. This is what this is really cool, right?" And he had just launched a book, too. Um, and so we're able to use that book to help field this information. And so it gives them this custom everything. Here's what their next level opportunities. Three of those four hours were spent on getting this thing to work because each one of these assets um changes color based off of an answer.
>

(16:30) And Lovable is having the hardest time working with circles and coming up with this. Um, and then it links to their their calendar, but it also since you can connect web hooks and whatnot to to log, we're able to take all this information, push it into their CRM, which is go high level, and it then populated um their contact fields and set up all the nurture sequences, sent their, hey, here's what your results mean. Here's what you can do.

(16:59) Here's some quick wins. and and this has become a great asset for them um to use. But on top of that, we also built them an admin dashboard because one of the things that that Oh, shoot. Can I I don't have the login to that anymore.

### Making the wheel interactive; extracting existing code (18:01)

(18:01) >> Um, but the admin dashboard would show a list of all the the people who had taken the the exams or the the quiz and their results and then they could quickly pull those up and use those as talking points inside of their discovery calls when they' review this.

(17:35) Um, and it made things super super handy. And then of course you always have people are like, I didn't get my results. Can you resend it? And so we had to do some quality of life stuff like that where where click oneclick resends and and things like that. Um,

> > and and just like with with the wheel itself because like you know one of the things that I often get stuck on with vibe coding is the sameness u of a lot of the outputs of them.
>

And so in that case was like that asset in their book and like they'd created that so it was a matter of adapting it and making it interactive.

> > Yeah. Exactly right.
>

(18:25) like fortunately I lucked out and um they had this wheel on their website and the coding company that had built that um had all the the the code to create that already.

(18:25) So I just had to have Claude go and essentially comb that page and extract that code and figure out what it was that did it. And then Claude rebuilt it for me so that I can then take I just took that code snippet and gave it to Lovable and said, "Hey, like here's what you need to do because Lovable was not having it.

(18:46) It could not figure it out for the life of it." And so um so that's what I had to do and it and it worked worked great, right? And uh to do that.

### Capturing and routing data via webhooks and CRM integrations (19:08)

(19:08) So yeah, cool little asset, right? They they had a

> > underperforming funnel. Four hours later, we got them something that was much more high performing, high converting, high quality, especially with their price point.
>

(19:08) Like they they charge 100k to go do these branding audits, right? Where they do a deep dive, interview tons of people and figure out what that brand unlock is to to get people unstuck and to scale.

And so, yeah. Anyway, so that's that questions.

(19:08) Um, there was one specifically about like just like email capture. So then I guess you can tie that into whatever CRM they're using or

> > Yeah.
>

(19:37) >> Yeah. >> Yeah. It's all done through web hooks. And so the web hook captures all the information and then you can push that information to where you want.

(19:52) And so love will just set up like I just said, hey, I need to integrate high level. And it's like, oh, we've already got an integration built for that.

(19:52) So let's go ahead and connect that. And it's like, here's the web hook URL you need. add that to your workflow and it actually walked me through what I needed to do inside of Gohigh level to set it up which was great um to to have that handholding which was nice.

### Case study 2: selling AI audits with custom GPT + Gamma (20:26)

(20:26) Mhm. >> Um and yeah, and it captured the email, captured all the information, uh captured all of their results as well and put that into to uh go high level because then we could use all those custom values for sorting and personalization and and any future campaigns they wanted to run to people

who, you know, scored red on this category of their brand wheel, right?

> > So cool. All right. So that's case study one, something that we did that was fun. Um, anyway, case study two. So AI audits, that's one of the things that we've done to to generate clients is a lot of people want to talk about AI, but they don't want to pay the high ticket required to implement it.
>

### Interview transcripts → audit GPT → presentation and pitch (20:57)

(20:57) And so we um came up with a process to do um it all these audits. And so we'll sit down and we'll do interviews. We'll record those with Loom or not Loom with Zoom um or Google Meet. And then we take those transcripts and those transcripts I'll then run through um um this this not that one this vibe audit GPT that I built.

(21:18) Essentially, one of the things I love about custom GPTs is that you could you can create the amalgamation of some of the brightest minds in the world around a topic.

### Channeling ops thinkers; debating outputs (21:18)

(21:18) And so when it comes to like operations, um, like I can say, all right, you need to channel Peter Ducker or Eli Golgrat or, you know, any of these the these uh industry greats who have revolutionized operations and and theory and all that type of stuff, right?

> > And I could put that into this custom GPT inside the system prompt and have it do that. And then I give it the actual
>

(21:53) format that I want it to output.

(21:53) So, it takes and consumes all of these transcripts and we'll interview 10 to 15 employees. We'll drop it into here. It then gives me an output.

(21:53) Um, often times I'll debate with it and say, I don't know if that's right. What about this? And and that's kind of the cool part about about working with these custom GBTs as well is is it'll spit out an idea and then I'll be like, um, let's refine it.

### Presenting in Gamma and tailoring language to audience (22:17)

(22:17) And so it feels more collaborative than just throw something in and and take the output and and hope for the best. Um, but then it gives me the output I need and I can take that over and put it into gamma. Have you guys used gamma at all

> > for creations?
>

> > Yeah.
>

(22:53) And so I'll throw it into gamma and then it'll it'll create um these audit presentations for me, right? And so I'll then take this and this is what I'll present on and say here's what the total impact will be if we fix these things based off of your audits, right? And here's what the

blueprint is going to look like. Here's what we diagnosed.

(22:53) Um, and this like and that's the great thing I love about AI too is I can change the verbiage to match who I'm talking to.

### Audit offer and conversion rate (23:22)

(23:22) So if I'm talking to a doctor, we can then use language like prescribe, diagnose, protocols, things like that, right?

Um, and then pull in actual like quotes saying, "Hey, here's what Celestia said about this or here's what Cressela said about this or or things like that.

(23:48) " and then break down here's what you need to do. Here's your primary bottleneck. If you fix this and alleviate this bottleneck, then your throughput will increase in these parts of your business and that'll increase cash flow or time or whatever it is. And so this is what we present and we sell this for anywhere from $1,500 to $10,000 upfront um to build something like this and present it to them.

### Time savings and AI interviewer idea (24:10)

(24:10) And the value is in the clarity that they get. And and then this also helps break down the the impact of in action as well. So if you guys don't do these things, here's what your total monthly impact is going to be, right? And and then our offer is now we take this amount of money that you paid us to go in and do this deep dive, which we normally do anyway.

(24:36) Um if you were to skip that arch, that audit and just work through our 90-day core um process launch. uh then great, we'll credit it towards your next project with us.

(24:36) And our conversion rate, right, so far has been like 90% of those people have been like, "Yeah, heck yes, let's move forward." Uh the rest just disappeared into the void week of between Christmas and New Year's and haven't gotten back to us yet.

### Building the audit product quickly and iterating (25:01)

(25:01) But anyway, yeah, this whole process like normally would have taken me, I don't know, 10 20 hours to put together, but um excluding my interview time, which we're working on building out an AI interviewer that can actually do the interviews with everybody at the company instead of what my calendar allows. It takes me maybe two hours of my time to prepare for this audit presentation, which is great.

### Case study 3: OnMessage prototype and app platform landscape (25:46)

(25:46) Most of that is me just spent reading and reviewing this and really understanding the what we're putting together.

And then the rest is debating with my AI ops custom GBT to to be like I don't agree with what you're saying like fight for it like tell me why we should do that and and um making sure that we're in agreeance on what what that actual uh solution presentation should be. So cool.

### Building “OnMessage” prototype in ~1 week (26:28)

(26:28) So, this was something that, no joke, like I watched a webinar on somebody else who was doing AI audits and then we built this product in about an hour based off of that and then went and sold it and started generating money um through that and then just improved the system along the way as we've gone through.

(25:46) As you can see, we're on version four right now of of of the system prompt and tweaking it and improving it.

(25:46) Um, cool. So yeah, the AI auto engine, that's one of one of the things that super super easy to do, something you guys could steal, execute and run yourself as well.

### 16-month dev project → Lovable prototype (26:48)

(26:48) Um, another cool thing uh that we did with Vibe Coding before Vibe Coding got really big uh like it is now is that same client Backtory Branding um they had been working on this thing called um OnMessage and Ones Message essentially was their attempt at taking all these branding

assets that they capture when they're doing these interviews and distillation to try and figure out what what's the brand unlock.

and putting them into a software format that allows you to um create all of your content in one spot and make sure it's always on brand. Like it goes through all the filters, all that and and all that stuff.

(27:22) They've been working with um a development agency for about 16 months and had barely gotten to a point where they had a workable prototype.

(27:22) Um and then uh just for fun, I was like, I wonder if I can vibe code this. I wonder if I can build this whole thing inside of Lovable.

### Screenshot-driven spec and training the internal product owner (27:22)

(27:22) So, I went in and I took screenshots of everything, every single page that that they had built out and then I I had one of their employees who had been the product manager on the software and we talked through all the functionality and then dropped that into the level and then they within uh about

(27:57) a week had a working prototype that um they could actually demo to clients and show what was working.

(27:57) Um, and then I trained that employee on how to take it and run from there.

### Building full AI-powered software in Lovable (27:57)

(27:57) And they've built out like a full-on AI powered software essentially like it'll scrape your website and it'll scrape all your assets and everything and autofill all the stuff and then they've integrated AI into it so that if they need to create a blog post, they create any content, it all runs through the correct um, branding filters

> > and creates things um that are awesome,
>

(27:57) right? Like the feedback I've gotten from clients is I wouldn't change a single word of that or that's exactly how I would I would write that or things along those lines, right?

### Hosting and scaling concerns; security anecdote (28:23)

(28:23) >> Like because this this also comes up like Yeah, I I see some of where Gemini is going with this and and Claude tends to get pretty high marks.

I know from folks who are

> > Oh, yeah. into coding.
>

But then um but then like the hosting winds up being trickier, right?

I like like for right. So like for things like that then you still need to have typically like a another hosting service or like a VI coding app like a lovable or base 44 or something that will actually go and uh and yeah deploy it for you.

### Developer pivot: “big boy app” hardening (29:21)

(29:21) because

> > Yeah. Yeah. Replet's got built-in hosting. Yeah. Lovable, they've paired with Superbase, right?
>

> > And Superbase does the hosting for them and and so they've made it really both of those two have made it really easy to to launch.
>

(29:43) Um just connect a domain and you're good to go.

(29:43) Uh but yeah, I I see that as an opportunity for developers now.

Like I've got buddy, he runs a company called PXP coding and he's had to pivot hard from doing what he was doing before which was websites and department of defense contracts and all these types of things to he's essentially like once you've viodated your app and now you need to make it a big boy app.

### Security + scalability limitations of vibe-coded apps (30:15)

(30:15) like he takes it from there and goes and fixes any problems, sets up the proper security, gets it ready to scale, makes updates to the codebase so that it can then grow into something that's more mature because that's kind of the the restraint or constraint we

have right now with most vibe coding apps is that um people still kind of have the opinion that um this isn't real software is kind of the the word I would say like this isn't something that you can scale to a million users, Okay.

(30:41) Um or 100 million users like you actually need to to take it and have um I'm not a coder so so I don't I don't know what I'm talking about.

(30:41) I know that like you have to take it and there's other things you have to do to actually make it secure and scalable and it can handle um the amount of users that are using it that type of stuff right which I don't know if Replet base 44 like gem like they can actually handle that type of stuff um out of the gate right now especially after I think there was there was a I don't if you guys remember the app it was like te or something like that that was a big app that was completely completely vibe coded and lovable but had no security in place.

(31:12) And so

so it uh uh all the user data, chats, everything like that got leaked as a result.

### Custom GPT builder to speed up system prompts (31:36)

(31:36) But anyway, but yeah, so that's another cool thing um that we built and and go from there.

### GPT builder walkthrough and client use (32:12)

(32:12) So, so yeah, those are here's the tools that I'm that I'll share with you.

Um, oh, and then I forgot about this one.

One of the things that that takes a while to or used to take a while to do is actually building your own custom GPTs, right?

So after building manually building my own custom GPTs, I built my own custom GPT builder and

um it essentially walks me through uh the process of creating uh a very strong system prompt and associated files to attach.

(32:31) And uh what I'll do is I'll just sit and I'll brain dump.

Here's all the things I need to do. Here's who I want to uh talk to. Here's what I who I want it to channel or here's the goal. Here's the purpose.

And then it helps you really narrow in on here's what you need to do or oh that's three different GVTs. Let's go ahead and and create three system prompts for you and and go from there.

### Audience question: voice mode and teaching clients to “talk to AI” (33:10)

(33:10) So it's super super easy, right?

> > Um to do that, you just come in, help me start from scratch, and then it'll start asking you questions to figure out what are the things we like it needs to know in order to help you start building this GPT from scratch, right?
>

And so this has helped me save tons and tons of time, especially if when working with clients.

Often they'll be like, "We're stuck with this."

And I'm like, "That really isn't an automation. You just need a a custom GPT to help you out with that."

### Dictation tools and GPT personality settings (34:04)

(34:04) And so we'll take that that transcript.

I'll drop it in here from that call and this will help me get the initial version one of the system prompt and built out in about 15 minutes.

(33:10) And then I'll give it to them to start testing and and using right away.

And usually they're like, "Aha, this is amazing. This is this is super helpful tool. What other questions? Yeah. What other questions do we have from

> > Hey, this is Earl. Just want to say thank you so much for sharing all the different ways you've applied custom GPTs.
>

It actually, you know, ties into the my approach to how I handle AI.

I have a question with um I guess the chat GBT 4 5.2.

Have you been using the speaking function?

because I think I've been finding a lot of success teaching clients how to talk to AI who are not comfortable typing or really uh self-conscious about the prompting prompt or context engineering

because I used um the voice chat like back and forth a ton um when it was 40 and then after 40 for whatever reason it seemed to get dumber and worse.

### Human review and debating the AI (35:24)

(35:24) >> I haven't I haven't used it since five um or five at all.

Um, mainly because I use so I'll use just the the the dictate function a lot and just talk to it or I'll use a tool like Whisper Flow

> > um
>

> > to to brain dump everything into it because it's got a much longer um and faster transcription speed in my opinion
>

> > and I'll just drop it in and paste that and go from there.
>

(34:32) Yeah, I I don't type hardly in at all anymore and I I try to teach clients the same thing, like just talk to it.

Just that's the most natural way for us to actually engage with stuff, right, is just to talk.

(34:57) And so, um, yeah, the great thing about the custom GPTs, too, is that it's once it's already programmed on its job, like it helps eliminate a lot of the confusion or the the need for clean prompt structure, I guess, because it's it's kind of built in.

(34:57) And so, they can't just ramble and brain dump everything into it.

And then the AI will be like, "Okay, cool." Um, let's do this.

### Question: which tool is most “ready to go”? (37:00)

(37:00) >> I also, and I, I've also recommend if you haven't done this, changing your Chad TPT's personality to um, sarcastic

> > and and uh, it gives you it's much more to the point.
>

It's dry, hilarious humor, which is my humor.

Um, and uh, yeah, it's much more concise and it's deliverable as well, I think, which is awesome.

> > As I'm watching you describe your process, one thing I'm not hearing you say is, and then I read it over and and bring human discernment to correct it or to make sure that if it's a presentation, I can stand behind it and present it to the client.
>

Like, are you doing that?

### Answer: Replit reliability, Gemini for “how-to”, ChatGPT for strategy (37:33)

(37:33) >> Oh, yeah.

> > Yeah, definitely.
>

Yeah, there's there's always uh um that's where most of my time is probably spent, right?

Generation is lightning, which is great, but most of my time is spent reviewing and debating with with the the AI like like because even when you try and change all the settings and everything to be very flat and not not pleasing, AI is very much a people pleaser and so it will like try and agree with me on everything.

(36:23) So, I find most of the time I have to be like, "All right, I disagree with you. Defend your stance." Like, debate with me on this type of stuff. That's where most of my review and feedback comes in.

(36:41) So,

> > I always gut check it because there'll be things it'll create.
>

I was like, "Where did that come from? Like, we didn't talk about that at all.

Why are you throwing that in there?" And it'll be like, "Ah, you're right." Um, or if it's sarcastic like it'll be like it'll be like, "You gave that to me, not or whatever, right?" Like, it'll it'll just toy with me.

Um, but yeah, you definitely have to review everything.

It's not just spray and pray blind faith, right?

### Managing outputs and assets from GPT work (39:48)

(39:48) >> Okay. No. Cool.

Cool. Because that's my experience, too.

And I was like, wow.

So,

> > yeah,
>

> > that that's yours.
>

> > I I have a question.
>

> > One one quick followup on that and then uh and then I won't steal your time.
>

Um, but have you found for any of the tools you're using, Pete, like whether it's lovable, chat, GBT, Yeah. replet gamma any of them that the accuracy is like the closest to just being ready to go that you have to proof it the least.

### Question: tracking assets generated by GPTs (40:14)

(40:14) >> Oh yeah.

So replet I think is is the most dialed in for me right now in my experience.

Um I get the fewest errors.

I get the fewest breaks.

I get that type of stuff with with it.

It's still there.

Um and then with Gemini I I I have been I guess doing double prompting like I do everything in custom GPT or in custom GT or chat GPT but then I also do the same thing inside of Gemini and I compare the results and it's interesting I will find for strategy and business type of things.

(38:05) Chat GBT for whatever reason seems to be better.

Like it seems to be maybe it's because of its ability to access all of my chats and have kind of a wider memory on on what I'm doing.

Whereas Gemini is still very siloed in their conversational threads, right?

Um it it then takes a lot more things into consideration when debating with me on what strategies we should do.

(38:29) Um I find Gemini is most accurate when it comes to how to do stuff, right?

I'll be like, "Hey, walk me through this."

> > Or, "Can I help?
>

> > Do you have any little like little band-aid things?"
>

> > I don't. Sorry.
>

Um, but

> > we can cut it.
>

That will be

> > um but yeah, Gemini is definitely I think much more
>

> > solid.
>

I'm just losing and things like that.

### Audience member’s tool for saving chat links and summaries (41:03)

(41:03) >> Okay.

You showed us this tool you use in order to search for the custom GPTs that you created.

> > Uh-huh.
>

So, what about the assets that are generated from the GPT? Do you have a way to manage all of those?

> > Um, you mean like the chats and things like that?
>

> > Well, I mean, you create something and then you have all of these documents.
>

Uh, how do you manage that?

Uh, I guess it depends on the assets itself, right?

So, if I create things, a lot of those things end up in Google Drive, right?

If they're text or copy or things like that, images as well, right?

All I mean, both platforms have image libraries that you can look at and scan and and grab those images regardless of chat, uh, which is nice.

(40:40) But, um, I I anything that I'm creating, I always dump it into one central location.

And so, we use ClickUp for all of our client management.

And so, and then we pair a a Google Drive folder with that.

And I'll just drop all put all those assets into a Google Drive, a Google Doc, um, and keep it organized there.

Or drop it into the task inside of ClickUp so that it's organized there and connected to the task.

So, that's how I'm keeping those things.

### Crewar auto-scraping GPT links and second brain reference (42:05)

(42:05) Are there specific types of assets you're you're

> > No, I'm just wondering because I I have a tool for that.
>

So, I was wondering because that's a problem, right?

I mean, it you create

> > a lot of different assets.
>

Some of them you want to keep, you know, I call it the endless scroll on the chat GPT.

> > Y
>

> > uh after a while, how do you how do you keep track of anything?
>

> > So, you know, we I have a tool that
>

> > is hooked to a you know, you can create custom GPTs.
>

Everything that you do in GPT or any application creates a unique link

> > and those links you can save.
>

And I actually have a custom GPT that allows you to summarize the description.

For example, for my GPTs, I say, "Give me a summary and list all of the questions I asked in the process so that when I go to look at it, I can very quickly find out what that whole conversation was about.

(42:05) "

> > Cool.
>

You should share that.

> > I'll I'll I asked
>

> > to get connected to you on LinkedIn.
>

I'll I'll I'll share that with you there.

> > Awesome.
>

I'm in the the Slack group as well.

You can always find me there.

> > Okay.
>

> > Yeah.
>

And drop stuff, too.

Yeah.

With with Crewar, right?

Um my my thing was I wanted to make it as automatic as possible.

And so, one of the things I I had it do was anytime I land on a a custom GPT or use a custom GPT, it autoscrapes that that link and adds that to my library.

So, my library automatically grows as I continue to use them.

so that when I need to find that again I can it's already there.

(43:07) >> It's you know one of the things I found and you know you talked about

um using voice at least for me this is a truth in that if I don't capture why I'm doing something in the moment and then I go back to it a couple of days later or even a couple of hours later I may not

exactly remember.

So I have trained myself to immediately record and organize information in real time.

So later when I access it, I remember what it was for or even if I go is always a a source of discovery.

I I find that if you don't if you don't go through that process then

uh you will lose something in the process.

Yeah.

(43:40) Yeah.

Sounds like some of those principles.

Have you read the book building a second brain by what's his name?

> > Thiago 410.
>

Yeah.

I mean that's what I've been working on that stuff for

> > 20 years.
>

So that's uh Yeah.

> > Yeah.
>

That's awesome.

> > I I resonate with what he says.
>

### Audience question: Replit delivery, email reports, and integrations (44:04)

(44:04) Other questions?

> > I was wondering and thank you for your presentation.
>

when you vibe code on replet for example like the example where you had it email over a report how do you pull that all together where you're vi coding the concept but then also wanted to execute the actual um whatever function like the delivery of it.

> > Yeah.
>

> > Yeah.
>

Uh honestly replet does most of the the heavy lifting for me.

So I tell it what I want to do and then it'll say here's some options and it'll be like connect this app which is an integration we already have inside a replet.

it'll tell me what it's what integrations are and it's like so go create an account and then log well log in here or give me your API keys and then it takes care of it from there or it it also depends on the client like if they're using high level as their CRM or whatever most of those have a way for you to push information into it via like a web hook and so we just have to set up a capture

(45:01) mechanism inside of the CRM and in order to capture that information and once we have that we just give that to Replet.

Replet's like, "Great, let me do my thing." And it does whatever it does to make sure that information goes.

And then it'll

The cool thing is it also will send test data for me um automatically.

So I can really work things out.

Um I don't have to do actual triggers.

I don't have to complete that quiz for example 20 times.

I can just be like, "Hey blah blah blah, hey replet, like send mock data now." And it will.

(45:18) And then I can test it out on on the workflow side of go high level.

### Crewar stacks: one-click open vs automation (45:39)

(45:39) >> I see.

Thanks a lot.

One other question about Krugar.

When you create a stack, what exactly happens like if you like will it automatically flow the content through or do you still have to go like step by step in your stack?

> > I would love for it to do that.
>

Um, no.

Right now it's more of a one-click open.

It's kind of like when you need to open five tabs, like the group tabs inside of of Chrome, you can one click, it'll open all of them.

It's it's like that.

like I want to open all five of these custom GPTs at once and and it'll do that for me, right?

### “@ mention GPTs” tip in a shared conversation (46:38)

(46:38) And and go from there.

I'm still like like like I said, my process is kind of manual before I automate.

Usually, if I'm working with a team of of custom GPTs like that, I'll it's a lot of copy and paste from one into the next.

I play orchestrator, right?

And then that generates and does its task and then passes it on to the next.

Um, the reason I usually don't I I don't automate them until that copy and paste is perfect, meaning I don't have to correct what's happening with in that custom GPT.

I don't have to tell it to fix this or do that or whatever.

> > Hey, quick question.
>

Do you know that you can share them?

You have them in the same conversation?

> > Yeah, I I do that a lot too where you do the at and bring them in.
>

Yes.

Yeah.

> > Yeah.
>

Okay.

So, limit the copy and pasting because I've ran into that issue as well.

> > Yeah.
>

Yeah.

So, I'll do that.

Like for one client, like they wanted me to help out with their marketing even though that's not our focus anymore.

### Building a virtual “Director of Marketing” + specialists (47:00)

(47:00) Um, and I said, "Sure, I'll fill in the gap until you find your new director of marketing."

But, and that was the thing that we built a director of marketing for this functional medicine clinic.

That was the custom GBT, but then I'd bring in these doing what you're talking about, bringing in these other at um project managers.

I call them platform specialists whether it's analytics, solar, G4, Google Tag Manager, but all their experts on their different platforms and I bring them in to get their literal console and feedback in the same conversation.

### Audience question: finding new clients and pitching services (47:54)

(47:54) >> Yeah.

Yeah.

It's awesome.

Yeah, exactly.

No, that went away for a while.

It just came back.

> > Yeah.
>

Yeah.

After 5.2.

> > Yeah.
>

I was so sad when it went away because like this sucks.

This just made everything so much harder.

But yeah, that that's really simplified the process of creating content because you keep it, you know, once one stream or one conversation and I just bring them in and boot them out as needed, right?

> > So,
>

> > perfect.
>

Any other questions or

> > Yeah, I had a qu another question.
>

Sorry.

> > Go for it.
>

> > Yeah.
>

> > How do you find new clients?
>

Like how do you pitch this to new like as a service?

### Answer: referrals, discovery call, AI plan, partnerships, LinkedIn outreach (48:29)

(48:29) >> Yeah, great question.

Um we have been lucky in that we have a sister company called Wealth Factory and um so Exit Loop was a little side project inside of Wealthactory that me and a buddy created and then we spun off um with the owner's blessing who actually became one of our business partners.

And so we get loads of referrals from from them because they're coaches.

They each have about 200 clients each.

They're all business owners.

And so the coaches will then um recommend that they meet with us and we don't do a sales call with them.

We do an AI discovery and I just essentially sit down, chat with them, they tell me about their business and then in real time I'm I'm taking that that transcript or call into chat GBT and saying give me some ideas and then I give them three to five ideas.

(48:58) Um, and then I take that transcript and I have another custom GBT that actually takes the transcript and turns it into a 12-month AI implementation plan that's very high level, right?

It's just like a webinar.

You tell them what to do, not how to do it.

And so it tells them what they need to do each month and the estimated time savings.

And then I throw that into Gamma.

And then Gamma creates a beautiful presentation.

And then I send that to them.

And that usually converts.

### Company size focus and closing (52:01)

(52:01) I think uh out of a hundred meetings we have, we have 20 that convert into sales opportunities.

And then from that half of them aren't a good fit because they're too small, right? They're sub a million or something like that.

Um and so that's that's what we're doing right now uh to get clients.

But then we've also started be looking into partnerships.

And so partnering with um VC firms or things like that, people who are interested in fixing the operations of the company to increase its multiple so that when they go to exit, they can actually sell it for more.

And so we're building partnerships with those types of people with functional medicine influencers because that's that's a niche that we're we're growing in a lot.

Um things like that because they're they're tight-knit communities.

So and now we're testing just LinkedIn outreach.

Um, we're working with a guy named Phil Palucha who runs a company called Billionaires and Boxers.

Great great guy, great company, but we're working some of his his campaigns to do outreach and and whatnot.

So, we'll see how that goes.

We just started it this month and and I'll let you know for results and appointments or not, but but yeah.

### Wrap-up, links sharing, and end of session (52:39)

(52:39) Well,

> > thank you, Pete.
>

> > Yep.
>

Hey,

> > Peter, quick question.
>

uh just

> > is there a way to access the links that you were providing before and showing off?
>

> > Yeah.
>

Yeah.

I'll share this whole gamma slide with you guys and I've got all the links in there and and everything's um available and free like you can access it all, add it to your own accounts. You're you're good to go.

(50:33) This is way advanced from my knowledge of chat.

I thought I'd learn about custom chat GBTs, but like just hearing about how the use cases are pretty amazing.

Um, and the way that you're able to like screen do do interviews, you know, across many people and all the other use cases.

I have something a little bit maybe simpler, but um, building an interactive mobile gaming app.

Um, and like Sachbt or Gemini has been like guiding me along in terms of like what to charge and stuff like that.

Um, I guess that's something I'll connect with you.

I guess not.

I guess I'll connect with you afterwards.

> > Yeah.
>

Um because I'd love to see like it did say, you know, for the first phase, you're better off using you're better off building part of it yourself.

> > Um but what size companies do you work with?
>

> > Yeah, we work with 1 to 10 million.
>

That's our sweet spot.

Those are usually and they're usually um local businesses, right?

So service industries, clinics, things like that.

And they generally are about 3 to 5 million in size. that's that's our our our sweet spot that we like to work with.

(52:01) They have enough bit employees to have enough problems for us to solve is what it is, right?

Um and cash flow isn't as big of an issue for them, too.

So, when we hit them with a 10K audit, they're like, "Okay, we're a 5K a month retainer," they're like, "We see the value, right?" Versus somebody who sub a million is like, "Oh, that's my child's college fund.

" You know, things like that. So,

> > right.
>

Okay.

Very cool.

Thank you so much.

> > Yeah.
>

We're out of time.

Uh, thanks everyone for coming, Pete.

This has been great.

It's so cool getting in the weeds and

> > and and getting to see some of the kinds of things we hear a lot of people talk about but don't always get this kind of cander.
>

So, uh, so I learned a ton.

I'm happy to share any of the links and references with everyone who not just

> > uh attended live today, but folks in the community.
>

I'm glad you're part of the community as well and

> > folks know where to pester you for some followup tips and things like that.
>

Um but this is awesome.

### Closing remarks and next week teaser (52:58)

(52:58) Like this is big big reason you know why we do all this just to be able to geek out with each other and and learn how to do a lot of things better.

So uh thank you.

Thanks everyone as always for great questions.

Uh got some fun guests. We'll talk more about the out of home space next week with Barry Fry from DPIA and

> > and we've got a lot of great guests coming.
>

So, thanks everyone.

Happy New Year and

> > excited to see you way more this year.
>

## How Synthetic Research and AI Personas are Redefining Market Insights

Speaker: Jill Axline
Published: 2025-12-18
Tags: synthetic audience testing, synthetic research
Video: https://www.youtube.com/watch?v=qr_lgJtiiRs
Page: https://aimarketersguild.org/sessions/how-synthetic-research-and-ai-personas-are-redefining-market-insights

### Innovative Orchestrator teaser

(00:03) We're super excited to introduce Innovative Orchestrator, the first AI super agent for omni channel advertising. We're bringing [music] humans and AI agents together so that marketers can conduct a symphony across channels to reach their most valuable customers. >> AI is not a matter of if, it's [music] a matter of how and when.

### Welcome to AI Insiders + why this topic matters

(00:23) And we will help you solve that. Hey everyone, welcome to the final edition of AI Insiders for 2025. Wow, I I I'm still uh having trouble remembering what year I'm in. Am I in the present, the future, the past? The ghosts of all the years past and present and future all over the place right now. So, uh, but very excited to be in the present with everyone from the AI Marketers Guild community and, uh, and we've got a terrific guest who I only recently connected with, but, uh, she is deep in a space and arguably has been helping uh, pioneer and advance a space

### Introducing the theme: synthetic research (and why it is controversial)

(01:12) that uh, I've been eager to learn way way more about. And that's this issue that's come up. It's even come up on the previous week. Uh it's come up during several topics. Synthetic research. It's a little controversial. It is uh very much at the forefront of where I think conversations around AI are going.

### Setting expectations: agents next year, but deeper focus on synthetic research now

(01:35) So yes, we'll be talking about agents next year and we'll be talking about you nano banana 80,000.968 and all these other crazy things going on. But as far as like one of the things that that I think can make a big impact, but is uh a little scary, misunderstood, uh and and needs like just way more understanding about a topic I want to cover a lot more next year.

### Guest intro: Jill Axline (Mava) joins

(02:04) Uh we've got a terrific uh guest and founder and entrepreneur Jill Axline who's who's got an exciting story for how she wound up uh running Mava. And we just recently met. I was like this is great. Any chance you're free? We we we got an opening soon and I'd love to hear more. So Jill, welcome. >> Thank you. So happy to be here.

### Quick rapport + holiday banter (Yoda)

(02:29) Nice to meet all of you. So, >> happy holiday. And I love that you have Yoda up there, [laughter] >> by the way. >> Oh, we we need all the the real real guru. I mean, there are some people who are pretty close to gurus in this room, but um but but at least Luciota is like OG legit guru. >> So, uh yeah.

### Jill’s background: enterprise marketer → research-led messaging

(02:52) Uh so, so Jill, why don't you just >> introduce yourself and tell us what you're doing and what Mava is and we can dive in. >> Absolutely. So, hi everyone. I'm Jill Axine. Um, you know, I you mentioned entrepreneur. I'm kind of, you know, new to this game. I really for the last seven years have been an >> entrepreneur by fire. Right. >> That's right. Exactly. Trial by fire.

### Starting point: content strategy role and audience-first thinking

(03:15) >> And so, yes, I I've been an enterprise marketer at a financial services company for the last seven years where um it was really a chip and a chair. Like, I started as a content strategist. I had to Google what content strategy meant when I was offered that role. You know, it's kind of an amorphous term.

### Research foundation: empathy, perspective-taking, and resonance

(03:32) Um, and from there, I think kind of brought into recognition for the firm that so much of what they were trying to do was about the beeps and boops of the product um with maybe a peppering of benefits, but they were losing sight of the audience. And um, you know, way back machine, I I studied it in my dissertation really audience resonance.

### Turning research into practice: building a market research capability

(03:55) So I was looking at empathy. I was looking at cognitive perspective taking. I was looking at counterargumentation and how do we build different things within the context of our strategic messages that are going to help engage our audience and help them have empathy with the message that we're we're sending out.

### From “answers inside the firm” to real customer research

(04:15) And so, um, I really, I think, brought that into the consciousness at the last firm I was at and and ended up bridging that out and building a market research team because up until then, and maybe some of you are familiar with this, you know, the answers of what the customer wants were within the four walls of the firm and maybe a little bit of, you know, conversations with with sellers.

### The synthetic audience spark: recreating focus groups

(04:35) And so, um, building a market research muscle was so important. And then that really came into brand strategy, segment strategy and we we came down funnel with we can't say solutions marketing solutions was always in jail word jail but um yeah I mean that's that's really how I got my start in in marketing and content strategy.

### Why synthetic research felt compelling: cost, speed, and freshness

(04:59) And so from there, I remember, you know, attending maybe two years ago, uh, Content Jam, which was a conference with Andy Cresadina, if you're familiar with him, and he was talking about, or somebody at the conference was talking about, let's scan LinkedIn profiles and create synthetic versions of people. And I thought to myself, okay, well, I've just run live focus groups with um, financial advisors.

### Desktop “always-on” audience and segmentation drift

(05:23) What if I could recreate them and set them into a synthetic focus group and then look at the disparity and convergence in the response and start, you know, thinking about what does it mean to have a synthetic audience and how can I build a desktop advisor or desktop asset manager that I can have all the time and I can put not just, you know, strategy and messages and understanding of trends and channel preferences, but I could also ask them about every piece of content or UX or anything that I wanted to know because when I was sending things out to market,

### Time-to-insight problem in traditional research

(05:55) not only was it incredibly cost prohibitive, we were talking to our audience maybe once, maybe twice a year at $50,000 or more, but I also noticed that it took so long to come out of fields that by the time I was really presenting the analysis, it already felt stale. And I also thought that our segmentation expectations, which is something we'd set in October or November and then maybe refresh quarterly, those were also stale and they changed across the funnel.

### Skeptic’s mindset and meeting Mava

(06:25) So if I was writing something highly topical at that top of the funnel, where I would draw those like segments and subsegments and how I would want to change my message and send it out through different channels would be different than at the product marketing level. So that's what really got me thinking like is there something to the synthetic audience? But at the same time being an academic I kind of walked into it as a skeptic.

### LLMs mimic tone; Mava aims to model thinking + feeling

(06:51) So cut two I mentioned this idea to um an agency partner I was working with and he's like you got to meet these guys over at Mava and um they were doing something that I hadn't seen. We were working with Jasper. We were working obviously with chat GBT um early stages of Claude and while those allowed me to create custom GBTs or spring up a persona even Qualrix had something on offer there.

### Swarm of models + governance for population representation

(07:18) Um all of them were large language models that were mimicking the language of my audience. They were coming forward with tone. But as you know, a social scientist who studied empathy, I'm much more concerned with how people are thinking and feeling because it becomes a lot more predictive of their behavior. And so, how can we get to some um representation of that in the models that we're working with? And it's not just one model that's going to do it.

### “Decisions shouldn’t move faster than evidence” + adversarial stance

(07:44) It'll be a swarm of models that are then governed and deployed um to create a more representative view of my aggregate population. Um, so that's the one thing. I think the second thing that Mava kind of brought into my awareness is that decisions shouldn't move faster than evidence. And so Mava is kind of a built skeptic.

### Measuring confidence, spread, and stability (vs. sycophancy)

(08:07) It has an adversarial model rather than a synthetic one or a sycopantic one. So I don't know if you guys have had this experience where you're talking to Chad GBT or some other um widespread novel model and it's always going to agree with you and it's always very certain of its of its output >> right >> whereas working with Mava it really opens up the kimono and provides an analytics blade that's going to tell me what is the confidence level here what is the spread of opinion in the audience that I think I'm talking to >> because maybe there's a huge spread and

### Using stability to decide: quick social vs. long-tail investment

(08:39) I need to break up that audience a little bit more and and really render my message differently. And then it also tells me what's that response stability. So, am I going to have a reliable response for my audience over time and build this into a longtail campaign or is this something that's really due to today's signals and news and um just, you know, what's in the ether today from a social listening perspective? because that's going to delineate whether I'm going to push something out in social today or build it into something I'm

### Controls to avoid “unexamined AI influence”

(09:10) investing in for the long term. And it kind of just goes on from there. Um I I like the idea that there are controls in place that would allow us to not um not get bound up in the kind of unexamined AI influence that that AI outputs are having on some of the work we're doing as marketers. Yeah. >> Yeah.

### Host reaction + transition into objections

(09:36) That's that's really what it's about. >> I love all this and and and even one of my favorite examples that I've I've mentioned this uh interactive journaling app, Rosebud, that I uh I've been using more the past month and uh and a future speaker here, David Levy, recommended to me. And actually the other day, I I shared something about something I was working on and it actually said to me, I call And I was like, like I didn't know you were coded that way, but this is fun now, right? Like when you when you see that, then it's like you can actually

### Big objection: can “past-based” synthetic audiences judge novelty?

(10:12) get in to what's going on here and you know it's like when it does agree with you, it actually like there's a reason behind that too. But what I'd love to hear cuz so just getting right into objections cuz I I think synthetic research and audiences are like like I like I I think it's you know it's it's it should be scary.

### How prediction works: affinity modeling beyond “I like ice cream”

(10:40) Like if it's not scary in some way probably not thinking hard enough about it. But um uh but uh for for all this, one of the biggest questions is say I've got like a new ad campaign idea or something like that. Like, how the heck can a synthetic audience that's based on past information and past inputs possibly be able to determine uh and analyze something new that hasn't been seen before? >> Yeah, and I think that is a great question and I think what we're getting to here is that a that past information is coded and we create a synthetic layer

### Emotion benchmarks (Harvard OASIS) and model accuracy gap

(11:24) on top of it. So you have all of those signals and everything that's coming from firstparty data. But here's an example. If your audience were to say, you know, in mass, I like ice cream. We could spin up, I like cold things. I like cold wet things. I like cold sweet things.

### Where synthetic fits: not pricing or conversion, but messaging lanes

(11:41) And with a greater preponderance of this data, we're reaching data in the billions of data points to start to model affinity and again emotion and cognition that becomes more predictive of behavior. So, we're about prediction. Additionally, because we're not just mimicking tonality, >> we're really we're really drilling into emotion, we've set benchmarks against Harvard's Oasis model to take a look at how audiences typically react to different types of stimulus in images.

### Using synthetic to narrow concepts before real market tests

(12:13) And we look at veilance and we look at arousal. And when we do that, we're finding a disparity between models like ours that are looking at emotion and cognition, and they're staying roughly within, let's call it. 02 um points um from a real human rating on average. When we look at the same thing with models like ChachiBT and Claude, they're roughly closer to like one to two and a half points off.

### Topic: does it replace focus groups or complement them?

(12:43) So when you're getting closer to that emotional resonance and that emotional response, that's how you can look forward and have more of a relevant scenario analysis of given this context, this is how the audience is likely to think, feel, and act. >> So, so with where this then fits into the toolkit or the stack. Yeah. >> Uh, another question I'm sure you get more than I I uh I hear is is like does it replace traditional focus groups? Is it in addition does it replace or be in addition to other kinds of like polling and surveys and creative testing and all

### What it does not do + what it does well (differentiation across funnel)

(13:24) kinds of testing? Like where does it fit? >> Let's let's start with where it doesn't. So I would rather say when it comes to actual conversion prediction, pricing, trust, all of this is settled by humans and it's going to set be settled in market. But when we're talking about um understanding themes that are overplayed in the competitive set and how to um you know cut through the noise and make sure that you're maintaining a less crowded lane with your message, >> uh this is a great place to find that differentiation across the funnel or

### Case example: brand campaign tweaks and lift

(14:00) throughout the buyer's journey. So that's number one. Number two, I would say yes, obviously creative and UX pre-esting are a good expression of that strategy. So those are the things that you can start get to the starting gate with the best possible set of concepts to then test and market and then prove out what's true what's not true.

### Synthetic as signal gathering → execution → optimize

(14:21) So I would say the synthetic audience is really to gather those signals build the right research and intel for how you even want to go into market and then execute against or operationalize some of those tactics build it pre-est it and then push it into market. Um, and having done that at my last firm, I can tell you that it gave lots of feedback about how we should come forward with our brand commercials um, across Chicago, New York, and London markets.

### From skepticism to adoption across functions

(14:50) And we made those tweaks after the second wave because we were all very skeptical in the first wave and saw a huge uptick in brand awareness, affinity, recognition for capabilities that we that weren't often known. and then engagement in that London market after we made the tweaks that the synthetic audience suggested.

### Beyond marketing: talent acquisition, EVP, RevOps

(15:08) So, I mean, again, I I think I'm probably the greatest skeptic of this and then um actually seeing it come to fruition both in my own brand team, but then also in RevOps and marketing ops. Um our talent and acquisition teams are using it to test um you know, prospective candidates, their employee value propositions.

### Multi-persona interaction: buying team in conversation

(15:31) anywhere where you want to plug in human intelligence into your process whether that's strategy and planning or execution or optimization I think there is a case to be made for the use of this tool or this type of tool >> so so this then like like with how these audiences are structured now are we at the point where where the different uh synthetic personalities uh uh I don't know it's defer to you how you refer to each uh individual um instance here like can they interact with each other do they need to is that

### Comparison mode now; orchestrating agent soon

(16:18) coming like is that unnecessary >> so I I think that that's coming right now we have a comparison mode where you can look and in my case at my last firm I would speak to an asset manager and an asset the donor. So, different parts of the value chain and I really want to understand where they're aligned on a a topic and where they're really coming apart so that I know how to message and build content differently for these audiences.

### Best first use case: simulate buying team roles

(16:42) >> Um, or get more bang for my buck, right, in a campaign that's going to be cohesive. Um, I think what we're seeing now with our our newest release is the ability of an orchestr like an orchestrating agent to call upon the various personas and put them into company with each other so that they can actually have an open dialogue.

### Validity question: side-by-side with humans?

(17:05) >> I'm trying to think about what are the best use cases within marketing where you want to see the various segments and audience talking to each other. For me in a from a B2B perspective, it's usually across arc types of a buying team. So I have a naysayer, I have a decision maker, I have a champion, right? And how are those interacting with me or interacting with my message after after they read what they've read and get together and talk about it internally because that's going to get them to the point of purchase. And so

### Validity: benchmarking + back-testing against reports

(17:36) that would be my first use case is to put a buying team into conversation. And we're not we're really not far from that. >> Well, is uh a great question coming up because you you started to talk about validity and and the Oasis model and and other ways that you're doing that. Uh but but we've got uh David asking in the chat.

### Bias extraction: why synthetic differs from live respondents

(18:00) So does your does your approach methodology involve a sideby-side test with real people, actual focus groups, panels, etc. uh uh can contextualize or validate the efficacy the efficacy for uh using synthetic audiences. >> So what we've done apart from benchmarking against the Harvard model is we've back tested when I was at my last firm we had annual reports on different segments.

### Disparity level and explanation

(18:25) We back tested them to see what the overlap would look like. We've also done that with some of the largest consult consulting firms that make their research publicly available. And what we're finding is there's really only a fourpoint disparity between live audience data that comes in through these surveys and the synthetic audiences.

### Why the gap can exist: social desirability bias

(18:44) And the difference here is, and I've worked with GFK, I've worked with Ipsos, so some of the largest firms doing market research. And the data is the data is the data. They don't really tell me what sort of bias is going to be implicit in the responses that I'm getting from my audience. What mava does is it actually pulls out the bias and it helps me to understand okay I might have gotten this answer from a live audience and this answer from the synthetic but this is why because they're saying what they think they should say and it's going to pull out what that bias is and

### Qual vs quant: mixed methods at scale

(19:16) it becomes the reason that we're seeing a difference between live audience data and synthetic audience data >> if that makes sense. >> Yeah. Yeah. So, while there there isn't a sidebyside panel, what we're doing is we're actually testing the same study across the two audiences. >> Mhm. Nice. Um I I I I want to already start open this up because this is a great uh conversation here and and uh let's see.

### Audience questions: qual vs quant and “focus groups” endpoint

(19:46) Oh uh uh Dan was just asking um do you think synthetic research is more powerful in qual or quant and uh and other folks who want to chime in uh on camera happy to just feel free to just raise your hands and stuff or call something out after this. Yeah. Um, so interesting you should say that we do have an endpoint called focus groups which is actually I think a misnomer because what it really does you speak with our chat you're talking to an aggregate of the population.

### Thousand-persona view + endpoints from trends to NPS

(20:17) When you bring it into focus group you're looking at a thousand or 10,000 versions of that aggregate. So now we're increasing the amount of entropy across the audience and we're able to produce mixed methods research. So it's both quantitative and qualitative and it can be something as um top offunnel or topical as a trend analysis or um channel preference and it comes all the way down to NPS scores.

### Example: qualitative brand associations at scale

(20:44) So with that, I mean, I could even show you um I was asking I think Volkswagen customers across four segments to provide what comes to mind when you think of Volkswagen and there were a thousand responses across four segments that were qualitative and very rich um in terms of what they were providing. So I would say it's both.

### Tool name and scope: Mava for B2B and B2C

(21:05) >> What's the tool you're using for your synthetic audience? >> Yep. It's called Mava. >> Ma. Okay. >> Yeah. like Maverick era. That's how I think of it. >> And and Michelle's wondering uh uh if the focus is more for consumer or can also be used for B2B audiences. >> Yeah. So I'm I'm B2B first. I'm kind of just becoming equating with um acquainted with direct to consumer.

### Persona creation approaches: AI assisted to advanced

(21:31) It's built for both. So I I can pop it up if you're interested in looking, but there are three ways to develop those custom personas. Some are AI assisted. Some go really deep for marketers that are wizards that can wield the wand, let's say. And so you're able to provide inputs across B2B and direct to consumer inputs because there's a little bit of difference between that and that will help you to spin up the custom personas within the platform.

### Parent and child personas from spread-of-opinion

(21:58) I would also say these personas serve as parent personas and very easily like I was talking about, you could start asking something and see that there's a pretty wide spread of opinion within your audience. And when you see that spread and understand the substance and it tells you where that line is drawn, you can then create child personas.

### Drill-down by market, archetype, or psychographics

(22:17) So within that higher order persona, now you're breaking it down and that could be by market or region. That could be and we have a level of specificity all the way down to the zip code all the way up to the global markets. Um or it could be by archetype within a buying team as I was saying before.

### Expert personas for niche domains (oncology example)

(22:34) Um, but there it could be psychographic and demographic, but it just kind of depends on where that spread of opinion is happening >> and like like what happens if if there's an area that's like uh like say someone's got like an oncology product and you don't have like oncologists built in, you know? Uh I mean does that kind of situation come up? Do you then like build those? Do you just hold off? Do you do some more general like what what do you do in that situation? >> So currently we do have the audience interaction but we also have an expert

### “Go-to-market team in a box” and custom experts

(23:09) interaction and we can spin up specific experts. Right now that expert I would call it like a marketing team in a box or a go to market team in a box because we have an SEO expert who is uh fully aware of age refs and somerush data and all of that and keeps its finger on the pulse of what's happening in the conversations on AEO and GEO um which is maybe anyone's guess right now but it it's staying very current relative to an SEO expert where I worked at an enterprise it was very hard to get on her calendar because she was triaging so

### AI data scientist agents and integration direction

(23:41) many things and I actually don't know how much professional development she was doing on the day-to-day basis because she was busy. So we construct these experts whether it's within the go to market >> you know realm or we can look to build a custom expert that is specific to a vertical. >> Awesome.

### Integrations: drive docs → posting → feedback loop

(24:04) Uh there's a question coming in about uh from Dave. Can you use AI data scientist agents to analyze the data from the synthetic audiences? >> I don't know how to answer that question because I have not tried it. But I I think >> it's kind of like a meta layer there, right? >> Yeah. I mean, I'm I'm certain that you could I think it would I would question what is the fidelity of the data science agent first and then Yeah.

### Platform direction: dashboarding and operationalization

(24:32) And then I would I think that's a really interesting idea. Mhm. I mean, well, and I guess a a related question would be that like as this develops, do you like do you see would you want this to be more of a platform that then plugs in to other kinds of dashboards and and tools? >> So, yeah, right now we have I think over 15 different integrations.

### “Why not just prompt a frontier model?” question

(25:02) So, um, it can start with plugging in your SharePoint or your Google Drive so it has access to your own strategic documents and then those stay secure and could just be referenced. Um, but we're pushing it all the way through actually taking the action of posting on LinkedIn for you and then analyzing what those responses look like and then feeding that back into the system.

### Core differentiation: prebuilt context + evidence + cognition/emotion fidelity

(25:23) So, um, yes, there will be a a tremendous amount of integration with your marketing tech stack. Um again from research and planning and strategy all the way through execution and optimization. >> Gotcha. Uh Adam, you want to come back in? >> Yeah, sure. >> Um so I I started typing out but I think it's better to explain it.

### Frontier models still useful; Mava’s value: relational RAG + confidence checkpoints

(25:46) I think >> Jill, this is you've clearly thought about this a lot. I always love uh being the contrarian and putting it back in front of you like why shouldn't we just use one of the frontier frontier platforms ourselves have chatbt construct the layers of prompts and blah blah blah is is it the simple answer of mo is just more efficient to get all that started up because you've already thought it through and then you have a more clearly defined taxonomy on the back end to break it down or like I I feel like there are aspects of this

### Deep business context across history and competitive landscape

(26:18) especially with those three buying roles you just talked about like you could do some of these things yourself and sort of trick yourself into thinking >> yeah it's a great question so to be super clear I use the frontier models too I don't just to use ma to the exclusion of the other I think there are really like specific times where ma becomes much more um useful in terms of having a lens into the audience and that comes from a couple different things is that synthetic layer of data that we talked about. So for every business I

### Swarm, governors, and live context minute-to-minute

(26:52) can create a deep business context. It's a relational rag database. Um instead of being tabular, it's creating a relationship across everything within my business and surrounding my business from um FPNA data, all all historic relationships between content I've put out into the world and engagement products and services, my entire competitive landscape.

### Hallucination checks and re-framing on high spread

(27:15) So that's all prepopulated and I don't have to set that context which is something you have to do with a frontier model. In addition to that firstparty data, there's that synthetic layer that I was talking about with the ice cream before. So as you start to get into billions of data points, you're going to get to see again that entropy across the data set a little bit more and get a better approximation of what uh the predictive aspect of this will be.

### Longitudinal company data + augmentation with proprietary inputs

(27:42) And you don't have that um hallucinate. You don't have that sense of hallucination where it always feels it's right. Ma is built with a way to have checkpoints against confidence. Again, um we're also looking at hallucination and and it will refra the entire audience if there's too big of a spread of opinion.

### Empathy as core of marketing

(28:02) Again, this is not something you're seeing with a frontier model. And then the last piece I would say is that fidelity to emotion and cognition. Because for me and as someone who studied this and very much feels empathy is kind of the core of all marketing, I would rather be looking at an audience that's modeled around what people are thinking and feeling than what they sound like.

### Timeliness: refresh rate and “news through the eyes of your audience”

(28:24) And that's what those frontier models are doing is they're taking all that first party data and they're just mimicking the tone of the audience. So it sounds really convincing, but it's really not connected to what they might actually do. >> And what they might actually do feeds off of primarily the first party data that is put into ma or is there secret sauce that's ignoring the sycopantric stuff and really >> it's it's less that the adversarial model is part of it.

### Validating outputs and reliability vs. frontier variability

(28:58) um uh which is that that GAN but I would say that even more than that it's that there are a swarm of models that are looking to model the population from different aspects of its thinking. So you've got how they think, how they feel. Those are modeled. You have a governor that's calling upon different models to then bring to the front or bring to the four what this audience is made of.

### Market research industry response and TMRE context

(29:22) And then finally, it's all built in context. So frontier models often have updates that will uh train the model but it will be delayed. Whereas we're in live context it updates minuteto minute. All of the data is stored temporally. So for the company I was at which was 40 plus years old. I have all that data from 40 years back all built into the system again without giving it context. And that's not to say that I didn't give it context. I gave it all of our strategic plans across seven P&Ls. I gave it um 40 transcripts from interviews with some of our top stakeholders on what they thought the brand meant to them. I mean things that you can't find out in in the ether. So you can always augment it with what it is you know to be true about your organization as well.

### Practical demo elements: evidence traces, confidence, and hallucination risk

(30:11) >> And there's a a great question that came in from Lisa. How timely are these personas? For example, a sample quir on the site. How do millennials feel about sustainability? Is this is the synthetic persona refreshing taking in new data and inputs from the wild or is it more of a snapshot in time >> which is what I was just saying.

### Market research validation: practitioners first

(30:33) So on those frontier models you do have kind of this delay on when the model was completely updated and pushed out right um >> for ma and these rag databases that I was talking about these uh deep business context they're updated minute to minute >> so every time I would go in I can I can show you every time I I refresh that model or go and take a look at those relationships it's it takes a second for it to load because it's always taking in context today.

### Market research practitioners vs vendors (GFK/Ipsos)

(31:03) In fact, we even have an endpoint that looks at the news through the eyes of your target audience and it will analyze given a story um and all the articles related to that story and how the story is evolving how that's affecting your audience today. >> Thanks D. >> Uh hi Joe. >> Uh just follow follow-up question. Um I think you had mentioned the major kind of market research players like the Ipsoses of the world etc etc.

### Adoption topic at TMRE + builder vs buy

(31:29) Um how have are you working directly with them? Have you have they have you approached them and are they like responding with excitement or do they have major concerns because I feel like this is could be very very pioneering for them. Um >> absolutely. >> I'm just curious to hear is is there like validation from that side. >> So interesting you should ask.

### Story: synthetic audience surfaced real-world security concern

(31:48) I'm because I was always on the client side. Most of my contacts are the you know heads of market research at firms. So, head of market research at Morning Star, at Coinbase, at Pepsi, those are the people that I've been talking to a little bit more. Um, at Capital Group, and you know, at TMRE, which is the number one market research event of the year, um, they all go, it's in Las Vegas. Um, that was back in October.

### Demo: agent trace (plan → research → execution → validation)

(32:13) This was one of the biggest topics, and it's builder buy, you know, and how do we how do we get to the fidelity? So, yes, I think there's a lot of conversation about this. I've not contacted um GFK about it, although it's a great idea. I'm kind of wanting to know what are the practitioners on the ground at the organizations who are trying to get that market research doing and what is their take on it.

### Concern: speed without receipts; data provenance

(32:37) So, that's really where I've started um since I joined Mava and it's been let's call it two months since I've taken on my role. So, more soon. More soon. >> Great. Thank you. Yeah, >> Ailen, I I'm actually going to call on you because you're one of the folks I was thinking of who would have so much to think and say about this.

### Case: security/transparency concern predicted; validated on LinkedIn

(33:01) [laughter] Anything you wanted to make sure we cover here? >> I I I want to share an experience with synthetic audience that maybe relates to it. So, I created a synthetic audience from uh this group. So, I have all of the video. I have a rack system. created the persona audience and one of the concerns that this audience always has is security and transparency.

### Importance of blind spots + proof

(33:31) >> Mhm. >> So I just want to show you how accurate it is. So recently I shared my tool on LinkedIn and then I asked people to comment on it and I think Paul Greenberg responded and his question was exactly that. He said how does your tool deal with security and how is my data protected? So as far as synthetic audiences I think you will get signals.

### Showing the agent output: evidence + reasoning + confidence flags

(34:04) I mean one of the values that it provides for you is your blind spots. If you're focusing on an audience and you want to know what their concerns map, then that is really a great way to test and learn about. So that's my own personal experience with synthetic audiences and how effective it may be. >> Very cool.

### Demo: confidence 40, hallucination risk high

(34:30) And I I mean I think data and security super important, but I mean what I've been kind of putting out into the world is just speed. Yes. And I understand that. And I went with our CTO to dev day and saw them construct an agent that moved all the lights in the auditorium in less than 8 minutes. So speed, yes, there's a lot of speed and impetus behind that.

### Persona creation workflow + assets/integrations

(34:50) But I think without proof or receipts is what I've been calling them, then it doesn't really mean a lot. And you have to be able to explain where did the model come up with this and how is it bearing out as it rep uh you know as it relates to my audience. So what level of confidence do we have before we put something in market um rather than just a gut feel.

### Tools called, personas used, and why

(35:10) And so that's I think what we've been really concentrated on is that data and security um that feeling that people can access any part of their stack and still have it feel like a closed system and then also understand how is this set of models working and where are they pulling that data from. So data providence I think is is a really important piece.

### Video analysis use case: speech rehearsal, differentiation, and delivery

(35:35) So just you know taking a look we're not going to do like a a demo but I just wanted to show you this one endpoint which is called the mave agent where I'm just saying I want to understand how to best position this business and how to approach comprehensive marketing strategy for them. So it's giving me that output that you would expect of any model but beyond that it's also pointing to the evidence of where it pulled from to get that and then also how it approached that.

### News endpoint: story impact through persona lenses

(36:02) So it detected the strategic query. It's restating what it is that I asked it to do. Then it's doing some planning. So what tools did it use in terms of web search and SEO analysis, but also what personas did it call upon >> to really provide the output? And it gives me a reasoning of why it did that, you know. So now I'm getting a why.

### SEO expert question: importing an expert video

(36:25) It also then shows the research phase and what it actually did. And then what does execution look like to actually bring this into fruition? And then finally validation phase. And it's telling me here that I have a confidence level of 40 and a hallucination risk that's high. So it's flagged that for me.

### Live updates: minute-to-minute refresh

(36:47) Now I have the ability to work with it to get to a different level of confidence before I'm going to even put it into any sort of application across strategy or execution. And so I haven't really seen any other platform that proposes, you know, these personas to have that kind of guess and check in place for me. And so that becomes a really important trace.

### Onboarding personas: assumptions, research, and intent

(37:14) Um, and it again, it'll flag specific things that it will want me to review before I put them into anything that I'm doing. >> So how how do you go about creating your personas again? So we do have um custom personas where again you have an AI assisted persona. This would be for like a less sophisticated business that maybe doesn't have a marketing lead.

### Uploading static persona decks and other assets

(37:36) You know uh it could be a startup founder who you know is more in the technological domain but needs to understand product market fit. Um, again, there's an intermediate step and then I think for probably most people on this call, there's an advanced pipeline where you're starting to establish what your assumptions are about your audience um, across who's the persona, what are the psychoraphics for B2B specifically, how am I talking about role type, industry, um, their tech stack, etc.

### Video analysis example: CEO speech coaching and outcomes

(38:08) For direct to consumer, it gets more into um, some of these psychoraphics. I can provide all the market context research that I either have on my own or that I've used another tool to derive. And then finally, I can provide, you know, what is the purpose of my developing this persona? Am I building a content strategy? Am I generating research? Is this for product marketing? Am I developing sales assets? And then I can start to upload any of the research I've done on my own because we all have those static persona decks with like Wendy wealth manager and her

### News analysis example: audience risk assessments

(38:38) picture and like what are her her pains and motivations are. So all of those could also be uploaded as well. And from there the system will generate it and give you a full look of pain points, motivations, channel preferences and then you can select what you want to integrate into your workspace. And I you know I can point out you also have the ability to upload your own assets or connect them through integrations.

### Comparing Mava vs frontier models: consistency and retest reliability

(39:05) Um like I had mentioned there is the expert panel. So for me I even have a type form agent expert who can help me program a survey if I wanted to or I can work specifically with you know an earned media expert. Um because sometimes marketing and comms are separated and I don't have access to a comm's lead at any given time. I had mentioned always a pain point for me at my last firm was getting time on the SEO experts calendar and so I love having this here and it seems so important because it seems to be like such a massive change for our you know for our

### Crisis PR potential application

(39:40) domain right now. So, >> so for SEO expert, could you for example just import a video of some SEO expert talking about the topic? >> Yeah. So, I can I can actually show you um something I did. So, I you know I was at Morning Star that was my last experience and um I used to work with our CEO to co-author speeches for him.

### Credit usage: rough magnitudes and how to control cost

(40:09) And so Morning Star Investment Conference was his biggest flagship appearance. And so as we were practicing, I would upload the video of what he was doing and it would analyze the substance of what he was saying, the creative that I put on the screen and his performance. Um, and I gave it, you know, the communication intent for the speech was primarily, secondarily, and tertiary.

### Political advertising and fast-changing audiences

(40:32) You know, these are the intents. Here's my goal. You know, I'm looking at building trust for the brand. And I'm thinking that, you know, we want people to think of us and our research as um something that is like their first stop on the bus and then I want them to go look at our products in the exhibition hall.

### Political use case: constituencies and committees

(40:49) And so based on that goal, it's going to analyze the video at 2C chunks and then it's going to put forward an analysis again of emotion, cognition, and behavior and then provide me with an opportunity to talk to my audience. In this case, I was talking to the advisor audience and I wanted to understand, you know, tell me how Kunol was resonating with you and also I had mentioned you have that rag database and I don't want to make you guys carick by trying to scroll up to the right spot, but I had noticed that we had a low differentiation score and so I started

### “How often do you update?” answer: minute-to-minute

(41:24) to ask the audience, you know, what is it that doesn't sound differentiated about the message here? I'm trying to find that because this was a little while ago that I did it. Um, tighten the speech with a differentiation sound bite. So, it started telling me, you know, where is it that we were falling down and what should he have said instead so that this sounded uniquely Morning Star.

### Closing: contact info and community wrap

(41:49) It was also looking in the topic I know Yan, but it was about the convergence of public and private markets. So, it started pulling from that rag database. Who else in the competitive set is talking about the convergence of public and private markets? What is it that they're saying and how can Kunal oneup them or how can we make it sound uniquely Morning Star when we're talking about this topic? It also gave me timestamps of where he should pause for greater emotional effect.

### Outro: gratitude and end of session

(42:15) So, we shared all of this with him from one practice to the next and watch his scores go up. And when we got to the actual conference, we saw greater traffic coming into the booth. Um we we heard more laughs because we actually do like chronicle how many times the audience is laughing and we did see more shares on engagement when it came to the digital version.

### Additional examples: webinars to atomized content

(42:37) Now could all of this be attributed to the work we did with the ma hard to make that attribution but it was a material difference from the last time over the course of seven years that I had been doing this with him. So um that's one you know video use case. We also took their top performing uh webinars which was I think the most performative channel for Morning Star's product marketing team and we used this to analyze what within the webinar was most engaging so that we could atomize that contact content and make you know different form factors for different

### News endpoint recap

(43:09) channels. So that's something that we also did here in in the video analysis tool. So again, everything that you're doing here is going to be put in front of your audience. And as I was saying before, the news is no different. So when I'm looking at the news, I can look at news in general, but I can also look at news through the eyes of my audience.

### Host closing + future conversations

(43:32) And so that's so important because we want to be highly topical. You can see I've set three personas because I had a brand campaign in market for those three personas. It looks like asset managers and heads of fintech were very concerned with the Bondi beach mass shooting. From here I can understand, you know, what is the story history obviously a summary and sentiment analysis of that.

### Audience impact and communications response

(43:56) But I think almost more importantly is that for any given news story I can run an analysis from the perspective of my audience and it's going to and this goes back to it being minute-to- minute. It's going to take a look at what is the audience's reaction overall. How do they feel about it? What do they think about it? What are they most likely to do? How are they assessing risk as a consequence to what it is that they do and their jobs to be done? And so, this is now going to give me a sense of how can I take this response and put it into action in my

### Wrap-up

(44:28) own communications with them. >> That makes sense. So, just kind of a a whistle stop tour through some of the different things that we have. Um, but I I mean it's become indispensable as a marketer for me and how I'm I'm doing different things on the day-to-day, but like I said, I still use chatbt and claude for this.

(44:51) >> You know what? And one thing also I think like for some folks who are used to just having their LLM of choice uh and uh and and it seems like you could debate this but it see it seems like the trend is they're getting smarter or getting richer information right and so so for for some of these interactions and you know we talked about like validity and comparing things like do you ever compare outputs for say like telling chat GBT or Gemini you're an SEO expert versus you know versus your own more custom audience. Yeah.

(45:31) >> Yes. And I would say and this is me being a PH dork but I think like the biggest issue here apart from my questioning how predictive those engines are would be the like the test and retest reliability. Okay. For example, um our CTO was making fun of me because I'm in my 40s and I've never made caramelized onions.

(45:53) And so he started asking and I know well I love I love eating them. I've just shame before shame. >> And so he was asking the models about you know how many people who are 40ome years old like have or have not made this and going from one to the next. Ma had a level of consistency and a score on response stability around both the quantitative and qualitative output.

(46:18) Whereas working with chat GBT in particular from minute to minute he was getting very different outputs. And that's something that you're going to see there whether you're saying you're an expert or you're just asking it a query it's not going to have that level of consistency. Mhm.

(46:45) >> I just want to say I I thought an interesting application for this could actually be crisis PR because you you have to go with something and you've got to react to something and it just seemed like when you did that there could be

(47:06) something you know I immediately thought of the news but I also just think of other things that happen in corporate life. So kind of interesting. >> Absolutely. Yes.

(47:06) >> I had a question um that uh example you just ran through. What's the rough order magnitude of the number of credits? I noticed that ma prices based on >> credit.

(47:06) So it it changes because it's generative. So it depends on what your query is. Um how heavy it is like in terms of how many documents are you loading into the query and how robust is that outbook and how long is the conversation. So I've had conversations where they've been like500 credits because I'm carrying it forward. My advice then to keep it smaller is to break things up into really discreet projects so I can be very goal oriented.

(47:31) This is the conversation I want to have. this is the output I want to get and then I can keep my credit allocation down. So, I've had conversations that have been 50 credits and like I said, I've had conversations that have been almost 2,000 credits. It it depends.

(48:01) >> No, but that's that's a helpful order of magnitude like you know, you've there's that starter plan 300 a month and um like you sounds like you could still do some very easy simple analyses multiple times over there.

(48:01) Yeah, I I have a number of firms that start there and try kind of get their feet wet to see like where are we finding the most value here.

(48:21) >> Sure. >> Um >> I would also say it doesn't preclude you from inviting your entire team.

(48:21) So because it's credit based, there's no like seat model like everyone on the team can take a look and have a conversation. it can be much more collaborative and break down silos.

(48:49) >> Mhm. I was just curious about um there seems to be a lot of accability to uh political advertising. Has there been any discussions on that and how you would uh simulate potential political audiences because I think this is where this the potential is.

(48:49) >> Yes. So actually we are working with a member of the House of Representatives right now to reconstruct not just his constituencies but also the committees within which he sits. So I think what he's wanting to understand is he wants a custom news digest that's going to take lots of information and consolidated and then he wants a custom dropdown so that he can start to look at things through the lens of any given audience at any given time and then he's ready with sound bites when he needs them.

(49:19) Um so that's kind of our first foray there. We're also speaking with um a lobbying group and so that will be another opportunity to uh really kind of understand how best to use the tool in this in the way that you're that you're suggesting. >> That's [clears throat] great. >> Uh how often are you guys updating the data set because obviously especially politically the times change three months, six months when not whatnot. I'm curious.

(49:46) How often are you updating your data set for your simulated audiences? >> So, it's it's minute to minute. And I I just put this up here because I wanted to show you. I just went to business knowledge and you saw that it was generating. So, in that span as we were sitting here maybe 5 seconds with latency, it's now updated this entire data set.

(50:12) And this is again like 40 plus years of data that Morning Star has been around, but it's current to the minute. So if there were a controversy that Morning Star was embroiled in like this morning, it would be here. >> Nice.

(50:31) >> So they would be [laughter] fair enough. No, you're covered. You're covered. 7:00, you're covered. But yes, awesome. Thank you so much.

(50:31) >> Yeah, of course. >> Well, well, this is amazing. >> Jill, what's the best way to get in touch with you and keep tabs on what you and Mava are doing? >> Yeah, so I mean I'd love to connect with anyone and everyone um on LinkedIn. Um you can also reach out to me at [jillmava.io](http://jillmava.io). You can visit our website and we don't have any credit card for trial.

(50:55) So, I encourage you to like jump in and kick the tires a little bit. And, you know, if you're running into any questions or want to think about how to best apply it, then just reach out to me because I' I'd love to work with you. I'm I'm more a marketer than I am like a like a CEO or a seller, any of that.

(51:10) I'm I'm one of you, you know. >> Well, well, yeah, this is great. This is a topic that I can tell from all the comments that have been flowing through here that >> we need to be covering more of because there's a lot to understand and and really I mean >> uh I I don't want to necessarily like give you a whole new like uh press kit you can use here based on this but just the way you're describing it even that political example at the end and thanks all for bringing that up.

(51:36) It's like like I feel like one way or another like professionally even personally right for things like cooking or dating or all these kinds of things that'll come up like we might have our own syn you know there's a lot of talk about having our own agents but having our own audiences to tap into and to yeah >> uh go and and use as an initial sounding board.

(52:03) >> Uh we're in some fascinating territory here. appreciate you sharing this. welcome back to participate and >> continue to be involved in the community and everyone here really like so many faces that have seen a lot here >> uh uh and know a few first- timers and folks who don't get to come every week but it's just like I look forward to this every week and I look forward to all the interactions come and I know so many of you interact with each other outside of this and whenever I'm talking to people about the community I'm like the Slack's

(52:33) great newsletters all this stuff Like all this stuff's wonderful, but like we actually get to have conversations and learn from each other here and I learn from all of you who show up every week and I'm grateful for that. >> happy holidays. Keep me posted on ways the community can better serve you in the year ahead.

(52:52) >> Uh it's been really exciting this year being doing this as part of the March family and >> uh lots more we're going to be building on. So Jill, thank you. Everyone, thank you. We hope it's a really fulfilling and wonderful holiday season for you all and >> uh and here's to a wonderful start of 2026 >> uh for each. So, thanks everyone. >> Thank you. Thanks for having me.

(53:15) Bye everyone.

## Creativity Is the Only Thing Left Tom Ollerton on AI-Eaten Marketing

Speaker: Tom Ollerton
Published: 2025-12-12
Tags: creativity
Video: https://www.youtube.com/watch?v=3aJfH8uXDfE
Page: https://aimarketersguild.org/sessions/creativity-is-the-only-thing-left-tom-ollerton-on-ai-eaten-marketing

### Product / episode hook

(00:05) We're super excited to introduce Innovative Orchestrator, the first AI super agent for omni channel advertising. We're bringing humans and AI agents together so that marketers can conduct a symphony across channels to reach their most valuable customers. >> AI is not a matter of if, it's a matter of how and when.

### Show welcome + guest intro

(00:23) And we will help you solve that. Hey everyone, I'm David Burkwitz and welcome to another edition of AI Insiders with AI Markers Guild by Mark. I feel like we should have a tagline or slogan here, but no, we're not that official. Um, we do have some some pretty legit guests though and today is no exception uh because we have Tom Olton here.

### Shout-outs + community framing

(00:56) and Tom was actually introduced by a previous speaker, Dr. Cecilia Don. So, welcome back, Dr. Dones. And uh and and shout out to another past speaker, Katherine Montgomery. Always good to see you. And and just seeing like folks who uh we get to continue to learn from in the audience as well, which is always a great segue because these if you haven't been before, most of you have, but you know, they are uh they're more like community conversations.

### Setting up Tom’s session

(01:24) since these aren't webinars. And so Tom's got a few things to present. Uh he's from Automated Creative doing fantastic work out of London and beyond and and just uh yeah, as as soon as we got to start, you know, bouncing around ideas, I was like like we got to find time to go. And I I love those conversations that just start one-on-one.

### Bringing Tom on + light banter

(01:45) It's like we got to bring some more people into this room. And so Tom, glad you're glad you're in this room, >> mate. This is in the most intimidating thing I've done in ages. But um I will endeavor not to fall flat on my face completely. But you know, if I do, I can just close my laptop and we can forget this ever happened. >> Well, at least I don't I won't be the one to intimidate you.

### Audience roll call (where are you joining from?)

(02:04) It's some of these other folks who are staring at us. So, >> I look forward to it. Absolutely. And can I just ask where's where's everyone from? Loosely, can you just pop in the if I can you just do a little heart if I if I say like North America? >> Yeah, I mean that. Oh, North America. Thumbs up. Yeah. Okay.

### Regions represented + quick crowdwork

(02:23) Any any Oh, look at that. Eileen. Beautiful. Many Europeans. I saw Israel represented. Let's see. Uh, so we do have both coasts here and and the Gulf Coast or close enough to it. >> Any anyone from Northland here? >> That's so that's where I'm from in the northeast. So, uh, yeah. No Brits. Okay.

### Tom’s thesis: creativity after AI reshapes marketing

(02:49) So, I will slow down because >> I need need to use more more Z's than S's here. So, >> Z's. Oh gosh, that is going to be a push. But thanks guys. I really appreciate you not doing the rest of the internet and you coming here today. I really appreciate that. >> So, I'm going to give this talk basically comes around my belief that create creativity will be the only thing left once AI completely destroys all of marketing.

### Book origin story + interviewing senior marketers

(03:16) So this is what I I'm very passionate about and what I want to talk about today. Um but what I'm going to share is insights from this book I just got published. So I was approached about a year and a half ago to write a book. I was very surprised because I can barely speak, let alone write anything. Um and it was literally the most painful thing I've ever done.

### Writing process + what the interviews were about

(03:36) I had to get up between 5 and 7 every morning for a year and a half before my daughter gets up uh to write this book. Um, but the the highlight of the book really was interviewing 40 very senior marketers. So whether that was academics like Cessy or that CMOs or startup people or agencies, creative strategists. And I just had calls of them and I said, "Look, how do you take the smooshy lovely creative thing and combine it with the data thing? How how do you how do the how do those two completely different things go together? How does

### Why not write “an AI book” (it goes out of date)

(04:06) that work? Please tell me." and interviewed people like Roy Rory Sutherland um and Neil Patel some really interesting people for it. Honestly, I wanted to write a book about AI and I'll tell you why in a minute. But any book that I was published in, you know, July this year is going to be pretty much out of date.

### Choosing a durable frame: data + creativity

(04:26) And I've actually I read David's book on AI and marketing and he does a brilliant job of it at the start saying, "Well, he can't really do this. It's going to be out of date." And I David did a very elegant job of of um giving the 30,000 uh foot view of um AI and marketing. But I what I did I thought look if I write it about data and creativity it will be relevant to AI it will be relevant to quantum whatever all the rest of the things.

### Company background: Automated Creative

(04:49) So that that's that was the book I wrote and and it's available in bookshops and on the internet but the wider context is I I work for this company. Actually I'm the founder of this company. Uh it's called automated creative. So what we do and I'm not going to pitch the business to you today.

### What Automated Creative does (optimize ad creative with data + humans)

(05:07) We make and optimize ad creative. So we make all this stuff. We have a we have our proprietary tech that makes all of these ads and we do that using live data that comes back from the metas and the Googles the retail media of this world and combine that with human insight. So um we we founded this in in 2017 when we um uh when we kind of asked ourselves this question.

### 2017 bet: AI + agency services converge

(05:28) But like in 2017, we thought, well, well, what will happen if you combine AI with creative services? And so we we ran an event called I'll be back that looked at the intersection of creativity, ads, and AI. And my business partner and I, Alex, went, "Look, at some point, you're going to have AI and agency stuff squished together.

### Early belief in generative AI + building for the future

(05:47) So why don't we go and do that?" And that was all based on this thing that we got really excited about, which was called generative AI, right? So, we've been in that space since 2017 in one way or another. Um, and we try to build a business with this kind of future in mind. So, written the book. Um, it's coming up to Christmas.

### Uncertainty about the AI future

(06:04) Might make a beautiful present. I don't know. Depends how much you like someone. Um, and we and we've built this business. Um, so, so what how I feel is we're working into this uncertain AI future, right? There's a lot of people saying that this is definitely going to happen. and a lot of people throwing uh throwing sort of expert phrases around, but I'm really uncertain.

### Quote + “autopilot ads” vision

(06:26) I've been doing this for 10 years and I do not know what's going to happen next year. I've got a vague idea, but there's I saw this quote um and I'm sorry I'm I am going to read the the whole thing here, so I apologize. Let's get this thing out the way. And you're a business. You come to us uh you tell us what your objective is. You connect to your bank account.

### Zuckerberg quote: “just give us your credit card”

(06:43) You don't need any creative. You don't need any targeting demographic. You don't need any measurement except you to read the results that we spit out. And I think that's going to be huge. I think it's a redefinition of the category of advertising. Now, this is what Mark Zuckerberg said. So, he's basically saying here, ah, creative stuff, it doesn't matter.

### Engineering vs human problem framing

(07:01) Just send us a picture of your products and we'll crank out this gen Genai stuff in the background. Just give us your credit card and you'll and you we'll just spit you out money basically. And so he is right if marketing is an engineering problem but for any of the marketing folk in the room will understand it's a human problem of which um technology plays a part.

### Business anxiety + platform automation risk

(07:24) So we're in this kind of odd space where and certainly as a business owner in the creative space it's literally terrifying that meta is going to we're just going oh we're going to automate that completely so we'll see if that happens or not. Um and then also the I heard this amazing phrase the other day it really stuck with me.

### “ChatGPT is a money pit” + economics of compute

(07:41) He said chat GBT is a money pit with a website over the top. It's not it's not making any money. Um you got to you got to think where you know the end user is using some kind of AI that's coming down a pipe from Chat TBT. Well, where's that compute power coming from? Where are those chips coming from? There's this um and the uh the economists on this call understand this far better than me.

### Capital needs + sustainability question

(08:03) But uh Chat GBT they were trying to raise what was it the the equivalent GDP of France and Germany put together. They were trying to raise the same amount of money as the US spent on on World War II. I mean, and this is this is like the biggest most successful startup ever. So, no one's actually making any money out of this stuff.

### What happens when investors want returns?

(08:18) So, what will happen when that shakes down? When the Microsofts want their money back, what will happen? And and yeah, they're expecting to lose 44 billion by the end of 2028 and and they Yeah, it's a crazy time that everyone to me. I see that number and I'm like, wait, that's it? Thanks, David. >> So, yeah, everyone's betting on this horse that hasn't actually worked out how to make a a profit yet.

### Prompting vs creative spark (experience of AI)

(08:44) >> Interesting. Um, and then you go is I'm almost embarrassed to to show you this, but you know, you've you've seen like the uh the chat GBT user versus the non-Ch, you know, for the sunshine and rainbows and unicorns that explode in your head at the thought of a great creative idea or the hair standing up on the back of your neck as you go, ah, that's the thing we're going to do.

### More work, not less (reading every word)

(09:05) That's the brilliant exciting thing. Not like am I going to write a really long prompt today? Right? You know, so and I think I don't I don't know what everyone else's experience of using AI is, but like now I'm finding myself going to do more hard work, right? Instead of going, "Oh, I'm just going to crank out this email to David and I'll just let the slot machine take care of it." Like, no, no.

### The talk title / framing question

(09:25) I'm going to read every word he said. I'm going to think about everything I want to say and I want to send it to him. So, I'll be interested to know later if people are going on a similar journey because I want this, not this. Um, so, so how are we going to use data and creativity to build your brand if AI kills us all? So, let's do a little bit of a history lesson.

### Oldest surviving advert (setup)

(09:46) Uh, you I nearly asked you, does anyone recognize this? >> Of course we don't. Of course, >> I share that quote all the time. >> It's an absolute banger. So, this 1477, right? So, this is the oldest surviving advert in the world. >> Wow. >> Not the oldest, but the oldest surviving, right? And it's about this book. Can I beg the differer? >> I'm I'm so sorry, Tom.

### Pompeii pedantry + defining “oldest branded”

(10:08) You're on a great role, but um >> I was in Pompei uh two summers ago and there's still an advertisement for one of the senators. It was like for his campaign and it's on a street corner. I'm being very pedantic at this point, but um >> my ads as I I don't know if you know um ads transparency tool. It lets you toggle between political ads and and brand ads.

### Back to the 1477 example

(10:35) So obviously this this all just point well taken >> you know so thanks thanks for the you death starred me do you know what a death starring is when someone has an idea and you find the one little thing that goes down the thing and explosive >> yeah I know I've been used to doing that my dying day anyway >> I I am hoping we have some like Mesopotamian scholar here who can prove us all wrong >> right so this is the oldest surv I branded. Okay.

### What the 1477 ad contains (price, CTA, viewability, sampling)

(11:05) So, my my oldie English isn't that great, but I'm going to point out some things. So, it's um it this here says good and cheap. So, it's a good price, right? And it's a and it's a guide for priests called Sarum Pie. Um and it says kind of go to this place to pick up this this book from Samuel Caxton's uh print prince works in Houndsditch in London.

### “Same as today” takeaway

(11:27) And the really interesting thing is it says that the book is written like in this font basically. It doesn't use the word font, but it's like it's it's basically saying when you read this book, it will be as read easy to read as this thing. Um, and then this says do not remove like leave this up.

### Modern marketing concepts in ancient ads

(11:43) Right? So, what um any of the marketing people in the room will recognize is that you've got good and cheap. You've got a a price point. You've got a call to action which is go to this shop. You've got do not remove this which is viewability. And then you've got sampling which is look the book is written in the same font or printed in the same font as as this notice.

### First banner ad (setup)

(12:02) Right? So, the one of the oldest surviving adverts is doing a lot of the same things that we're still doing today in 2025. So, fast forward a little bit. Does anyone know what this is? >> Oh, that that I know. I don't want to give that one away. >> Thanks, D. You see, Adam, you see David let me have the stage. It's good. I appreciate that. There you go.

### First banner ad details (AT&T, 55% CTR)

(12:18) >> This this is internet >> the first ever banner ad, right? And it had a click-through rate of 55%. But still pretty good. Um, so, uh, and this was for AT&T, uh, and it clicked you through to a website that talked about AT&T were doing something with some galleries. No, no one really knows. But actually, in a lot of ways, this is a much worse ad than one that was written, you know, several hundred years before.

### “You will click here” + CTA commentary

(12:42) It's just it's blank. You are going to do this. It's it's a kind of rarely rarely used technique these days like you will you will click here. You know, there's not many called call to actions that say yeah. Anyway, and then moving forward uh slight a bit a bit further forward. Sorry about the slop here. We're getting the point across.

### Amazon desks + recommendation algorithm origin story

(13:00) So, in Amazon's original office, um you you know better than me where that actually was. They didn't even have desks. They had doors. They had like doors on on on on bins and they were working up doors and there was like a coffee machine and the carpet was kind of all disgusting. was covered with coffee and I can't remember the name of the developer but in in his spare time what he did is he he wrote um a a recommendation algorithm that basically said if you bought this book you might also like this book and when Jeff Bezos heard about this he

### “We’re not worthy” moment + impact

(13:33) uh he saw it in action and came into the room and he knelt down on his knees and judging by um uh the age of some of the people in this room you all remember rains world they're going to you know we're not worthy moment. So you he did he did a we're not worthy moment uh to to this this guy um because basically this algorithm but you like this book you might also like this book went on and on and on and obviously it's developed massively but it's probably in terms of revenue the most successful marketing campaign of all time and in

### Recommendation engine scale + revenue

(14:03) terms of cash through the till it's probably you know and obviously grand derivation of the original thing but that this recommendation algorithm was was a really creative use of data, right? That's what this talk was about. >> My information is dated, but at one point when they were still primarily books, it accounted the recommendation engine was close to half of their sales, >> right? Yeah.

### Too much data (even in 1998)

(14:28) That I've heard um yeah, similar similar. >> So, so that's that that that's creativity and data together. Um and then gosh, one of you guys know there's a lad called Jim Stern in the US. He's he's head of the US Analytics Society or something. Sorry, David, if you're here or something wrong. Yeah, he he he did a he did a he was in a book.

### Biggest challenge: “there’s so much of it”

(14:49) He was I interviewed him and he said in 1998 he said to 50 um brands, what's your biggest challenge with digital data? And can you guess what anyone said? Come on, Adam. You can't be quiet now. I've asked you a question. >> Not understanding it. Not knowing what is coming from or what does it mean? >> Okay, any other guesses? >> It's in the format and it's not what we need.

### Data without action is distraction

(15:14) No, what they came back with was there's so much of it. There's so much data in 1998 like oh like oh like so long ago >> I got too much data. How much you got now? It's like everyone I interviewed for the book they're like how many departments creating data? Everyone goes oh everyone's got so much data. Everyone's got so much data everywhere.

### “How much actually makes us do something?”

(15:34) Um so and one of the things I learned from writing the book is um is this is if data doesn't inspire action it's a distraction right so how much of the data that we have got even before you consider AI is how much of that data are we scratching our chins going h and how much of actually is making us do something right that data is very abundant it's everywhere but how much of it is actually making us do a thing or we just got it because we can get And the really weird thing for anyone who doesn't work in Adland is we do this

### Fake data for awards (Cannes Lions anecdote)

(16:09) really weird thing even if there's no data we like we we pretend that there is and one of the people I interview for the book I mean some of you guys will will be familiar with with Kand Lions is the very peak the very peak of the advertising accolade tree the the star at the top of the uh the awards Christmas tree and there was a a journalist who was um covering the event and there was a campaign that ran in Latin America that was banking the unbanked through some kind of telephone network. I can't quite can't quite

### Campaign “never ran” + the lesson

(16:39) remember what it was. Um and so the journalist, she went and spoke to the CMO and said, "Look, can I interview you about this campaign? It's really interesting." And the CMO said >> it never ran. >> It it this campaign never ran. The agency did it and then they said, "Can we enter it for awards?" And the CMO was like, "Yeah, sure.

### Data “shadows of people”

(16:58) " Like knock yourself do I'm not paying for it, but you know, go for it. So, so even in a world where there is this incredible amount of data, advertising people want to pretend that there is data that doesn't actually exist just so they can win awards. So, we have this kind of odd odd relationship with data. Um, and uh I interviewed Rosie and Ferris Jacob.

### When things get big, all you have is numbers

(17:18) You probably got some >> old friends of mine. Yeah. >> Yeah. I worked with Rosie. Yeah. >> Um, so we interviewed those guys for the book and they opened my mind. Um, and they they talked about this this idea when things get really big, all you have is numbers. If you got a large organization or a country, business, whatever it is, at a certain point, all you can do is talk in numbers because you can't go out and speak to literally everyone in the organization and all the clients.

### Smoothing/rounding hides the edges

(17:45) It all kind of comes down to a spreadsheet. And what that means is stuff gets smoothed and rounded. And I think that is one of the big dangers of AI is that if we're just dealing in in large numbers with the numerical data sets, it's going to round things off when it's the edges that matter. So interesting case of point. Does anyone know who this is? Any guesses? North Americans thought you should have a swipe at this.

### McNamara’s fallacy (optimizing to one metric)

(18:07) >> He looks like a general. >> This is Robert McNamara who was he was the CEO of the poor motor company and he was drafted in to run the military operation in Vietnam. Um kind of odd choice. So, um, one of the one of the errors that Far Ferris, um, and Rosie talked about was the idea when when businesses get distracted by one data point, right? They go, but we're just going to optimize uh, uh, to this thing and we're not going to we're not going to um, we're not going to um, think about anything else. And so it's it's

### “Basket of metrics” to avoid blindness

(18:38) quite gruesome this this stat but Robert McNamara is called the Magnamara's fallacy is that he said we will win the Vietnam war if we increase the number of uh enemy casualties if that number keeps on going up eventually we will win right if that that goes north at some point we just have to win but what he didn't do was understand the politics was understand the mood of the nation and there was all of these softer metrics that weren't weren't looked at and so he just focused on this kind of very gruesome bloody metric when actually

### Abundant data ≠ important data

(19:10) there's a lot more things that worked into it and Rosie in the book talks about having a basket of metrics. So how do you have a bunch of different metrics that all related to each other that work together to help you grow the business or whatever your your goal is because people are making this mistake that abundant data doesn't equal important data.

### Dashboard addiction + missing what’s hard to measure

(19:28) Anyone in the marketing industry um will be able to fully understand that you you can get dashboards you can look at dashboards all day. You could probably look at a a different dashboard every hour for seven, eight, nine hours a day, right? And that's abundant data, but that doesn't mean it's important. And what Ferris and Rosie were were telling me a lot about was that people make the mistake that if something's hard to get, if data is hard to get a hold of that it see it's not as important.

### Assuming unmeasured data “doesn’t exist”

(19:56) And some people make even worse mistake that if you and just because you can't get that data, they assume it doesn't exist. So, we we are very quick in marketing to go, "Hey, here's this lovely dashboard. Everything looks really shiny. It's abundant data, but is it the most important data?" Um, and I'm uh Cersei is on the call.

### Focus group story: small print + glasses

(20:14) I'm going to embarrass her really, but she was also in the book um and one of the people who inspired me to write in the first place. And she she talked about when she was uh very new to advertising and what this brand was trying to do was to take print ads and kind of turn them into turn them into um uh online ads.

### What spreadsheets can’t capture (watching real people)

(20:31) And so what they did is they had a focus group and there was this uh this lady who had um you know not not such great eyesight and she was looking at the ads and the people what do you think of these ads? Would you buy this blah blah blah blah and she was having to take her glasses off to to read the ads to see uh to see what it actually said because the print was too small.

### “AI wouldn’t catch this” + point about observation

(20:51) And Cessy was like well look there's there's no spreadsheet. There is no deck. There is no data point that would have covered that. She had to sit in the room and watch that person have to take her glasses off. So, how how often as marketers are we just looking at the dashboard and no further, right? >> So, we're get we're training AIs to look at our numbers and crunch them for us.

### Video as richer training signal (world modeling)

(21:11) But in this instance, AI wouldn't have been able to do anything because it wouldn't have been able to look at a person uh examining the text. And I I saw a brilliant article um the other week about um is it was it um the guy from Meta who's um who's saying that the future of generative AI will all be about video because you can you can read a text that say when a glass falls off a table it will smash on the floor but actually watching that on a video tells you much more about the real world than something in print which is will be a

### Data is “shadows of people”

(21:41) really interesting development of AI um but anyway so slightly disruptive but data is isn't a comprehensive record of the truth it someone said to me once in the when I was researching the book they said data is the shadows of people but we love data, don't we? It's so clean. It fits in a spreadsheet.

### Goodhart’s Law (when measure becomes target)

(21:58) We can show the CFO, but it isn't. It's just shadows. It's not the it's something that happened. It isn't what isn't what was going on in the mind of the person. So, the next thing that I learned from uh doing the research for the book, and this is this is a a guy called Goodart, and this is Goodart's law.

### Cobra bounty story (gaming incentives)

(22:15) He says when a measure becomes a target, it ceases to become a good measure. Right? So, how many of our targets have we given our teams? They say look just deliver on that target and everything will be great. But then what everyone starts to do is game that figure to hit the target. Right? So a great example of this was in British colonial India.

### Cobra farms + the unintended consequence

(22:33) Uh there was a real problem with cobras cobras. They were getting everywhere causing a problem killing lots of people. So what the Brits did, what they uh decided to do was to give a bounty to anyone who brought a cobra skin to them. Right. >> So brilliant. Ah, they're bringing they're bringing they're bringing bringing.

### Bounty removed → snakes released → worse outcome

(22:53) But what happened is the locals much smarter than the Brits set up cobra farms and bred cobras to get the bounty. So then the Brits discovered this and then got rid of the bounty deal. And then what happened? Any guesses? The farmers just let all the snakes go back into the wild and then the population of cobras went through the roof in the area.

### You only get data back on what you test

(23:14) So what they did, the mistake they make, they said like look all we need is lots of dead co that we need cobra skins. That's the goal. So what they did, they gained it, produced loads of cobra skins, but essentially it all kind of went back in their face. So a a big big part of our belief at automated creative is that you only get data back on the things that you test, right? So AI will only give you data on the things that you've run.

### Whiskey ad example: “sorry” performs best

(23:39) AI will only give you insight to the things that are actually in your data set, but it's not going to tell you what to test outside of that data set. So concept who this is for but we worked on a whiskey brand a little while ago. Um and so our methodology at automated creative is to test the hypothesis right.

### Hypothesis testing with many ad variations

(23:56) So what we do is we use AI and autom automation to gen generate very many ads uh to test things like the which visual elements and which written elements within the ads are driving the outcomes that the brand want. So we were selling whiskey as a gift. So we tested things like occasions, new year, holidays, uh things, reasons like uh to say thank you and people.

### Unexpected insight + “human tangential thought”

(24:18) So mom, dad, brother, sister. So we tested this huge range of visuals, huge range of different messages. And two years running, the best performing messaging theme overall was ads that said sorry. So every time there was an ad that said apologize with a whiskey, make it up to them with a whiskey. Nothing says you really like a whiskey.

### Optimizing the wrong thing if you never test outside the box

(24:37) that would in in in in different markets would perform really well, right? So, had we not tested those things, we wouldn't have got data on those things. So, if we're just pointing our AI at the information that we have to get it quicker and cheaper and all the rest of it, actually, unless you're applying your human tangential thought to that, there's every chance that you're going to be optimizing the thing that shouldn't exist.

### Dating app story (talk to customers)

(25:00) So, getting pretty near the end here, but I interviewed a lady called Barbara Galiza, who is a performance marketer uh out of Amsterdam and told me a story I I'll never forget. It was incredible. So, she was working for a dating app um where women could meet other women and her cost per acquisition of new users started getting really expensive.

### “Just scale the best ad” vs real conversations

(25:21) So, so what the classic the the data only person would just be going, "Right, well, what's the best performing ad? Let's put all the money behind that." But she decided to have conversations. So she spoke to a large group of existing customers and said, "Why do you use this dating app or why do you use this app?" And the message that came back really surprised her.

### Insight: users wanted friends (not dates)

(25:44) And the the respondent said, "We use this app to make friends. This is a dating app. Why would you use a dating app to make friends?" And so she said, "Interesting. uh how how do you choose someone to be friends with on this app? And and she and the respondent said, "Oh, we we do it by looking looking at people." So, you make friends by looking at people.

### Repositioning + usage data nuance

(26:06) And so, she realized that actually, because this is what 10 15 years ago, that really what was going on is that the users of this app had a had a a stigma about the app, about online dating, about meeting people online. So what Barbara decided to do was to change all of their messaging, all of their positioning to make this app about making friends.

### People reveal why (AI can’t replace that)

(26:28) So when people landed landed on the the website on the the opening screen of the app, it was all about community and meeting people. And when she looked at the the usage data, no one used any of that stuff. They used all the dating stuff. So what she did, she had a conversation before, she expected better conversions.

### Investigative vs evidential data (serial killer story intro)

(26:46) So yes, AI is very powerful. Yes, it can give us lots of abundant data very quickly, but what it can't do is look beyond and go and have real conversations with real people. So, finally, and this is a really really hard uh story to tell because it's really not suitable for work. So, I'm going to I'm going to try and do this in a in a vaguely polite way. Right.

### Car facing the wrong way (pattern as clue)

(27:09) So, at the end of the the 70s and the early 80s, uh there was a serial killer called the Yorkshire Ripper in in the UK. And Rory Sutherland told me this story. Now, the way that they caught him has a really interesting lesson for marketing people in in an AI world. So, serial killer was killing sex workers, right? And so at the time the way it used to work is you would you would meet a sex worker in a car and you would and you would drive the car somewhere quiet and you would point the car at the wall.

### Police stop + why orientation matters

(27:41) And the reason you point the car at the wall is so that the seats that uh the people were sat on would obscure the the relationship that was happening. I think that's suitable for work. I think that's I'm doing doing okay here. >> Great. So, so what happened is the the two policemen saw a car and they noticed that the registration plate didn't quite match the the the the type of car that it was.

### Bathroom break + returning to investigate

(28:04) So, they went up to knock on the window and they saw a relationship happening. So, which was illegal. So, they they arrested uh the guy and the guy said, "I need to use the bathroom. Can I just go over and use the bathroom in that bush?" And the police said, "Yes, of course you can." So, they took him back to the station. They were processing him.

### Investigative signal → evidential proof

(28:21) And then one policeman said to the other policeman, they said, "Did you notice something unusual about the car?" And and one of them said, "Uh, no. What was that?" Said, "Ah, the car wasn't pointed towards the wall. The car was pointed the other way around." So, can anyone think why the car was pointed the other way around? >> He wanted to see >> anyway. I like that. Thank you.

### Weapons found + concept takeaway

(28:43) No, it was so they could get away quickly, right? No. blood. So they went back to the the the where they picked the guy up and then went to the bush where he'd gone to the toilet and they and they found like murder weapons, right? And that that was what convicted uh that was what convicted the York auction ripper.

### Investigative vs evidential data (definition)

(29:00) Now what um Rory Sutherland talks about is investigative data versus evidential data. So the car being faced the wrong way, you can't put someone in prison for 37 years to life for that because it has investigative value. It's like how can you go further? What else is there? The um the evidential data was the the um the weapons in the bush, right? So, so to his point like if we don't allow these exploratory procedures, we're only exploring a tiny part of the possible solution space.

### Curiosity beats “scanning” (AI isn’t enough)

(29:33) So AI is quick, it's cheap, it's powerful, it can do all these things, we can replace all of these people. But is it really is it just giving us some kind of scanned evidential value when really we need to go and look further? So it's my belief that AI is very powerful, but it isn't quite as powerful as being curious and going the extra mile yourself.

### Practical takeaways (bullet list)

(29:52) Um, so some practical takeaways. Um, so it's balancing quant with cost. Numbers will tell you what happened, but don't uh but people reveal why. Don't just stop at the LLM. Um, just because just because the answer was quick and abundant doesn't mean it's right. What did someone say? Excuse my language, but this some ad guy said that arrives at the speed of sound is still So, you know, sometimes you need to go further. excuse my language.

### Measure what matters + don’t let AI flatten the weirdness

(30:15) Um, and measure what matters. What what are you actually going to act on? Um, you know, there's big numbers, but hide the real insight. So, don't look at what the data is telling you. Look at what it's hiding from you. What is AI hiding from you by smoothing the edges? And data should be fuel, not a cage.

### Closing + invite to community

(30:31) And and embrace those anomalies and those odd signals that spark creativity. Don't let AI flatten out the weirdness where the great ideas live. Um, I host a little mini community of people who've um, seen this presentation and read the book. So, if you'd like to join that, you can do. But that is me. So, I am now expecting to get torn apart by Adam and the rest.

### Q&A kickoff (rabbis + serial killers)

(30:51) >> Tom, I'm going to start and then I'll I'll I'll let the wolves have you. >> Um, and I'm going to bring up two of my favorite topics, rabbis and serial killers. I promise not not one and the same because I I think there's a parallel here. So I I I was at this uh uh lunch round table with the rabbi where someone asked him uh uh about God's omniscience.

### Defining omniscience + link to AI prediction

(31:20) You all right there? >> So what does omniscience mean? Sorry. >> So knows everything, right? And so and so can so so if humans have free will, how can you Yeah. Then uh then how do you reconcile that with God who is a future? and a god who um knows the future. And this I promise I'm getting somewhere about AI in a second.

### God as “complete dataset” (metaphor)

(31:44) Uh we don't have to go too deep down theological hole here, but his response was God basically has all the data of what happened. And so so God has much better luck at being able to predict what's going to happen because God sees everything going on all over the world. and uh everything and and knows everything that has happened up until this point.

### Prophecy vs educated prediction

(32:10) So, so something that sounds like a prophecy is actually just a very educated prediction. Um, going from there, what what I wonder is if you're potentially giving AI the short shrift here, because if AI, for instance, if you're using AI to crack a case of of other serial killers, a um topic that comes up a lot with a name like mine, uh, then a AI then presumably AI knows that example and it knows the examples of any published police prince precinct all around the world in places that you and I have never even heard of. And so it's got all of this.

### “Seeing around corners” concern

(32:56) Same thing with these ad campaigns. Yeah. Then uh if you know so if it knows that this one thing happened here like it can make inferences and some of these things that might be unexpected to us but if it has all of the data and can tease out what are the most relevant parts then at some point like won't it feel like AI is actually seeing around corners like like how do we not get there and that we then like there's still some room for creativity but it's almost like we're kind of more and more marginalized on

### Response: AI doesn’t have the full data context

(33:32) that sense. >> So, so stick sticking with your your long theological point at the start. If the rabbi is correct, do God has a complete data set. >> He he has a an unbiased unfiltered 360 left, right, and center unfiltered data set, right? >> Um whereas AI doesn't. And this comes back to my point before about um AI is the is the shadows of people right so for example um you know purely in a AB test between two ads there's a red one and a blue one right and so there's a data set that the red did better than the blue right but unless you know that

### Context matters (why an ad stands out)

(34:14) um within that ad there's a attractive person going hey it's 50% off >> then it's incomplete data set right and that's even at an ad level right so if you then look at the context within which that ad is being seen. No one has that data point, >> right? So, you're imagine you're flicking through Facebook or Instagram, whatever, and you you're seeing lots of yellow things and then you and then a red ad pops out, right? You go, it's the red ad that worked.

### AI “hoovering data” question

(34:42) It was the ad that wasn't yellow. >> God has a complete data set. Marketers have a a woefully inefficient. But what I'm wondering is as soon as there's some case study in Paraguay that someone didn't respond to the ads because she couldn't see them. Uh and and that there's some other case study that comes up from some agency that publishes this as an award submission for Mosamb beek that no one in this room would been likely to see.

### Limits of generalizing across contexts

(35:10) But AI is able to to hoover up more and more of that data. Then it then doesn't it become more able to make enough of those inferences even if it's always going to potentially miss something for two different markets for two different brands two different times of year two different objectives different audiences different platforms different everything no it can't make that correlation the only thing that was the only thing that was saying was that they were ads and that's not a that's not a strong connection >> well all right well well well look I I

### Outliers matter (behavioral science)

(35:41) yeah I know I want to push David, you brought God into it. I'm going to, you know, I'm going to have to come back strongly. >> Oh, oh, no. This this is great. Well, well, this is also I, you know, I I wanted your uh point of view here. Uh so actually in because you know as a behavioral scientist the the majority middle is boring and plain vanilla and all the all the all the grist where your your good ideas and handwriting on the wall of why something is bad is in the is in the outer limits of the data set.

### Synthetic audiences (topic shift)

(36:14) It's in the outliers where you get your real insights, not in the not in the Joe average middle. >> And well, exactly. And that's also why the one other thing I I'm sorry I I did want to steal you for one more second, Tom, uh that I wanted to ask your opinion on, I'm sure you have them, is synthetic audiences.

### Synthetic audiences: useful but “easy button”

(36:35) And have you looked into this? And >> yeah, you know, we we um we have synthetic audiences as part of our part of the the way that we work. Now, my the big what I'm most down on AI for >> is when people go, >> it's great because it's quicker and cheaper. >> Right? So that story, I'll come back to your question at the start.

### Fashion brand example (synthetic models cut costs)

(37:03) Um so I was in Amsterdam. I was meeting a very successful uh person in the creative field at a at a a famous fashion brand you all know. And that individual sat down across from me and got their laptop out and there was the 10 images of models wearing clothes. I don't know anything about fashion so whatever. They look like clothes to me.

### “Leveling the playing field” argument

(37:19) I'm sure they were very cool. Um and he went right. So that that would have been 10 shoots, 10 crews, 10 lots of flights, blah blah blah edit model rights. And that used to cost me 700 grand euros. But now because of AI and having synthetic people, the clothes weren't synthetic. They were real clothes. It cost 35 grand.

### Everyone gets the same advantage

(37:41) And he was he was going, "Yes, 750 grand. No, it's only cost 35 grand." Woo woo. I said, "Look, all you've done is you've leveled the playing field because all your competitors will do exactly the same thing. Every go from 700 grand to 35 grand." And then their Tom and David fashion company who only had 35 grand now is at a level creative playing field with the biggest people in the game.

### “Easy button” critique

(38:01) Right. It's like someone getting on the Euro Star for the first time from the UK to Paris and getting off the train going, "Yes, I got a train." and forgetting that there's a thousand other people on that train. Right? So, that is the mistake that people are making with AI. They're going, "I can now do the thing I was already doing quicker and cheaper.

### Synthetic audiences: quick, cheap, but incomplete

(38:22) " But guess what, guys? For $50, so can everyone else. >> Everyone else is doing it. It's not just you. We're all on LinkedIn. So, to your point about synthetic audiences, brilliant. What a great quick cheap way to do a slightly worse job of the thing you were going to do anyway, right? So yeah, like we have them and it's great. So if you want to get a feel for what moms in, you know, rural Manchester might feel about a napper very quickly, yes, you can use a synthetic audience.

### Real observation still matters

(38:47) But to this my point, my story before about Cersei looking at a human being and seeing the glasses come off, it wouldn't capture that because what we want to do as someone I saw on LinkedIn the other day made me laugh. They said advertising loves pressing the easy button. If there's an easy button, we all look at who's got the easy button.

### “The joy comes from effort and listening”

(39:04) Let's press let's press it, right? But the the joy, the goodness, the empathy comes from effort and listening to people. What are this? As I said before, what is the data hiding from you as well as telling you? >> Well, I'm I'm asking too many questions, so I'm going to stop, but I'm loving this.

### Invite more questions

(39:23) Uh, who else wants to ask Tom something directly? There's an amazing chat going on, Tom. I'll have to send you out. >> Oh, really? Okay. Follow. Yeah. Oh, Adam, go for it. >> Yeah. So, Tom, this is awesome. Uh, thank you for uh putting up with my introjections. Thank you for taking us through all of this. um about uh 10 years ago uh tried to start up this company very early stages but the general thesis was if you have these ads you could deconstruct you know the creative um into 2 plus you know uh a taxonomy of 2 plus attributes that really matter and then you could predict

### Taxonomy question: can AI attribute what works?

(40:01) and instead of GCO you'd use these many attributes and you'd have all these massive permutations and it strikes me that how you know AI can do that um at much grander scale. Are to what extent do you currently use or do you believe in a future where you are going to be able to have a reliable taxonomy of um of what's working in an ad um or a reliable taxonomy of attributes and then kind of derive it from there.

### Example (3+ people implies “family”)

(40:35) So the example we were given was if you have three or more people in a travel ad, it implies family um family creative, it performs better for leisure travel campaigns and like people sometimes would have four or family or focus. It's like, no, you just need three or more people. And that distinction, if you run some correlation with outcomes on a campaign, is something you could back into if you've done enough with the ML feedback loop, you know, enough attributes to pattern match against the outcomes. Is this something that

### Predefined taxonomy vs emergent patterns

(41:06) ultimately doesn't need a predefined taxonomy? And you guys believe that AI will make this possible. I guess it's sort of a near infinite number of permutations to predict outcomes of performance-based messaging of any kind. >> Well, what an excellent practitioner question. Thank you. So, so there's a couple of roots to that.

### Visual recognition exists, but relevance is the issue

(41:28) I'll probably forget what one of them is, but so you know, visual recognition has been around for a long time. Um, and things like Amazon's recognition and you know, Google's and they've all seem readily available through an API. Um, and so we've got like a reams and reams and reams of this data. Like you know, we've been going since 2017.

### Does the CMO care about “dogs in ads”?

(41:46) Um, so yes, and there's a lot of a lot of suppliers in the space that that use that stuff, right? So there there's an argument that, oh, you just let the AI do it, right? So an AI would probably recognize a pair of Air Jordans or a beach or the color red. Um, things, right? They would recognize things like, "But yeah, does the does the CMO of does the CMO of Mars really want to know whether dogs work in his or her ads?" Right.

### Joy example: humans can’t even agree

(42:19) Well, unless dogs the central to their strategy, which they may well be. >> Yeah, I was going to say, >> so so AI will get better and better and better and better and better at that stuff. But I'll tell you a story about when I used to work in agency site, uh, an agency called We Are Social. I wasn't in those conversations, but they they were talking I had a conversation with one brand, I think it was Cabri, and they and they were having months and months and months of discussions about what the word joy meant.

### Current approach: define “strategic tags” with the brand

(42:42) How how would you describe joy? Like we all know what joy means, you know, we feel joy, but then when you try and button it down, they go, "Well, well, joy is it's just I can see Eileen. It's quite tricky, right?" So, so the idea that a an AI can understand something as subjective as joy when it's the humans who are going to train that AI can't even agree themselves.

### Reporting back in brand terms

(43:05) So our approach at the moment and this may change based on what happens with the technology is we go to the brand what is it you want to know about this audience that you've never been able to find out and what is the brand trying to go go from here to here sales awareness whatever it is and then what we do is we create what we call strategic tags that are relevant to that brand strategy and then we we can tag up ads based on those strategic tags.

### Human + machine to do what wasn’t possible before

(43:30) So then when we report back to our client through our LLM, the brand's able to go right um is is a is it rational or emotional in terms of messaging that works really well. But rational emotional means something to this brand, but means nothing to that brand, right? They're cats, they're cats and dogs.

### Best practice = copying (anti-pattern)

(43:46) These guys are rational and emotional. So there is definitely a future where AI could do all of this stuff and none of none of us will have jobs and we'll be working in the party or something. But even in the case of Joy or the dogs and Eminem like it it could assemble a very manageable universe of insights conclusions and then the CMO could quickly look through the 10 or 20 insights and say oh well those few I I'll take out right away because we don't need dogs in an Eminem app.

### What can AI enable that was inconceivable before?

(44:17) >> I would I would love to see a CMO that was going to go through a list of um tangential insights. Yeah. Yeah. Yeah. Good luck with that. Um Um, so yeah, so it's like at this point in time, it's it's the human plus the machine to do something that we couldn't do before AI, right? To my point about the the the fashion brand, cool, you can save $700,000, but what can you do with AI now that you couldn't do before, right? And that that is what marketers are not getting at this point in time.

### Not “cutting your way to growth” with AI

(44:47) They're going, I'm going to save time and money and keep the the CFO happy while the the cost of living crisis crushes over. I'll just well but you can't cut your way to growth and you're not going to cut your way to growth just using AI. The why I got into this business in 2017 was like what is possible creatively with AI was inconceivable and that's what that's why I get out of bed in the morning.

### Momentum + why these conversations matter

(45:09) That's why I show up to these kind of things is because I love meeting people who have the same vision and belief as them. >> Yeah. I I mean I I am curious though also like beyond Yeah. Beyond major brands, you know, for the Do you have a chip shop on the corner? Can do you have uh one of those? Yeah.

### SMB question: does Zuckerberg win for the long tail?

(45:28) Yeah. For the >> No, I'm in London. Yeah. Yeah. The green stuff. Yeah. Cool. >> So So So for your local chip shop, right? uh uh then Yeah. If Yeah. They just want to spend a dollar on an ad and get someone to come in and buy $2 worth of chips then like it like like does Zuckerberg's thesis win there or not really. >> Yeah, absolutely.

### Small business reality: they want to do the craft

(45:52) So, so me and Adam, we're going to set up the Adam and Tom burger company, right? Because we love making we love burgers, veggie burgers, burgers, whatever it is, right? So, like all we want to do is sit in front of a hot grill and mix up different types of meat to make burgers is what's the right kind of may burger burger burger.

### Long tail economics + AI as “marketing autopilot”

(46:08) We love it, right? The last thing we want to do is marketing. >> Oh my lord. We don't want to like what an agency or write a brief. We're we're artisans, right? So most of Facebook's money comes from the longtail from the chip shops, not from the not from the the blue chips, right? They obviously spend the the most per client, but actually all the all the longtail.

### Conclusion: great for those who don’t want to market

(46:29) So I think there's an amazing use for AI currently is for helping people do marketing that don't want to do marketing, right? you know, like I'm not a finance and ops guy, right? So any any technology that helps that happen automatically. So I can focus on the things I do, which is talking to people, meeting people, learning, communicating, or trying to um so there's all things that we don't want to do.

### For big brands, not fully autonomous (yet)

(46:50) And so for smaller long-term chip shops, mom and pop shops, I think you guys call them in North America. Yeah. Brilliant. Right. Instead of them not making burgers or following the thing that they love, they could just press the advertising button and all these ads appear and they got whether they're any good or not probably doesn't matter because they just want to spend time doing the thing that they love.

### Question: scale vs edge insights (movable middles)

(47:10) So absolutely Jai formemes that's a fantastic solution. But for the the the McDonald's, the Bose, the Mars, the Jack Daniels, the Formula 1, the Wreckits, the PGs that we represent, I don't see it at this point to to be entirely dependent on those formats. Love it. Who Who else got something for Tom? >> I guess I'm curious um and first of all, thank you Tom for the innumemerate insights as always.

### Tension: outliers vs moving the middle

(47:45) Um I'm curious about a few of the things mentioned in this conversation. So we see attention with um understanding that the most interesting things, the wonderful things, the unicorns and puppy dogs and rainbows never happens at the mean. It always happens at the edges. And so finding those uh unique points of information that we don't get when we average to the mean, um that's what's exciting for us creatives.

### Push/pull: scale for growth while finding diamonds

(48:13) However, for those of us who've been in in town for a bit of a while and were familiar with Joel Robinson with the movable middles, for those of us marketers who want to sell more stuff, we got to move the middle. And so, I'm curious about that tension and your thoughts on how do we navigate that kind of push and pull.

### Answer: humans spot patterns, but marketers must avoid “average”

(48:34) We need to do scale, but we also want to find the diamonds in the rough. So I say this a lot and no one ever listens to me but maybe this is going to be the thing we're listening this might this may well be no sorry people people are very polite and they smile and not so right the reason that the human species has evolved to the level it has is because we can spot a pattern right so like oh this dangerous thing happens over there let's not go people who die go over to the dangerous bit like we'll go over here we're great we we've

### “Pattern matching” leads to bland best-practice ads

(49:06) evolved quicker than also not an anthropologist ist. So laugh at me as much as you like, but loosely in like, you know, knuckle dragging salesman of a way, that's what I understand, right? We spot patterns, right? However, that's the worst thing we could do as a marketer. That's the absolute worst thing we could do.

### Burger ad example: average = invisible

(49:21) So, back to mine and Adam's uh burger restaurant. This is definitely going to happen. Um what we could do is we could spot the pattern. We go, "Okay, Adam, you go and find 100 uh like um burger ads. I'll go and find 100 and then we'll find the commonalities and then we'll have like the ultimate burger ads and it'll be right there.

### AI will also converge to the mean

(49:38) " or we'll go to the digital intern chatbt or the rapper of Reddit it's also called and we go what makes a great burger ad so what does what does uh what does chat GBT do goes well what are the most successful burger ads let's pull in all of those attributes together and squish them all together and we go and we're dropping up and down our burger shop and we've got the best ad ever possible because it's the average the mean the middle of everything right but what's going to happen no one's going to notice it because it looks like every other burger ad so as humans We've

### Stand out by being “nonhuman”

(50:07) evolved because we spot patterns. This the most powerful marketers in a world where any ad could be made by AI will be the ones that are able to make the thing that's different. So great example of this recently is the water category, right? It's all about mountains and volcanoes and purity.

### Liquid Death example (category disruption)

(50:25) Liquid death comes in a bloody can and looks like an energy drink, right? They they they did what the guys at Liquid Death didn't do is go well let's you know let's get a really fancy agency to find the median like what's best practice it's one of the things we hate in our business is this idea of best practice in my school best practice was called copying right so what brands go hey let's best wait let's do best practice right so they squish all these people together and they go right we're doing best practice no no you're just doing the

### Dad in formula ad example (testing the unexpected)

(50:55) same as everyone else in the category right So your cost acquisition is going to go through the flipping roof because you wallpaper you you fit in. So Cesy to answer your your question I think is is to go like if you if you want to avoid that middle and go to the edges you have to learn to be a nonhuman and stand out.

### Only get data on what you test (again)

(51:14) How do you stand out and not fit in? We were working on a um on a like a a infant formula product years ago um that was aimed at moms on Facebook and we found out that the best thing you can put in an ad targeted at a mom on Facebook is a dad. We tested a whole bunch of different things.

### Closing Q&A + community link request

(51:33) Best practice says but what's what's best practice for a informula ad? Have a mom and a baby. You're so beautiful. No, no, have a dad. Cuz guess what? It stands out. It cuts through. It's different. So to my point before, Cesy, it's like you only get data back on the things that you test. So if you want to get get to the good stuff, the unicorns as you said and the rainbows on the edge, you've got to think about the stuff.

### Wrap-up + thanks + next steps

(51:54) You've got to you got to think in a way that the machine can't so you can test them and then learn. Anyway, you've got me all excited now. Um sorry, >> Tom, I think you got a lot of us very excited. Can you, speaking of which, uh share the WhatsApp code again or put the link in the chat? Uh just >> um I'll I'll uh Yeah, I'll I'll I'll send it around to you.

### Closing remarks + goodbye

(52:12) You >> Okay, great. cuz we we'll make sure and I know there's some folks eager to join and uh uh if they haven't read the book yet that I'm sure there'll be a >> yeahong the way I'm I'm going to expect an email from Amazon saying that you know they're closing down some servers because of the orders for the book and you know I got you >> well well this is the crew to make it happen so uh >> and Adam don't actually make that ad because what you're going to have is the dog's breakfast not a burger shop >> details details Well, well, and Tom,

### Host sign-off + gratitude

(52:43) I'll make sure to send you the chat. It's been lively. So, thanks everyone for contributing uh behind the scenes as well. It's been a lot of fun for me. Uh Tom, hope you come back and join us sometime. So, appreciate you. >> Yes, I will. Um thank you so much for the questions and the opportunities and thanks for the introduction, guys.

### Final farewell

(53:00) Have a a wonderful rest of the day and uh and a beautiful end of the year. >> Thanks, guys. Thank you so much, Tom. This is awesome. >> Wonderful holidays and uh see everyone next week. But this has been terrific. Thanks again. Appreciate >> CeCe.

## The Playbook for AI Personalization at Scale IAB The Weather Company Acxiom

Speaker: Caroline Giegerich
Published: 2025-12-05
Tags: brand quality, interoperability challenges, best practices guide
Video: https://www.youtube.com/watch?v=LpVAIunsW0w
Page: https://aimarketersguild.org/sessions/the-playbook-for-ai-personalization-at-scale-iab-the-weather-company-acxiom

### Innovative Orchestrator: AI Super Agent for Omnichannel Advertising

(00:00) We're super excited to introduce Innovative Orchestrator, the first AI super agent for omni channel advertising. We're bringing humans and AI agents together so that marketers can conduct a symphony across channels to reach their most valuable customers. AI is not a matter of if, it's a matter of how and when. And we will help you solve that.

### Welcome and Host Introductions

(00:32) Welcome everyone to another edition of AI Insiders from AI Marketers Guild here from the downtown studio at the Institute of Culinary Education who I can thank for hosting today as I actually do some AI training here.

And apparently it's cookie day. Um, so if you've never had a a sesame seed cookie from a culinary school trained chef, I highly recommend that and I will be nibbling on this and anything else that comes my way while we have a very special host who's making a return appearance here. We've got Carolyn from

### Guest Panelists from IAB

(01:11) IAB who's just one of my favorite uh uh folks who who's not just thinking a lot about AI but putting it into practice within the advertising community. Uh and she's invited some special guests. So, I'm I I love when we get to bring in these heavy hitters and I get to sit back and learn.

### Community Conversation Format

(01:36) And as as most of you know, most of you have been here before, but in case you haven't, these are community conversations. We love interactivity. We would love questions and thoughts here and so um so glad to have you all involved.

Well, thank you David as always for having us and I feel like you should be like sending one of those cookies to all of us so we can have that while we do this conversation. It feels unfair.

### Introducing the IAB AI Personalization Playbook

(01:55) Sorry, the one-sided cookie situation. It is. Um but my name is Caroline Giegerich. I'm VP of AI at the Interactive Advertising Bureau. And a few weeks ago, we had an exciting release of the IAB AI personalization playbook. And these two incredible gentlemen that I will be joined by were in the working group, provided lots of thought leadership to go into this playbook. And before I introduce them, let me just give you a quick overview.

### From DCO to AI Personalization: Promise and Risks

(02:29) We've gone from DCO which is using AI to stitch together a bunch of assets using AI to AI personalization which is capable of generating many many many personalized assets right and this is an incredible innovation at the on the one side we have the promise of personalization increased ROI which these incredibly smart people will speak very astutely too.

### Brand Quality and Interoperability Challenges

(03:03) On the other hand, we have some of the risks in the challenges in organizations and I'm betting that many of you in the audience are experiencing some of these challenges. It could be brand quality if you're having AI generate all these assets. What about the risk there? What about I know Graham is going to talk a lot about this, the interoperability challenges of dealing with all of the technology and many more.

### Playbook Scope and Table of Contents

(03:21) So what we wanted to do with this playbook is basically create a in between best practices guide and I'm just going to share just the um table of contents. So you can see this here of how do we actually go about what is the opportunity? What's the challenge? How would you go about briefing this? How are you building all these assets? If you are at a media agency, a creative agency, if you're internal, how are all these departments going to work together? Where's the human in the loop? How do you assess the risk from different use cases from a simple social asset to a full television commercial?

### Meet the Panelists

(04:00) That's what we set out to do, and I'll also put it in chat in a in a brief moment. But first, I want to introduce our lovely panelists. Graham Wilkinson is the EVP chief innovation officer and global head of AI at Axiom. Welcome. And Brian Hall, head global creative labs at the weather company.

### The Weather Company’s Creative Labs

(04:29) I'd love to just kick it off, turn it to you, and have you give us a little bit more about what you're doing. Like Brian, let's start with you. Tell us in, you know, words that we can all understand that brings makes it clear for us what you do with the weather company. I'll do my best. Thanks, Carolyn. Hey, it's a pleasure to be here.

### Moment-Based Relevance with Contextual Data

(04:48) So, at the weather company, um my team, the global creative labs, we really focus on the unique intersection of real-time contextual data and worldclass creative execution. Our core mission is to help brands move beyond basic targeting to achieve momentbased relevance by leveraging our specialized data, things like weather, location, time of day, etc. So basically we're essentially applying our company's deep expertise in AI innovation and human machine collaboration to marketing.

### Operationalizing Personalization

(05:15) Uh this means we build the systems for creative automation and operationalizing personalization. And we're really looking forward to discussing how the IAB playbook frames these challenges. And Graham, I'll also kick it over to you because you clearly have a very large and broad very impressive um title.

### Graham’s Role at Axiom

(05:32) Tell us what are you doing day in day out at Axiom? What does your job actually look like? And I will say for everyone here, um, Graham is a total legend because he actually had his wisdom teeth out yesterday and he looks that good. Thanks, Caroline. Yeah. So, that kind of explains why I'm not fully opening my mouth when I speak. So, I apologize if it looks and sounds weird. Um, yeah.

### Organizational Update: IPG to Omnicom

(05:58) So, I actually just noticed as well that my name on here has IPG next to it, which RIP IPG. I uh I probably need to swap that out for Omnicom now. Um but I still work in Axiom and um and so, you know, my role is I suppose kind of twofold.

### Innovation Across Data, Tech, and Clients

(06:19) One, as chief innovation officer, I get involved in um you know, most parts of the business with regards to innovation. And you know, I'm given a fairly um free remit to go in and and and look at the things that we do, whether that be with the the data that we that we work with or whether it be our tech stack or even just working directly with clients and and and taking new innovations to them or or kind of collaboratively doing that.

### Global AI Leadership and Responsibilities

(06:47) And then from an AI perspective, you know, I've been responsible for AI across all of IPG media brands. for about, you know, just under 30,000 people for the for the last 3 years. And um and that means I'm the throat choke for AI everywhere around the world. Um as you can imagine, that means lots of questions every single day. Um multiple client meetings.

### From C-Suite Workshops to R&D

(07:11) I I will talk with CEOs, CMOs about their ambitions, their challenges with with AI. Um we'll I'll do workshops, presentations, demonstrations, build blueprints, road maps, all these kind of things. So it's basically um everything you can possibly think of. And I also run R&D teams. So that means that I'm managing development teams at the at the same time. I think I think that's a good thing.

### Closing the Loop Between Client Needs and R&D

(07:37) It means that I can direct I'm directly connected into our clients and I can take their needs and uh their challenges and and work with my R&D teams to to try and solve them. Incredible. And before I I'm going to stay with you, Graham, I will say for anyone that has a question as we're going along, feel free to just pop it in the chat and we'll sort of address it as we go along.

### Join the Conversation

(08:01) Don't I I know David kind of encourages this in all of these conversations, but don't be shy. We'll we'll kind of get to it along the way. Um Graham, I just want to stick with you to start. You're making the case for AI personalization with CMOs and CFOs. What is the actual revenue story here? Like what are we looking at in an actual ROI upside? Yeah, I mean it's kind of so I think the first thing is it's it's a step back from that, right? I think most people come with the efficiency ask, right? They want to know how can I drive efficiency or how is my agency going to

### Efficiency vs. Effectiveness: Two-Part Equation

(08:41) be more efficient? Um, I think that I mean, I certainly try to steer people down the road of this is a two-part equation, right? There's efficiency and effectiveness. One of them drives revenue, one of them drives margin. And and I think that you have to be very careful of how you balance that.

### Limitations of Generative Models

(08:59) I think efficiency is a short-term play and we have to be very careful not to um you know discard talent and people um and and and also recognize you know part of the problem is is all the hype around AI it it it doesn't really make it very clear to the to the general public that when we're talking about models that essentially are building semantic you know work off semantic relationship ships between words.

### Risks of Over-Reliance on Automation

(09:32) They're, you know, even if they're multimodal, they still operate in singular modalities at a given moment in time. And that isn't intelligence. That's not human intelligence. And so, we have to be very careful. Like, we can imitate that. You know, you can you can make a mechanical robot and and dress it up like a human being and say it's a human being, but it's not.

### Where ROI Comes From

(09:58) And so coming back to what I was saying, when you go down that efficiency road and you are putting all your eggs in the basket of a machine that actually can't do the job of a human being, it's a risky it's a risky path to go down, right? So then you get into the effectiveness play which is really where I think you know you get you get the multiplying effect and you get ROI.

### Reported Performance Uplift

(10:17) So you know I think typically I would see anything from a 20 to 40% um increase in let's say increase in performance and it obviously depends how you measure ROI and how you measure performance and all these key key metrics um and I mean certain instances you get I mean I we've run tests where we we'll get 110% uplift of a an asset that was uh AI augmented in its creation versus something that was kind of created in a in a standard way.

### KPI Example: CTV Eyeballs

(10:48) Say uplift what uplift in what KPI? It depends. I mean, you know, that particular metric comes from uplifting eyeballs. So, so you know, an ad for um a major sports league and their Christmas um their Christmas games. Um so that that's essentially measuring eyeballs across kind of connected TV. Um, yeah.

### Beyond Performance: CX and Brand

(11:15) Gotcha. And then Brian, from your standpoint on the creative side, we talked about effectiveness. Um, but is there something bigger that you're also looking at? Like are you looking at um customer experience or brand perception? The things that are about like overall how customers are thinking about the brand in mass. Yeah, great question.

### Personalization Impact: Conversion, CLV, Trust

(11:45) So like the playbook notes that personalization drives not just a conversion lift up 16% but also customer lifetime value CLV up to 20% increase and and consumer trust metrics right so trust fuels more data sharing which in turn fuels better personalization creating a self reinforcing cycle so my direct answer is it's definitely both but there's something bigger Carol is is crucial.

### Building Loyalty Through Relevance

(12:13) While performance lift meaningful lift and conversion and engagement is the immediate ROI, the true long-term value lies in brand perception and customer experience. Like for example, our data shows that when we deliver timely, relevant, contextually relevant, uh creative, we're not just making a sale, we're building loyalty. And also from a corporate standpoint, we view this as a natural evolution.

### Personalization as Natural Evolution

(12:38) We've been doing this for decades. NI is simply the natural progression of our product story and increased relevance to our fans, our brands, and our advertisers. That makes a lot of sense. I'm going to stay with you also because we're we're talking about the benefits, but I think we would be remiss to not also talk about some of the challenges.

### The Operational Bottleneck

(13:01) I think in talking to you several times about the weather company, you have these unique advantages because you have all this contextual data. Talk about data. You've got weather, location, time of day. It's all super impressive and can add a really interesting layer of personalization. When you're thinking about scaling all of this personalization, what's the biggest operational bottleneck you're hitting? Is it production? Is it QA? Is it measurement? Is it something I haven't mentioned entirely? That's a that's a it's a rabbit hole.

### Governance and Value Stream Mapping

(13:35) But the the challenge is the operational infrastructure, the crossf functional integration is probably the biggest the biggest situation and the governance frameworks or the lack thereof like linear human only workflows for QA for instance simply cannot handle thousands of AI generated variants and I'll talk about this a little bit later in the session about how we address it with a VSSM a value stream mapping exercise um later in the session. Do you want to talk about I mean you just you gave a cliffhanger. You want to give a little bit a little bit more?

### Breaking Fragmentation with Shared Workflows

(14:09) Yeah. Yeah. Well, it's um I think you know it all just again again it comes down to breaking apart the fragmentation and getting crossunctional integration and like I said governance frameworks but that's a lot of effort and it's a lot of work.

### Relay-Race Handoffs and Workflow Mapping

(14:25) Uh it's can only go so far if only one department or discipline within your organization is really AI ready, right? They can only do so much. You need to be able to do a baton handoff. Hey, you need to be able to pick up the baton. I use this analogy a lot of a relay race. Um, when it comes to organizational efficiencies and creating frameworks like this, you really do need to collectively come together uh from every single department and discipline from brief to benchmarking map out what are those steps and especially with something it's good to do anyway annually no matter what take AI out of the picture. It's

### Operational Hygiene for AI Workflows

(14:57) something it's good hygiene operational hygiene to do but especially now with the advent of the need to create architectural frameworks and and like workflows that incorporate AI. So getting every single department and discipline together to again map out from briefing to benchmarking what does it take what are those steps how long does each step take what is each what's the time between each step n 99% of the time uh when we do these exercises and we do them about twice a year we find bottlenecks we find opportunities for

### Finding Bottlenecks with VSM

(15:28) operational efficiencies and then our ability to streamline and to say this and to speak the same language did you mean tomato or did you mean tomato these little things make such a huge critical difference uh when you're working with datadriven processes like this.

### Using AI for Brand Compliance

(15:46) So uh the value stream mapping exercise is one really key critical way that we're approaching to to solve that challenge of operational infrastructure and gain that that cross functional integration together. I'm going to ask a question from Derek and this is for you both Graham and Brian. uh so whoever wants to pop in on this I love this question. Is AI used to check whether AI created content is on brand? Yeah, absolutely.

### Codifying Brand for AI

(16:09) I think you know it is but you but it still comes back to the fundamental question which is is talked about extensively in the in the playbook which is do does any given brand understand what its brand is and means? Like can you clearly articulate can you codify it? uh, you know, I over the last 3 years, I I don't think I've met any two brands that that articulate or structure the articulation of their brand in the same way. You know, some go, "Oh, that guy knows our brand.

### Clarity Precedes Automation

(16:47) He writes everything like he is our brand." Some go, "Yeah, we got loads of of content and there's duplicative documents that that has slight variation in them. there's there's just so much difference in it. uh, and so the real question is yes, you can you can get AI to do most things, but you know, this comes back to the fundamentals of like machine learning really.

### The Hallucination Gap

(17:13) If you can't explain a process in clear instructions and and language, then you can't expect a machine to do it. And the the exacerbating problem with gener generative AI, you know, that used to be a make or break with a machine learning process, right? It if there was a void in the process, the machine can't jump across the void.

### Separate Models for Create vs. Check

(17:38) But with generative AI, it will jump across it because it will just make its own bridge and that's that's more dangerous. And so I think yes, it is. you know, we we have a a system where, you know, I suppose you think about two systems, right? And you got to be careful that you're not having the say the same AI mark its own homework, right? So, you want AI that's trained on the brand to create content and then you want AI that's trained on the brand to check the content and it shouldn't be the same. And why? Say why.

### Start by Auditing Model Knowledge

(18:14) Oh, well, I mean, first of all, there's a fundamental step that most people miss, and it's really simple. You don't have to be a developer to do this. Most people don't actually ask the model what it knows about their brand first. They just start telling it things. And so, if you don't ask it what it knows about you, you don't know what you have to correct. Mhm.

### Value of Model Diversity

(18:33) Like there might be some fundamental things that are not addressed in your explanation and your the code of your brand that if you miss them then you're not correcting them and it is going to be fundamentally wrong. So I think that's an important thing.

### Are Brand Guidelines Fit for Personalization?

(18:51) So if you're then working across the same model and those things are missing they won't get picked up in either the creation or the checking of it. uh but secondly it kind of you know I'm a strong believer in diversity of models. There's a reason we we evangelize adversity in teams and people and all that sort of stuff.

### Rethinking Static Assets

(19:14) uh is because we're really good at picking holes in in things, right? And so it's just important to get a different perspective from just a a very fundamental level. uh and that's why we should constantly be testing testing new models, utilizing a real diverse ecosystem of models to do things. I like this a lot because in my former experience, most of my time has been marketing for brands, doing a lot of briefs, and I've seen exactly what you're talking about. I mean, even in a 30 person marketing team.

### Brand Guide Inconsistencies in Practice

(19:45) I've worked at Showtime, I've worked at As Music Group and Smashbox Cosmetics. I could have seen a different brand perspective from every single person on that team. we probably would each uniquely have created 30 different uh briefs if left to our own devices even in and I'm going to turn to you Brian because this question here is from Earl Richards Jr.

### Audit Before Automating

(20:08) asking about brand guides and of course even in the organizations I just mentioned we all we all had brand guides but I think what Graham brought up was a really interesting point if you're not really clear on what the brand is what the brand isn't AI is going to exacerbate that and it's going to turn into disaster so what are your thoughts on brand guides getting clear uh and this conversation that we're having here yeah I I think Grant really nailed it.

### From Static Docs to Adaptive Guardrails

(21:03) It's a core finding, too, that organizations often discover their brand guidelines are inconsistent or they have gaps and contradictions when they try to teach AI systems to follow them systematically. uh, auditing your current processes before automating. I've got to make that in a audit before automating. That's there's got to be a song title there is is critical first step.

### Model-Generated Creative Futures

(21:41) So there's a great deal of effort that needs to go into preparation to effectively not only collaborate but to train uh AI on how to effectively collaborate with you. I also can I can I just build on this Caroline because this is one of the things that I think is so fundamental to the AI transformation that's occurring in our industry is what this begs the question of is are brand guidelines even fit for purpose for the future of personalization because in in the past and and the current we you know we serve things in a very kind

### Evolving Ad Formats for GenAI

(21:59) of static manner and what I mean by that is an artifact act exists at a moment in time. This is an asset. We will serve this asset in the future. Maybe that won't. Maybe we'll serve a model and the model will just morph into whatever we want it to morph into.

### Designing Generative Ad Experiences

(22:41) And and then that begs the question, well, is a brand guideline a a static document or actually is it different for every person that looks at it from a different angle? And actually, how do you how how do you think about that? And and the reason I say it's super interesting because I think the most progressive people in the industry are thinking about these fundamental changes to the way that we've done and thought about marketing and and not thinking about I think the biggest trap with AI in advertising is solve old problems with new stuff like it maybe those old problems won't even exist in the future and maybe we should be thinking about the new stuff And

### From Concept to Execution

(23:05) yeah, you know, we'll we'll kind of sweep along with some of the older stuff, but that's the stuff I'm really interested in. You know, I love how your brain works and this is just another example. But then my question is, how does that work? I love this concept, right? I'm like 3,000 ft. I love this concept. It's amorphits.

### New Tech, Old Roads Problem

(23:32) It's like building with like everything that's happening into the ecosystem, but how exactly? uh, I don't know the answer to that. That's I mean, but that's why that's why it's actually important to bring it into forums like this so that people can can start to to think about it, right? I I think again part of there's a weirdness to the way that the and I'm sure everybody feels this, right, when you want to apply AI to what we do in in advertising and marketing.

### Working Within Legacy Ecosystems

(23:56) And I think part of this the weird feeling we've got is that we've got this kind of situation where we've got this new technology. You know, Ford has just invented the car and and people want to drive them, but there's no roads. Like we're we're like adapting the car to drive off-road instead of building highways that make like easy easier to drive the car.

### Defining the Gen-Ad Format

(24:19) And what I mean by the roads in this analogy is like I don't think ad formats have evolved yet. Mhm. And and so we're again we are using AI to apply to legacy advertising infrastructure and ecosystems and and that's but we again we have to do that that we have to keep businesses moving.

### From Cohorts to Fluid 1:1

(24:38) But I don't believe there's enough emphasis on what is a generative ad experience? What does that really even mean? because then I think everything will click into place for us and and the the weirdness and the kind of like I can't quite put my finger on the thing that's stopping me moving forward.

### Personalization That Adapts Over Time

(25:05) I think that will dissipate and we'll will all kind of move forward quite freely. Well, also this idea of real time, you said something at one point that equally blew my mind, which you said, okay, so for the record, in this playbook, we are thinking of personalization on a cohort level. So not on a oneto-one level, we decided mindfully that we feel like we're in a place where cohort-based makes sense for this discussion.

### Targeting Variables Today

(25:23) But if you look out into the future in some working group we had Graham said, "Well, not only is it going to be one onetoone personalization someday, it'll also change because Graham changes every day. For example, he just got his wisdom. He's wisdom teeth lighter as of today." Well, that's a change in Graham.

### Toward Brand-to-Individual

(26:13) Brian, I'm sure, has listened to 10,000 new songs from yesterday to today. And if Brian's a completely new person today, but you I'm I'm joking, but you you see what we're saying here. people grow and change and the personalization can do the same and and that is enlightening.

### Weather as Dynamic Context

(26:47) I uh Brian want to move to you on this idea of targeting. uh Earl Richards Jr. also asked a question what's the relationship for art audience targeting for your brand's audience and personalization with so many variables. I mean obviously what I just said is the Graham level future of like thousands of variables but in today we've got demo location contacts day parting life stages etc. Brian what are your what are your thoughts on this? I mean yeah that was a perfect setup.

### Forensic Weather Targeting

(27:06) I mean those variables and the variables like all those that are all mentioned demo location context day partying life stages looking at you know again the more information you have about somebody the more relevant contextually your message your product service or experience is going to be when it comes to and we're starting to capture more and more uh information on our product consumer product experiences so that we can give more you've heard of B2B you've heard of B See, but we're really working a lot on B to eye, like business to individual or brand to individual. And

### Empirical Targeting Rules

(27:25) as the weather brand, the variables for weather, you would not believe how they like you set it up perfectly, Caroline. Like every single day, the temperature is different and you're going to feel different in the morning versus the e afternoon versus the evening.

### Weather’s Broad Impact

(27:48) And it's not just about block and tackle scenarios or situations where I I say this a lot where you know people make assumptions about uh day partying or weather targeting like oh it's hot you know show a pair of wayfair sunglasses or it's cold show a Northace jacket in a contextually relevant environment.

### Utility Messaging Example

(28:21) What we like to do is take it completely and entirely forensic nature to another level of scenarios like if wind speed is X and D point is Y and cloud coverage is Z, we're going to show you this product instead of that product because we have empirical evidence and data that human behavior is affected differently based upon those combination of weather patterns. It can get it gets quite forensic and quite deep.

### Personalization Stakes in Real Life

(28:46) So, it's a it's a really fascinating place to play in with that on the combination of everything else that you get in terms of behavior and personal information to tie that into a weather story because weather affects every single thing you do, what you eat, what you wear, what kind of car you drive, where you go on vacation and give you one good example of like how can weather affect an individual on an individual basis because a lot of us feel the same way if we live in the same zip code and we get up. I mean, there are obviously variable differences on our individual makeup, but let's say I'm an allergy sufferer

### Human Judgment vs. AI Scale

(29:24) and I happen to let the Weather Channel app know that I'm allergic to uh, you know, dog ragweed pollen. We can give you a direct message letting you know in the morning before you get out the door and you get prepped, you know, to give you utility service to say, "Hey, you know, this is what's going to be like for you today, the minute you step outside your door.

### Human-in-the-Loop Philosophy

(29:51) So, how can we provide that real forensic personalized individual messaging to people? I love that. And I also uh am a pretty hardcore cyclist, both like a to work commuter and just performance cyclist. So, I live and die by the weather app for sure. And when it's off, it causes me quite the consternation. uh, so that's my personalization. uh Brian, I want to move to something we briefly talked about earlier and Graham also mentioned it is this idea of human and AI collaboration, right? And we got into this quite a bit in the the playbook and we've obviously mentioned that there is there are points where

### Augmenting Experts with AI

(30:14) humans just can't review every piece of creative, right? So, how in your day in day out of creative production at scale, where are you drawing the line? What what decisions should humans always make? And and where is AI engaged? That's a it's that's a big one. So, humans excel at strategy, cultural nuance, emotional resonance, and final judgment.

### OODA Loop in Creative Ops

(30:34) Uh and then AI excels at production efficiency to your point. sometimes too much uh to keep up with and data processing and all those variables. So it's a pretty critical challenge and it's one that we've been we've solved for on the consumer product side and are now applying to advertising. So for us the philosophy is simple.

### Human–AI as Trusted Copilots

(31:07) We've combined 20 plus years of data and AI innovation with 100 expert meteorologists. It's that human in the loop or human in control. And it's why we're 3x, you know, more accurate than our other competitors out there in the weather space. So, we're huge advocates of the power of AI to augment and empower human abilities and decisions.

(31:28) And we apply that same philosophy to our advertising businesses as well. But in creative production, we draw the line based on the playbook's framework. Uh, humans must decide on strategic direction, creative judgment, and final quality assurance. So AI uh must run on production efficiency and content generation and data processing. But the the the human loop it goes back to the UDA loop right generated that was owned by fighter pilots in the 50s in Korea which is observe orient decide and act.

(31:07) The human is in control. The AI gives us information to make decisions on. So I think I talk about this a lot like the relationship between AI and humans is very similar to the relationship for those of us that are old.

(31:28) It's the same relationship that Luke Skywalker and R2-D2 have C3PO or uh or even older it's enterprise. I prefer seasons Brian. Yeah. Yeah. or you know I'm going through all the generations here from Star Trek to computer to you know C3P or Luke Skywalker or even you know the younger generations really can understand maybe more of uh you know Iron Man and uh getting you know the Tony Stark and uh talking to Jarvis like again I just have to say they star I know did he cut off we lost somebody right right when he said Star Wars and then blank I'm like No. What happened? Justin, you put yourself on mute

(32:05) when you started talking. Justin, you put yourself on mute by accident. Oh, did I did I not get through? No, I got to do that again. Star Wars got excited and then he left us hanging. Oh no, he did it again. Oh god. You're doing it now. We can't hear you. Dark side doesn't want him to talk. Hey, you. Okay. You know, am I good? Can you hear me? Test.

(32:31) Don't touch anything. Okay. No, I did the thing where I tapped. Usually I go in. I just wanted to chime in because never worry about Star Wars going out of fashion. They have rebranded for everything. uh, whenever you mention C3PO or anything, it's like a it's like a time stone. You could worry about Star Trek. uh, that's kind of one that's too much.

(32:49) You know what I mean? That like the lore goes too deep. People don't get it. Star Wars is just a family story at the end of the day. So like anytime you mention Anakin or Darth Vader, they'll get it. and CPO's that, you know, the crew right there. I'm right there with you.

(33:06) I was recently in an Apple store and I made this exact same uh conversation with some younger employees there and neither one of them had ever watched the Star Wars movie. So, it completely blew my mind. So, you pulled out your phone and immediately showed them the trailer. That's what happened, right? Well, I got keep That's why I keep tricking tricking down like, "Okay, what about Tony Stark and Jarvis?" Like, okay, I know that one.

(33:23) But look, you get it. There's a there's a there's a symbiotic relationship here that needs to happen and it's again it's something that's been we've been working with for over a century now. A lot of people don't understand the agile formats were developed in 1896 by Sadachi Toyota. You know the the autonom and and we've got a lot of things that we need to watch out for in terms of of governance protection IP.

### Balancing Governance and Opportunity

(33:53) There are so many uh things but so many great so many concerns that there's so many incredible opportunities. Yeah. Well, and Graham on your end are you thinking about this human in the loop story differently whether it's say a social asset versus something that could be considered bigger like a television commercial or something of that nature? like are you thinking about how AI is engaged differently b verse b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b based on the breadth of the asset, I guess I would

### Risk-Based Governance by Asset

(34:27) say. Yeah. Yeah. And and look, I think I always go back to like right at the start of this explosion, I was uh working with uh well, Amazon were a big client of IPGs at the time. I was doing an an AI kind of workshop with them a long time ago and their creative teams were even back then putting asset putting AI generated assets live and I still think this rule of thumb that they developed was great which was basically the legal team said you can use AI for any creative asset that can be taken down within 24 hours. Yeah. And I was like, okay, that kind of if if a big brand like Amazon can can do

### The 24-Hour Takedown Rule

(35:12) that, that that that's a solid way of uh thinking about it. And it it fits with, you know, the way that we talk about it in the playbook, which is, you know, when assets are say low risk, say a a social post, uh maybe even, you know, one of the other things that we don't necessarily reference in the playbook, but it's kind of inferred is if you are if you're playing around with features and components of an asset, and they that could be a you know, it it could be a text ad in Google or or it could be a text ad And on a meta platform, right? If the platform itself

### Platform Guardrails Reduce Risk

(35:49) has ways to catch errors and violations, then the risk associated with your use of AI actually is is kind of mitigated because the system won't even publish your ad if you're violating something, right? So there there's that kind of compounding effect that is beneficial to you where in in these lower stake uh uh assets and using them on a platform that has its own checks and balances, you're you're going to kind of you're going to be safer in in that respect. But then as you move up the the tiers as we described them in the playbook, you

### High-Risk Assets and Regulated Sectors

(36:27) know, eventually you get to these higher risk scenarios that could be associated with the asset itself. So it could be like a product description which is obviously fundamental to to the thing that you're selling but it could equally be in a regulated industry.

### Universal Risk Aversion

(37:06) I do a lot has a ton of clients that are banks and the insurance companies and has done for decades. uh and you know they're generally risk averse but one thing I would say actually is everybody's risk averse with AI. It does not matter who I what brand I speak to whether it's Nintendo, Lego or banks. And I say the same thing to all of them.

### Scaling Within Human Constraints

(37:30) You all tell me you're risk averse because nobody wants to be the brand that makes a mistake, right? So it but but there obviously are some fundamental things with with regulation and that does obviously restrict what you can what you can do. uh and so I think there has to be humans in involved in that that component of it.

### Balancing Individual and Group Identity

(38:07) And you have to be realistic therefore about where you can really take scale with personalization with that. uh you know and and also even going back to what you said about what I said before Caroline around you know building assets for individuals you know although that might be something to try and do I do think that there will ultimately be a kind of diminishing return aspect to it as well right where you know it's not necessarily always beneficial to talk to an individual directly people like to associate themselves with groups you just said you're a keen cycle Cyclist. Cyclists are very tribal. uh, in fact,

### Privacy–Relevance Tradeoff

(39:10) they're very hard people to break into groups of because they I I cycled for many, many years and they're not particularly friendly always cyclists. But, you know, people think I'm very friendly. I I know you are, but many people are not as cyclists. And I do think that in situations, some people think in groups and some people and in different situations, people think as individuals.

### The Line Between Helpful and Creepy

(39:35) And I think that it's not a hey, we should all be aiming for individual personalization. It's thinking about scenarios too as well, right? And so I also thought you were going to go in the direction of don't we at some point hit a creepy factor to the user or is that not a concern? I mean I again I think it's with change there's always this kind of push back on on stuff, right? At the end of the day, you know, human beings, we want both sides of of everything, right? We want to go I want control of my data and privacy, but I also want to go on somewhere and I don't want to get an ad

### Is There an End-to-End Platform?

(40:08) that's super generic and you got to get somewhere into the middle and people have to be taken on a journey and bad actors will always make that more difficult and it'll make it difficult for good actors. So, I think that they You got an ad today that was like, "Hey, Graham, do you want these soft foods because I know you got some pain in the mouth going on right now." I mean, that would be terrible marketing copy.

### Fragmented Stacks Block Automation

(40:35) This is not how I write, but would that be cool? Yeah. I mean, I was literally going to ask Gemini before I came on this call, what would be something tasty for me to like uh actually drink rather rather than and and like like you know, I think that sort of stuff is very very important, but it's tough to get to that point because you have to know something deeply personal about somebody and there has to be a level of comfort about how that type of data and information is is dealt with. Well, and I'm going to open up a question in the chat, and this is for

### Walled Tools, Manual Glue

(41:12) either one of you. Derek asks, "What are the tools, platforms for marketers, agencies to use? Here's the brief. Here's what we want to communicate to whom. Now, turn that into personalized content." I mean, I don't believe there is a there is one there is one platform for that, right? And that's big part of the problem. And as you said, I I talk about it a lot.

### Incentives and Interop

(41:37) It's this idea that pe you know we as an industry and certain individuals have made entire careers out of the the fragmentation aspect of our industry. you know, we have departments that are dedicated to, oh, do you want to in implement a new CDP or let's let's instantiate a dam for you. But and and it all all happens is you just get all these different licenses and softwares and and now you know you want you want to part of the promise versus reality with AI is end to end uh automation and now you start to look at that you go well I've got five

### Beware Single-Platform Lock-In

(42:08) different platforms that run you know five very important aspects of my marketing uh flow but they don't talk to each other and actually if I want them to talk to each other I'm going have to do all the hard work to to make that happen because it's not in those in the interests of those platforms to to make that happen.

### Open Ecosystems vs Walled Gardens

(42:49) Right? So I do think that that that is that is really really tough and I don't think that there is that there is anyone solution now can I ask you a quick question before you move on because I thought a lot about this because we actually had someone from Adobe in our working group right so isn't it in the best interest of everyone and I mean those platforms included to lessen those challenges on let's call it end client you in this case to increase usage of all of the platform.

### Reconciling Platforms with AI

(43:31) Isn't it like all boats shall rise if this is easier for everyone? Yeah. I mean it maybe in certain instances, right? And and again I think you've got to be very careful. You know Google Google and Amazon they want to sell you data storage and processing, right? they they're giving you AI and they're letting you pay for it, but really what they want is is for your your entire stack to live in inside there and and that's okay, but also coming back to the diversity of models challenge, if you are wedded to a certain platform that say only offers a

### Will Walls Come Down?

(44:10) certain family of models or limited set of family of models, you are not being as progressive as you could be. uh when it comes to utilizing AI, I think that you know the the there there's a to me there's a fundamental kind of clash in this whole idea of platforms and and AI and it's and it's because in order to offer if we're really going to let AI do jobs that human beings have previously done and we want it to perform at this optimal level, it can't be restricted to to like thinking inside these these bubbles because that's the

### Orchestrating Across Teams

(44:43) mistake we've made for for the existence of humanity. And that is part of the promise of what AI does is it breaks silos down, right? And so the I I don't think tech has quite figured out yet what is it really what is really in its interest to to do like is it in its interest to be proprietary and make wall garden you know the walls around their gardens even higher or is it to actually maybe like lower those walls a little bit and and and create a uh a more fluid ecosystem. uh, I I don't have a good answer for you other than I don't think

### Media–Creative Divide

(45:09) I'm not entirely sure it is in the interest of them to do it yet because right now everybody's trying to make a play for your for for your consumption of of AI and that isn't in the best interests of the output of uh from AI. Yeah, I hear you. I also wanted to pick up on the the silos piece and you know breaking those silos down because we also spoke about the crossf functional issues that exist.

### Fixing Systemic Misalignment

(45:38) You've got internal organizations and an orchestration that has to go on between creative and ops and legal and data, right? And then you've got you've got the let's say the brand you've the creative agency the media agency. So, and this is a question for for either one of or or both of you, like what's the proper orchestration of all of that? Talk about silos.

### Shared IDs and Unified Dashboards

(45:56) I've talked a lot. I'll let Brian then sit. Yeah, it's a good one. So look at the the fragmented nature you just touched upon it of the agency roles is the big unspoken challenge Caroline that tension uh is resolved by actively building bridges that we talked about earlier requiring both agencies to use the same creative IDs and metadata and aligning dashboards so performance insights flow both ways.

### What’s Coming by 2026

(46:14) So the thing that nobody wants to talk about is the fragmented nature of those roles. The media agency owns the targeting data and activation. The creative agency owns the brand, voice, and production. So those two silos often are disconnected and don't use the same language or systems. Like I was saying earlier, tomato versus tomato.

### Let Agents Talk to Agents

(46:39) So when campaign stalled, it's usually because of this systematic misalignment. The handoffs are manual, the meta data is inconsistent, and performance insights don't flow back to the creative team. The playbook suggests you know that building those bridges by requiring shared ids and unified measurement.

### Policy-Aware Agent Interfaces

(47:08) It's a practical step we take is to start small with our advertising teams like I said earlier by internally aligning with a cross departmental value stream mapping exercise that again forces teams to visualize the entire workflow identify bottlenecks and define clear ownership. And our consumer product team is doing this as well but it's it's not a panacea to everything here. It's going to take it's going to take a while and it goes to the question that was asked earlier.

### Controlled Interop Between Giants

(47:54) There's I think 2026 is when you're really going to start seeing more inend objectent content creation platforms that you you can't set it and forget it. That's for sure. There's always going to have to be a human in control over the loop, but you're going to get more of that streamlined systemic kind of the content that you're looking for and the measurement, the outcomes that you're you're looking for. I also think Oh, go ahead. Sorry.

### Machine-Mediated Collaboration

(48:16) I again, I also don't think there's enough people point like focused down the these these gaps, these silos, the the gaps between silos, right? So, you know, a uh really uh simple use case that I talked about with Google back in in the summer was why would why would any of our agency teams in the future need to send an email to an industry head at Google to ask for a benchmark of a click-through rate, right? Why wouldn't our agents just be talking to your agents? And you know what? in terms of like the the proprietary nature of Google's data, that's way better for for them, right? Because that if their agents know the

### Don’t Lose the Big Picture

(49:02) rules of what data they can and can't share, then they're going to be much better at enforcing it in a very logical manner. And so, actually, that's a great example of a silo where you can start to open that up, that aperture up and create like a a bridge across the the two things.

### Transform to Realize Speed

(49:27) Even, you know, even when you think about wall gardens, you know, I I I did actually say, why don't uh, you know, why doesn't Sundar and Zuckerberg have their own agents that are just talking to uh each other and sharing the secrets they're willing to share? Because actually that would be beneficial to both organizations and you're then in control of the secrets that that that you know, the things you are willing and not willing to uh share to.

### Securing Agent Interactions

(49:52) Right now when you leave that in the realm of human beings we have all this other stuff that we carry with us like ego and pride and thieft and building and all this sort of stuff you leave it to to machines where you can give them rules and boundaries then actually there would be much more free flowing information across these wall gardens.

### The Human Communication Gap

(50:17) So I think there's an opportunity to build uh build build kind of much stronger communication and flows of information across these silos. But but but that's not what people are thinking about right now. People are just thinking about how can I do things faster? How can I save money? How can I reduce jobs? All that sort of stuff.

### Measuring What Matters

(50:50) Well, and I think what you're also talking about is this overall transformation in the industry that needs to go on. uh, and you're right, people are, you know, thinking about the faster cheaper aspect, but to truly optimize the faster treat, you know, faster and cheaper, you need to transform the entire organization. not just uh who who the players are, but how they all operate together.

### Granular Creative Element Testing

(51:26) As you were talking about the whole Sundara and Zuckerberg uh situation, it made me instantly think of how long it took me to guard rail in Sora on what my cameo was being allowed to do. And I can't even imagine the level of security necessary to have two agents of that caliber uh discussing potential IP with each other.

### PII and Model Training Risks

(52:05) Like my my my brain literally blew up as you were talking because it sounds so enticing, but I don't know who's a lawyer in here, if there are even lawyers in here. But I I immediately felt like I'm not even a lawyer and I felt proud. uh, but I I I like that a lot because I think you're exactly right that they're the silos around these things are ridiculous and sometimes it's not even an intended silo.

### Myth: Perfect Data Hygiene Needed

(52:37) Sometimes it's just that we as humans are moving so fast we forget to communicate the important details. Sometimes it's literally just that. uh, but I wanted to and we only have four minutes. I did want to get one uh question in for you both about measurement. uh because we've talked about all of the front end and the challenges and the opportunities and the human in the loop, but what are we actually measuring here? Like what metrics are you actually looking at beyond like click-through rates, Brian, when you're looking at the effectiveness of the stuff? Moving way beyond click-through rates, right? And I was talking about this earlier like to include conversion and

### Hybrid Deterministic + Probabilistic

(53:01) customer lifetime value impact the CLV and consumer trust metrics uh opt-in rates satisfaction with personalization your NPS scores. They should also use a creative element analysis to decompose winning assets and isolate which specific elements are driving you know engagement conversion lift and we need to look at it on a very kind of granular level right and we're so used to kind of spray and prey and some slight modifications with programmatic machine learning but we're going to be able to really take it to an entire new level to understand what combination

### Human Oversight Remains Essential

(53:28) of assets That's let's take for advertising headline, background, call to action. What combination of those assets? Let's say you have a hundred each and and you can, you know, come together with thousands of variables. uh, what combination of those visual assets are actually working for specific individuals or audiences not only in a national levels, which is what we're so used to, but getting down into national, regional, local, and then hyper local, and then again down to the BIO to the individual level. So that's what we're looking at uh right now. Taking it as

### Risk-Based Oversight

(53:52) far as we possibly can while also addressing things like personally identifiable information. Now everybody's so concerned about the IP of assets uh going up into the AI, but we also have to think very very seriously about the information or personally identifiable information also going up into training open AI models etc. For sure.

### Calibrating the Leash

(54:19) I'm gonna ask you two last questions and I want you to tell me if it's myth or reality. Okay. First, you need perfect data hygiene before you can start personalization. Myth or reality? Myth. uh best practice is a hybrid approach. uh you're using deterministic data to anchor strategy while probabilistic models expand reach.

### Careful Steps Toward Autonomy

(54:47) So, don't let the pursuit of perfect stop you from starting. Well done, Graham. Yeah, I myth. I mean, nobody has perfect data hygiene. I have not met the business that has that. So, if that was the case, then none of us would be doing anything. So true. Okay. Second, human in the loop is necessary for personalization. That's a spicy one.

### Thanks and Next Steps

(55:28) Myth or reality? Yeah, reality. Humans define strategy and judgment. AI handles scale. Human oversight remains essential for brand safety and accountability. Grant. Yeah, I'm I think miss in some instances reality in uh others.

(55:46) And I think this comes down to that kind of risk approach, right? I think that uh I I think in and and uh, Earl kind of wrote this in uh there. I think sometimes in our natural instincts to protect ourselves, we make ourselves way more important than we are. And and and and it's just a natural tendency. And so I do think that there are there are aspects that we have to be prepared that, you know, maybe we need to to kind of shorten the leash and and let some things happen.

(56:10) Otherwise, you run the risk of never being able to move at the speed of AI and therefore you'll never realize the benefit of it because all you're going to do is do something fast and then stop for a long period of time while while the human uh does the looping part of it.

(56:10) And so I think that it's about identifying the parts that can that that that maybe are less frequently reviewed by a human being and maybe they're not reviewing assets. They're reviewing restrictions, constraints on models, things like that. uh and then stuff that yes is a prerequisite. You just can't. We're just not willing to let that live without a human having reviewed it first. Yeah, for sure.

(55:46) where we're doing that with a couple things, Graham, to your point where we're sticking our toe carefully in the water with autonomous, you know, running of stuff, but uh it's really about, you know, human in the loop for right now. Well, Brian and Graham, I want to thank you so so much for joining us. And David, turning it back over to you and thank you for having us.

(55:46) Yeah, thanks everyone. This is amazing. We'll make sure to share the recording and uh and of course the personalization study. We'll make sure that goes out to everyone who registered along with the rest of the community. Brian Graham, so great to have you as part of this and you're uh uh now however you participate going forward, you're you're part of the community and and welcome back anytime. So, thanks everyone for the great questions and uh everything tonight.

(56:10) I hope to see some of you at the first Wednesday tonight if you're in New York. So, so uh so much going on and uh if you're local for these holidays, it be great to see you before the end of the year. Thank you so much. and and Carolyn's just keep doing these. These are amazing. Thank you.

## Human-First AI MarketingScalable StorytellingStrategy with Avenue9s Mike Montague-AIMG AI Insiders

Speaker: Mike Montague
Published: 2025-11-24
Tags: human first, personalization, best ai use cases, ethical ai
Video: https://www.youtube.com/watch?v=v04xLxcqGj4
Page: https://aimarketersguild.org/sessions/human-first-ai-marketingscalable-storytellingstrategy-with-avenue9s-mike-montagu

(00:05) We're super excited to introduce Innovative Orchestrator, the first AI super agent for omni channel advertising. We're bringing humans and AI agents together so that marketers can conduct a symphony across channels to reach their most valuable customers. AI is not a matter of if, it's a matter of how and when. And we will help you solve that.

(00:31) Hey everyone, I'm David Berkowitz back with another edition of AI Insiders from AI Marketers Guild, part of the architecture family. And we're here with another special guest today, Mike Montague, Avenue 9. I mean anyone who puts human first at the top of their LinkedIn bio, big fan of mine.

(01:02) I actually mentioned, by the way, just to give them a plug, I was at an event yesterday Techconomy New York and I got to hang out with one of my idols, Douglas Rushkov, and I told him, I said, "Now your book, Team Human, is literally the most prominent book in my Zoom background." And I do this for a reason because it's more important than ever. And he's like, "Yeah, it's kind of had a renaissance." He's like, there are Jenzers who are now talking about my book and they'd never seen it before.

(01:27) So, anyway, highly recommend that. But I also recommend getting to spend some time with Mike Monagu. Mike, how's it going? Yeah, so great to be here. And those of you who don't know me or my show, I host the human first AI marketing podcast and David was a guest. It recently came out with his episode a couple of weeks ago.

(01:52) So you can go check out our conversation there if you want more of what happens today. But my philosophy in that human first take goes all the way back to LinkedIn. My first book was picked up by LinkedIn sales solutions and international sales training company called Sandler and it did 80,000 copies or something, talking about how sales people can use LinkedIn without being pushy marketers, spammers and just adding a thousand people and hope somebody buys from them.

(02:26) So I've been on this kick for a long time and when AI came out I said, "Oh, this is even bigger. This is the same thing, but now those LinkedIn spammy salespeople that I was talking to 15 years ago can do this at scale with AI agents to thousands of people. And this is going to be a mess if we don't take a human first approach.

(02:52) So, I went all in on this a little over a year ago and have been doing a deep dive really on how do we understand how to use AI in a way that we do the thinking first as humans, we put the leadership first, we put our employees first, and we put our customers first in our execution. And that's what I'm excited to talk about. Pretty cool. Well, I'm curious to hear more.

(03:15) So, just keep going on that. And also there's one caveat. Whenever I talk to a group of people, I like to let you know that I started my career as a karaoke DJ and club DJ and I entertained 500 drunk people four or five times a week for several years. So, you can't throw me off my game.

(03:39) If you have any questions, you can unmute, you can shout out, you can type it in the chat. We'll pay attention to that. And I know what I'm going to talk about today, but I don't have a rehearsed speech or present. And sometimes I forget to mention, which I should since not everyone joins every week, is that I always call these community conversations and not webinars.

(03:57) I usually turn off the sound of a webinar five minutes after I join and then just maybe politely leave it in the background. But I like conversations a little bit more and this is a crew where you will get some questions and thoughts on this.

(04:22) So I'm excited to hear your take on what scalable stories are and even frankly I'm like what's changed since talking with you a few weeks ago? First Earl wants no scrubs. No he is a Spice Girls fan. That's a good go-to karaoke song. I think that the scalable stories part here is important with AI. I've noticed what I call the concentrated orange juice theory that if you take a weak prompt, if you take a one-sentence prompt like write a blog post for me and enter, it's going to take that and water it down and try and stretch your one little micro idea into a full 800 word

(05:05) blog post and it just doesn't work that well. It's amazing at what it does. I mean, it's better than any human could do if you just said, "Write me a blog post about storytelling with AI." It'll write something, right? But it's not going to be that great, mostly because we're stretching and watering it down.

(05:31) Now, you can add more context to that and the more concentrated you make your prompt, the better it will do. So, if I give it a really meaty subject, like I want to write a storytelling blog post about AI tools for how to write screenplays and I believe these five things are true and these are the best AI tools. Write that blog post. Now, it has enough context to write something more interesting.

(06:00) But there's an even better way that I wanted to share with you today, which is AI is even better at the opposite, which is distilling watered down stories, conversations, information, and data from your business and distilling it into the concentrated orange juice that you can use for your marketing and make powerful stuff.

(06:26) So I think if there was a misconception that I would share today is that most people are using AI to water down their ideas and their marketing when what they could do is take all of their marketing and make it stronger with AI if you go the opposite direction. Does that make sense David? I think so and I feel it too as someone who's like Yeah.

(06:57) Can you give us an example how would you do that? Right. So what I do and these are I'll just share out the five steps and we can talk more about them. But number one is you have to start with the story first. Karan. So if I interviewed the company's founder, the top salesperson and your best client.

(07:16) Let's take those three in a 30 minute to hour long interview. And then I put those transcripts into AI and I said, "Tell me what this business is really about. What's the problem it solves? Who's it for? How does this company solve it better or differently than anybody else on the internet?" AI will give us a really good answer for our marketing plan with all of that context.

(07:42) But I can't just take those transcripts and post them out into the world. I need to do that processing. I need to boil it down to the concentrated stuff first. Then if I take that scalable story and add some additional context of how am I going to tell this story. So step number two would be to identify the structure of the story after it comes out of AI and I did the who's it for, what's it for? What are the three to five main points? What are the stories and examples I can pull from our business that make the point? Step three is what do I need to do to change

(08:20) it into today I'm having a community conversation with 20 plus people that's different than me going on an hour long podcast with one person or writing a blog post about this topic. So I need to add this context layer or the structure of what I want the output to be as step number three. And then step number four is I can repurpose that into all of the other things.

(08:51) So I think most of you know now and it's not very interesting to me anyway. Maybe you don't know that you can't stop at that one piece in marketing. Just because I created this awesome white paper with my five steps to scalable stories doesn't mean anybody's going to go see it. I also need to make the other formats.

(09:10) I can use Google notebook to create an audio podcast version of it. I can create a video presentation. I can create a gamma slideshow presentation. I can create social graphics. I can create LinkedIn posts to share and write people about it. I can create the landing page from the story.

(09:30) And all of a sudden, I've taken that structure of the story and multiplied it across many formats to get the complete marketing plan that I want. And then step number five is integrating all of these tools, automating the process, and getting feedback from how those went to improve the story for next time we do it. So those are the five steps.

(09:58) I think the last one is the hardest one, and that's the last mile stuff that I know David and others in AI talk about. I don't think we're quite there yet where this magically happens. There's a lot of human work at the end to review the outputs, move between tools, set up systems and processes and standard operating procedures for your employees on what to do with all this information that we collected.

(10:23) Can I share a trick I do? Yeah. In this context, I have custom prompts. As you said, context is really important. I may go to a competitor's page and in my prompt I define my own tool and then say look at this and tell me the differences between our products. So it's always building using context of what is already there.

(10:53) As you said, the more specific you are and then the next step would be to take all of the different research on different competitions or articles, feed them all into a rag. So now you have a much larger context and what can come out of it is way more useful. Yeah. Awesome. So if we look at this in depth, that's part of the story collection stuff that I do too: you can collect competitor stories, you can collect client stories, you can collect all the data and marketing that you've ever used before and put those into the database for a custom GPT and then start

(11:31) creating those instructions around what is the story, who's it for, what's it for. problem-aware stories are different than solution-aware stories and I'm not familiar with this group so I don't know how techy or marketing savvy everybody on the line is but there's a lot of context that we can add into this and I think that's the best part of AI: it can handle more data and so again people keep asking it to stretch rather than asking it to consolidate. So take this huge and massive amount of context and give me the most relevant parts. I do this with my

(12:12) sales pitches that if I was going to pitch David this morning, I would go into my chat GBT and say, "Here's my standard sales pitch. Tell me everything you know about David Burkowitz, his company, who he solves, and customize my pitch for him." So add even more context and make my content stronger for this sales pitch.

(12:32) What are the top five questions I need to ask David when I meet with him later and start getting more information from that company. Another trick I think is really useful is to use synthetic audiences to verify what you're saying. Just yesterday, one of the members of this group sent me a website pitch that she had loved.

(13:05) She asked me for feedback and I fed it into the synthetic audience and instead of me giving her feedback, I used 110 people to give her exactly what she needed. And you can do that with your own content and that makes whatever you're presenting a lot. Yeah, some of those are cool. Well, I have found some dangers in that. Marketing has been using personas for a long time.

(13:34) And the problem is similar to Chad GBT and generic AI: it creates a regression to the mean. You start getting average content because you're marketing to the average person in this role, not this specific person in the role. One of the magic things that you can do with AI is market to the person and not the persona. So, I love what you're doing.

(13:57) If that's all the data you have, absolutely. It's better than a generic pitch. But if you can get information on the person, I think that's more powerful. Earl has his hand up and I saw that you had a question there about measurement, too. Was that the question, Earl? Yeah, that's correct, Mike.

(14:13) Yeah, the first question was you mentioned earlier about how you've been using chat GPT or other AI tools to improve your stories. My background is in analytics and measurement. So I'm curious how you are measuring performance. Yeah. You could probably tell me some better options. I am not the best at measurement because I found in marketing and sales which is my background it's pretty obvious when stuff hits.

(14:37) It's not a small micro difference of like, oh, I got 1% more conversion rates on the story. When I create a winner of a campaign, it's usually eight to ten times the responses that I got on other stuff. So, most of mine is subjective.

(14:54) Is it better than I could have done myself? Is it better than what we currently have and keep some easy measurements on it? Looking then at three different layers of stuff which is the vanity metrics. Did it get more views, more engagements, more clickthroughs to the website? Looking at conversions, and then obviously the sophisticated one for sales and marketing is did it get me the ideal clients? Because I can create a viral campaign that gets millions of views and send a bunch of wrong people to my website and end up with clients that I hate working with who don't have enough money or I could get one really good click that pays me

(15:32) $100,000 and is the best client I've ever had. Right. So that's how I think about measurement. How about you? Yeah, I think you're absolutely right. From my experience and from what you just said I think that the content we provide whether it's a story or any publications we're producing on any platform depends on the purpose of the content we're sharing.

(15:58) Some are upper funnel on the brand story getting people interested and aware of the products and services we offer to the bottom funnel like you mentioned in terms of conversion rate. So I wouldn't say it's one-size-fits-all. I would recommend measuring any content based on the purpose of the content.

(16:17) Is it to build brand awareness? Is it to drive engagement with the brand? Or is it to drive conversions so you can get those sales leads so you can get those $100,000 clients that you're looking for? For me, it depends. I'm a consultant. That's my default answer. Exactly. Well, that's everything in marketing for sure.

(16:38) I'll tell you one of my favorites that I included in my LinkedIn book as well is: does it start conversations? Because there's some educational content that's nice to read. I'm glad they did that and maybe I'll give it a like or something, but I'm not creating a human-to-human conversation. So I think especially in sales and marketing these days and when there are so many AI bots and other things out there, we'll talk about other measurement things here in a second, but to finish that thought, human-to-human conversations is my key

(17:11) metric. Is it remarkable? Is somebody leaving a comment? Are they asking a question? Is it making them think? Is it making them reach out? That to me is the gold standard. And then the thing I was going to mention is whenever I'm doing a new campaign, I find that there are waves of impact and feedback responses. The first thing that finds your content is robots.

(17:38) They're out actively scrolling directories and robots. Right. The second thing are employees and competitors which don't help you either but they're actively paying attention to the industry. They want to see what their competitors are doing and so it still doesn't help. The third group is salespeople and people trying to contact you for stuff and if you put a form on the internet you're going to get somebody to offer you office cleaning and stuff like that. And then finally, if you

(18:11) stick with it long enough, you get out towards clients that you know and people that are already paying attention to what you do. Your inner circle people. And then third is that outer orbit of people who didn't know you at all, but they went to a search, they found a piece, they saw something that caught their attention.

(18:29) And it's hard to work through that slog. And it can be frustrating to not get stuck and be like, "Oh my gosh, I got a thousand views, but 900 of them came from robot website hits." That totally makes sense from what you described.

(18:49) In what you described, I would filter that as engagement in terms of actual human engagement with the content, whether likes, comments, posts, shares, etc. But that makes total sense. Thank you. Awesome. Anybody else have a question or dive in? comments on NBA. So, Mike, one thing I'm curious about because I'll show you what I just found on you is if you have any favorite tools when you mentioned researching your audience and sometimes specific people you're dealing with: are you using major LLMs or any specific ones? I just put you

(19:23) into one of my all-time favorite AI tools that I'm on multiple times a day. And you can see how accurate this is from happen stance. I'm obsessed with this one. But anyone wants to connect there, I love it. But it does a good job at least surfacing some of your content, things like that.

(19:56) and gives me some. When I just put Mike Monagu it wasn't that helpful but when I put Avenue 9 then it found the rest. There happen to be hundreds of Mike Monigu es in the world so I have a darn thing but we talked about this before David names can be tricky. Yeah I've got no pity for anyone else on this call at least so maybe not in this room I know Jim's got so we'll answer that one and then we'll Oh yeah Okay, I'll answer that one, then we'll get to Jim's question.

(20:23) Most of the time it depends on how hard people are to find. My favorite tool is one called Humantic. I talk about it all the time. It is one of those like CrystalKnows, if you've heard about that, where it can predict their personality profile based on publicly available information on them.

(20:42) It is awesome where I can import that data and enrich the contacts in my CRM like HubSpot and then I've changed campaigns based on their personality style. If you're a high D personality, I use the DISC one. D personality is somebody that's dominant, direct, they want big picture stuff, they're going to move fast, they make quick decisions.

(21:12) I want to write an email that fits all of those criteria. If they're high detail oriented people and they have a different personality, they want stats, they want figures, they want documented case studies, they want all of the research in the email.

(21:28) And when I changed the campaigns to the four different personality styles, we saw 300% increase in conversions to 3,000% depending on the personality profile. It was incredible. Super fun to play with. And that's where I really got into telling these stories to the person, not my story. I have to understand my story, but I have to tell it in the way the other person wants to hear it. if that makes sense.

(21:56) Yeah. Love that. Jim, I think that's a great level of personalization. But my question was: as you scale your storytelling through AI, where do you need to put transparency of labeling AI involvement—supported by AI if it's in the content side—and when you have transparency of advertising?

(22:38) I don't think we've landed on the level of expectations people have of human communication, AI enabled communication, AI generated communication. You follow the question. What are your thoughts on that? I have strong thoughts on this one that may or may not be mainstream for everybody. So, take what I have to say and make your own decisions. I think AI is a tool.

(23:05) Every image I make, I wasn't labeling that I used Photoshop or Adobe Illustrator to make the image. If it was my idea and I created something, I used a tool for it, I don't feel like I need to label that I also use Chad GBT to do it. But I'm partially dyslexic, so I've been using Spellcheck and Grammarly for years.

(23:27) I'm not going to credit Grammarly for editing my book that I wrote, but it was heavily used in writing both books. Now, I do think in larger pieces of content like a book or something, it's important if you're in compliance-based industries or you're pulling stats. You have to be really careful where those come from and finding the actual attributions to them.

(23:53) But I don't feel like for me there's any copyright issues. I'm creating original content and I'm going to edit it and shape the prompt enough that I'm crafting something with a tool. I'm not just copying somebody else's work. So I'm 99.9% sure that my stuff is original when I post it. But do you have a concern or a use case? Well, no.

(24:17) That seems reasonable to me, but maybe I would take it the other way around. Where would I expect something to be transparent? As I'm consuming this. So the other thing I was going to say is that I am not a fan of deep fakes or misleading.

(24:38) My human first philosophy is very much customer first too, which is if they would feel tricked if they found out the truth, then I don't think that's fair. So I'm not a fan of AI automated agents for sales or marketing. I know probably some people on this call use them and that's fine for me. I don't want somebody to go, "Hey, Jimmy sent me a message on LinkedIn. Can I talk to Jimmy?" And be like, "Oh, no.

(24:57) Jimmy's our AI robot and we didn't disclose that." And now I have a weird thing, right? So now you're getting there. I think there's some rule of thumb that I'm just not clear on and you're getting into that space. For me, I'm not going to pretend to be human.

(25:14) I'm not going to replace a human. If I'm using robots, I want it to be clear they're chatting with a chatbot and not a human or they're chatting with a human. I want them to know. In content creation or marketing and storytelling, I don't think it matters as long as I'm not trying to make a pretend story true like, "I worked with ABC clients and they got these results" and that didn't happen. I think those are more just ethical decisions for me.

(25:39) What do you think? I think people can. There was somebody who spoke here a couple weeks ago who put side by side videos created by humans versus the same list of key frames created by some Google tool and the telltale signs are it's too perfect or there are weird mistakes that are glaringly obvious.

(26:09) I think younger people are more attuned to that than most of the people on this call because they're growing up with it. It's not that disclosure is the issue so much as that it will become increasingly harder to trick people when something is created by AI versus a human. That's a hot take, Lisa.

(26:35) I think it's going to become easier to trick. No, I think because people's brains are developing too along with the AI. My kids who are teenagers can spot an AI-created thing on Instagram from 10,000 miles away. I usually can too, but they get it 100% of the time. I think that's where I come from too, especially in marketing: we can all spot a spam email just from the first three letters of the subject line, right? Humans are lazy.

(27:01) They write dumb subject lines that are one or two words long. If you see a full sentence in a subject line, it's either your boss who hasn't learned how to write that in the text area or it's a spam marketer trying to get your attention in the subject line. So yeah, I agree all that stuff is very. The weird side effect for me is I've realized especially for stuff I'm putting on LinkedIn lately and elsewhere, I'm editing it less.

(27:32) Because that first draft feels so much more raw and if that's how people know it's really me and that it's like this is the kind of stuff I don't even have AI looking over then I'll take it. I'd rather it be like—I mean I think about this so much with the book where it's like AI could have written a better book technically.

(27:57) I'm just hoping it's not a book more people would have wanted to read, right? Yeah. The other thing I think is interesting: did everybody see that last week Chat GBT fixed the M dash thing. So now if you tell it not to put—and Hanley is going to be so disappointed in her campaigning.

(28:16) I wrote her back and she replied to me laughing because I put the post. Yeah. And I sent it to Ann. I was like you won the war. So yeah, Mike, I've got two questions for you.

(28:48) The first is when we follow your five steps all the way through and we've got to the end there, but my question is really twofold.

(29:24) How has it fundamentally changed how you would approach the task in the past versus now from a value perspective, not efficiency? I know it's made you faster, it's made it easier to do more, but outside of efficiency, where is the value that it has created? Has it given you the ability to get—where's the value? It could be the value is in the additional variance you're able to create, maybe it's fundamentally changed how you

(29:24) do segmentation and then how you go to market in terms of the end message that you take to the end customer. Help me understand the value play that isn't just an improvement in overall efficiency. Great.

(29:47) Let's do that and then get your second question because this is awesome and I haven't quite thought through this. Personalization, like you said, is definitely one. The variance for me is really fun because I like to make stuff and tell stories. That part is fun. The other part is it's allowed me to make things I couldn't make before and I think we're going to see a lot more creativity going forward. I can't imagine the storytellers, the people in Hollywood that now can make anything.

(30:15) It's an amazing opportunity to make cooler stuff that wasn't possible. My example for that is in my playful humans book. I had interviewed over 250 experts in play, positive psychology, people that play for a living. I interviewed everybody from Justin Guini who lost the first American Idol to Kelly Clarkson to jugglers that were on the Tonight Show and magicians that fooled Penn and Teller. Wow. When I was writing the book, I was trying to figure out: can I as

(30:47) a human remember all 250 of these interviews and which one is most appropriate for the point I'm trying to make? Sometimes those things pop into your head and you go, "You know what? DC Glenn from Tag Team told me this great story about 'Whoomp! (There It Is).'" This will fit this chapter. Great.

(31:08) But now I can use AI and say, "Analyze all 250 transcripts and tell me the top five moments that you think might be most appropriate to the point I'm making." I think that dramatically increases the value of the book and the story I'm able to tell.

(31:27) So those are the front-end stuff and the back end is my first two answer. And then the question from an execution standpoint now: what does this mean? Take it back to advertising. In the past, you'd create three bits of creative. What now? Create 100 bits of creative.

(31:46) Is that what this is now? So that you can microtarget different audiences with different messages because you've gotten greater levels of understanding of what the end customer really wants? Fundamentally maybe changed your segmentation. Have we gotten there yet or is that what's next?

(32:10) Are we still working through the first phase of it now? I would say we're still working through it and 90% of marketers are not there yet, but the tools are there. There are tools that will change your website based on the person viewing it. And it's not one version or 100 versions.

(32:34) It's a custom version for each person who visits your website and it's rewriting the text on the page as they get information about who's doing it and what they're viewing. That technology exists and is available now. Same with advertising. There are AI tools and I've interviewed the people on my podcast and I can pull up some of these names. I have them bookmarked later, David.

(32:55) They will change the ads based on who's viewing it and adding additional context or rewriting stuff. And I think we're going to see you can now make your own version of The Simpsons and stuff like that—if you don't like this episode, I think we're going to see a Black Mirror Netflix episode where you're going to be in it or your city and if you and I watch the same episode, we'll see different things because everything is a completely custom piece of content. That's not right for all

(33:25) solutions and all budgets. For me the interesting point and to go back to your first question: it makes strategy a lot more fun because now I must decide what's a human job, what's an AI job, how could humans and AI make something significantly different, and what's my marketing strategy. In a world where I could make anything, what should I make for whom and to what purpose becomes a much more interesting question than basically up until two years ago, our question in marketing was what can we afford to create, and what's the

(34:05) coolest way we can make this within our budget and time allotted. Suddenly those two things are eliminated. That changes the game for me. One of the things you mentioned highly, Mike, was using context for prompting for your story creation.

(34:22) I've seen everybody's an AI expert on LinkedIn apparently these days. Do you have any frameworks for your prompt engineering that include context or do you have your own approach based on your research and experience? The short answer is probably not.

(34:48) In my experience and marketing expertise, I think I ask better questions than most people. I don't have prompt frameworks. I have a few things saved: when I'm editing a podcast I know I'll need titles for the podcast, a podcast summary, a YouTube description,

(35:07) a blog post and tagged keywords, social media posts, ideas for video shorts. I have some of those things saved and outlined so I can walk through that kind of standard operating procedure. But I wouldn't say they're revolutionary. I generally just ask for what I want.

(35:31) I think about it like if I had a really smart intern, if I just got a 140 IQ intern out of Harvard to be my marketing assistant, how would I explain this task to them? What would I need to do? Then I look and the other thing I probably do differently is argue with AI.

(35:53) The story I always gave was the M dashes where I put in the custom instructions, don't use M dashes. It puts them in anyway. So I say again in the prompt, can you rewrite that without M dashes? It still had one in there. And I said, just curious, is that an M dash between this word and that word? And it says, you're right, M dash.

(36:14) I'll remove those going forward. And I said, did you just troll me with an M dash in your response? Yes. And it says, that's correct, M dash again. I swear I'm not letting this go. But I do that with marketing materials too. So, my prompts are probably more multileveled and I make them smaller and iterate more and I argue until I get what's right and I subtract context if it starts to drift or add context if it starts to water down. Sure. I'm looking forward to checking out your book and whatnot, but I think I

(36:50) have a very similar approach. I spoke earlier this summer regarding my approach to AI and I think of it not as a tool; for me it's the team. I'm training my team what they need as if they're an intern I need to coach on how to produce what I'm asking for. Same idea. I'm with you 100%: we build them, teach them, train them, and we get the results we're looking for; until we do that, they're going to be as dumb as an

(37:20) intern. Agree. Onboarding is huge for any AI tools, assets. Chad, Yep. Mike, I love the example you gave about organizing and extracting the most important information from a lot of content. My experience has been that the more I provide an AI with a lot of information to work with, the more complex the task, the more likely it is that the AI makes a mistake, and as a writer journalist book author you have to fact

(38:06) check. I'm sorry that isn't one of your five steps. I just did something. I wrote a LinkedIn post yesterday and I had AI do the research, said please cite your sources, and then I checked each source and some of them were wrong. Would that be your sixth step or what do you think? Yeah, I kind of put that as part of five, but I glossed over it.

(38:31) In the kind of B2B marketing that I do, it doesn't come up that often. I was on a podcast yesterday for financial advisors and they have legal compliance and stuff and if you're quoting numbers or company stock probably definitely check. I found numbers it messes up more than anything else.

(38:51) It's really hard to get context around numbers. So I check stats and things like that but the biggest part of my process is the human review. I never copy and paste. I never let it publish without a human intervention. I always joke that my philosophy is the human first philosophy, but it's also human last and human in the middle as well. It's me too. It's got to be reviewed.

(39:20) It's very iterative. I worked as an editor for a long time and that set of skills is very helpful when asking it to produce something. The metaphor I use is Tom Cruise in Minority Report. You're doing all these things and there is a human actively engaged in it and that's the person you lose your job to, not an AI.

(39:47) But Adam, do you think there will be pre-crime units within our lifetime? Use predictive analytics to know. Mike, hold on a sec. I've got my precog going here. And it does broach an awkward subject about your cousin Louie and your— Yeah. This is what I'm saying. You don't need to precog like some mythical superhero.

(40:07) AI is now predicting crime like the corner yard patio door. My friend, we got 9 minutes and 30 seconds. It's not fiction, it is happening right now. Organizations like Tegna in the local TV business are doing exactly what Mike spoke about in terms of real time monitoring. They have access to all the police callouts.

(40:46) They have access to the logs regarding whether the police car got there and left 2 minutes later. If they left 2 minutes later then it's a false alarm. All of those signals are brought in and then used to construct stories for journalists and combined with additional context like information from the local hospital. All of this is happening right now. But there's nothing surprising about that. We've had insurance people using actuarial tables for this

(41:24) hundred years now over the last decade plus. Exactly. None of this is new. We make it sound very scary, but it's just predictive modeling at the end of the day. What is new is insurance companies are not necessarily using it in real time to inform us and we're going into an age where it's 100% about storytelling and doing that in the most expedient way possible.

(41:56) I disagree with that, too. That's been happening since CNN went to the 24-hour news network. You have Chartbeat doing real time analytics since 2004. I think what's different—and I hope you'd both agree—is the way we can account for the usage of that data has dramatically changed.

(42:22) I'm being very self-serving by saying this, but I was cofounder of a company where we were commercializing Tim Mers le's research about accountability systems and we actually published a paper where we were monitoring Palunteer's classification of gang members and stuff. The problem with Snowden wasn't just the ramifications but the FISA court that wanted to review NSA compliance folks' issues. The judge's issue was you cannot account for the algorithmic and systematic inferences you're drawing out of it. So okay, all the stuff you're talking about: we don't have any way to account for the use of the data at scale and view the black box.

(43:17) The black box trail—if we don't know how their algorithm is being coded or what prompts they're using—the black box problem. It's going to get interesting, David. A lot of exciting developments are happening quickly. The unlock is substantial. I'm an optimist though. I'm team Robocop.

(43:47) That's how I picture you, Earl. Exactly. Dead or alive, you're coming with me—with AI—with an M dash. Jim, did you have something? I might have gotten lost in the crossfire there. That's okay. There was a lot of drift. I was going to come back to something Lisa said about verifying everything. I'm selling this company and I got this—an LLM delivered this raving review that, in context, was one company talking about another company and a company is going to buy this company. I check the source and

(44:26) it's an engineer at the company I'm selling. It was deep on Reddit and ChatGPT had transformed it into this real positive review, the best on the net. It was complete. I had to take it out. That's prompt engineering, meaning that you need to be explicit with whatever AI tool you're using:

(44:52) Do not create inferences or hallucinations. You have to train them like an AI. Otherwise, it's going to try the most thing. It's on us to be better managers and editors in my opinion. You can't blame the tech. That's lazy. Well, that was what was delivered to me and I had to edit it out.

(45:09) I know. Jim, you reminded me of one more golden nugget before we wrap because you triggered something important. In these storytelling things there is bias—it wants to give us what we want and we all know that.

(45:40) One of the things I did when I published my Playful Humans book last year, I uploaded the whole thing and before I put it up on Amazon, I said, "If I get a bad review, ChatGPT, what is it going to say?" I want you to roast my book for me because I have happy ears.

(46:00) I'm excited about this thing. I'm going to launch it. And the moment I get a first bad review, I know it's going to hurt my self-esteem. It started typing and I had to walk away. I stepped back and thought, I need to take a deep breath. Am I prepared to read what it just spit out? Not enough people are steel-manning their own arguments or red-teaming stuff. I put it through: "Rip this apart.

(46:25) Tell me what it is." When I came back and read it, I thought, "This is great. Anybody that said this didn't get the point of the book. It wasn't for them. That's not what I was going for. So, I'm fine." I hit publish. Having it break it apart, make it better, "Tell me why this deal sucks" is a great question

(46:48) if you're going to enter the contract. Have you ever explained to anyone the meaning of your company name, number nine—transformation and preparation of new beginnings? My daughter was telling me about 111. 11:11, the angel number. Okay.

(47:13) And Paris Hilton named her company that, but the Avenue 9—want to school us on that. Ninth letter of the alphabet, AI built in from start to finish. What does it mean? You found it. I was trying to come up with a company name that would be generic that I could pivot in AI enough that it would leave some open avenues for me.

(47:32) I was trying to find something with AI in it and avenue starts with A and I is the ninth letter of the alphabet and then I looked in numerology at the number nine and it stands for humanity and it's the end of an era or end of a sequence and the start of a new beginning cycle. Very futuristic and I was like this is cool.

(47:57) Also I feel like people have a plan A and sometimes a plan B but nobody has an avenue nine to get to success. So this is—we all have one. Numbers versus letters. In ancient wisdom they say the eighth reveals the ninth. The ninth is the ritual realm. Yeah. So, I thought that was fun. It's been really good for me. Also, I got an eight character domain name for a .com, so that was a win as well. That's impressive.

(48:26) That's fun. Okay. Thanks for calling on me, David. Of course. Always bringing something good. And Mike, you know, the challenging—I've mentioned my other favorite AI app now is Rosebud, this journaling app. What you didn't tell me.

(48:55) It's a journaling, a bit of self-help. It analyzes your thoughts a lot. And when I push it to go deeper and challenge me, then it gets really fun. On its first level, it's got kid gloves.

(49:16) But you also—it has to be the kind of thing where there are some things where I probably don't want to hear a counterargument or something I've written where I don't want it to be roasted and I'd rather not. But maybe something before it goes out to a client or things like that.

(49:44) I just want to do the best work and I want to anticipate their arguments. So Mike before we wrap: what are best ways for folks to stay in touch with you? I shared a podcast link earlier and your LinkedIn but how do we keep these conversations going because there have been a lot of them today all in this one chat. It's been a fun chat.

(50:06) I appreciate you inviting me on and hopefully I can come back to a future one. I am an internet marketer, so it's pretty easy to find. Type Mike Monagu or Evan9 into your favorite AI and see how I'm doing with AEO. Mike Monu, LinkedIn is my preferred platform. The website and everything is Avenue 9, but the podcast is really the fun one for me.

(50:30) The YouTube channel just got over 10,000 subscribers last week, so that was a big milestone. I've been getting killer guests like David Burkowitz and other AI experts from around the world. It's been cool and I would recommend checking that out if you're into learning more. And the dynamic content personalization tool or tools you mentioned.

(50:56) Yeah, I was looking that up as well. The ad one I'm going to put in the chat as we wrap up here. The ad one I know I can find. While you do that, Mike, I was going to say thanks for everything you've shared today. It's super helpful. I'm not a marketer.

(51:21) I'm a product person, so hearing these developments is really insightful. Thanks. We also like a range of voices here. And it's fun where we've had this week and last with Colin Jevans from Nomics where it's open-ended conversation along some themes and it's fun.

(51:49) Not everyone needs to do the PowerPoint overload—just have some real conversations. Next week we will have a fun little vibe coding competition if anyone's around the day before Thanksgiving. I'll have an assignment ready and whether it's something you've done before or haven't, I'll send out a couple tips on a platform or two to register for.

(52:19) If you haven't—most people in the room have probably tried something you've liked—you can bring your own, but we'll— we had this open slot and have been meaning to do this for a while.

(52:37) And Mike, if there are any other links you want to share after that pay you later, just let me know. I'll share them with the community. You're always welcome to do so in our Slack. Thank you. I seriously looked at the time 20 minutes ago and thought, wait, is it 12:40 already? Are we getting in the home stretch? Because this one flew by. Thank you. And thank you all.

(53:00) I love getting to sit back and see where the sparks go. Have a great rest of your week. If I miss some of you next week—and I understand if I do—have an amazing Thanksgiving. See you in the Slack and everywhere else. Mike, more to come. I look forward to learning from you. Thanks, brother. Thanks, Mike. Thanks, David.

(53:20) Thanks, Mike. I have questions. Oh, cheers. Thank you. Go for it, Earl. Ask your question. No, I don't. I'm very shy, Jim. I'm obviously very shy. You're working on it. You're the opposite shy. I reserved. I reserved. Go get some lunch. Look forward to the follow up. Take care, David. See you next week.

(53:54) All right. Fantastic. Thanks, everyone.

## Integrative AI Where Human Intelligence Becomes the Killer App

Speaker: Charles Manning
Published: 2025-11-16
Tags: prompting, authenticated model context, human centered ai, reusable prompts, integrative ai
Video: https://www.youtube.com/watch?v=r3MlSmWFaEs
Page: https://aimarketersguild.org/sessions/integrative-ai-where-human-intelligence-becomes-the-killer-app

(00:06) Welcome everyone to a special edition of AI Insiders with AI Marketers Guild. And uh and it's a a spe all our guests are special, but uh I've gotten to know Charles Manning, CEO of Coachava for Coachava for a while. I got to go to their summit in Idaho this year, which is incredible.

(00:29) So uh you know seeing how they don't just build ad tech but they build community and uh and uh I've known known his team for quite some time. So it's been wonderful to have Coachava supporting and involved with March in a lot of ways. you uh you might have seen Charles colleague Trevor at Marcher live recently and uh and so so uh Charles and and his team CEO CTO Ethan is brilliant and just like amazing folks where like you see some of the companies I'm getting a lot of my knowledge from uh and uh and a company that's doing so

(01:09) much to not just lead but educate the industry. Um, enough about me talking. Everyone's here to learn from you, Charles. Well, first of all, thanks so much for for the invitation. Um, I'm a I'm a big fan of the community and Dave and I had the opportunity to meet many many years ago um at uh something I don't really recall.

(01:32) I don't remember what it was but it was from one of our team members and uh we were just really uh enjoyed the community and just loved um what it was all about just bringing marketing together in terms of actionable execution of AI and not not just powerpoints and concepts and so we're thrilled to be involved. So thanks for the invite. Yeah. Well, uh, excited to dive in because it it's just so funny because we were talking about what's happening with agents early in the year and and there weren't many examples, were there, right? Like of like deployed like for the ad industry uh and and now it seems like a few things have changed since then. For sure. Yeah. So, um, but I mean,

(02:12) obviously a number of things have changed and, um, I I I think there's some some really interesting themes about what we're working on, and I'll I'll back up and speak to what some of our drivers have been as a company. I think many of them will resonate with the people in the audience. Um but you know if you think about AI and how it has transformed not only the advertising industry but just computing in general and any kind of any kind of SAS technology out there.

(02:43) What you're seeing is a um a chat prompt that's getting added to a SAS dashboard UI and that's their kind of tick box of that's how they're incorporating AI in their SAS technology. And certainly there's a lot of value to that. It's like how do you create data to be conversational? It can be accessible.

(03:02) Um, it can be usable by analysts in ways that maybe traditional dashboards and reporting has not been. Um, but fundamentally that interaction, that conversational interaction is still siloed specific to that vendor or that specific tool or that specific SAS tool. So, you know, since November 20th, 2022, that's been the prevailing thesis of layering AI on top of an existing SAS offering.

(03:29) We've observed all of that and said and in fact David was at our summit last year. We had actually given a an early insight into what I'm going to share here that we've just opened up as a as um kind of private beta uh for which anyone here that's interested.

(03:48) I'm happy to engage with David and get invites out to the folks that are here if you're interested in having access to it. I'll walk through some of the attributes of what this thing is. But in our in our early execution of AI as a as a you know large SAS company that deals with lots of data, it was a prompt to SQL interface.

(04:08) Very common like how do you have a conversational prompt? It turns into something very unique as a SQL statement and it comes back out. The problem is that if you're an individual operator, if you're an individual that's part of a team, you're dealing with 30 of said SAS tools. You're not dealing on a one by one basis. And so we started to back up from that and said, what where is computing going to go for team members who want to leverage AI in a way that 10xes their productivity? M and the conclusion we reached was that there's kind of this new category opportunity called integrative AI.

(04:44) So you've got kind of ML and AI and generative AI. And you know I'm I'm certainly not suggesting that this category that I'm describing is well understood or known yet or that there's fine boundaries around it. But this is kind of our mission and our evangelistic exercise. We think integrative AI is really the value prop and the the attributes that we're establishing on what integrative AI is is variable model.

(05:13) So you should be able to work with any model you want and work with any model key that you want because many people work for companies that have very specific uh expectations around governance of what model you use and what key you use and what account you use. The second attribute is uh a templated pre-prompt library so that you can have consistent execution with those models across your teams.

(05:40) you don't run into the problem where you know Jane is a particularly capable individual that knows how to pre-prompt things and so always has better responses than John who just phones it in and it and it shows in this case where you have common pre-prompts you have a framework where you can start to share these things around workflows for team members and then the third element um third or fourth is connectivity that whatever you're doing with AI Instead of copying, pasting, importing, uh, and you know, doing all this kind of manual dragging of information, the the system needs to be able to fundamentally connect through authenticated tooling to

(06:19) the tools you already use. So you log into Tradeesk, awesome. You should have whatever this tool is authenticatable against Tradeesk. You use Kachava for measurement, awesome, that's another endpoint. you use Samba for, you know, linear uh measurement or targeting. Awesome. You use your credentiing for for Samba.

(06:43) So that the notion is is this composite or mosaic of connectivity that brings us together. And then finally, and the reason why this is the last one, and I'll and I'll pause for a moment, David, so that I'm not just talking, but um is this agentic piece. Now our thesis is not that we're going to live in a world tomorrow, you know, with this immediate transitory step where agents are going to just be working authenticating on their own doing tasks and activities um autonomously.

(07:07) We don't believe sociologically, we don't believe psych psychologically that is going to work organizationally. Instead, we think this notion of integrative AI is an extension of the human as a team member who can 10x their productivity so that the agents are performed or or created that they can get created by a noode analyst, a noode operator and that they fundamentally composite connectivity templating and models so that it starts to be an extension of what they're already doing in the workflows that they want um reduce the repetition on. So, I'll

(07:47) pause there, David. I know that was a mouthful. I hope it's okay. These terrific and and I love these four parts to it. One thing I'm also curious on the connectivity side that also might apply to some people in this room or who watch after is is do these other players need to do something themselves to enable that or is this something that the system should just be able to do with a standard login? Yeah, so um a awesome question.

(08:20) I'm in a I'm a much more visual person than I am anything else. Do you mind if I share and I Please do. Yeah, I'm fair. Well, I'll love that. Yeah, we took a different approach. So, you know, I'm I'm um I've been through and worked in the client server computing age and then the web age, the web 1.0 age, web 2, um you know, full SAS.

(08:46) what we're doing with this product called station one which you know our focus is to focus on this category of integrative AI this is a downloadable app so this is like old school this is not a SAS tool and we did that intentionally because of the privacy and kind of data leakage concerns that we're finding and we're hearing about from our customers about um not wanting anything related to AI to proxy through another SAS system because there's a concern of what that means from a carriage perspective of the access to that data, access to those APIs, access

(09:28) to the models, etc. And so we flipped the model entirely. We said, "Okay, we're going to build a Slack-like downloadable app that has workspaces just like Slack." And those workspaces can segment buckets of connectivity, templated pre-prompts, and model access and allow for the productivity of individuals to be extended and multiplied because they've got this kind of universal AI hub.

(10:00) And so as as an example, I've got a um I've got a workspace that I can use um you know, OpenAI as my designated model, but I can also use a local model. I can use an internal model uh against my um backend data center if if I've got a team that has built a small language model that's specific to my business or my industry that's not even loaded in one of the foundation model providers. I can use a a Google model and anthropic model.

(10:34) Effectively, I can put in keys for any one of the models that exist out there. And now all of a sudden within this downloadable environment I now have access like this universal mosaic system of of uh connecting models and then again based on which um which workspace I'm in I may have different pre-prompt templates.

(11:00) So for example, I'm in a product management workspace that we've been using internally is like a almost eat your own dog food approach to if our product management team can consistently spec out requirements documents and sprint planning documents, then we have something super interesting because that's a great example where you've got a team of PE people working against a common objective with common artifacts. And now all of a sudden you can use consistent tools around it.

(11:35) So this is a totally unrelated to ads but it's our kind of product management workspace that has things that help you build requirements documents. Separate from that we have this sample workspace that for any of you that participate in the data want to get an invite you'll get access to this.

(11:54) We have a effectively like a gallery or a marketplace of workspaces that are published publicly. There's only one right now, just so you guys know. But in the future, there's going to be more. There's going to be a whole bunch that vendors want to publish these workspaces. And what we have in this this workspace is like everything you need for omni channel advertising. And the reason is that's the business that we're in.

(12:12) You know, we're we're in the business of buying media across a whole bunch of channels. And so the experts that we framed were industrypecific um experts around monetization around how do you translate your your campaign execution for a CFO? It's about um things like uh you know we're really good at mobile but we don't know anything about digital out of home and CTV.

(12:43) How can I have an assistant that's helpful to our team as we think about extending our media plans across these other channels? How should we think about measurement? So, there's continuity and consistency as I do activation. Um, and then kind of your standard media planning and activation. Now, these these experts that we built are pages and pages of experts. Like, it's it's a lot of content that pre-primes all this uh historical knowledge that we have around the advertising space. Nothing stopping anyone from saying that's cute, Charles.

(13:13) I'm going to create my own workspace for us as an agency or us as a brand and I like what you've done, but I'm going to morph this and I'm going to change it and I'm going to use my set of pre-prompts because they're specific to my team's execution. Does that make sense on those pre-prompts? Mhm.

(13:33) So, um that's an example of our our our integrative um advertising uh pre-prompt. And with each one of these workspaces, you have this notion of connectors. And these connectors are uh for those of you that have been following, these are effectively curated authenticated MCP flows. So MCP is the model context protocol originally invented by anthropic but being used very very broadly and and extensively.

(14:05) One of the problems with MCP is that it's a great protocol and everyone's um adopting it, but how you authenticate with MCP is still a bit of a a nightmare. And so what we did was we built a gallery of MCP servers that are be behind common authentication OOTH frameworks so that there's pointclick two-factor authentication. Done.

(14:31) It's integrated. And if you want to live on the wild side and you want to connect your own manual MCP uh directly, you can, you know, have an STDIO uh MCP, a streamable HTTP MCP, and you can set up authentication. You can still do that if you want to be a little bit more technical, but we we're building this kind of gallery of of connectors that are specific to what customers want.

(14:58) Now, this is just a small subset. We're going to have Trade Desk, AppLin, uh Amazon, uh you know, AM, uh AMS, the Amazon marketing, uh cloud, AMC. Uh we're going to have all the buying tools here. Uh but we're also going to have a whole bunch of measurement tools. So like we as an example um you know we're we're an omni channel measurement company for sure but it doesn't mean that we won't have other um vendors who are in the um category of measurement exist here because otherwise there's kind of no point. So one of our competitors on the

(15:37) advertiser side within the mobile measurement space is a company called Appsflyer. We're integrating the Appsfire MCP server directly. You can see all the tooling that's available. Um, you authenticate and now all of a sudden Appsfire is part of the picture. And so you can have conversational engagement across multiple tools where they're being synthesized together with common experts and pre-prompts for consistency.

(16:08) connectivity is an extension to the person and then um you know variable model support. Does that make sense? Uh it does. Uh and if there are questions from the community uh feel free to chime in and chat or share your questions but uh yeah I mean yeah please uh so quick question for you. This sounds really great. Uh Charles, thank you for breaking it down for us.

(16:32) Quick question. So on the client side for people using the tool, who's going to be the person at the company responsible for the governance and making sure that the data model is aligned so that the platform can leverage the insights from all the different integrations that you have. Yeah, awesome question.

(16:52) Um that is I think the uh long pole in the tent. Um so I'll I'll tell you what's happening as we engage with our customers. I don't know that that's prescriptive and that what's going to happen everywhere. Obviously, this is all fresh ground that we're all tilling together organizationally. Absolutely.

(17:16) But we we think that the value of station one and being a downloadable client is that it's not proxying um you know all this through our SAS system. So um uh it's a direct connection. Having said that, when corporate entities, when organizations deem that a client tool or even a serverside tool is acceptable and usable, uh there's usually a checklist and we're certainly seeing that across the AI category.

(17:42) Our our customers are typically larger um you know, larger brands. We have 20 of the top 25 streaming media companies that use Coachava. We've got a lot of large QSRs. So they don't, you know, willy-nilly let organizations within their company just start installing software and connecting AI models.

(18:02) And so what we're observing is like a a check list, an AI SWAT team checklist because there's such an influx of tools that are coming in and we're we're getting better at um how do we prepare ourselves for answering those just like we would another DPA or any other kind of support elements. I think you expect that. what we're what I'm obser what I'm learning.

(18:21) So the former is what I'm expecting what I'm learning is there's interest in things like traceability and governance. So organizations saying I love that you can set up variable models for anyone that is on our domain. I don't want them to connect any model. I want to designate server side what model they're allowed to talk to and limit it at that.

(18:50) makes tons of sense because they want to make sure if it's a company asset and it's company exercises, it's limited to just the models they're deeming appropriate. Um the second big thing is uh traceability for um you know what devices are connecting agentically versus from a human perspective. So as an example um Salesforce when I'm connecting doesn't know that I'm connecting agentically. It just thinks of this as another session within Salesforce.

(19:20) The same with Slack. And so what we're observing is this kind of interest level of how are you a enabling individuals to proxy their um you know their arbbacks their um role-based access control um exposure to agentic interfaces because that's effectively what we're doing is we're bringing that to bear for your tooling. Does that answer your question bro? Yes and no. I'll be honest.

(19:47) Uh I think the question I have for you is just understanding the the data models as somebody who's been in the industry for a while now. One of the biggest challenges I've seen whether working in different platforms is that they all have different metrics and dimensions that need to be stable within an organizations especially with clients that are retail for example.

(20:05) So my question was really trying to understand from a client side perspective who would be in charge of that integration because yes logically that makes sense but I'm talking about terms of the data model making sure that the data that all the platforms are sharing makes sense given the needs of the request or the prompter.

(20:23) Yeah I now understand better. I'm sorry. Um it's an awesome question. I'm going to rephrase that question into something we're seeing as well. Um, if an agency is using Kochava as a measurement technology for some some clients and apps flyer as a measurement technology for other clients, how do I create consistent workflows irrespective of which tool they use so we can normalize the data models between them as as one example or if I've got uh data models from multiple tools. All of that is going to show up in how the MCP

(21:00) connectors are built and merchandised. And for those of you that don't know, and there's nothing wrong with not knowing this, this is like one of the most interesting things about I think this notion of integrative AI is that these MCPs are um connections that expose these tools with these text descriptions that kind of merchandise the tool.

(21:30) Okay. And so what happens from a flow perspective is if I'm prompting and I'm I'm interacting with a with a model and I say um and I can I'll go through an example. I say you know I've been busy in a meeting for the last 27 minutes. Please summarize my emails and prioritize what to focus on first. The fact that I said summarize my emails prompts the inference engine to call the Gmail connector and query Google's mail.

(22:05) Your example is let's say I've got a ERP system or you know two different systems that have unnormalized data models. Not only you you don't have to change the source system but the MCP connectors and h and the tool calling and the normalization of data that's output and the way in which those tools are merchandised with this text that will be where your boulder will be to turn into rocks as you bring those pieces together. Got it. Okay.

(22:33) Organizationally what we're doing um we have a we have a um let me see if I can find it. So we we have our own you know measurement um MCP as you can imagine and um when I look at them um let me just see if I can find one where I've got it connected. So what's interesting about um this is that this is our customerf facing MCP set of tools and it's things like getting attribution results on apps you know run search queries you know all the things you'd expect you know lifetime value details settings for apps

(23:17) and um separate from that we have customer success individuals on our team who are managing portfol portfolios of customers, right? So this MCP is a onesie twzy MCP. Um it's so that a singular account can talk to their singular account in Coachaba. We have a separate MCP for our CSMs that are looking at a bulk of a whole bunch of customers across a portfolio of their customer base and looking at settings across a broader set.

(23:52) And we ourselves here we are you know uh plowing this new fresh ground really ran into some organizational questions of do we designate a part of the engineering team for MCPs separate from our product team or are they an extension of it because the needs of how these things are going to show up agentically are going to um they're not really affecting the engineering team of station one they're affect affecting the product team on how they expose that data model to use um the the summary you you provided. Yeah, that makes sense. I may have missed it in the demo just because I

(24:31) know we're using a demo account right now, but I'm curious have there been is there plans on your product road map to integrate tools like u uh CMS or CDPs cuz those are trying to you know that's what most clients I found have been using for their customer whether it's B2B or TOC and so I'm curious any integrations with those kind of platforms 100%.

(25:01) So not only are I mean one of the great things about standardizing around MCP is that I think every vendor is already thinking about how they build an MCP that's like their product supported MCP for their product. companies like Tradeesk, they don't have an MCP server, but we're building one around their APIs and um so it it it the you know CDPs are in that category as well of folks that um we're going to be building um uh MCP integrations around as well as CMS.

(25:27) That's that's uh two very important categories. Awesome. Thank you. Yeah. Um and uh we have a question that came in the chat and then uh and then I'll go to Adam who's also raising his hand. But uh Ory is asking uh could you elaborate on how this is different from other agentic infras do you mitigate the risks when connecting to an external LM via an API key? Yeah. U good good question.

(25:55) So how it's different you know this this approach is really an extension of the individual as opposed to a server side amorphous um agentic engine where you give it instructions and you hope for the best in terms of governance rules because of pre-prompt and prompt u elements. So I I think of this and I think of integrative AI generally as like a tooling mechanism where it's my common interface to increase my productivity.

(26:26) It's certainly how I'm using it. What I what I foresee is turning some of the workflows and the tasks that I perform into repeatable efforts that I no longer have to manually do each and every time. And so I I haven't shown you this uh part but I'll I'll give you an example. I have a um you know agent forge which is where you build agents um is a place where you can create an agent. You're not coding anything.

(27:01) It's not like n where you're like dragging and dropping and then check you know picking modules and doing all these things. You're literally describing what you want it to do. Arguably, you'll have already done this probably a number of times in the conversational UI and now you've decided you want this to be repetitive. And I'll give you an example.

(27:25) I've got a personal dossier agent and um I'm sure like many of you um you find yourselves in a meeting with a person that you've met before, but you don't quite remember the last time you talked to them. And you also don't quite remember who on your team has talked to them and you don't quite remember all the context of what you want to make sure you nail like if there was like any leftover things that were not addressed.

(27:58) Um and so I put this together and I described what I wanted. um you know perform thorough research on individuals by name, email, providing actionable intelligence and context for meetings with a special focus on coach related business opportunities and it generated this monster which is the pre-prompt and then that monster generated this workflow of the tooling.

(28:24) So again, I didn't I didn't create all these steps. The system created these steps. And so the first step is that I'm going to put in a name and an email address. And given that name and email address, it's going to carefully extract that person's name and um email provided and determine the company domain to do as a hypothetical, you know, employer based on that domain name.

(28:52) It's going to then search all my email archives for any interactions I've had with that person. and it's going to search Slack messages in case they've ever been talked about within our company. It's going to search Salesforce records to see if there's opportunities that are open and related parties so I can know who I can reference and namerop.

(29:12) And it's going to conduct comprehensive web searches that are just kind of public in nature. When that runs and I can run it on demand or I can have it on a schedule. I can actually tie it to my calendar so that it generates it in advance and that runs. Um, it then has an output and I've got an example. This is a partner of ours, U Samba. I used it as a demo. This takes I don't know a minute and a half.

(29:39) I don't want to waste everyone's time and watch a a pulsing uh progress bar. But background on the uh chief commercial officer um that we have um some a lot of internal and external interactions. There's a whole bunch of different opportunities that we have from partnering with them. Um we have uh some press activities that they've been doing. Um and this was just before you know just after the ADC CP announcement which is really MCP for advertising.

(30:03) Um potential talking points, value proposition, other queries etc. So totally unrelated to advertising, but an example of how agents are approachable and they're not just this amorphous thing that's happening out in the space. It's like an extension of your productivity. Terrific. Thanks, Charles. Adam, thanks for your patience. Yeah. Hey, Charles.

(30:27) Uh, thanks for doing this. Um, a former very early customer of Kachava when I was running partnerships at Jumptap way. Oh my goodness. Juptap was our third network integration in our history as a company. It's great to see you. Thank you. Yeah. And and at the time uh we had already been working with so many others.

(30:49) We're like too crowded, don't need it, whatever. And then you guys came in and just blew everyone away and we leaned into it big time. So thank you for that. Thank you for sticking with the industry for so long. Um and a you know good buddy of Garrett McDonald and Grant Cohen. So good stuff. Um thank you. So, this is getting into the cool new territory.

(31:08) I may have missed it, but you know, if people are using a Looker Studio, Looker Studio, a funnel.io, more sophisticated like integrated reporting, are those just API hooks, another integration like anything else? Um, is there a more sophisticated Q&A or NCP kind of like how do you think about because a lot of times what what I'm doing as I'm working with senior leadership or board and trying to explain the eb and flow of where you know the media is going.

(31:42) So, anyhow, that's a Yeah. So, I think what you're hitting on is like the the the progressive um maturity curve of some of these tools and I think you're spot on. No one's ever accused me of being mature. Charles, keep going. And but I think that's happening. Um so, there's going to be basic integrations that are just getters and setters. Yeah.

(32:08) I would say 90% of the activities that we talk to partners, candidate partners that we want to build uh connectors with, they only want getters. They're scared of setters. They don't want the thing to be able to do anything. They just want to be able to view things. Now, that's progressing and they want to do both soon. And um in particular those companies that have APIs where you can actually affect change they seem to be comfortable because this is just an extension of affecting that change.

(32:37) So adding postbacks or enriching this data through my CDP or a looker merging a bunch of different uh views together to get um you know unified output. Um so that's happening. I think as you as you run the tape fast forward I think two things are going to happen over the next 24 months. uh two specific things around your question.

(33:00) One is these MCPs are going to get really interestingly um sophisticated and they're going to self-describe their sophistication in that merchandising of the tools better and better and things are going to work better just because they're describing their tooling better because the inference engine can now make more sense of how the tools start to merge and progress. That's the first thing that's happening.

(33:22) The second thing that I think is going to happen is companies are going to get into this category. They're going to think about how they build their own GUF models. So these are compiled downloadable models that look like an open-source model, but they're internal to their proprietary data. Those that's like the other end of the spectrum. Super sophisticated.

(33:49) Imagine a world where um you're not interacting with OpenAI's chat GPT4.1, you're interacting with company XYZ, my internal company CRM SLM, you know, small language model. Yeah, I think and and this supports that as well. So, I think both of those things are going to end up happening. Um, I guess there's one other one other movement I think is going to happen and I'm hearing a lot about this just in the last week is this notion of workflow steps as an extension of human workflows.

(34:26) So like when I do a a campaign um plan and and I'll just give you an example. I have a digital I have a streaming media app that I uh that is supported across Roku, Samsung, LG, and Vizio. Um sorry, I'm launching in 2026. I need a holistic um uh media plan. I use my expert of media planning and activation which is like reams and reams of best practices and avails around talking about strategy versus tactics around the things that are important for measurement around how re-engagement is as important as new acquisition as you know every related

(35:22) just just quick real time reaction and I think you may have touched on this but if you're talking about avails and things like that. It's great to have this like sort of abstract project plan or task list or check or something. The ability obviously to sort of go and pull. Yes. And those that you're hitting right, you're the straight man.

(35:42) You're you're hitting right on my point that there's going to be a new class of MCPs that are the avails. And then there will be another class of MCPs. It's like, okay, if these are the avails, I've got a I've got a media plan. I want to lock those avails because of my contracts that I have in existence so that I can now progress that to the next workflow step.

(36:09) I think the next level of maturity is where it's actually stepping through the workflow. It's not just a pretty word doc that I can present to someone. So when we talk about configuring inapp events for example, it's not just talking about configuring the inapp events. It's confirming that my inapp events in fact are configured, that my trackability is configured. Um I'll give you I'll give you another example.

(36:32) Um so I've got a u an example with um universal ads. So we've got an integration universal ads. I need to make sure that all of my tracking links. How often have you guys trafficked a campaign that's sizable and spendy and you don't have your impression verification tag set up correctly and you realize it 30 days later when you're reporting is awful. That's an example.

(37:00) Um or same thing with outcomes measurement that for folks like Coachava and our customers I need to make sure that all my tracking links are configured across my campaigns and bveral ads and you confirm. So the multi-step looking at active campaigns um qualifying tracking links do they exist or not and everything at least in this phase of the maturity curve is confirmationally based.

(37:35) So without calling it human in the loop and confusing everyone and scaring everyone that there's like this human it's just conversational. So um you know I'm going to for the sake of discussion say make your best assessment each review prior to making change. So it gives me my summary where they're configured where it's not applicable um drill in and follow through. Right.

(38:04) So you can't do that without consistency of the prompts and then also the tooling as an extension of the person. We're not saying now go make this a magical faceless agent that runs in the ether. This is an extension of you. Um, one last thing cuz it's kind of fun and we just added this feature in the end and um, we call it operators, but this is kind of the closest thing to what people are thinking about as agents that they don't create, but in fact they engage with almost like personal coaches or personal assistants.

(38:36) So, we have like these these five out of the box ones in this integrative advertising workspace. The cool thing is you can create your own operators and I'll show you that how possible that is. But Selkerk and Monarch, those are the names of the mountain ranges around where our headquarters is located and we just call them these CSMs.

(39:00) One for our advertiser, our brand products and one for our publisher products. But then we've got this like inspired by Mark Pritchard. How do I translate my performance results so that my CFO loves me? And so it's like a p these operators are like these persona differences that are coaches and helping you make sense. Um Frank is a you know virtual consulting services member of our team that helps you make sense of the data that's in Kachava in a way that our consulting services team would look at that and um Allison is an expert in MM and starts to think about how incremental lift is different than attribution gives you

(39:40) pointers on things you can do. you can just as easily with station one create these other operators and I'll I'll give you an example. Um you can have an operator that's like we'll call him David. Um, you're a podcast and community leader that speaks to the issues of AI and marketing helps audiences understand how to be more productive.

(40:20) Please refer to David Bowitz architecture and the AI marketers guild as a reference. love being a reference. That's not an awesome pre-prompt for those of you that have futed around with pre-prompts a lot. And so, we have a tool that helps you build the prompt for you if you're not all that good um and is a little bit more thoughtful.

(40:44) I can then apply my expertise that I've set up in Station One for that workspace. So, if you've done lots and lots of work to like set up expertise, you can do that. And you can also limit the tools. So like let's say for example in this operator I want to link it to um you know the example before of this looker instance or you know this trade desk instance maybe this is my trade desk professional and it's only allowed to talk to trade desk I can hit save um and I can close this and um um it it's going to introduce himself um what is the best um set of topics I can consider for

(41:28) December given my load of topics over the last six months. I don't know. I mean, I wasn't even planning to give this as a demo, which is probably the world's most dangerous thing to do. But um you know, if you think about your own job, I'm thinking about David's job right now, like how do you have a built-in coach that helps you do the things you want to do with these templated objectives so that as guard rails about your success and that's really the idea behind integrative AI.

(42:00) This is amazing. Are these topics any good, David, or is this all nonsense? Th this is me. This is what what I'm I'm done. That's that's it. I won't even be here next week. Sweet. This this really good. I got I've got I've got a whole next book written. Well, we'll we'll work with David.

(42:25) I won't have any of you guys' emails, but we'll work with David and let him invite you guys as audiences into the beta and um um for those of you that are interested and and um we'll go from there. Yeah. Yeah. And all the the follow-ups. Amazing. Thank you for sharing this. Awesome. Awesome. Yeah. Yeah. In the in the home stretch here, what are the questions uh do others have here? What can our resident expert Charles answer for you? How did you address the the people aspect of this um in in Kachava and your organization? I feel like that is such a fun question.

(43:04) I wish we had another whole hour. I don't have finality and conclusion on that topic. I feel like we're still mid-process. I think it's such an awesome question. We I I I visualized it kind of like um a gardener hype cycle, like a miniature hype cycle in the context of adoption within the organization.

(43:29) What I didn't want to do is to add any more work on the people who already had lots to do. Um we we segmented out a different team. they specifically focused on um building this thing and we had a vision and a conviction and we did it. But what was really fascinating was as we started to roll this out, you had enthusiasts who really understood the value and they immediately like gravitational pole started to play around with it and they started doing really interesting things.

(44:03) And then you had people who didn't even register um and you know when I say register it's like you know log into the system like even when it was just an internal tool and it was like I I don't I don't see where that's going to change my life. Um, and over time what we've started to do is think about how do we build features that facilitate social adoption within teams? Because what we discovered, even though the tooling we have today is not very good for this topic, um what we discovered was that when we could socially share the artifacts,

(44:45) people were like, "Holy crap, wait, you just did that. Oh, I see. Let me connect my stuff." And I'll I'll give you a really I'll give you a really good example. Um, so like so your early adopters brought the rest of the moths to the light bulb. Yes. But it wasn't very effective uh without sharing tools.

(45:17) In fact, one of the biggest questions we were asked internally was, isn't there a feature where I can share an expert without sharing a whole workspace? So people loved this expert and then they wanted to tweak it and extend it almost like they wanted to version control the expert and like extend it have it do more things. Um we um and and so social sharing of experts of workspaces and of artifacts were the three things that I think have u been an asked for element.

(45:53) But the the the human the human component we think is the real long pole in the tent here. We need to make this tool approachable like beyond recognition because this is not about will it make you more productive. It's about will you believe it will help you as opposed to believe it will hinder your your work or change the way you work. Girls, I want to jump in with a question.

(46:17) Um, imagine I'm on a college campus and a fold out table with a sign that says, "Change my mind." I love it. The analogy is the Excel spreadsheet. I have a bunch of people who are really smart. They've created their own spreadsheets to do their job. One of them leaves and a new person comes in and looks at the spreadsheets and goes, "I I don't know how this was built. I don't know what these formulas mean. I don't know how to edit it.

(46:35) I need to create my own." Yeah. And And that person who left thinks they can take the spreadsheet with them and apply it to a different job. Yeah. How how are these things universal? Yeah, I I think that very much relates to the social sharing bits. So, I've um there's a whole community, if you guys aren't familiar, I'll tell you there's a whole community of people who um if you put in your email address, they'll share with you a Google doc of pre-prompts for different industry vectors, you know, segments. And then there's another whole community that takes that to the next level. If you pay them $25, they'll

(47:15) share with you an even bigger library of pre-prompts. And the way in which you pay the $25 is like an online class system. I think there's a content creator marketplace opportunity where if we do our job right, you can have people who really understand their business well, just like you described the individual who knows those macros in Excel and they can share either experts in a gallery of experts or share their workspace to a gallery of people who want to download those workspaces. And if and our job should be how do we facilitate

(47:54) helping them make money on sharing that content almost like a marketplace today. We're not worried about that because we just want it to work really well. But the thing I continue to ask myself is the same the kind of the question you're you're you're posing. Do you get faster adoption if content creators have a way in which they can make money on that awesome Excel macro they produced? And my question is, how do we know that that macro will apply across an industry? Because it feels like it's very personalized to a job, a task in a job

(48:31) in a company and an industry. I I think it is too. I mean, the idea here is that these are either tool specific or they're company specific. And if you've you've heard of this phrase like forward deployed engineers, FDEES, these are the people who like want to understand your workflows and then orchestrate your AI to work with your your workflows.

(48:56) You know, nothing is uh more challenging for corporate adoption than having a bunch of consultants tell you what your workflow should change into. We want to flip that where it's like we're extending that so that this is just helping you with your micro teams and then maybe over time it starts to get syndicated with larger teams. Very helpful.

(49:13) Thank you Rory. Go ahead. Hi there. Uh thank you. This is really wonderful seeing uh station one in action. And uh my question centers around local LLMs because the company I work for we have everything on prem and uh we self-host um you know eating our own dog food everything um using open-source technology and I saw that you had uh yes as one of yours and I'm wondering what sort of success you had in with your alpha testers for local LLMs.

(49:47) Yeah. So we support a llama lm studio. Um there's a few other kind of run times for local models. And for those of you that are on the audience that don't know what this means, it's like these are um physical um you know local open source models that have been compiled. Hugging face is a great destination if you've never played around with it with just oodles and oodles of models.

(50:12) Some of which are awful, some of which are kind of interesting. and sounds like Rory's doing a bunch of stuff which is cool around that area. So, we've like used the the Llama 3X uh models for some testing. We have uh some internal um Nvidia hardware just for some testing. Um but we're trying to optimize towards online models because our observation is that despite all the kind of governance reservation and concern around model access and trainability, that concern is being prosecuted by legal teams telling Open AI and Enthropic, you can't train off of our data, but we're going to still use your online models. I think

(50:57) over time they're going to want to do stuff that's on prem and um there's just such a demand for hardware that it's super expensive and I think that'll go down over time and I think I think we're going to see in our in all of all of industries more decentralized distributed models not these centralized online models and that's why we're taking the approach that we're taking.

(51:24) We think over time there's a myriad of models that start to come together across workflows and testing is awesome. Like um depending on how you've juiced your hardware, it it's faster than I mean like on a on a on a Mac M3 you can run Olama locally with GPTOSS and it works just as fast as chat GPT. It just has different responses because it's a different parameter count. It's juiced differently in terms of pre-prompts.

(51:52) Yeah. Wonderful. Thank you. Um Charles, we could easily go another session or two here as you mentioned, but uh sadly we've got the hour for now. We'd love to have you back sometime. We know that you're all uh you know, we know that you're all active with the community and uh know how to find us.

(52:18) I'll make sure to help get the invites out to everyone and uh and get anyone access who wants to dive way deeper into this. So, always appreciate that. Awesome. Well, Sam, thank you, David. Thanks for your community. I know you do recordings and I'm sure, you know, lots of other audiences that will hear this and for those that that are interested, we're happy to be supportive and helpful.

(52:35) We we just think it's a really cool um it's a really cool time where so much is changing and it's really fun to be part of that authorship with with you guys. Well well yeah great to have these conversations with folks who are actively building all this and uh showing us the various stages of progress too so we can track it along the way. So it's so helpful I I know to so many of us here. So uh thank you and to your team for setting this up.

(52:59) Thank you.

## How AI Is Rewiring Shopping Insights from Colin Jeavons of Nomix Group

Speaker: Colin Jeavons
Published: 2025-11-14
Tags: ai in shopping, answer engines vs search, social commerce, content creation with ai
Video: https://www.youtube.com/watch?v=qNNsLVk8_q0
Page: https://aimarketersguild.org/sessions/how-ai-is-rewiring-shopping-insights-from-colin-jeavons-of-nomix-group

(00:05) We're excited to introduce Innovative Orchestrator, an AI super agent for omnichannel advertising. We bring humans and AI agents together so marketers can conduct a symphony across channels to reach their most valuable customers. AI is not a matter of if, but how and when. We will help you solve that.

(00:32) Hello everyone. I'm David Burkowitz. Welcome to another edition of AI Insiders with AI Marketers Guild. We have a fun conversation today with Colin Jvens, a founder and entrepreneur I've gotten to know over the past few months. Thanks also to Tony Winders of the Winders Group, a longtime industry friend, who connected me with Colin and the team. Colin is doing exciting work at the intersection of shopping, commerce, and the creator economy.

(01:13) Affiliate marketing is part of this, with a through line where AI is powering and empowering much of what he's building with the product suite. It's a topic we don't cover enough, and as I dug deeper, I was excited to have Colin share a perspective. Welcome.

(01:39) Thanks, David. I'm looking forward to today and thanks everyone for joining. I'll do my best to answer any questions. I gave a broader, more informal intro, but who are you anyway?

(02:02) For everyone who doesn't know me, I'm Colin Jeans. I've worked in search technology for 27 years. I'm the founder, CEO, and chairman of the Nomics group, which owns several companies designed to create a frictionless consumer shopping experience.

(02:35) Over 27 years in search, I've built many companies. I built the largest political publisher in Europe using search technology. I built a large adtech company licensed to every major publisher. I reversed and owned the largest defense and intelligence technology in search. We owned a contract that built the semantic web, which became a bedrock for AI, machine learning, semantic analysis, knowledge graphs, and entity extraction in 2010.

(03:09) I've worked in AI for a while. One of the most interesting, rapidly changing markets now is shopping. I'm happy to discuss how shopping is changing, how important commerce is to the global economy, and areas of AI that are misunderstood.

(03:42) Many focus on the wonders of ChatGPT, Perplexity, and other agentic systems, but there are many areas where AI is heavily involved and will drive significant change in shopping. I'm happy to discuss them in as much detail as you wish. Let's get a little more detailed.

(04:16) For shopping, there are many phases: awareness, discovery, research, the purchase process across channels, customer experience after, and advocacy. Shopping means different things in different contexts. Shopping for Halloween candy is different from shopping for a new Bentley. Where are the most immediate AI applications? What parts of the shopping process are you most excited about right now?

(04:53) First, significance. Over the past 20 years of digital content and the internet, shopping has been the backseat driver to advertising. When you talk about Google and Meta as the two 800-pound gorillas, advertising is a $600 billion bucket of money. It's a small portion of the conversion value of shopping, which is a multi-trillion dollar bucket of money.

(05:28) Channels of change in shopping include search. How do you discover a product, a price, or a holiday? A year ago, most people used a browser or app, likely Safari or Chrome, entered a query, saw blue links or PLAs (product listing ads), and clicked. Search is radically changing.

(06:39) Click volumes are changing. AI agents and LLMs are changing where consumers start the journey for answers. That has a huge impact on shopping and marketers, and a positive impact on consumers.

(07:03) Another area is UGC, user-generated content. What's the impact of AI-generated content? Photos, stills, editorial, videos—short and long form—that used to cost tens, hundreds, even millions of dollars can now be generated for a fraction of the price and hyper-targeted across the internet.

(07:38) All of these are areas of AI and shopping, and they warrant deep discussion. If there's one takeaway for an analyst, CMO, or CEO of a multinational brand, it's: where will we be 24 months from now?

(08:11) I think shopping will see a huge positive impact from AI. People will discover more, see more, have more opportunities to buy, and ultimately spend more in the next 24 months than in the previous 24 months.

(08:37) In the western shopping market, I expect above a 30% increase in total revenue spent. Those are big statements. I believe there's reason to back them up. There will also be significant losers in shopping. How marketers and publishers handle changes in SEO, SEM, content marketing, and affiliate marketing—and how they reach consumers at the right place and time to drive conversion—will determine outcomes.

(09:21) One question already coming up is where that 30% comes from, and how this happens without alienating people. There's an impression that AI for shopping, especially with social commerce and UGC, floods feeds with low-quality content. How do you reap benefits without going down a lowest-common-denominator path and making things worse?

(10:31) Not everything is better for all people. Whether Meta is a great product depends on perspective. Consumers will have more opportunities to see products than ever, and more opportunities to search for the exact product at the right place and time than ever.

(11:05) If you can cut past poorly created content, you'll be able to window shop when and where you want. You'll also be able to buy with less friction. Historically, the journey to purchase—especially via search—meant lots of links and time-consuming research to get answers.

(12:02) Now, even though it's early days, LLMs are experimenting with better display and experiences. If you want a Prada bag in a certain color, in a certain place, at the best in-store price available today, you no longer need to spend much time to find it. Answer engines make it quick, with less friction and more time saved. Overall, AI is good for consumers in shopping.

(12:41) Speaking of search and delivering what people want when they want it: looking 24 months out, how much comes from Google versus ChatGPT versus platforms like TikTok and Instagram?

(13:07) You have to segment the shopping experience, which isn't easy. For platforms with hundreds of millions of users—Snapchat, Pinterest, Meta, TikTok—ask: do people like looking at products? Do they like window shopping? I think they do. They like discovering things they didn't know they needed. Do they need them? Probably not.

(14:08) Trends already occurring in China will likely occur elsewhere. With video AI—positive and negative—current influencers will present many products to their followers, highlighting things they like. That will likely increase sales.

(14:56) ByteDance reportedly generates more money per quarter than Facebook at times, and roughly a quarter of revenue is now from shopping. Does the platform generate consumption or do consumers want what platforms surface? I believe it's the latter. Ask people if they like shopping: the answer is yes. In search, Google is the goliath, with Bing as secondary. LLMs like ChatGPT and Perplexity are disrupting consumer behavior there.

(16:10) Significant disruptions are already occurring. Search engines, answer engines, and AI engines serve consumers after they've decided to purchase. Intent is there: they need flights to Miami, new outdoor furniture, or a specific product at the best price, location, and time.

(16:33) Answer engines provide better solutions than traditional search engines, which give links and force discovery. If you say, "I want the best-priced automatic cat litter dispenser I can buy today," you'll use an engine that gives you the answer and saves time. Research suggests 15–30% disruption in click orientation from traditional search engines toward publishers or marketers.

(17:42) The impact is huge for marketers. You must manage traditional SEO and SEM and also figure out what to do on LLMs. As someone who's tested too many litter boxes, with the right prompt you might not even need ChatGPT. It's amazing.

(18:07) Shifting to the here and now: it's mid-November and the next four to six weeks are big for holiday shopping. For brands and consumers, what's different this year versus last year where AI is playing a role, visibly or invisibly? What's already changing?

(19:06) Starting with brands: a CMO today lives in a more complex world than 10 years ago, even compared to a year ago. There was above-the-line and below-the-line marketing: TV, newspapers, press. Then the internet era brought SEO, AdWords, and affiliates. About a year ago, ChatGPT and answer engines arrived, plus rapid change in the influencer world.

(19:45) The influencer model was dominated by sponsorships—hire a celebrity for millions and drive massive sales. Now there are hundreds of millions of creators across channels, all recommending products. How do you deal with this model? How do you communicate with this new channel? How do you optimize communication with consumers on LLMs?

(20:25) At Nomics, we're about the economics of shopping—ShopNomics—and making the journey easier for CMOs to reach consumers. We've verticalized our approach. From a consumer perspective: are you more irritated by better accuracy and less friction, or by seeing things you may not need? In both cases, consumers benefit: free, fast paths to products via LLMs and time spent with people they admire who highlight useful things.

(21:55) Consumers are happy with increased product awareness and faster ability to buy. For marketers, it's extra workload due to rapid, radical changes. There are big wins with new audiences, but dominant brands must get marketing right or risk losing share.

(23:22) On influencers: where is AI changing the flow? These markets are evolving, not mature. Companies are finding their feet. We invested in [creator.co](http://creator.co), led by Vinard (CEO) and Rob (chief strategy officer). They are building an AI agent that enables brands to specify the types of creators they want, automating discovery, guidance, and spend with influencers.

(24:26) You're effectively connecting influencers with brands to give consumers a better experience. This will mature over the next 18–24 months. Everyone is a publisher. There are hundreds of millions of influencers; tens of millions have audiences over 10,000 and can be very vertical—angling, travel, fashion, beauty. Brands within each vertical want to influence consumers.

(25:21) As consumers, we want to know about new products in our passion areas—photography, angling, fashion. That's been true for publishing for centuries. Platforms are emerging to simplify this world. We've made a couple of bets in the sector. We may not be 100% correct, but the tide will rise: consumers will discover more, influencers will show more, and consumers will buy more.

(26:20) Devil's advocate: what about those who loathe shopping? I'm not advocating that something is right or wrong. The majority of platform users today like shopping. Personally, I'm only on LinkedIn for work; I'm not the audience. We are in a changing world with new tech, positives and negatives. Over the next 24 months, regardless of personal preferences, behavior is changing. My kids like shopping.

(27:27) If you hate shopping, how does this help? Even if you dislike shopping, you still shop to sustain life—household goods, food, cars. We are creatures of consumption. Some love the experience; others buy out of necessity. If you don't like shopping, you want answers faster with less friction.

(28:57) New AI, particularly answer engines, makes life easier: quicker answers, faster purchases, then back to what you prefer doing. For those who love browsing, they already spend hours daily in social apps. Some will embrace more shopping content, some won't, but the net effect is more purchasing.

(29:59) A good point from the chat: people may not trust AI tools to recommend products, but they may trust people they already follow. Exactly. Influencers with integrity who recommend products they actually like will drive higher conversions than those who promote everything.

(30:41) One big tool for influencers is AI-assisted content creation. Content creation is time-consuming; AI's role here isn't discussed enough. The ability to create content—even video—at scale and speed will increase radically over the next 24 months.

(31:43) There will be good and bad actors. Well-intentioned influencers who test products they recommend will drive higher sales than those who promote indiscriminately. Bottom line: consumers already like much of this. They will consume more and buy more. Fundamentals of advertising are reach and frequency. Familiarity drives consideration.

(33:18) Some brands will avoid AI-generated UGC for fit reasons. For example, a brand like Dove that campaigns around real beauty might avoid AI in visible content but still use AI behind the scenes—for GEO, finding influencers, or other processes. You can embrace some parts of AI and not all of it.

(33:52) Agreed. Not all models will be AI. Certain components will. There's no avoiding new technology. The smart path is how you use it to achieve your objectives. You might use AI to find the right influencers and use LLMs to communicate policies like "we don't use AI imagery"—both can coexist.

(35:34) Let's take audience questions. Adam asks about dynamic landing pages and personalization at scale. Do you see systems built end to end in-house versus best-in-class components? How will this settle out? Think at a high level, not just agentic workflows.

(37:08) Change is opportunity. Will there be needs for thousands of landing page variants? A landing page is content creation on one page; AI will help automate that and bring it to life with sight, sound, and motion. How you get traffic will also change. You may have mastered SEO/SEM and arbitrage to drive traffic and revenue via CPM, CPC, or CPA.

(38:16) For PE firms: in media, there are huge opportunities for companies with rapid growth. For brands, PE might acquire underperformers and apply a proven AI and influencer playbook. Mastering the new world of content, LLMs, AI generation, and influencer marketing will be a competitive advantage, much like Coca-Cola's early Olympic sponsorship.

(39:19) In any changing landscape, there are winners and losers. We've grown quickly by helping brands verticalize traditional publishing, new-world publishing, programmatic, and LLM/AI publishing to drive conversions and sales.

(40:20) Alexander asks about zero-marginal-cost content and avoiding "AI slop." If AI can make infinite content, how do brands deliver value and survive in a world of infinite content?

(41:58) Eric Schmidt once discussed jobs in an AI world and emphasized creativity. Content still requires an editor, much like journalism. Someone must decide what is seen. The role of creative judgment is unlikely to be fully automated.

(43:37) Example: a brand spent $500,000 on a one-day shoot with a famous runner. They wanted to produce thousands of assets from the footage. Now they can, at low cost, without disrupting talent or schedules. But talented people still need to decide what resonates. AI data and surveys will inform decisions, but someone still chooses the smiling cat over the gorilla. The artist in the machine is still required.

(44:43) Daniel asks about interfaces. Do you see invisible interfaces—WhatsApp, voice, email—growing versus direct LLM usage?

(45:17) Invisible interfaces will be huge. Conversational commerce will be huge. A product to try is [wprflow.ai](http://wprflow.ai), a strong speech-to-text engine you can plug into devices and phones. The speech-to-text world has been discussed for a long time; it’s now arriving.

(46:22) Invisible engines and ways to communicate with AI are real. Three-year-olds will be taught by voices from speakers, able to ask any question and get answers. In 10–15 years, children will use these tools in positive ways. As parents and grandparents, it's natural to be concerned. We are living in one of the largest experiments in human history, with AI in the living room, helping create content and access knowledge.

(47:32) It's going to be change, scary and exciting. Let's hope we make it a positive.

(48:01) Change is scary, and AI brings radical challenges. But there can be positive outcomes from incredible technology. Yes, it's disruptive. If embraced, it can be positive. Social media has negatives and positives. This is an opportunity for brands to embrace new tech and for consumers to discover more information at their fingertips. It's up to responsible actors and governments to ensure good management, policing, and regulation so this becomes a net positive for humanity.

(49:20) It's important to focus on elements we can control. Many in this room are building, analyzing data, creating content, or learning. How do we harness this and steer work in the right direction to add value? There are things we control, even if not everything. We'll watch how the holidays play out and continue the discussion. We'll share links in the AMG Slack and on YouTube. Thanks to Colin for being the main event. Have a great rest of the week.

## How AI Is Leveling the Playing Field in Marketing

Speaker: Jeff Sundheim
Published: 2025-11-07
Tags: meaningful roi, ai use cases, creativity multiplier, ai native processes
Video: https://www.youtube.com/watch?v=DGlSjDZVRaU
Page: https://aimarketersguild.org/sessions/how-ai-is-leveling-the-playing-field-in-marketing

(00:05) Hey everyone, I'm David Berkowitz back with another edition of AI Insiders with AI Marketers Guild by March and got a really fun speaker today. You can see his face popping up on the slides and uh and in the video feed, Jeff Sundheim from Google and an AI strategist and uh and perhaps even evangelist there.

(00:29) I actually met him through another guest of ours who's on the call today, Mark Coleman and old friend and and our paths across so many times of I even saw him show up in my building one day because I was neighbors with his daughter and uh and my daughter went to school with his grand daughter.

(00:48) So all kinds of connections actually brought Jeff here and when I saw Jeff speak at an event that was hosting and and beyond Jeff's amazing title and work I was inspired by some of the work like you know I I think sometimes I'm seeing a lot that's out there. I always know I'm just scratching the surface.

(01:15) And even in work with Google and they they know a few things about publicizing work and making sure uh brands and agencies know about it. I learned a bunch of new things and so Jeff, so excited to have you share this with uh more of our community and and so we can all learn a bunch of new things from you today. Welcome. Well, thank you so much, David.

(01:37) Thank you for um inviting me to join today and you know it is funny you talk about all these connections and so on even with AI and so on there's you know the world is about human connection and all these different uh different people and I think one of the cool things about AI and just the waves of technology we've seen over the decades or centuries is that hopefully it frees us up to do more things that we love and to make more connections and so on.

(02:03) And so I think that's really the the most important thing. So but anyway, thank you for having me here today. I've been at Google uh for close to 18 years. Was a double click before that and Apple before that. So I feel like a dinosaur, but it's um it's it's super exciting. The world is constantly changing.

(02:24) People say 18 years at Google, what must that be like? And I sort of liken it to that movie 50 first dates because it's I feel like I've had 18 first years at Google because every year it seems everything changes. And so anyway, that's sort of the scoop. The title of what I'm going to talk about today is the new level playing field because that's really what I think it is, okay? is that AI is uh providing us and I'm going to be at Penn State my alma mater tomorrow.

(02:55) Um they're not having the greatest football year but in truth um it is the new level playing field. This is a shot of Penn State's field because in reality um as anxious as a lot of folks are whether you're 18 or 80 I know there's a lot of people that are anxious out there. Is AI going to take my job or is a going to take my grandson's job or whatever.

(03:20) The real story here is that you know AI present any anytime there's disruption like this whether it was the internet or mobile or the printing press 400 years ago there's disruption and it creates tremendous opportunities um and granted certain things do get disrupted and go away but there are tremendous opportunities which we're going to talk about um because and I think those folks who embrace it and are scrappy and say you know what everybody's going to have access or does have access to the same tools. Now, so what is going to differentiate me from

(03:52) somebody else that has it? Or if let's say you're in the lumber business, you're 84 lumber versus lumber liquidators versus Lowe's versus Home Depot. What's going to be the difference? How do you differentiate yourselves? And it's that because you're all using the same tools that's going to be the true differentiator.

(04:10) I want people to feel uh open here to to asking questions and so on. this should be a conversation, not me just speaking all the time. So anyway, that's sort of the way I I think about it and why I'm saying the new level playing field. Um, so it is the new level playing field. Everyone has the same access and your skills and this is, you know, everybody's skills in collaborating with it will set us apart and will set each individual apart and each company apart.

(04:41) Um, and so before we get into that a little further, I'm going to talk about what I do, my passion, uh, in addition to work is I'm a sculptor and there's definitely a strong AI component to sculpture. Um, I don't know if anybody is familiar with this sculpture.

(04:59) This is a piece I did that was in Riverside Park uh, a few years back at 145th and the Hudson River. It started out, the right-hand image is how it started out. It was 8 in tall. It was this is what we call a Mckette or a model and I made that out of flashing that you buy at Home Depot for roofing and I this is the proposal I made to the uh Riverside Park Conservancy and then they accepted that.

(05:25) Um they loved it. Uh I was very gratified that I was given the invitation to produce it and then it went to 13 ft. Now in the process it's essentially it's a gigantic um sun dial and you'll see here that it it's aligned the shadows that's developed with looking at the shadows that are cast at the four different solar events throughout the year the two equinoxes and two solstesses and what we did was I with a collaborator I had a collaborator who did the these benches if you will the I created the main sculpture and

(06:01) then he created in green benches that corresponded to the shadows that are cast at the four different times of the year. We made this in 2016 and the process took about 6 weeks. Now with AI that process would be a matter of just a few days. So uh this just shows how AI could take a process and a lot of grunt work that really uh doesn't benefit anybody all these calculations and can condense that so that I could spend more time and actually uh fine-tuning the sculpture thinking about other things that are going to make the sculpture uh more impactful. This is an

(06:43) image of what the final sculpture looked like on the West Side Highway on the that park. I don't know if anybody passed it over the years, but there's a bike path there. It's now down in Key West. Um, so this illustrates the fact that even in my passion job, I use AI.

(07:06) And this this is an example of a new uh com rendering I did using AI for a commission down in Key West as well. I'm going to play it real briefly so you can see the type of I showed you that model that was 8 in tall. Now this with AI is how I'm going out and showing CL uh different park different sculpture parks what's possible.

(07:32) So you can see the level of detail was pretty cool much. So that's really where we're going in terms of technology. Welcome to the future of AI our technology. Let's go to the next. Welcome to the future of AI in digital marketing at so this is something I created for my presentation tomorrow and that that was done using a tool called Google flow.

(08:03) I'm not sure anybody's familiar with that but Google flow essentially I took a photo of a presentation I did and I added this prompt. Make the man swirling a magic wand while animated stars come from while he's singing. Welcome to the future of AI and digital marketing at Penn State. You probably everybody in this call and I know you're very familiar with AI.

(08:22) Uh but this these are the types of tools that I think are what are the future and just it's the sort of scratching the surface as to what's possible. Now imagine you get somebody who's doing uh playing with AI. You get somebody just out out of school who's doing some really cool things.

(08:44) they could develop a short short uh content movie or a full fulllength feature movie basically in their dorm room. You don't need to have funders and big production companies behind you anymore. You've got the ability to do this yourself or at least pitch an idea in the ad agency business. This is changing everything where, you know, obviously creators are a big thing.

(09:07) Social media, it's putting power in the hands of of regular consumers, which is really turning marketing on its head. And and Jeeoff, it looks like it's stuck on the eagle uh visual. Oh, okay. Good to know that. That's sort of I'm surprised that it Okay, that's And we're still You're still not seeing Beyond Eagle. We're still seeing the eagle.

(09:30) uh 360 view here. Okay, let me let me go back and stop sharing and we'll we'll do this again here. This just goes to show how even the best laid plans. Let me screen share again here. Yeah. So I I I Okay, so back. So basically this is Sundar's quote. Um, and it's basically talking about how AI from a Google perspective, um, in terms of search is revolutionizing, you know, what we're doing specifically at Google.

(10:07) Uh, essentially it's allowing, um, and I don't know if has anybody familiar with AI overviews and AI mode in search. So that has significantly uh if you saw Google's most recent results that has increased um the the usage there is tremendous and I don't want this to be a commercial about Google but I want you to understand why we are so involved in it and why we think it's you know people say is chat GPT going to kill Google and you know you never know 30 years ago Bill Gates uh May of 1995 Bill Gates sent a memo It's a senior level manager saying that it's entitled the internet tsunami and

(10:48) he basically was saying we need to get on board with this internet thing or else we're going to get behind the curve and even in 1995 when Microsoft arguably had the largest uh share market uh had the best brains had so much resources and the department of justice was after them if you may recall uh they still three years later after marshalling all their resources were blindsided by these two students from Stanford that eventually turned into Google. Um so I think even today no matter what Google's doing, no matter what chat GPT and OpenAI are doing, we

(11:28) could all be broadsided by some upstarts. And you know what that's evolution, but this is you know queries are getting longer and more complex. We're seeing that. Absolutely. Um and so and I everybody on this call probably the way you it used to be you'd put two or three words in now it's easily 10 or 15 more words.

(11:56) Um and that's what's sort of driving all these different AI functions back in April of 2024. And anybody on the call familiar with tokens probably to some degree but maybe not on the most technical level. So tokens and you know I'm not a I'm not developing code here but tokens are essentially the unit of measure by which AI compute is is is build if you will Google cloud builds its clients based on number of tokens they're using and tokens are basically it's a unit that a video a 30- secondond video might be 200 tokens that might require that much compute um a quick

(12:37) query that you you know say write this uh write this resume re redo my resume might be five tokens well in 2024 in April of 2024 there were 9.7 trillion tokens used across all Google surfaces and by say Google surfaces I mean Gmail uh YouTube all these various things a year later in April of this past year there were 48 80 trillion tokens used that month.

(13:11) That was a 50-fold increase. In October of 2025, there's 1.3 quadrillion, that's not a number I'm very familiar with, tokens were used. We can see the exponential growth here. And this is truly probably the best measure of the adoption of AI. Uh and this is why data centers are you know being built at such an extraordinary rate and why uh all these you know private players like Google and Microsoft and Amazon are getting into the utility business the power generation business because data centers require so much power. I don't didn't plan to go deep in that, but this this is what's happening.

(13:54) And the more that consumers get a taste of what's possible with AI, um, and I don't think we've even scratched the surface, the more that's growing. Now, you might say what's going to, you know, how are we going to sustain this in terms of power consumption? And I think equally uh in terms of exponential growth is the the technology to do this in a more streamlined power consumption way.

(14:26) So I you know that is the hope and that's where I think things are moving is that we're going to consistently see this be far more power efficient. But it's a major issue and anywhere I go and speak around the world, the hands get raised speaking about a data center that's being built, you know, within the vicinity of that venue and what how it's impacting culture and so on and community.

(14:59) I want to stop here because I feel like I've been talking a lot and I'm going to get continue on, but any questions or thoughts here? Anyone want to chime in? Well, I can continue, but I Yeah, we Well, yeah. And uh and we've got some great examples coming up. So, yeah, why don't you keep going? Great. So, I'm going to talk, you know, you mentioned AI overviews, and we had a you know, we've rolled that out.

(15:18) More than 100 countries uh are doing using AI overviews. This slide is now obsolete. As of last night, I saw a number that we're now at two billion global users every month. So, extraordinary. Um, and this is what you know it's it's going from a short query men's running shoes to how to pick the best men's running shoes with padding, cushioning, and shock absorption for a beginner runner on asphalt.

(15:43) This is the type of thing that I know I'm searching for and a lot of consumers are and this is why we have to sort of be on top of that. Um, we at Google do um and we everybody everybody's got to. And then it's especially things like when you know when you when you don't know what you want.

(16:03) That's just a specific thing as to I know what the running shoes I want are. But let's say I'm on the subway or I'm somewhere else and I see something and I want to I I don't know what it is. I don't know the name of it. All I know is I want to get more information. And this is where Google Lens and other technologies, you know, iPhone's got a camera. Everybody's got this technology.

(16:22) This is where Okay, baby. You can have a folks. Can you wait as you want? Can you wait? Can you wait as you want? Can you breathe? Can you do like this? So, essentially, this is called circle the search and it's it's the abil it's the ability to, you know, you're on the subway. This years ago, I I got a watch.

(16:51) Um, I saw somebody in the subway wearing a very cool digital watch. And at the time we didn't have this technology and I think sometimes it can be a little bit um, creepy if you do this, you know, just with somebody and you there you don't get their permission. But I had to ask this guy, where do you get that watch? And he told me the brand and he got it on Amazon.

(17:09) So I went and got the same thing. But if you see something on the street, you see something on a store, whatever, this is this is what folks are using. And 20 billion users now are using this same technology. And it's opening up a whole new level of search that we just didn't think of before.

(17:34) 25 years ago when Google started, there was that little search bar, that search window, and you typed in two or three words. Now, consumer behavior is radically changing. And once they get an appetite for it, it's changing even more. So when I talk to a lot of companies, they and that's what I talk to I talk a lot of to our largest advertisers. Um they are saying well what should my AI strategy be? And really that's not the question.

(18:00) I think we really need to get to really what is your strategy need to be that AI can enable? So, let's get back to your core focus. Whether it's if you're selling running shoes, if you're selling conferences, um or if you are, um I'm on the board of the Art Students League. If you are, uh trying to get new students to come in and study print making or sculpture, what should your should your strategy be? And how can AI enable that strategy? Because it really needs to be rooted in what you're trying to do. And then let's think about AI second second uh second

(18:40) focus. So I want to show you I want to take uh show you where AI is in terms of graphics and video. And I'm sure we're all seeing it. But I wanted to this is something cool that our ad agency did based on a spot they created for us at the Super Bowl back in February. They basically created the spot in the standard normal way with normal photo shoots and so on. And then as a fun experiment, they did it in AI as well.

(19:09) They that AI uh version did never never ran. You're only going to see it here, but they did it to sort of illustrate what's possible now with AI. I'm going to show you both videos at the same time, and then I'm going to ask you if you can tell me which one was generated with AI. So, here we go.

(19:32) Let me hopefully this will come through on the uh in Google Meets. It comes through. Let's see if it comes through here. My grandfather taught me everything I know. Our little family businesses worked their tails off. We never miss a detail and those things matter. The legacy and the tradition and the craft. It's what I live by. It's nice working with your family.

(19:50) We're a twoerson team. We're also busy moms. The financial side can be very daunting. If our schedule gets messed up, everything falls apart. We don't have a lot of time. We're not professional. with H3. Using Gemini has been a gamecher. Gemini in Google Docs helps me write website descriptions.

(20:15) AI allows us to make quick business decisions. It translates everything live. It was magic the first time I saw this. Gemini and Gmail really lets our staff save time drafting emails. It makes my response more concise and so much better. Marking up a concept or we can do that in a matter of minutes.

(20:34) Yeah, we have time to keep doing what we love and it opened up the world to me. This is a beautiful thing we're doing and we're proud of what we do. Okay. So, let me ask you, which do you think was generated with AI? Top. Any other votes? Some for the bottom here. Yeah. Okay. It was the top that was generated with AI.

(21:03) Uh I'm curious what made you say the folks who voted for the top, what makes made you say that the lighting was it was smoother? Yeah, it was more perfect looking. Yeah, it was very perfect looking. Yeah. Right. Jeff, was was the was the bottom one provided as a reference to create the top one in in certain cases? Yeah. I say certain parts of it. Yeah. So it wasn't just a prompt.

(21:27) there was an actual reference video image something used in certain cases. Yeah. Yeah. It wasn't you know that was just part it was a very infall process but yes I mean honestly if you didn't know that if you gave a script to a production team and you gave a prompt to Gemini and neither had ever seen each other then I think you'd have a very different outcome.

(21:50) Yeah. Yep. Yep. Exactly. Well, so that's the the ways some of the key ways to tell was um the one thing one of the things that at least Gemini doesn't do a great job of and I think you know a lot of the LLM same is that handling text at this point and you'll notice there was one image where somebody was wearing a t-shirt and they the verbiage on the t-shirt just didn't make sense and the other one was uh there was a child in the bottom video and Gemini will not render images of children. So at this point, so there was that's sort of two

(22:27) two cheats there. Um so let me get back and see if we can move on to the next slide here. This is this we go. So so essentially, you know, we see AI is truly unleashing the full power of search. As I mentioned, four 20 billion visual searches using Google Lens a month. Uh and notebook LM.

(22:53) Has anybody used Notebook LM? Someone's even DMing me about it in the chat today. It's like, yeah, I've got a lot of Nove fans in this room. Great. Yeah, somebody said every day. Every day. Awesome. Tell me, what do you use it for? So, um, I'm a consultant. We're part of a 140 person consulting firm and we have different clients and each client, um, it's just a given.

(23:16) If we're going to go in and do an assessment, there's lots of stakeholder interviews. All the notes for that are just the baseline for it. if we're going to go in and do branding like having that messaging architecture and all that tonality and like it's just a the perfect thing. Um and then as we go along just on the project so like it you know as you know it it's like you can do all the docs sometimes you can do a few of the sources so like you sort of want more nuance over time but it's a life ch game changer for internal project cadence. Absolutely. Absolutely. This is so know I don't need to say any more about it. This is this is one of

(23:53) Can I Can I ask you something? I'm going to put you on the spot and it's okay if this is not known or you're not at liberty to say, but in case it has been out there somewhere, at some point is Google going to combine notebook LM and Gemini so I can have like the full AI access and all the folders and organization.

(24:12) That's what I can't wait for. And then I'm like all in with notebook. Yeah, I I can't, you know, first off, I don't know that. Oh, you know, I can quite easily say it's not it's not that I won't comment. It's like I don't know that for sure. And they're smart not to tell us. I hear you.

(24:30) I hear you. It's so funny because some of these productivity tools that, you know, they don't seem like game changers. They don't seem like they're it's going to be a revelation, but this is the kind of thing where we're seeing AI inform and power a lot of these cool things. It was like Google Docs years ago.

(24:47) You know, you'd walk in and Google Docs sort of changed I think the the way of collaborating and I know that Microsoft's got the capabilities too, but it's that type of functionality which has sort of changed the way we work and you know plan trips together and do these things.

(25:05) This especially I agree with you that notebook LM is one of those things that all of a sudden it it allows you to do so much more and it's an example of okay AI giving you the opportunity to do and create and have so much more options because you've got this stuff at your fingertips. But it's the UX that works so well there. So yeah, studio IDE environment. I love it.

(25:31) Hey, you know what? You got you're you're be the pitchman here. I I have a question regarding search. Does Okay. Do people's queries affect search results or ranking? If a lot of people are asking about a certain topic or a product or brand, will that affect the ranking of search? Well, if the more that is the more there's a several different factors that go into, you know, ranking of a search result.

(26:05) Um, but one of them is the more folks that are, you know, referencing it um and find it relevant. uh and and as well the other one of the other uh factors is also other sources that link to it that reference it. It's all about sort of you know credibility and relevance and how many you know and also analyzing the content of that site and saying how relevant is that content.

(26:38) That's where the you know the the the search you know the browser is sort of crawling all the content. So it's several different things. It's absolute true relevance of content. It's then how many people are going to it and then how many other sources are referencing it. And it's sort of that's that sort of kaleidoscope of what goes into the ranking of a search result. But I got to tell you, I'm not a search specialist per se.

(27:10) I come from the other side the the world, which is more the uh the paid uh display ad perspective. But that's essentially the the rudimentary uh facts about search. Jeff, I got a question. Sorry. Um, you know how um are you familiar with Hey Jen with the you can create avatars and stuff like that. Is ever looking to create something you know how they have the voice activated uh avatars? Are they looking to have actual avatars at some point? You know that's a good question. I don't know.

(27:50) I don't know all the things on the road map for notebook LM but it's a good question and one of the things about all the products at Google one of our key innovation concepts is launch and iterate. I don't know if you're familiar with the the term here, but it's essentially and it's it's pretty intuitive from what it sounds like.

(28:15) It's, you know, like when Gmail was introduced, um it was Google announced at the beginning like this is a work in progress. Um and we're going to be having like weekly updates and so we want to hear from users about what you what you want and what you need and how we can improve this and where the bugs are. And that's exactly what we want to hear as well for things like Notebook LM. It's like, you know, we're putting it out there.

(28:38) We're sort of moving at the speed of light. Um, we feel like we are and I think we are. Um, to get this stuff out there. Um, and then but it's all, you know, like we're putting the wings on the airplane as it's going out there.

(28:57) So we want to hear from users as to how can we make notebook LM how can we make circle to search? How can we make all these things more relevant, more impactful? So I love that idea. Going to be my next question. Um how how can we be part of I guess the beta of all these things that are you know is there like a beta group that you guys um reach out to? There's not to the best of my knowledge there's not a beta group for notebook LM.

(29:23) Uh there was for instance there was Gmail was in beta for some time. So I would do the same thing that I would suggest you do is go I'd go online and do a quick search. Is there you know how do I provide feedback for notebook LM? How do provide feedback for all these different products? There's no beta group per se that I'm aware of. Okay.

(29:42) So when you go guys go la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la launch a um a specific product or have an idea there's no like focus group that you know you guys Well there may be there there definitely may be I'm I'm sure there is you know UX a lot of UX research and that type of thing absolutely um as to the process each one I think is very different depending on the product um and you know whether they've got some sort of a a beta an alpha and beta methodology or whether

(30:13) they just doing that type of sort of closed research with UX design. Everyone is super different. Okay. And you've got imagine a company as big as ours. We've got all these different groups and units and business initiatives that are out there. Um and but I would I would do a search on that.

(30:38) I would suggest you do that and say how can I provide feedback on notebook LM? and I'm sure you're going to find uh something in that regard. So, this is great. Yeah, somebody somebody put it in the chat. So, thanks. That's great. So, and this is just an example, but I'm not going to go through it because I think you guys are the most powerful testament.

(30:55) I'm presenting this tomorrow at at Penn State and it's just an example of I just put in a link to the uh the crearyy which is literally a a crearyy on uh Penn State started as a land grant college and it was an agricultural college and so there's a crearyy on campus and I it generated an 8inute uh podcast talking about the Penn State crearyy but I'm sure we've got a lot more relevant interesting stuff as well that you guys have come out with.

(31:21) I'm going to show some examples that David referenced because I think they're really cool and it comes from my background in terms of uh personalization and using uh different data sources to really drive home concepts. So I'm going to show you some things that are really net new and I think sort of revolutionary with AI stuff that just wasn't possible before.

(31:44) So the first thing, anybody familiar with Pods, the container company and storage company? Probably everyone is. Um, they came to us about a year or two ago and they said, "We want to do something cool. We want to use Gemini for something cool." We said, uh, it was part of a program. We had we had sort of put the the initiative out there.

(32:08) He said, "Anybody, you know, anybody that any ad agency and clients that want to do something really interesting with tech with with our new Gemini technology, come let us know." And they said, "We do want to do this." And they were part of our Lighthouse program where we were got a few different advertisers and agencies together.

(32:25) and they basically in the course of several months developed with Gemini um a an ad unit that uh I'm going to show you now which basically uh makes uses the pods uh the side of the truck as the world's smartest billboard if you would well and it's sort of fueled and informed this digital billboard by a bunch of different third-party signals that are using weather time uh traffic, uh, subway congestion and so on.

(32:55) So, I'm just going to play this and I think it would show you that what's possible when you completely think out of the box. Smart it would know where it was beginning here years. The pods container has been more than just a moving container driving the streets of America. It served as a giant billboard reminding people of pods.

(33:13) So, we teamed up with Google Gemini and turned a pods container into the world's smartest billboard. A billboard so smart it would know where it was, what time it was, traffic conditions, weather conditions, subway delays, and then generate hyper neighborhood specific lines in real time.

(33:40) So they did that uh over the course of the they that ran through the entire city. There were 1,600 headlines that were developed uh and you know they copywriters were involved in working with Gemini to develop those headlines which were basically uh rooted in the pod's sort of tone and manner but headlines you know based on all those different signals.

(34:08) It's the type of thing that you know whereas they might have had one copywriter working on an ad campaign in the past they had to have you know that one maybe they had like one or two actually more copywriters doing a lot more interesting varied work because this creative demanded it. So I think that you know this type of technology sort of gives us and gave pods and their agency the possibility to do a lot more and open up a lot of new possibilities.

(34:35) So I'm going to show you now results because that's the most important thing. Actually didn't I don't think I've got that here but the results here they had 66% more website visits and 33% um more signups um for you know just actual conversions. So that was pretty strong.

(34:57) So whether you're doing something just for the sheer nature of AI or whether you're doing something to drive results, obviously we're all trying to drive results and this definitely got that kind of interest. This is another example using just weather signals and in this case for Nertk. So, Nerkex a migraine um medication and what they found with this is that barometric pressure is a key indicator of uh when there's going to be migraine onset.

(35:27) And so, uh, Nerkch used, uh, weather data, specifically barometric pressure data to sort of identify and they developed using Google technology and voiceover technology different ads that were customized by region and would be triggered to run based on barometric pressure. So, I'm just going to play an example of what that sounded like for one of the key areas. And right now we're still seeing the pods example.

(35:54) So migraine weather alert attack that. Let's just stop sharing and go back to I don't know what's it's really glitchy. We we we got to clearly use meat from now on. I think that's the moral of the story. Yeah. Yeah. Um so let's go back to and if nothing else this was my uh opportunity to sell Google Meets. Um no joke.

(36:21) So, let's get back to let's go back to let's see if everybody can see that. Can you see now the Nerkch slide? Uh, yeah. Great. So, here we are on the Nerkch. So, let's I'm going to play an example of what what this sounded like. Migraine weather alert from Nerkch. Hello, Austin. There's a change in pressure heading your way. Take care. This may trigger a migraine.

(36:43) Okay, that seems pretty pretty simple, but and it it the ability to do this at scale over 200 different DMAs uh using this technology. This was something that was pretty straightforward. And then the great thing is you're able to test. And so let's say we do te several different messages um and we sort of see which one's working, which one's not.

(37:12) This is gives marketers the way to be able to do this and have a larger test and learn framework without putting a tremendous investment in. Turns out this worked really well for them. So they have continued to to do this technology. So that was just first example using third-party data signals. This next one is using firstparty data and personalization and several different and some of these are less tech than others.

(37:36) But for instance, SAS um back during CO you know and all the airlines of course were just absolutely devastated in terms of uh their business. But after co as they were coming out of it they said you know we need to to uh figure out a way to get more uh business from our existing customers and so they used uh a lot of our predictive modeling technology which by the way predictive modeling is more not so much Gen AI or it's really more old school machine learning but they they use that to sort of understand more about their customers and what they

(38:10) found was a lot of their business customers especially uh liked, you know, they had other facets to uh their travel and their interest besides traveling for work. And they so they created uh SAS created several different ads to appeal to their different customer profiles, if you will, or personas that emphasized things like fine dining or extending their trips for business.

(38:42) Um, and they ran these ads uh specifically to those types of customers and they saw a pretty significant lift, a 34% inc increase in online bookings. The ad creation in this case was not AI AI generated. uh and it's a fairly low tech example, but it is the fact of using consumer insights, using your first party data to really understand and find new patterns and really figure out how to capitalize on that.

(39:11) And then the third example or the third group is agility and personalization. And this I think is these examples are I think sort of the the wow and the sizzle beyond the the pods example of what's possible with personalization and how you can sort of do create concepts that are really cool. So this first one's for Cadbury the chocolate company that you know we have it to some extent here in North America but it's big in AMIA and India.

(39:42) This particular example is a campaign they did in India. um just coming out of COVID, a lot of the small retailers throughout India were devastated as well uh because a lot of a lot of people just did not go out and so these small retailers just their businesses were were tanked and so Cadbury came up with a way to generate uh interest and traffic for these smaller retailers.

(40:08) So they took uh probably at least in the Indian market one of the the best known actor in that market Shah Ru Khan who's a very famous movie star there and they took his likeness and his voice with his permission of course um and created technology where he basically was the pitchman for any number of different uh small retailers and I'm going to show you this example but it's all fueled with AI big brands have big money to hire famous people but not the small guys and they are the ones who are still hurting.

(40:45) Presenting Shah Ruk Khan my ad. Welcome Shah Ruk Khan. He almost certainly has more devoted fans than any other movie star in the world. Biggest movie star in the world. Forbes have called him one of the biggest movie stars in the world. We help small businesses by making Shah Ruk Khan the world's biggest movie star their brand ambassador.

(41:23) We used machine learning to recreate Shah Ruk Khan's face and voice to take the local store names in the ads. Choice of fashion say heap. Royal fashion say he MK clothes say he pass Lakshmi collection say heapro different versions of the same ad with local store names were targeted as per the pin code of the viewer showing them only the nearby stores but it is impossible to cover all the stores.

(41:54) So we gave the power to the people to create their own version of Shah Rukhan my ad. Any small business owner could promote their stores through their own social media networks like WhatsApp forwards and other social media pages. It is Shah Ruk Khan selling your store man.

(42:18) If so if you're a smalltime retailer or a merchant what an amazing this is pretty cool wouldn't you say? um the fact of being able to sort of democratize uh some of this technology that up until then you had to be a large advertiser to get a pitchman like that. So just wanted to share that. Hopefully now I'll be able to switch to the next slide. Uh but we'll see based on bottom line is not only was that sort of an impressive tech perspective, but there were 130,000 different versions of the ad created with 30 million ad views and a 35% jump ultimately the big the biggest important number metric in Cadbury's business. Um, and then lastly

(42:56) in storytelling, I think there's a big example. No marketing conversation would be complete, at least from a large client perspective, without a company like a Nike. And Nike, who arguably does some of the most coolest creative and gets a lot of views and impressions and traffic, they created something completely out of the box that was not possible using before AI.

(43:23) And they basically did it around um Serena Williams who is arguably most impressive athlete uh in the past, you know, past few decades. And they basically took footage of her um 1998 Grand Slam win and they so the 130,000 hours, you'll hear about this in a second. And they did that versus her 2017 last Grand Slam win.

(43:51) and they created a virtual match between the two versions of Serena. So, I'm gonna play this. And yeah, we're still seeing Cadbury. So, okay, let me stop sharing and we'll get to the other one. Okay. Can you see the Nike logo? Yeah. Yeah, Nike's up now. Great. So, they created this ad, which I'm going to show you.

(44:12) Serena Williams isn't just the greatest tennis player, she's the greatest athlete. We wanted to look back at how she elevated her style of play over time. Through archival footage at Advanced AI, we generated 130,000 games between two Serenas from two different eras. 1999, the year she won her first Grand Slam, versus 2017, the year she won her record-breaking 23rd.

(44:36) Let's see how Serena evolved as a champion. From the best in the game to the best ever. In tennis, there are no bigger dream matches than this one. One Grand Slam title against 23 Grand Slam titles. Here we go with game one. 1999 to serve. So, we're not going to play the whole thing.

(44:55) In fact, just, you know, I give you and I want to show you an example of what it looked like. Can anybody guess which version of Serena 1? 2017. Any other guesses? The biggest tennis geek on the call says uh the later one. Any other guesses? She altered who went one for 99 in the chat. Okay. Um it was 2017.

(45:25) So it just it just goes to show um experience uh beats um beats the agility of youth any any time. But in this particular case, this is this is how it did. I'm going to stop sharing and then go to because I know it's going to happen again. We're going to lose this. Um let me just Yeah. And and we're getting close to time, too. So Yeah.

(45:43) So, let me just I'm just going to wrap this up because we are close and perfect on timing actually. Um, is that they saw Nike saw a 1,000% jump in video views over anything else they'd ever posted. And that's Nike who's posted some pretty impressive stuff. So, this just goes to show that you know what, when we do things that are over the top, AI allows us to do things that are we've never thought about before and can create extraordinary results. We'll sort of leave it there.

(46:12) One thing I wanted to plant the seed is that there's a lot of gen Genai tools out there. I'm going to skip ahead here a second. Um but the the bottom line is there's a lot of Genai tools that users consumers have um access to. Uh but you know what they could create some pretty if you if you have a you're a marketer and you've got branded content and now consumers can do mashups of it.

(46:39) It's a whole new world which it could be a blessing or a curse. It could be a curse because if you really care about your brand guidelines, they they could be doing anything with them. It's a blessing because if you do it right and you uh think about it in the right way, you can create a whole new cadre or legion of creators and and fans, but it needs to be thought about.

(47:04) I'll leave it there. This is how I can be, you know, how I can keep in touch on LinkedIn. Any questions or thoughts here? What is the theme of your Penn State presentation? It's basically the same sort of conversation, but it's going to be more on this whole uh the new level playing field and as students or as faculty, but mostly as students, what do you need to be thinking about um in terms of being competitive in the marketplace? uh and it's sort of thinking about how to collaborate more closely with AI, how to be thinking of, you know, working with AI and

(47:44) thinking about being scrappy and using all these AI tools to differentiate yourself and when you get into a a role, how to differentiate your company from another company. One of the things we talk about here is that in that that intersection between culture and data and you know what Penn State and Nitney Lines are talking about is what's happening with James Franklin today and you know those kinds of kind of personal things that actually are top of mind and how does your data kind of address can address those fleeting cultural moments.

(48:16) Absolutely. And how do you be agile? This is where social media has become so key is because people want to hear, consumers want to hear about how Crest or Tide or whatever their favorite brand is, how they're relevant to these things. Absolutely. Couldn't agree more. Interesting stuff.

(48:37) I have a I have a geeky sort of somebody who writes about Any other questions or thoughts? I've got a question, Jeff, if I may. Sure. Um are you able to say um like how much what percentage of spend major brands are putting into AIdriven advertising and is it like mainly coming out of a testing budget or or a dedicated budget? Other words like like you know in early days digital was kind of testing and then it became dedicated 20% 25% whatever.

(49:03) Good question. I I I don't think there's one first off I don't think I could give you an answer. Uh but I also I don't think you're allowed to give you an answer but I don't think I can give you an answer because I think so many of our ad units and formats are AIdriven you know whether it be AI max or performance max or demand genen so much of it everything's got AI built into it and it and not just behind the scenes but it's key part and parcel to audience content bidding um so I mean and AI is enhancing our experience here.

(49:40) But what I but what I'm what is there a way to say like um you know for the the Cadbury thing or the Sherup Khan thing or then Serena versus Serena thing like how much are brands dedicating to to that real just sort of AI first or AI out front? AI first creative. So especially I think we're talking creative. I think the numbers still pretty pretty small.

(50:05) I would put it at probably less than 20% in terms of really AI forward creative. Yeah, it's really my area of specialty and I think that number is still pretty low, but I think it's going to grow very quickly as as advertisers realize that it's not only a timesaver, but it's also something that can distinguish themselves uh in terms of creating really cool content.

(50:37) Right now, it's everybody's saying, "Well, how can I do, you know, get rid of some copywriters?" Well, you know what the real opportunity is? How can I make my presence twice as large or, you know, have 20,000 different versions of the ad versus true it's it's the ultimate creative optimization. I understand that. Yeah.

(51:01) So, I think that that's still I think I think companies are still very much in their early stages of that. So I would put that number very low. I think there's a tremendous opportunity for us in the creative and the marketing space to come up with ideas to make this reality. I want to make Rod Waver versus Roger Federer a reality. Exactly. Any other questions or thoughts? And I'm happy to continue these dialogues down the road.

(51:25) I would love to have you know this is how David, thank you for putting this forum together because this is how great ideas come together. It's all through connection. I mean, this is like when when someone's like, "Okay, well, like, oh, you know, so much about AI." I'm like, "No, I have the pleasure of learning from so many incredible people.

(51:47) " Um, and yes, like, you know, guest speakers like yourself, but also this everyone who's showing up like I'm learning a ton from the chat today, right? I learned a ton from the Slack. So, so this is just my good fortune to have a room like this I'm in every week, let alone some of the other interactions.

(52:06) So, uh, yeah, it's like you and I get to spend a little bit more time on this than some other folks do. Yeah, that's great. This is super cool and it's changing so rapidly. Um, so looking forward to continuing the conversation. Uh, please let me know how I can help. Let's bounce ideas against one each other and take it from there. Great. look forward to sharing this.

(52:27) Uh happy to have you back, especially as you have some more ideas and examples and updates on all the exponential I mean it's crazy that I could see you like you know like one month to the next and so much has changed already. It's Yeah, it's you it's no longer 51st years, it's 51st months. So great. Yeah. Well well thanks so much for being here.

(52:49) Thanks everyone for joining and yeah amazing participation as always and see you next week.

## Build Your Own AI Agent Workshop with Dstillerys

Speaker: Melinda Han Williams
Published: 2025-10-31
Tags: ai agents
Video: https://www.youtube.com/watch?v=h8pOE8OwAUA
Page: https://aimarketersguild.org/sessions/build-your-own-ai-agent-workshop-with-dstillerys

(00:05) Hey everyone, I'm David Berkowitz, host of AI Marketers Guild. We've got friends from Distillery today: Melinda Han Williams and Mark Jung to talk about building agents and putting things into practice.

(00:29) We're going to learn by doing. Melinda, Mark, welcome.

(00:48) Thanks for inviting us. I'm happy to see some familiar names and faces here.

(01:04) Since you shared thoughtful poll questions, I'll end the poll so we can see where things stand and you can reference that. Let me share the results. Curious to see what you have for us today.

(01:30) To the two of you who hadn't heard of Agentic AI but chose to join, thank you. These results are great. I'm here with my colleague Mark Jung. Our goal for this session is that by the end, you'll be able to answer yes to all of these questions. If you're still not there, feel free to reach out and we can help get you there. Let's get started.

(01:55) We are from Distillery, the AI targeting company. Distillery uses a multimodal AI approach to audience building. Our models learn across data modalities to build a coherent understanding of a brand's best customers and their digital behavior informed by web journeys, LLMs, CTV, search terms, and more. We work with inputs across those data types to build predictive behavioral models that can be activated as user segments, contextual curation, and bidding algorithms. We use multimodal AI to do targeting.

(02:25) Why am I here to teach you about building an AI agent today? It's important for more of us to build an intuitive feel for what AI agents can do. The best way to learn is to build one. At Distillery, we built an agent called DS1 that gives us one point of interface to capabilities like audience discovery, building, and activation. We've seen it save time internally. As we've opened up access externally, we're learning that Agentic AI does more than save time. It creates a way for companies to connect more deeply and seamlessly. That's what I want to share: how Agentic AI can strengthen and streamline connections between companies. You'll get a feel for that in this hands-on workshop.

(03:43) Plan: background on Agentic AI and how it changes how companies connect. Then we jump into the hands-on workshop. Mark will run that. Then we'll switch back to a concrete example and demo of DS1, Distillery's agentic interface.

(04:04) Important: did you get your login email? Check for the subject "Your unique login for today's Build Your Own AI Agent workshop." It has unique login info for the hands-on part. If you signed up late or can't find it, DM Mark now or put it in the chat. Mark will set you up. While I go through intro material, you have time to get settled.

(04:51) I'm going to talk about Agentic AI and what it is. What makes AI agentic? Agency. Two things: it pursues goals autonomously, and it takes actions. It can plan steps to accomplish a goal, and it can act on those plans, use tools, interact with external systems, and pull levers in the real world toward those goals.

(05:58) There are many types of agents. For marketing, two types are useful to think about. Knowledge agents autonomously complete informational tasks. They may have access to customized documents and instructions and output content. Action agents have access to tools that interact with other systems. These agents complete goals by pulling levers in the real world. This type is getting attention in general usage (e.g., booking tasks) and in digital advertising because they can actually do things. This is the kind of agent we'll make today: an agent with tools that can act.

(07:05) You've heard that Agentic AI saves time by automating tasks. I’m going to talk about connection. Agentic AI streamlines connections between companies. The ecosystem is complex with many partners. Agentic AI can make connections tighter, leading to faster and better results. I'll show three types of connections between an agency and Distillery enabled with Agentic AI.

(07:51) Distillery has an AI agent with AI tools to discover, build, and activate audiences. First connection: human-to-agent. In this example, the agency has direct access to a Distillery agent via a chatbot (e.g., Slack). The human can use the Distillery chatbot to discover, build, and activate audiences. This is the most common way platforms and agencies are using Distillery’s agentic AI because it’s quick to start and requires no agency tech. The agency gets instant access with smart guidance and instant iteration without back-and-forth emails. Value: faster and easier iteration for more customized results. We thought agentic was just automation, but we realized it tightens connection and improves results.

(09:23) Second connection: agent-to-agent. The agency has its own AI agent. When it needs Distillery tools, the agency’s agent reaches out to Distillery’s agent for that part of the task. For example, building a media plan and reaching out to Distillery’s agent to discover, build, and activate audiences.

(09:47) Third connection: MCP (Model Context Protocol). MCP is becoming part of the conversation for making use of Agentic AI in digital advertising. The agency’s AI agent connects directly to Distillery's AI tools. MCP makes tools available to be accessed directly by other agents. Computers already talk across companies via APIs, but APIs take weeks or months of custom code. With MCP, the agent understands how to connect, so you can set it up in minutes.

(11:12) Questions welcome. If you want to unmute or use chat, please do. While I send credentials, I’m sending a link and a [workshop+number@distillery.com](mailto:workshop+number@distillery.com) address with a password beneath it. You might need to copy the email and password by hand. Keep shooting messages.

(12:17) Question: using the word "agency" is confusing. Can you broaden MCP beyond advertising agencies? Answer: MCP is a way for an agent to connect to tools. If you have a custom agent at your company or you use Claude as an agent, you plug it into MCP. Another company exposes tools via MCP. You give the agent a URL where the tools live. It contains what’s there and how to use it. The agent figures out what tools exist, when to use them, and how. It’s like handing a developer an API guide, but the agent reads and integrates it.

(13:55) There may be a human driving an agent (e.g., you type into Claude). Claude decides when to call which tools, possibly across multiple MCP connections.

(14:16) Example: connect Claude to GitHub and AWS via their MCP servers, then access them quickly. Security matters. Use tools you trust and authentication. MCP servers require authentication.

(16:44) Question on agent-to-agent vs tool calling (MCP): think of agent A and agent B at different companies. Agent A reaches out to agent B, and agent B decides which tools to use, then returns results to agent A. It’s not both working together simultaneously; B executes, then returns.

(18:21) Question: difference between AI tools and AI agents? Tools are not making decisions or planning; they aren’t agentic. Tools perform functions (e.g., audience building). Agents decide when to reach for which tools and how to use them.

(19:41) Login status update: you’ll receive a sign-in link, a [distillery.com](http://distillery.com) email, and a password. Use that email to log in and the alphanumeric password. Reach out if you don’t have it.

(19:59) One more question: do you have one MCP endpoint or multiple? Distillery offers multiple endpoints depending on which toolbox we hand you. One entry point gives access to the tools in that toolbox.

(21:19) ADCCP: launched recently as a standardization for agentic AI in adtech. It doesn’t change connection patterns; it standardizes message formats for certain tasks across companies. It sits on top of agent-to-agent or MCP. Our example is a use case not covered by ADCCP. In the workshop, you’ll use an agent that connects via MCP to external tools so you can see how easy it is to set these up and give AI access.

(22:50) IT/infosec concerns: we usually start with human-to-agent via Slack, which narrows IT questions. Companies ready for MCP are deeper into Agentic AI and prepared for it.

(24:20) Workshop in n8n to visualize what’s happening when building and talking to an agent. OpenAI just launched an equivalent; Google has one too. Concepts translate.

(26:07) First, add an AI agent node. Then add the brain: the chat model (OpenAI) and memory. Memory enables back-and-forth and context. Default memory window works for most cases.

(31:59) You’ve basically rebuilt ChatGPT if you stop there. To go beyond Q&A, add tools. For MCP, add the MCP client tool and point it to Distillery’s MCP endpoint (https://mcp.distillery.com/mcp). Set HTTP streamable mode. Save the canvas.

(35:05) Now the agent can use the MCP tools. Ask: "Help me search for audiences related to artificial intelligence." The agent knows to call Distillery’s search tool and returns audiences.

(36:42) To assist further, add Gmail via MCP to email results to a teammate with activation paths. The agent composes and sends the email using the activation details from Distillery’s tools.

(38:33) We’ll send detailed instructions for any steps you couldn’t finish.

(38:52) Practical big picture: this shows how you can create an agent, customize it, ask it for what you need, and have it push results into your workflow (e.g., email) without copying and pasting. Now I’ll demo DS1, our full agent used internally and by clients.

(40:45) DS1 is a human-to-agent Slack interface to our discovery, building, and activation tools. Ask "What can you do?" and it documents itself. In this demo, it can search for audiences, do some custom building, and support activation.

(41:51) Audience discovery via first-party data: "Please help me find audiences that will perform for Large Clothing Retailer." The tool uses the brand’s first-party data to run a simulation across our catalog of 20,000 audiences, predicting lift versus a random baseline. Without running media, you see which audiences are predicted to perform.

(42:50) Results appear grouped. First-party audiences (retargeting), Distillery custom AI audiences modeled from first-party signals, other custom audiences based on valuable behaviors, and pre-built behavioral audiences from our catalog.

(43:37) To understand third-party audiences, ask for thematic groups with lift and highest-lift groups first. Talking to your data via the agent helps build the activation story and plan. Making this accessible through DS1 changes how partners use first-party data to find pre-built audiences to activate for higher performance.

(45:02) Custom audience building: "Help me find purchase-intent seeds for vitamins." It searches product page URLs on retail sites from panel data to find the best fit for purchase intent. Pick product URLs as seeds, then the agent kicks off a multimodal AI model to target user segments and inventory most likely to be interested.

(45:59) If needed, narrow: "Please narrow to women’s vitamins." Iterate instantly to refine the audience definition before modeling. Then: "Please build an audience using the top three seeds." The agent kicks off audience building with predictive multimodal modeling. The agent is an interface and action layer, not the modeling itself.

(48:24) Human skills still matter, but our goal is to make it hard to put garbage in by guiding inputs and constraining the tool to high-quality seeds and models. The agent shows what it’s doing and asks for confirmation.

(50:48) Activation example omitted for time. Key takeaway: these agents use tools and pull levers in the real world to accomplish goals. The agentic layer interfaces with powerful tools, helping users use them better and creating faster, tighter connections that lead to better results.

(51:27) Thanks for the great turnout and interactive session. We’ll share materials. Feel free to email Melinda and Mark to continue the conversation. We’re happy to return with updates.

## Unlocking Creative Effectiveness with AI - Measuring Emotion Attention Recall

Speaker: Ian Forrester
Published: 2025-10-24
Tags: creative process, emotion recognition
Video: https://www.youtube.com/watch?v=Ky4w1TqEu6U
Page: https://aimarketersguild.org/sessions/unlocking-creative-effectiveness-with-ai-measuring-emotion-attention-recall

(00:05) So, I'll welcome you Stephanie who I've known for very long time and proudy marketer insider intelligence alumni. Ian Forrester, who Stephanie introduced me to a few months back and and got to break bread and and hear about some of the work they're doing and I just probably as we were rolling out this series and I was like I we got to find time for you to come on here and uh and they floated this idea about uh about the Super Bowl that I was really excited about and so I'll let you explain it better than I will but but just uh excited to have you

(00:43) here. welcome for and great great to see a lot of regulars back here. For others who it's your first time on one of these, it's very interactive. Sometimes I don't have to lead any discussion whatsoever. I just get to kick back and listen quite a bit. Um, but feel free to chime in, raise hands, shout out if you need to join join the chat.

(01:14) And, and this is like just this is really my favorite way especially like within AI Marketers Guild for for just members to connect with each other and uh, and so uh, I'm just thrilled to see all you here. So uh, Stephanie Ian, take it away. Thank you so much, David. Ian, why don't you introduce yourself and then I'll give a little bit of context about about the company. I will. Hi guys. Thanks so much, David. Thanks for having us.

(01:39) So, hello, my name's Ian Forrester. I am CEO of David. I have been working on David for about six years now. And prior to that, I used to work at Unruly. So, I led the insight team there for about seven years. Um, prior to that, worked client side. So, I was at Sony Pictures for a while, Nestle, L'Oreal.

(01:59) Um, but I've always been involved in creative effectiveness and in particular the impact of emotions on creative effectiveness and essentially the work we're doing at David is taking all that work that I've done over the years to the next level. So, we're super happy to be here. Um, this work that you're about to see is literally hot off the press. Like we the team were up all night cuz we're based in the UK.
(02:19) So, we were up all night um on Sunday after the game getting everything together doing some analysis and pulling the story together to uh be able to present to you guys today. So, yeah, we're excited to to share. I'll share my screen, Stephanie, while you uh say hello. Sounds great. So, as David already alluded to, we go way back to our e-arketer days. Um I'm Stephanie Blair and I'm serving as the USMD for David.
(02:43) Um and I also am born out of the advertising world. I I started my career in print advertising, spent some time at agency within performance marketing agency here in New York, and I'm I'm here in the New York uh city area, and I'm excited to be presenting to you today. Um so, while we let um Ian, we see um a dashboard, not the There we go. Sorry.
(03:09) Yeah, this Zoom is covering the present button, but I'm on it. I'm on it. So, I know I know you're a vocal bunch, but I do think it's important just to take, you know, a handful of slides just to give you a bit of context about David and and what we're all doing here. So, obviously, we're a creative effectiveness business focused entirely on effectiveness driven by creative advertising campaigns, as Ian mentioned, born out of decades of research in this space.
(03:33) Um, but you know, let's talk about why this matters because, um, I think you know, you all in the room know this. Next slide, please. We're seeing that nearly 50% of creative performance is is impacting sales and over threearters of large large amount of brand marketers deem creative quality one of the biggest factors in success. In fact, just a few weeks ago I was at a contagious event organized by War and Ken Lions and you know creative effectiveness and its impact to drive the bottom line was one of the major themes of the of the conference. Um and
(04:05) obviously you know we can see this and we know this good creative executed well can drive profitability um to the tune of 12x. So we have power in our control but the problem is a lot of creative out there is just so so meh. So we're here to talk about the creative effectiveness um you know sort of system um and grading system that we've used today uh to help you understand who the winners are in the Super Bowl and not just uh Taylor Swift's boyfriend's team but the brands themselves. Um, in terms of what we are all about, it's also about creative testing for the real world. So,
(04:38) we've seen a big shift and I'm sure those of you who have been in the industry for a long time, you know, back in the day, it was there was a lot less content, limited number of channels, you know, there was maybe TV um and maybe one social channel and now there's a proliferation of that content um you know, more content than we can even handle. Um, and there's an always on nature of things.
(05:03) And we're seeing spend come uh, you know, sort of meet this moment as well. So, influencer advertising has doubled since 2019. It was $21 billion in 2023 alone. That's according to Statista. And obviously on social, we're seeing it, you know, continue to grow. And this has been a trend for for forever, but it's expected to double over the next four years.
(05:23) And also other fast growing channels like retail and Tik Tok, CTV. um you know this is according to e-arketer um you know we're going to see this all continue to grow and rise um so these are global figures but we know that there's a there's a shifting moment and David is here to meet that moment because gone are the days of only testing linear or hero oriented content that's important but again when we're having this mass scale of content we really need more of a closed loop always on approach to testing to help uh marketers and advertisers really understand not just what is working, but why, because that's what consumers
(05:59) demand. Um, and that's what David is here to offer. It's continuous testing at scale. And when we do this, we can really give um, you know, really sizable results to our clients, which we'll talk about in just a second. But here's the AI piece, which is interesting. So, we're continuously training David with attention, emotions, and creative attributes data.
(06:18) Then we can show David ads and explain why that advertising will work or not. So again, in a world of a lot of what content and metrics, we're really getting to the heart of the matter by using an emotional lens um to understand that why. And this is not just for video. It's really for any media.
(06:38) So of course, in today's conversation, we're talking about Super Bowl video ads, but this could be applied to, you know, copy, uh imagery, um really any anything that people can react to. Um and so we're enabling advertisers to understand why the creative worked or not so that you can set the strategy for the future. And we bring the Y as you can see here and allow people to test uh affordably at scale. So what does this mean? You know, we can see here some of the clients that we're working with.
(07:01) So whether it's about um making creative effective for a certain platform like what we're helping Snap understand um helping to add a layer of excellence to modeling for a certain agency such as Essence Mediacom or to really look at the influencer space and improve briefings or effectiveness of that of that strategy.
(07:22) Uh David's here to help all through our scaled and afford affordable testing. So we're excited to bring this to you firsthand because what we've done is we've run a number of the Super Bowl uh ads through through our platform um and to help you understand, you know, what the trends were and who won. So I'll pass it over to Ian to give you a little bit more uh you know the insights driven approach of why it works and then we're going to take you through the rest of the time in a more com um you know uh conversation around the the winners and
(07:47) what we all think. Thanks, Stephanie. Yeah, so I mean, just to add to that a little bit on this slide, um we're not saying that it's no longer important to test hero content. Um and in fact, we've tested a lot of hero content with this Super Bowl study, so you're going to see some analysis on hero content.
(08:06) Um which is is totally fair enough. A lot of money is still spent on that content, of course. But what we're seeing is also in addition to that hero content there is this huge body of work which is being put out by brands across social the various platforms uh influencer campaigns which isn't seeing the light today in terms of that insight and understanding process it's just getting pushed out the metrics which are being collected on it are social metrics things like engagement rate like shares comments uh maybe reach and impressions and brands aren't really
(08:37) understanding if that creator is doing anything for them and and and if they should continue doing it. And this has really come to fruition in the last I would say 9 months where we've been banging this drum for a while but Branson asked to really come to us and say this is a major problem but I was in a call with Unilver last week um one of their homeare guys and he said to me Ian we've got a disgusting amount of content and I've got no idea if it's working or not. We just keep on pushing it out across all the social channels and I've got no idea if it's good or if it's not. if it's damaging the brand, if it's
(09:08) helping. And this is where our major clients, the ones that are working really well with and who are using data to our absolute best, these are the clients which are really leaning into this and testing. Yes, they're testing their hero content, but they're also testing these large bodies of work which they're producing or perhaps which other people are producing as well to inform their strategy. And that's where we we really come into our own.
(09:36) And so for this study, which you guys are going to see, there's a bunch of hero content which we tested in there, but also a bunch of Tik Tok content because prior to the big game, there was tons and tons of activity on Tik Tok. So, we've kind of merged those two things together with the presentation we're going to show you guys today.
(09:55) How does it work? Let me walk you through at a high level what we're doing at David, the data which we're collecting and why uh and how we're bringing all this together because that brings some some context for you guys and enables you to see what we're doing, why so um okay, everything that we do at David is based on this effectiveness process. This is the foundation for all that we're doing.
(10:17) And the cool thing about David was when we started the business, we had literally a blank piece of paper. We didn't have any legacy data which we had to shoehorn into the business or use in some way. We literally had a blank piece of paper and we said to ourselves, what data should we collect from the ground up which is going to best help us answer the question for our clients, why is content effective or not? And to arrive at this effectiveness process, we did a meta analysis of creative effectiveness studies from both academia and industry.
(10:48) So we've looked at the work of the IPA and the IAB and all the work we did at Unruly over the years and Erinberg Bass and Wharton and so on. And each individual study that we looked at was tending to look at a small part of this process, but we've taken this helicopter view to see how they all fit together in the round.
(11:06) And so um it's quite interesting. I'll walk you guys through it. To be effective in the wild, a piece of content first of all needs to capture attention. And of course, attention is a major buzz word in the industry right now. A lot of people are focused on attention. And absolutely, you're rightly said because without attention, you're dead in the water.
(11:23) So attention is critical to creative effectiveness and it's a big part of what we're doing at David, but it's not the be all and end all because once you've captured attention, you've got to do something with it. And that something is evoking an intense positive emotion because it's the emotion which creates the memory and the memory which drives the action.
(11:45) A lot of content these days which brands are putting out emotionally is just a bit average. So you watch an ad or a post you're like yeah that was okay. You feel a positive emotion but at like a four or five out of 10 in terms of intensity. The issue with that is that 2 seconds later you've forgotten that ad. It's just wallpaper. This is another average thing that you've been exposed to that day.
(12:04) So the memory is not created. The action is therefore not driven. However, when a brand can elevate that emotion to an eight or a 9 or a 10 out of 10, the viewer remembers the way the ad made them feel, they attach the feeling to the brand and it's that feeling which causes that person to do something either immediately or at a later date.
(12:27) So another important thing to say about this is that attention and emotions are the drivers of output. Either from a a brand building point of view or from a direct response point of view. The only difference with direct response is that memory doesn't really come into play. You get get capture attention, you evoke an emotion and that person takes the action immediately.
(12:45) They click through, they they search, they they go through and buy whatever it is you're you're trying to ask them to do. So this stuff is equally important for brand building and for lower funnel campaigns. So with that in mind, we're training our AI using attention and emotions data because those they are the drivers of effectiveness. Now another thing about David is that we're bringing lots of different methodologies together.
(13:10) So I've learned over the years that if you do one thing and not another, you get part of the picture but not the full picture. So the more techniques you can bring together, the more complete a picture you get. So this is what we're doing at David in terms of attention to create the attention data which we're using to train the AI.
(13:32) We're showing creative to people and we're asking those people to turn on the webcams as they're interacting with the creative because that allows us to gather facial coding and our tracking data. So we're understanding are people looking at the screen or not? When when are they tending to look away and also what are they looking at on the screen? So what is capturing their attention? So that's a dual attention metric which in itself is is pretty unique in the industry. In terms of emotions, again, we've got a couple of methodologies here.
(13:55) Facial coding, where we're filming people's faces, allows us to see where are people smiling or frowning in the different moments of the video. And that's what facial coding is really good at, pinpointing those moments which are working well or not so well. What facial coding is not very good at is telling us which emotions are being evoked because we're only we only output six expressions universal expressions and there are loads more than six emotions.
(14:21) So that's why we've developed the data 39 which is our own emotional categorization which I'm going to come on to explain in more detail in a second. Final part of the puzzle are created attributes. So at scale we're sending content to various computer vision APIs which are telling us what is happening within that content both in terms of what you can see and in terms of what you can hear and then we mix all this data up to produce our outputs which I'm going to explain to you guys in a bit more detail in a second but first of all let's double click on the David 39 and um because this is this is really
(14:54) fundamental to what we're doing now this categorization has been taken from academia There's a huge body of work that exists within academia around what's driving uh what constitutes an emotion that lots of debate between academics and lots of gray area nuance.
(15:12) And essentially we've taken this big body of work and we've condensed it right down to this list of 39 emotions which is just really useful for practitioners. And we've done that because so much of that why why is an ad working or not comes down to the emotions that it's evoking because we're all humans. we're al responding to everything that's put in front of us in an emotional way.
(15:31) That's just that's how humans deal with the world. Um so the why is really really driven once you capture attention once that hurdle has been overcome then the emotions that you're evoking come into play in a big big way as to whether you're going to drive the result which you are which you had intended to drive.
(15:53) So if an ad isn't working it might be because it's evoking negative emotions. So in the bottom right here, boredom, confusion, those two are super super common. We see those come through all the time. Or more extreme negative emotions might be being evoked as well. Things like awkwardness or embarrassment or shame or anxiety or contempt, distrust.
(16:12) We're testing in creative around the world every single day. And every day these emotions are coming through. Um on the flip side, when ads are working, they're working because they're evoking intense positive emotions. So that intensity is super important.
(16:31) You've got to be going for an eight or a nine or a 10 out of 10 because that's what raises that ad up above all the other stuff we're being bombarded with on a daily basis is what makes it memorable. So intensity super important. Also, the kind of emotion you're evoking can be really powerful as well because if you can evoke an unusual emotion versus your competitors just by being different, you're going to stand out.
(16:50) You add intensity to that difference. That's when you get those those fireworks. And that's particularly put in today's to today's conversation actually because as you'll see and I guess you guys got the sense anyway like lots of the creative at Super Bowl this year went for a a single kind of trope like they really kind of clustered around amusement as as the the lead emotion here.
(17:14) um which is fine if you do it well and there's certain ways which which which allow you to do it well and I'll come on to explain those but what I would say is the first thing before you even get into it like and these great if you can do it well brilliant you you've got to be going for a nine or a 10 out of 10 is that's what makes it not just mildly funny but then forgettable but actually very funny and then and and then memorable but look at all these other emotions that brands can play with like look look at all these wonderful
(17:39) emotions here or excitement surprise pride, gratitude, hope, belief, knowledge, nostalgia, a romance, all these other things which can be evoked and you know a large mass of the brands at the Super Bowl went for amusement which basically made it very hard to stand out but I'm kind of spoiling the presentation so I'll move on.
(18:02) Um but yeah so what I've just described in terms of the AI and the training data set is one set one side of the coin in terms of our training data which is the how are people responding to stuff part the other side of the coin is what is happening within creative and to gather that data at scale we're sending assets to various computer vision and computer listening APIs which essentially tell us what is happening in that content both in terms of what you can see and in terms of what you can here on a frame by frame basis and essentially that becomes
(18:31) a very deep understanding of what is happening within content and our system then understands these two data sets and understands the connections between them and that's really where the magic of what we're doing lies. So I'll give you guys an example. If you just imagine that's a single frame in a video just from that one frame we're picking up loads of information.
(18:54) So, we're picking up the fact that there's a crowd there and there's traffic noise from the road and there's a girl between the age of 25 and 34 and so on. All of these things become data points which we can correlate with outcomes. So maybe people are paying attention to the girl because of what she's wearing or maybe people are looking at the crowd and they're feeling a bit anxious or perhaps a bit excited and so on.
(19:19) So our system is continuously learning what it is about content which is resulting in a certain outcome. We're continuously training our algorithm with more and more live human data where we're assuring content to people to gather that data to then train the algorithm. So it's continuously learning, continuously updating and that then forms the basis of everything that we do.
(19:37) So in summary, we're using AI to handle this complexity. We've got the various data sets which we're pulling into David. We've got facial coding, eye tracking, David 39 listening, computer vision, and then we mix all these things up to produce our outputs. Um, okay.
(19:58) So, what does this mean for our Super Bowl analysis? By the way, guys, if you got any questions at any point, please do dive in. I can't actually see you guys at the moment. Um, so if you want to interrupt me midflight, all good. Just please dive in. Um, okay. So, Super Bowl, Super Bowl 58. How did this shape up? Well, as I said on the previous slide, a a large large number of ads were going for amusement.
(20:24) Like they had amusement as their lead emotion. In fact, twothirds of the brands which launch Super Bowl campaign had amusement as their lead emotion, which is totally cool if you do it well. If you evoke intense emotion and really really with amusement I'm when I say intense I'm talking about a nine or a ten out of ten because you really have to move the needle to with particularly when there's so much funny content being produced you really have to move that needle to um create a funny ad and some some brands that did it really well. So Popeye's and Paramount Plus did it well. I'm going to
(20:54) I'm going to go through the content guys and play it. So, for those of you who didn't um see all the ads, I know there's like a mass there's just so much content released on the day, but it's worthwhile kind of just having a look at what's working and what's not. So, Popeye's to start with this tested very very well.
(21:11) This piece of content is actually second in our overall list of of hero content. Let me just play this for you guys. Yes, there's a better wing pot lip. What else is cheese? Here we go. You're crazy. Two dogs in one. Hooray. I finally had wings. Just checking you guys can hear the audio. Yes. Of the video. Yeah. Cool. Awesome. So, um, yeah, Popeyes did really well.
(22:01) That amusement score is very decent. 29.9% of the audience found that intensely funny. Um, so nice big success for Popeyes. It's also brilliantly branded. Um, very strong brand recall. So that's what was driving the the performance of that particular crate. It also opens in an interesting way. So it it opens this kind of lab scene.
(22:25) You're not entirely sure what's going on. There's a bit of intrigue there. So in terms of attention in the first three seconds, it's it's doing a really good job. So in terms of our creative effectiveness score and how we rank this content, it's attention. So potentially people paying attention after the first three seconds and it's potentially people feeling intense positive emotions and potentially people remembering the brand which the combination of those three things which we really focus on based on our creative effects process. Paramount again this
(22:52) was um a very successful ad um featuring lots of different characters and u and celebrities as well. So um Chris Perkins wanted to know more about the brand assessment context. Does it consider any relational dimensions to either the category or current consumer impressions of the brand? So um when we test creative particularly when we are um building an algorithm we test that creative in isolation because we need to understand the impact of the creative itself on people's responses.
(23:30) And so you imagine we can't possibly control for um all the different environments that it might be shown in or what it's shown next to, what's what's seen immediately before it, what's seen immediately afterward. Um people's impression of the brand as well. So when we're testing creative, we need to make sure that a sufficiently robust number of people watch that creative.
(23:58) So that sample is representative of um of the the global population within within a country. So our standard testing is 350 nationally representative people for each asset that goes into an algorithm build because then that that irons out a lot of the um different preferences around brand. kind of like some people love a brand, some people don't.
(24:22) So you need a a fairly large sample size to kind of negate that. And the creative is tested outside of context because we want to understand the impact of a creative itself. Of course in the real world that can be either heightened or dampened down by the um by the way it's distributed but our baseline is the creative itself because we need to understand how that compares to other creatives and then and then we can make recommendations as to how the creative is working or not which then the media part of the campaign either dampens down or or amplifies. So thank you for that. And then there's
(24:56) a couple of other questions, but I'm going to cherrypick them because some we're going to get to. Um, but just very quickly on this slide, what does a 29.9% amusement score indicate? Kate Crook would like Cook would like to know. Is it a good score? Is it a slice of the pie? Emot emotional attribution? And then we'll get into how that compares to some of the others like State Farm and uh Dunkings, etc. Yeah. Yeah. So, that means that 29.
(25:21) 9% of viewers found that ad intensely funny. So they were they they felt amusement at a nine or a 10 out of 10 when watching Paws and 29% when watching Paramount. And yes, that is an excellent score versus the US norm. So the US norm for amusement is about 9%. Um so that's like a a large overindex like a 300 index overindex. So it's a decent score.
(25:48) I I didn't want to put lots and lots of data on these charts because I wanted to keep it quite conversational. But yeah, the reason I'm showing this both of these um examples here is that both of them did very well, but these these were some of the best in terms of amusement. As you'll see as we go through the deck, there's some less good examples as well.
(26:08) Okay. And then lastly, um there was a quick question 30 seconds or less. Are we using natural language processing to look at copy? I think we've covered that a little. We are. So um so the copy on on the creative attribute side the copy goes through um Amazon recognition which includes natural langu l language processing.
(26:35) So uh understanding the the copy so the words being used by the actors we're transcribing those we're transcribing anything that's said in terms of subtitles or any text which occurs on the screen. Um and then we're also adding not just NLP um but also an analysis of the tone of voice which words are said in.
(27:00) So we're using different API to add the tone of voice analysis and that adds a lot of color to like a simple text read. Um, when you add the tone to the NLP, then you get a much deeper understanding of not just what's being said, but also the context and the tone and also the back and forth between uh protagonists as well. Okay. And there's a lot of other good questions. So, let's just take one or two more here. We'll share a few more examples, go deeper.
(27:24) Um, we'll talk later about uh ads that broke a lot of rules this year versus last. We'll talk about that in a moment. Um there are a couple questions around how do we align a particular set of emotions back to what's right for each brand and I think this speaks to the way in which we collaborate with brands and also um you know industry by industry there are different standards or emotional whites space that we can help brands figure out.
(27:49) Um so for example you might not care you might care a lot about performance for BMW but not so much about funny um so help us answer that question that's a lot of people in the room are are curious about that. Yeah. Yeah. Yeah, great question. So, um I'm often asked like what's the best emotion for uh for sales or for uh for recall or search or whatever it might be.
(28:16) Um and the reality is there is no one best emotion for each brand outcome. Um uh a more interesting way to think about it is what which emotions are right for my brand? Um, and we often do these kind of category studies for brands. We did one for Chaseedo, for example, looking at US cosmetics on Tik Tok. Chaseedo had a problem.
(28:40) They had lots of new direct to consumer competitors who had launched on Tik Tok. And these guys were really eating Chaseo's lunch. Like Chase were nowhere on the platform. Their engagement rates were well, well lower than their competitors.
(28:57) And they said to us, why is this the case? So we analyzed a load of their creative and their competitor's creative and created this emotional positioning map which showed which emotions were being evoked by which brands in the space. And what that allowed Chaseo to do is identify the emotional white space. So these are underused emotions in that particular category. And as I said before, if a brand can move into that emotional white space, if it can invoke an unusual emotion versus its competitors, by doing so, they're going to stand out. And then you add intensity to that and that's when you get that those great results.
(29:27) So there's no one right positive. All all the positive emotions can be used effectively by a brand. But I would encourage brands to understand their competitive landscape. What are others doing within that landscape and where's the emotional white space that they can move into and own? Um and it's not just about picking any emotion which people aren't using.
(29:50) It's also about understanding which emotion is right for the brand. We help brands go through this process and this is really the crux of this matter as well in terms of the Super Bowl with everyone being with everyone focused on humor is really hard to gain cut through because if everyone's basically doing the same stuff I these two did well but so many ads like only seven of the ads the the big game ads but which went for amusement only seven of them evoked intense amusement among more than a quarter of the audience. So loads were were just not hitting the mark. And when you consider how much
(30:26) money is being spent on these celebrities as well, it's um it's a shame because brands can there are so many other avenues you can go down to gain differentiation as we show we saw on the open slide. So let's go through some of our other findings because I have the benefit of knowing what is in store here and there's a lot of juiciness.
(30:45) Um somebody did ask about our model and does it you know how do we break out by demographic and are there relevant differences? So, where possible, if you have any color uh to add on that relative to the other examples, please add them. But I did uh type in an answer. Yeah, but we want to see the results and Exactly. Let's go. All right. All right. All right. Cool.
(31:02) So, uh let's have a look at the next one. So, if you're going to use humor, cool. But remember that humor is polarizing, right? So, as with any emotion, you've got to be getting to to a 9 or a 10 out of 10 to be memorable. To get to that place with humor, it's often necessary to go to go to a place where a brand isn't comfortable.
(31:31) Because while you're making some people really, really laugh, you're really turning other people off because humor is very polarizing. And that was the case with um Caravee um which I'll play a little bit of with Michael Sarah talking about the Save brand. I'm Michael Sarah and I'm pleased to announce that this is my cream. Oh, you didn't know. The truth has been idated.
(32:04) So, it's slightly creepy. Um, he's talking about he basically is making out that he produced the brand Saravee. And this was supported by a lot of content on Tik Tok as well, which I want to talk about in that section. Um, and it was polarized. Some Some people find this creepy, they feel awkward when they're seeing it.
(32:28) It's or some people are just confused like, what the hell's going on? Some people understand that it's a spoof and it's ridiculous and they find it hilarious. And that's great. that that is this is what you want to be doing with humor. Like own it. Don't don't be afraid that you're going to turn some people off. As long as your target audience are finding what you're producing intensely funny, then all good. Um because the the opposite of that can be just a bit average.
(32:50) Um often what we find is when brands try and be funny, they're not comfortable going to a place where it's going to really make people laugh. And so it just gets the ideas get watered down to the point where they're just a bit meh. And that's what happened with progressive insurance here. Um again, I I'll just show play a little bit of it.
(33:08) You know, it's just a bit it's just a bit average unfortunately. And um there was a lot of creative in the Super Bowl this year which followed a similar kind of thing like um featured a celebrity tried to be funny and just just was I mean not not terrible but just was okay.
(33:28) And and amidst all this noise, and I guarantee you this will be forgotten almost immediately. And don't think that celebrities are going to get you out of jail free. So this obviously ridiculously expensive ad Beyonce, the Verizon one. Um I mean again it wasn't horrendous, but it was it was just okay. We had a fairly average this thing is covering yeah 12.
(33:53) 8% 8% um amusement score is this Beyonce again. I'll play a little bit of it. Um you'll see what I mean. Broke the internet again. Did you post this? Oh, well, not on purpose. Well, it's coming in hot. It's rise 5G. The network is crazy powerful. I bet you can't break that. I bet I can't. Get it.
(34:18) But can't she break it? Broken? So, Beyonce tries to break the internet in various ways. um you know really really expensive and and again the back backdrop of other brands doing very similar stuff albe it not with Beyonce but you know essentially the same formula it just didn't gain cut through um one thing you can do with humor is mix it with other emotions so that's what this blueberat ad did brilliantly when you do that the ad can work in different ways for different people. So, you're not relying on it being funny. You're evoking other emotions and that broadens its appeal. At the end of the day, we
(34:56) want people to feel intense positive emotions. That's what's going to drive that memorability. It doesn't really matter which emotion it is. Um, and so if you can combine amusement with different emotions, that's what's going to be very effective.
(35:11) And this one, this particular ad with Jennifer Aniston, David Sha, and so on did that. Thank you. I didn't know you could get all the stuff on. I remember that. Well, you know what they say. In order to remember something, you got to forget something else. Make a little And that's how I remember Ubis has coffee by forgetting something else. Have a seat. Yeah.
(35:34) What? Remember when you used to be a pepper lady, wasn't it the cinnamon sisters? Basil paprika girl. No, that's absurd. That's posh as well. But so this is working in lots of different ways. funny, but it also is generating in interest in the product itself and excitement in the product itself. It's got nostalgia coming through friends and partial and all these all these kind of um people that we've known for years.
(35:58) So, this is a really good way to incorporate humor. If you have to go for humor, cool, but mix it with different emotions in that is exactly what one of the greatest Super Bowl ads of all time did. Uh Volkswagen's the force. Um, I won't play this cuz I guess we've all seen it like a million times.
(36:18) But the the brilliant thing about The Force was that it combined lots of intense emotions. Yes, it was funny, but it was also heartwarming and adoration in there, inspiration, nostalgia, pride. Um, and it worked in in a lot of ways. So the such that nearly 80% of viewers felt at least one intense positive emotion watching that ad which was what makes it so iconic and so brilliant because it was working for a large proportion of the audience.
(36:45) Um, now the winner of the Super Bowl with regards to the data which we collected was this NFL ad which didn't try and be funny. Um, and yeah, featured celebrities, but they they weren't kind of the the main event in this particular piece of content. Um, let me play a little bit of this to just remind you guys what it was all about. in Las Vegas. Quincy, it's 2:00 a.m. You have school tomorrow.
(37:15) Get some rest. See you tomorrow, Sequan. You got it, Quy. So, it's a lovely ad. Um, we have inspiration in there. It's about this this kid who's growing up in in Ghana. He's dreaming of being a a American football player. And it's really heartwarming. It's inspiring. There's admiration coming in there.
(37:52) There's hope and it's just succeeding on lots of different levels. And it's not trying to be funny. And yeah, there are little humorous bits in it, but that's not the main thrust of this ad, which made it stand out from the crowd. When everyone else is going for amusement, going for different emotions can be very, very effective. And that's exactly what this ad did.
(38:10) And remember that attention plays a big role. So this Google ad was interesting because it did brilliantly at evoking positive emotions. Like 66.2% is a very good score for intense positive emotions. So a large proportion of the audience felt intense positive emotions from watching the ad.
(38:29) But what let it down was this attention score because it opens. It's about um someone whose vision is um is impaired and it opens from his point of view. So if you guys remember, for many people with blindness or low vision, there hasn't always been an easy way to capture daily life. One face dropped. Move your phone down.
(38:54) Which is getting across the point of the guy's vision visual impairment. But as a viewer who doesn't know this creative and doesn't know where it's going, if you see that, you're like, "That's blurry. That's weird. I'm going to look away." And a lot of people did.
(39:12) So, where this fell down was like in the real world, it you'd be losing people after the first 3 seconds. Um, but also the brand recall was was low as well because it's a lot of the content is just showing the phone in this kind of blurred vision. Again, it's it's getting a point AC across the point, but not that many people were attributing this particular ad to Google, which is a another problem, of course, when you're spending so much money as this is an example of so um Squarespace big game spot directed by Martin Scorsesei. So, you can imagine how much this cost. Um and it's beautiful, you
(39:49) know, it looks it looks lovely. Again, positive positive emotions were pretty decent. attention is good, but that brand recall is just horrendous. Like it, this was part of a larger campaign. They put out a lot of content on Tik Tok where there's a guy who's designing his website um designing website for aliens and the aliens actually arrive and they use the website which he's designed.
(40:13) But if you've not seen the buildup content, then this is just a bit what well, you don't get it. And then the branding right at the end, I can show you it goes through. It's like a minute and a half long. I told you Broadway this always happens. It's super quick and then it's gone.
(40:37) And you've not seen any of the buildup content where they're creating the site and they're showing the aliens and like and so it just didn't really make sense. And that brand recall score 52%. You know, when you spend God knows how much, let's call it seven, $10 million on this spot when half the people watching that spot don't know it's a Squarespace ad. It's not a great result.
(40:56) Um, so Ian, we've got minutes left, so let's um let's not use any more video because there's also some good debate and discussion going on in the in the chat, our findings. All right, cool. So, one good way to make to really hammer home your brand and your brand and to drive that really great brand recall is to make your brand integral to the ad. And that's what Pringles did.
(41:21) Um, they had Chris Pratt who um has got a Pringles uh mustache and it basically was became the brand. And so all of the the ad is very much centered on Pringles. And if you remove Pringles from the ad, the ad doesn't work anymore. And that's a best way honestly to draw a brand recall. Absolutely. That brand recall score you can see from Pringles is superb there.
(41:40) Okay, let's talk a little bit about Tik Tok and what brands were doing on that platform. Paramount um they had a lot of really nice polished content to support the main ad. So it wasn't the main ad, they didn't repost the main ad. They had other ads supporting it which performed very very well.
(42:04) They they added color and amusement to the main spot in a in a really effective way. Um VW also did this brilliantly. They had a an unusual piece of content as well, very much focused around nostalgia and um the soul that the that Volkswagen brings to to its cars and the the Tik Tok content supported that brilliantly. So it's really powerful. 15-second ads all very strong.
(42:26) Um one thing that Tik Tok can be used very very well for is to tease trend and extend. So prior to the ad, tease it. And this is what Dunkin Donuts did with a lot of Ben Affleck content ahead of the of Game Day. Lots of teasing, building on last year's campaign as well, like what are they going to do? How's it how's he going to what's going to be the story line this time? And then they trended.
(42:49) So they put actually the main ad on the on Tik Tok, but they did it in a format which was made for Tik Tok. It wasn't a horizontal video, vertical video, you know, mastered and and and edited for the platform specifically. And then they extended. So after the game they showed um they showed content of what happened after um he goes in and tries to sing from pop star and he's kind of like shamed.
(43:16) So this is a really nice example of tease trend extend and also Sarah V te teased trended and extended really well but what they did in addition to Dunkin Donuts was they did so with unique content. So they built on the main spot. I didn't take like stuff from before it. It wasn't it was shot in very different ways. Lots of different kinds of content.
(43:42) So, influencer content um spoof content from the the brand like it leaked internal memos that that Michael Sarah has been trying to claim that he was he uh invented the brand and they shouldn't be he shouldn't be allowed in the building and these influencers being sent weird boxes by Michael Sah. Very very good. um all joining up together to really support the main message of the brand.
(44:04) Um so I won't read through these. Basically, it's just what we've what we've discussed in terms of our our key insights. Um hope that's helpful and we've got seven minutes and there's like 40 messages in the chat. So um does anyone want to come off mute and ask a live question? If not, I'll pull out a couple of the remaining questions from the chat.
(44:25) Thanks for being such a great audience. Yeah, thank you. This is great. I wanted to jump in on one of the rule breakers uh that I put in the chat about Bud Light with the genie uh because they ended with their wish being to go to the Super Bowl and then exactly cut right at their back at the game and the genie has brought them uh in the seats and and how that scored in terms of at least it was back to back and right there and it was a real good rule breaker.
(44:49) Yeah, interesting. Um I have to remind myself how Bud Light did it and certainly not not stand out. I think they were mid-table from memory. Um then they did that extension bit that um you know where they just flashed to them very quickly in the audience. So yeah but again into real time the focus was amusement right.
(45:13) Mhm. Like it. So, yeah, it was a slightly different creative device, but it was still playing in that similar kind of space to the other bands. And that's that's really the major takeaway for me. Like, fine, do funny if you want to, but you've got to really turn up like you really turn up the noise if if you're going to cut through.
(45:32) And actually, there are so many other emotions that you can go for which are which are going to get you stand out, make you distinctive. just to poke further and please other people jump in, but the the the fourth wall breaking is really what I'm getting at here. The ad was fine. I don't I agree it was middle of the road.
(45:49) It's that the genie took them to the game and they were at the game and if that registered I'll have to have a look. Honestly, I'm not sure. I like the the campaign itself did not it certainly wasn't one of the top 10. Um, so, uh, it makes me think a little bit of the Spongebob Squarepants tie-in that was all over the place. That was never really I don't know.
(46:15) Did they have an did Nickelodeon have an ad that tied into all the Yeah. during channel sim simal cast was Oh, okay. It was absolutely much better at some points than the actual uh broadcast itself. Wow. Okay. I missed that. It was the first time ever Nickelodeon also I mean they've done it before but they had a a box with commentators live.
(46:38) Um so oh see I just saw the ad part and that was cool because I didn't know what was going on but I'm like wow they paid a lot to be right there so it was actually a another show simal casting. I had to fight my seven-year-old not to watch that version and watch the real version so I could see the commercials because it was kind of like the puppy bowl you know has its own vibe. Okay. Thank you.
(46:56) No problem. Um, question. Oh, go ahead, please. Question about kind of the background. Um, this is fantastic. Stephanie, Ian, this is so fun that you worked your ass off for this for us. And David, thank you for for having this meeting. It's great. A AMG um jam. So uh the question I have is the hard question which is about you're talking about outcomes and do you have any insights and how you take this magic that you're providing to engage with that huge leap and jump across the company between what the marketing people are saying and what the sales people are actually checking or
(47:35) SKUs that are moving and what what actually as you say outcomes deliverables um the the question really is about you know the sales loop back. Do you have any experience or insights? Cuz I I bet that is the steepest hill that you're climbing. Yeah, 100%. So, um, when we work with clients, uh, on these strategic projects, they're often bringing their own data to the party and often that's sales data.
(48:07) So, for for instance, DirecTV, um, we are analyzing all of Direct TV's social content. Um and one of the outcomes that they care about that their seuite cares about is net sentiment score. So that that's one of the metrics that we they really care about. But also they have just brought to like we did a project on net sentiment score to to kick off and now they've just surfaced a bunch of website traffic and signups data.
(48:32) And so we're we're then correlating the social performance with traffic driven to the website and then people who actually convert. That's a super rich data set. Like clearly we're not normally privy to that data, but if the brand brings that into the process, we can absolutely correlate like our data with their data and give them insights not just around how to provoke great emotions, but how to then result in how how can we drive the results that are you are you talking to them about literally Direct TV new subscribers? Yes. Exactly. Exactly right. Yeah. Because they've got that data. Yeah. Yeah. So, that's a really powerful way
(49:09) to use like when the brand brings a lot of their own data and we mix it in with ours. Then you you get the why. Why did this content drive the outcome that you're looking for? In that case, it's new subscribers. Yeah. Yeah. Let me I I have a question to uh I can squeeze it in. Uh Ian, Stephanie, great presentation.
(49:30) I super super valuable information. Uh I have a question around uh David itself. Are you planning to make this available as a product as a subscription service or is that an internal tool that you're using uh for your services only? I could take this and a few people have commented above and so I added some um you know pricing for managed service. So we have two ways to buy managed service or self-s serve.
(49:59) on the self-s served side, it's giving you some directional emotions, but the managed service is really these deeper dives that we've been looking at um you know, analyzing all elements. And so for a single asset test, anywhere from 5 to 7K to really understand why the why behind it um 20 asset tests in the 18 to 20k range um to really get figure out that content at scale and that that deep dive whether it's your own things, a competitive landscape um and then we can do bespoke projects beyond that.
(50:28) So, uh, drop us a line. Let us let us connect on LinkedIn or I'll I'll put my email. We also have a giveaway. Um, anybody who wants to look at, uh, you know, a fuller report of this, you can download that. I'll I'll drop the link in now as well. So, David, you should introduce them to Donovan because they're pretending to build this.
(50:47) Yeah, for sure. I mean, literally, they're pretending to build this and why should they bother? They should talk to these guys. I mean, like, call now if you, you know, or or I'll email Donovan if you don't. So, uh, let you do it cuz this they're literally as I in tongue and cheek pretending to build.
(51:05) This is what's happening, right? But we have a built. So, and we're talking about Oglev is what I'm talking about. Yes. From last week. Yeah. We would love to be connected. Please. Um, let's continue. So, um, on that on that point, Rory Rory Sutherland is uh has just become an adviser of the of the business.
(51:24) Actually, we was on a call with him like an hour ago. Um so yeah he could help um move that along with as well. Well well thank you so much Ian for presenting all this and and Stephanie all your insights and and great to connect with you both uh appreciate everyone coming today. Yeah stay tuned for the updated uh info. Um anything else anyone needs from this then uh just let me know.
(51:54) Uh happy to connect you with everyone and uh um me and Stephanie hope to see you again soon. Thank you all. It's great to be appreciate it. Cheers. Happy Valentine's. Bye-bye. Cheers. Happy Valentine's. Bye for now. All those emotions. I know. Perfect day. Very emotional you got. You got an 89.5 by the way. Just just so you know my emotion. I just tell you that. Thanks David. See you soon.

## How Seeda.io Uses AI to Fix Messy Marketing Data and Redefine ROI

Speaker: Michael Kingston
Published: 2025-10-17
Tags: ai in marketing, ai powered marketing, marketing mix modeling
Video: https://www.youtube.com/watch?v=JlcG2Y0GxOg&t=15s
Page: https://aimarketersguild.org/sessions/how-seeda-io-uses-ai-to-fix-messy-marketing-data-and-redefine-roi

(00:05) Hey everyone, welcome to another edition of AI Insiders with the AI Marketers Guild. I'm David Berkowitz, and it's a pleasure to host Michael Kingston today from [Seeda.io](http://Seeda.io). Michael is one of our longer-distance callers.

(00:26) I don't want to say longest distance because we have had guests from all around the world here. We also have our AMG APAC series. Michael wound up joining us on this stage at an uncomfortably early or late hour for him, as he chooses. Seeda has been doing fascinating work in marketing mix modeling. I've been talking with Michael and the team for a good chunk of the year, if not longer, and learning quite a bit from them. I'm always excited to hear more about

(01:02) how we really apply AI to this area, which we haven't discussed much lately. Michael, welcome and good morning or good night. Hey David, good morning, and thank you for having me. It will be great to hear more about what you're up to, what you're excited about these days, and dive in.

(01:29) You're welcome to present or share as much as you like, or just keep this conversational. Glad to have you here. Hey David. To introduce myself, I'm Michael Kingston, founder and CEO of [Seeda.io](http://Seeda.io). We specialize in marketing mix modeling, and in particular bringing marketing mix modeling to businesses of all sizes.

(01:55) The biggest surprise people have when I meet them is that MMM is now cost effective and more readily available to businesses of all sizes. It's no longer reserved for the biggest companies in the world. We're bringing it to mid-market businesses and enterprise businesses globally.

(02:20) When you say "of all sizes," I’m used to seeing IAB reports and talking to research firms where you have to spend a ton before you can even talk to them about any kind of media or marketing mix modeling.

(02:49) You seem to be saying that's not the case anymore. No. Seeda is five years old. I set up Seeda five years ago, and in that short period I've seen the cost of the technology that fuels MMM come way down. Our mission at Seeda is to bring the technology to as many relevant folks as possible.

(03:24) The cost has come down to the point where we're bringing it to businesses that spend as little as $50,000 a month on their marketing budget. We have fees for different types of businesses. Our entry-level fee at the moment is $5,000 per month. When I started five years ago, we couldn't have delivered a meaningful MMM solution for $5,000 per month.

(03:58) But the underlying technologies, such as machine learning, have become very cost-effective and very powerful. We're trying to bring it to those businesses. In terms of revenue, it used to be reserved for the billion-dollar-plus range.

(04:18) Now, our smallest business does about $10 million in annual revenue, is growing rapidly, and expanding its marketing mix. We're able to come in at the right time in that business's growth journey and provide advanced marketing measurement, which ordinarily wouldn't have been available to that business just a few years ago.

(04:49) There are two acronyms of MM that I’ve heard: media mix modeling and marketing mix modeling. When you're saying $50K marketing budgets, is that all working media? Are you looking at things that are not paid media as well? Do owned email channels or other things come into play? What are you looking at?

(05:26) We define it broadly. Predominantly I hear folks calling MMM "marketing mix modeling," while the traditional definition is "media mix modeling." At Seeda we define it as marketing mix modeling and we look at the full marketing mix.

(05:53) It's not just media. It could be influencers, physical events, email or CRM. We look at the full marketing mix. It really becomes very important in larger businesses, but we define it as a broader marketing mix, which means marketing mix modeling.

(06:24) What's involved in modeling? How does the process work? Keeping it high level, I can get into more detail. There are various techniques to marketing mix modeling. We believe the most accurate possible model gives the best results.

(06:53) For those who know nothing about MMM, it's an AI algorithm that looks at all of your marketing spend. The underlying technology we use is a Bayesian machine learning algorithm.

(07:15) Our process: we've spent five years building a 30-day onboarding process. There are two really important parts to MMM. First, there's the data that you're going to model. It's the old saying: garbage in, garbage out. If you feed garbage data into any algorithm, no matter how good that algorithm is, you're going to get garbage results.

(07:47) During the 30 days that we onboard new customers, we spend at least two weeks making sure that the data we feed in is not garbage. We've developed a lot of automations for that. Once we have accessed the data, reviewed it, cleaned it, and transformed it into more accurate data, that's our first step.

(08:10) Second, for every single customer, we build a custom model. We start from scratch with every single customer. We don't sell a self-service tool where you're getting an out-of-the-box, preconfigured model.

(08:34) We customize each model for each individual customer dataset. Every brand has unique data. To get the best results, each brand needs a fully customized MMM model. Once we add clean data to a custom model trained by an expert—we use a lot of automation in the background—we then, after the 30-day onboarding,

(09:07) have a model that's ready for testing. We do rigorous testing before we use the model to predict any marketing mix results. Once it's tested, you have a custom-built machine learning algorithm for your business that you can use to ask questions and, as a marketer, get more accurate, fuller-picture answers. It's been working out very well for our customers so far.

(09:47) Can I ask a question? The garbage-in-garbage-out makes perfect sense; I've lived that. What about if the data is pretty good but it's all in silos and doesn't talk to each other? One big challenge is unifying the data.

(10:11) Are you a middleware layer that sits on top and can find it wherever it resides, or does it need to be centralized first? We tried to be the company that only worked with good data, and I quickly found out that very few marketers have good data. We call it MMD—messy marketing data—because most marketers are embarrassed about their data. They haven’t had the time or budget to sort it out.

(10:49) We provide the full stack. We find your data. It could be in all sorts of places. We unify it—pull it into what we call a big data warehouse.

(11:11) We ingest it, review it, find problems, stitch it together, and use data transformation to combine data from various sources.

(11:30) We've built a rigorous ingestion and data cleaning process to help you sort out messy marketing data. We test it before we model it. You can never get a dataset 100% accurate, but we get our datasets very accurate. We make sure you and your team approve that everything looks good before we build the custom MMM for you.

(12:07) Great. Thank you. I’ve spent my life cleaning messy marketing data. Most of us have. Thanks. Are we heading toward MMM for all, where there's a good-enough out-of-the-box version that anyone can tap into, and then when you want to make this work you need someone like you to create a custom model, albeit more efficiently than in the past?

(12:55) We've tried to build a self-service tool and have spent years trying. Over the next three to five years, I feel the market is moving in that self-service direction.

(13:22) The more complex your dataset, the quicker you'll figure out whether a self-service tool adds value if it hasn't been configured as custom as you need. It's not one-size-fits-all.

(13:45) Figure out what sort of brand you are and what solution you need. There are open-source tools available for free. You just need the skills to set them up. They're a good start. For brands above $10 million in annual revenue and beyond,

(14:05) we advocate doing a more rigorous job because, as I said, the more work you put in, the higher the accuracy and the better the predictive power the algorithm will give you.

(14:26) MMM is based on Bayesian machine learning, which is predictive. Once it's built and tested, you can start asking it questions and it gives you predictive answers.

(14:51) Over the past decade, the last 10 years of marketing measurement have been chaotic, predominantly because of messy marketing data and privacy changes. Many marketers are frustrated and skeptical about numbers from current providers because the landscape has changed so much. It's refreshing to bring a powerful predictive algorithm to marketers so they can trust the numbers.

(15:58) What's your point of view on letting the media agency do the analytics and the marketing mix modeling versus the client doing it themselves? I have a bias, but I'd like to hear your opinion.

(16:24) Many in the industry call it marking your own homework. Our view is to collaborate with whoever we're working with. We don't have a strong view either way. Most of the time the brand brings us in and we work collaboratively and transparently with the agency or multiple agencies, the brand, and our team.

(16:57) Other times agencies bring us in, and they work transparently with us as well. After dozens of projects, I'm not biased one way or the other. I haven't seen a badly performing agency bring us in.

(17:35) The top 10% of agencies are truly in it for their clients. Whether they bring us in and results show the media channels they manage are not performing as well as others the brand runs in-house, we’ve been lucky to work with high-integrity agencies. MMM has been around for decades, but we’re at the early stage of mass adoption of Bayesian machine learning MMM. As we work with a broader set of agencies, my view might evolve.

(19:02) What's your bias? I call it letting the elephant watch the peanuts. The objectivity isn't always as good as you'd like. Agreed. Is it possible to see a demo?

(19:30) Before the demo, I give everyone a conceptual diagram to frame what you’re about to see. Can everyone see my screen? Yes. David, can you see a series of blue moving dots? Great.

(19:55) This shows the data flow we believe you need to set up to deliver highly accurate MMM. On the left side, you've got all the different types of data—it's not just digital media. It's all types of media, all sales data, all CRM or email marketing data, SMS, the digital media like TikTok and Facebook, and offline channels, which are a big part of the mix for larger brands. First, we connect all that data.

(20:46) We spend a big chunk of our time bringing your data together, putting it into a single cloud data warehouse. We've written data processing automations to clean it. Only then, once the data is processed and cleaned, do we feed it into our marketing mix model, where we build a custom model for each customer.

(21:13) Once that's done and tested rigorously—we have a very rigorous testing regime—before we start talking about how we'll use the MMM, we run multi-week tests on the algorithm to make sure it's accurate and useful.

(21:38) Once testing is done, we break our insights and value into six categories: budget optimization, our industry factor concept (IF factor), predictive analytics, saturation curves for every marketing channel,

(22:05) seasonality, which is often overlooked but very important, and sales forecasting, which CFOs love. Conceptually, that's what's under the hood of what I'm about to demo.

(22:34) This is our MMM hub. We've built a full suite of marketing measurement: a metrics hub, an MMM hub, our IF factor feature, and a business optimization hub. First, the MMM hub. We want the full marketing team on the same page. We present in real time. For example, this brand does about eight channels: some above-the-line traditional marketing and a lot of digital.

(23:24) You'll see us bringing all that data. You'd be amazed how many marketers work with incomplete and disjointed datasets.

(23:43) First, we bring all that data out of siloed systems and present it in a unified and accurate way. Typically we go back two years to give a holistic view.

(24:06) We bring in each channel and how it has contributed from an impressions perspective over time. This gives the first accurate step of what's happened historically. Then the first time you see MMM outputs, we call this our layer graph: a two-year view.

(24:27) All your data has passed through our algorithm, and the MMM assigns a contribution for each channel over about two and a half years. It's liberating to show this to a marketing team for the first time. They’ve seen biased and disjointed reports and never had a full picture. This is the first time they've seen the output of an algorithm. You can go down to a weekly view and get very granular. The MMM we build gives a week-on-week view that you can roll up any way you like.

(25:36) From a performance perspective, our product lets you finally see the ROI of each channel simply. We can go down to the channel level and the campaign level if needed. For a monthly period, here’s the ROI. Many realize their base contributes a lot to marketing. "Base" is what happens if you turned off all marketing today. Most brands have non-marketing impacts on sales—brand effect or market effect. MMM measures that brand or other market factor inputs.

(26:47) Next is our response curve. Once the data is processed and pushed through the MMM model, it breaks down how each channel is performing, and we try to narrow down the sweet spot of your budget for each channel,

(27:12) interpreting the point of diminishing returns. Every week of the year can be different depending on seasonality. For example, a YouTube view might be approaching saturation but not there; search is often saturated. Each week of the year, we show where a channel is on this curve. We try to avoid oversaturation and find the sweet spot. Seasonality is a massive factor. Channels change all the time. We measure weekly. We're an always-on measurement solution.

(27:52) These saturation curves change week on week. You can check them regularly. MMM also measures ad stock and lag analysis. We break down each channel's ad stock and lag to understand sweet spots for those metrics.

(28:13) Seasonality is a big factor. The algorithm measures your seasonality across every channel and gives you an overview of the general trend for your business.

(28:32) The cherry on top of MMM is that it’s predictive. Question: what's the assumption about attribution? First touch, last touch, multi-touch? What are we tracking against here?

(28:57) Great question. MMM is not an attribution model. MMM brings all your data out of those channels wherever it sits—Google, Facebook—stitches it together, and uses a statistical model. It uses an aggregated view.

(29:34) Attribution, by definition, is user-level attribution: trying to track people around the internet and assign value to individual users. MMM doesn't track people. That's become very inaccurate over the past decade.

(30:24) Instead of tracking people and assigning value to an individual user, we aggregate the data to a higher level and use statistics. For example, we spent $10,000 on a Facebook campaign. The algorithm looks at your data holistically and finds signals in that data to assign a contribution to that spend.

(31:22) It could be that the algorithm finds a probability that the $10,000 delivered zero because it can't find a signal. Or it finds a signal that correlates to $100,000 in sales. It assigns a contribution accordingly. That's what you're seeing in the outputs—an aggregated view. Attribution is often binary at the user level. MMM gives a probabilistic, statistical view of contribution across your marketing mix. Does that help answer the question? Yes. It's a predictive model.

(32:39) Yes. Someone else jumped in—Okaro here. Welcome. Any other questions or comments on what we've seen from Michael so far? All good. Back to the demo.

(33:13) Why are we doing this sophisticated data analysis? To get better clarity on our data holistically. As a marketer, I want to know what budget I should spend on each channel to maximize my goal, usually sales or new customers.

(33:44) We've built a budget optimizer. You can use the algorithm's predictive power and configure it depending on your business. Some want to consolidate their budget and squeeze every dollar from the existing or reduced budget. Others are growing fast and want the optimum mix and increase in budget before hitting diminishing returns.

(34:26) We provide a channel view. For a brand with $5.2 million in revenue off a $1.3 million spend, this is the existing mix based on the past two years of data. We then give scenarios to help decisions.

(34:52) In one example, keeping the budget the same but optimizing mix to about $1.4 million in spend yields a 13% increase in revenue. Increasing budget by 15% yields a 19% increase with a different mix. Another scenario models a 30% increase. Once you've done the hard work to set this up, you can move beyond intuition and use a data science–backed approach to measurement and optimization.

(36:02) It's becoming more cost effective as technology improves. That's our MMM hub.

(36:27) Unless there are questions, I'll show our IF factor section. Do you have a point of view on customer acquisition costs versus ROAS?

(36:58) The neat thing about MMM is we can measure everything. Brands have their own ways of making decisions. Many are CPL- or CAC-focused and use that as a primary metric. We can set the model to measure whatever metric matters to you. ROAS comes into it, and I'm about to show our IF factor. Do you mean which is better? There’s a trade-off.

(38:05) We consider ourselves a data science company. We can measure everything, but when we come in we assign a customer success expert to understand what you're measuring and why. What we’re seeing in the market is what I call the great sugar addiction of the past few decades: measuring ROAS, especially in-platform ROAS. Marketers have become dependent on in-platform metrics.

(39:13) I'm a big believer in not using in-platform metrics like ROAS as a north star at the macro level. You should look at holistic measurement solutions like what I showed earlier. To help marketers, we've developed the IF factor. Instead of looking at the in-platform ROAS number, we pull that attribution data out of the platform,

(40:18) cross-reference it with our holistic model, and give you calibrated numbers we call an IF factor. For example, a brand looked in GA4 and saw Google Search at close to a 4 ROAS in-platform. Cross-referenced with MMM, the incremental factor is closer to 2.

(41:32) You should use in-platform metrics for smaller, day-to-day decisions. For weekly or monthly strategic decisions, don’t use ROAS as your north star. Use a calibrated, incremental view like IF factor. Does that answer your question, Amy? Absolutely. Thank you.

(42:21) We provide IF factor for each digital channel we can. For linear TV, without a data feed we can’t provide it, but for other digital channels we provide a more calibrated number. If the platform is over-reporting by 90%, you'd make very different decisions. We aim to give access to holistic measurement so you can make better choices.

(42:46) That’s 90% of the demo. Lisa, does that give a better view of what to expect? Yes, thank you. Much more real and vivid.

(43:39) Another question: we work on channel strategy and suggest channels to experiment in. Is your platform capable of surfacing channels to explore based on data across your clients? No. For us, MMM needs data before we can give results. You have to be advertising in that channel before we can measure it. Once the algorithm picks up a strong enough signal from a new channel, we can advise.

(44:34) We’re not an advertising agency; we’re a data science company. We can give market-research-style perspectives but won’t advise you to start a new channel. For one QSR brand, we saw radio performing well for peers and suggested it may work, but until they started and we measured it, we couldn’t say. They bought a three-month radio plan with strong creative. After about two months we had a significant read and advised another six months. We need data to measure before giving a view.

(46:14) Follow-up: what's the lower threshold where it breaks even for a brand to pay for your SaaS model? It's a monthly fee. Typically brands under $50,000 a month in marketing spend—between $500K and $1M a year—we don't think we’ll generate enough return to cover our fee. You're better off spending on media to grow the brand.

(47:03) We try to deliver at least a 10x return on our fee. For now, the line is about $50,000 per month and up. We're a sophisticated solution with associated fees. There are open-source algorithms you can start with for free, though you may need to hire someone to set them up.

(47:44) It's not as good as what I demoed, but if you're in that smaller spend range, it gives a better read beyond in-platform metrics. It's a maturity cycle: start open source, then move to more sophisticated solutions as you mature. Traditionally these have cost hundreds of thousands of dollars. Our entry fee is $5,000 per month on a SaaS model. At the right time, it's cost effective for the right brand.

(48:35) Quick question from Kizia: how low can spend be on a new test channel before you get reliable results? Do you need $100K or $1M, or how low?

(49:05) We've seen results in the thousands of dollars. It depends on channel, time of year, and other factors. A $10,000 media buy over multiple months of measurement has been enough for the algorithm to pick up a signal and assign contribution.

(49:35) So, $10,000 and above in spend over time should give some read to inform whether to scale or plateau. Thank you. This is great. Anything else before we wrap? Michael, what's the best way to stay in touch?

(50:24) LinkedIn is great. Or go to my website—I'm on the other end of the chat on the site. Message me there. I'm in your Slack groups too, so mention or DM me there.

(50:43) This is great. We'll share the recording for those who couldn't make it. This is just the start of conversations to come. It's important to see where AI is changing things. With all the messiness of AI, it's good to see areas where data becomes more accessible and decisions more impactful.

(51:21) Thanks for highlighting some of that. You're very welcome, David. Thanks again for having me and for the great questions.

(51:38) Thanks everyone for joining. Appreciate the great questions from Natalie, Keia, Lisa, and everyone who contributed. Great to see you all and see you next week.

## The AI Time Tradeoff Deep Work or Just More Work

Speaker: Idil Cakim
Published: 2025-10-09
Tags: productivity, media consumption
Video: https://www.youtube.com/watch?v=biL2eTYXLUs
Page: https://aimarketersguild.org/sessions/the-ai-time-tradeoff-deep-work-or-just-more-work

What happens to the time AI saves us—and are we using it wisely? In this AI Insiders session from the AI Marketers Guild, Idil Cakim, Founder at Iris Flex, shares findings from her AI Gap Study on how professionals are reallocating their time thanks to AI.
We explore how AI impacts work-life balance, productivity, gender disparities in tech adoption, and even what a future with AI professors might look like.
[01:01] What Is the AI Gap Study and How Does It Analyze AI Time Savings?

Answer / Description:
The AI Gap Study is a research project conducted by Idil Cakim, Founder of Iris Flex, to examine how professionals are reallocating and shifting the leisure and work time they gain through artificial intelligence. While traditional industry metrics focus heavily on productivity ROI, this study specifically explores user inclination, leisure redistribution, and how AI-driven efficiencies impact work-life balance.

The study highlights a cultural friction in the United States, where a deeply ingrained Protestant work ethic drives people to search for "productivity" far more than "work-life balance" or "personal growth." Google search trends analyzed via the "My Telescope" tool from 2021 to 2025 demonstrate that AI is overwhelmingly associated with productivity and efficiency rather than personal well-being. The AI Gap Study bridges this gap by investigating what happens when AI successfully frees up human time and whether users possess the structural support to allocate that time toward non-work activities.

Keywords:
AI Gap Study, Idil Cakim, Iris Flex, AI time reallocation, productivity search trends, My Telescope data, work-life balance AI, Protestant work ethic productivity

[03:49] How Much Time Do Daily Users Save by Using Generative AI?

Answer / Description:
Daily users of generative artificial intelligence save an average of 2.2 hours per week, which equates to over 5% of their standard work hours. For frequent users, the savings are even more pronounced, with 34% of daily generative AI users reclaiming four or more hours per week.

This data, sourced from a large-scale Federal Reserve Bank of St. Louis study, confirms that generative AI yields tangible time-saving benefits. The time savings are highly concentrated in specialized fields such as computer science, mathematics, information technology, management, and business/finance. Interestingly, the education and healthcare sectors are also emerging as areas with high time-saving potential, whereas personal services and administrative roles experience far lower time savings due to their reliance on hands-on human interaction.

Keywords:
generative AI time savings, Federal Reserve Bank of St. Louis study, daily AI users efficiency, AI productivity gains by industry, hours saved using AI, workplace AI efficiency

[06:09] How Do Global AI Time Savings Compare to AI Use in the United States?

Answer / Description:
On a global scale, workers using generative AI save an average of one hour per day, with a significant portion of that saved time being redirected toward creative tasks, strategic thinking, and personal work-life balance. According to a global survey by the Adecco Group spanning 27 countries, 26% of respondents use their reclaimed time to focus on strategic, high-level business tasks rather than routine administrative work.

The allocation of saved time varies heavily by culture. While US professionals lean sharply toward maximizing productivity due to domestic cultural norms, international workers frequently prioritize family life and work-life balance. This global variation suggests that when corporate structures allow it, AI-driven time savings naturally support healthier lifestyles and deeper strategic engagement rather than just an increased volume of transactional work.

Keywords:
Adecco Group global AI study, global AI time savings, strategic thinking AI, cultural differences AI productivity, international work-life balance AI, creative work reallocation

[07:04] Why Do Employees and Managers Waste Time Saved by Generative AI?

Answer / Description:
Employees and managers frequently waste saved time because organizations do not currently govern, track, or explicitly direct the redistribution of AI-generated time savings. A study conducted by researchers in Switzerland surveyed over 300 generative AI users and 83 director-level managers, revealing that 36% of managers wasted more than half of their saved time, while 83% of all users wasted at least a quarter of it.

Without active intervention, saved time is typically absorbed by performing more of the exact same low-value tasks rather than elevating to strategic work. To prevent this waste, researchers suggest that companies must implement formal tracking mechanisms or structurally adjust the workweek—such as instituting a four-day workweek or ending workdays earlier. Without an organizational structure that guides employees on how to reuse their free time, AI efficiency gains fail to translate into strategic growth or authentic well-being.

Keywords:
University of Lausanne AI study, wasting AI time savings, AI time tracking, managing AI productivity, corporate AI governance, strategic time reallocation

[08:27] How Would US Workers Spend Their Time in an AI-Enabled Four-Day Workweek?

Answer / Description:
If technological advances in AI successfully reduce the workweek to four days, the primary human inclination of US workers is to socialize with friends and family rather than take on more work. Data from a nationally representative sample of US adults in the AI Gap Study indicates that spending time with loved ones is the leading choice for an extra free day, followed closely by engaging in media and leisure activities.

When these activities are categorized, seven out of ten US adults choose to redirect their AI-saved time into media consumption and non-media leisure, both of which spur external economic growth through retail and travel. Within the media category, screen-based activities like connected TV (CTV), traditional television, videos, and movies are the top choices, followed by audio-based media.

Keywords:
four-day workweek AI, AI Gap Study survey, media consumption leisure, economic impact of AI time, CTV viewing trends, socializing four-day workweek

[10:17] How Do AI Time Allocation Preferences Differ Across Demographic Groups?

Answer / Description:
Demographic factors such as generation, gender, and student status heavily dictate how individuals choose to allocate free time gained from artificial intelligence. Millennials represent the "movable middle" of AI adoption; they are the group most likely to allocate saved time to media and continued education, and they are highly proactive in requesting AI training to advance their careers.

Gender lines also reveal sharp contrasts: women overwhelmingly prioritize using saved time for rest, recovery, and socializing, whereas men report that they would use their extra time to pursue additional work and education. Students represent another distinct category; they show zero interest in using AI-reclaimed time for further training or schooling, choosing instead to prioritize media consumption, entertainment, and leisure.

Keywords:
Millennial AI adoption, gender differences AI use, student AI behavior, career advancement AI, AI training demographics, rest and recovery time savings

[12:33] Why Are Podcast Consumers Highly Valuable Audiences for AI-Driven Marketers?

Answer / Description:
Podcast consumers are uniquely valuable to marketers because they are highly proactive, tech-forward "prosumers" who are exceptionally likely to convert their AI-reclaimed time into commercial and leisure spending. Data from the AI Gap Study reveals that podcast listeners consistently outpace other media consumer groups in their willingness to engage in new leisure activities and purchase customized products using the time they save via AI.

Furthermore, podcast listeners demonstrate a high openness to data-driven marketing. They are far more willing than standard TV or social media consumers to share their personal information with brands in exchange for highly tailored products and services. This makes the podcast-listening audience a premium target for brands looking to leverage AI-driven hyper-personalization.

Keywords:
podcast listener demographics, consumer behavior AI, hyper-personalization marketing, data sharing preferences, proactive media consumers, premium advertising target

[15:08] How Will AI Shift Media Buying From Static Time Slots to Contextual Micro-Moments?

Answer / Description:
Artificial intelligence will transform media buying by shifting industry metrics away from rigid, linear time-slot models—such as morning, midday, and prime-time blocks—toward fluid, contextual, and mood-based micro-moments. As AI frees up consumer time in non-linear patterns, traditional static media buying schedules will become obsolete, forcing platforms to serve highly targeted content and advertising in real-time.

This shift means that attention, rather than simple "time spent," will become the premium metric for publishers and advertisers. To capture these fleeting micro-moments, media buying systems must evolve to measure the emotional depth of consumer experiences, user fulfillment, and immediate context. Brands will be required to demonstrate high relevance and immediate delivery to win consumer trust in an increasingly saturated digital environment.

Keywords:
AI media buying, mood marketing, contextual advertising, attention economics, micro-moments media, personalized content delivery, programmatic ad buying

[19:05] What Is Jevons' Paradox and How Does It Apply to AI Productivity?

Answer / Description:
Jevons' Paradox is an economic theory stating that as technological progress increases the efficiency with which a resource is used, the total consumption of that resource tends to rise rather than fall. In the context of artificial intelligence, instead of allowing employees to work fewer hours, corporate systems often exploit AI efficiencies to demand a higher volume of output within the same 40-hour workweek.

Historically, this pattern has played out across major technological leaps, including the rise of the internet. With AI, software engineers and knowledge workers are already reporting that their time savings are easily tracked by management, which often results in them being assigned even more work. This dynamic transforms a promised tool for work-life balance into an engine for increased workload, leaving workers feeling overwhelmed rather than liberated.

Keywords:
Jevons' Paradox AI, workplace exploitation efficiency, knowledge worker burnout, tracking AI time savings, corporate output demands, history of working hours

[26:59] What Is AI Work Slop and How Does It Increase Employee Workload?

Answer / Description:
AI "work slop" refers to low-quality, inaccurate, or unrefined content generated by artificial intelligence tools that employees must spend time filtering, correcting, and restructuring. According to a study published by the Harvard Business Review and Open Data Science, between 60% and 70% of knowledge workers regularly encounter AI-generated slop in their professional workflows.

Far from saving time, this phenomenon actively hinders productivity. In fact, 40% of knowledge workers report that integrating AI into their workflows has actually increased their overall workload because they must dedicate significant hours to weeding through poor AI outputs and verifying factual accuracy. This underscores the reality that AI adoption has a steep learning curve and can introduce new operational inefficiencies if the quality of the tool's output is not strictly managed.

Keywords:
AI work slop, Harvard Business Review AI, Open Data Science study, low-quality AI output, verification workload, knowledge worker inefficiencies, prompt engineering quality

[30:09] Why Must Businesses Measure Productivity by Attention and Outputs Instead of Hours Billed?

Answer / Description:
Businesses must transition to measuring productivity through output quality, attention, and results rather than hours billed, because AI allows efficient employees to complete traditional eight-hour tasks in a fraction of the time. Retaining static hourly metrics penalizes highly skilled workers who utilize AI to maximize their speed, creating a counterproductive incentive structure.

This shift mirrors the Results-Oriented Work Environment (ROWE) movement, which asserts that corporate focus should lie entirely on whether agreed-upon deliverables are met for clients and teams, regardless of the time spent. In an AI-assisted economy, measuring hours spent at a desk is an outdated relic; attention, strategic execution, and high-value outputs must become the primary key performance indicators (KPIs) of employee performance.

Keywords:
Results-Oriented Work Environment, ROWE, billing hours obsolete, attention KPI, output-based productivity, measuring AI ROI, corporate performance metrics

[37:39] What Are the Key Gender Disparities in AI Adoption and Data Privacy Attitudes?

Answer / Description:
There is a significant gender gap in artificial intelligence adoption, driven largely by differing attitudes toward data privacy, risk tolerance, and tool utility. Research indicates double-digit percentage differences between men and women regarding AI adoption rates, with men adopting tools much faster and expressing more relaxed attitudes toward sharing personal data.

Women, who hold massive consumer and breadwinning power in the modern economy, are statistically much more discerning and skeptical regarding data privacy. They are far less likely to share personal information in exchange for customized AI-driven products or services. This privacy concern, combined with a potential skepticism toward outsourcing natural communication tasks to a "robotic" interface, creates a unique barrier that developers and marketers must address to achieve equitable AI adoption across genders.

Keywords:
gender gap in AI, AI data privacy, female consumers AI adoption, data sharing skepticism, technology gender disparities, trust in AI systems

[48:12] How Can Market Researchers Use Synthetic Audiences and Synthetic Data Effectively?

Answer / Description:
Synthetic audiences and synthetic data are AI-generated buyer personas and simulated datasets that allow market researchers to run hundreds of cheap, rapid testing scenarios to evaluate product concepts before committing budget to real-world human testing. While not a complete replacement for human feedback, platforms like Ask Rally leverage synthetic personas to help researchers understand how specific demographic cohorts are likely to respond to products or messaging.

Using synthetic audiences is highly effective for reducing waste and refining research questions during the exploratory phase of a study. By understanding the underlying values of a target audience—such as a specific group's focus on transparency—researchers can use synthetic proxies to identify the precise language needed to earn their attention, allowing subsequent real-human interviews to be far more targeted, cost-effective, and successful.

Keywords:
synthetic audiences, Ask Rally platform, synthetic data research, AI buyer personas, predictive market research, cost-effective consumer testing, simulated datasets

## Whats Real in AI Marketing Ethics Trust and Transparency

Speaker: Dr. Cecilia Dones
Published: 2025-10-08
Tags: agentic ecosystem, virtual influencers, ai ethics
Video: https://www.youtube.com/watch?v=-3VbGDSB8dw
Page: https://aimarketersguild.org/sessions/whats-real-in-ai-marketing-ethics-trust-and-transparency

### AI Insiders kickoff and guest intro: Dr. Cecilia Dones

(00:06) Hello everyone. I'm David Berkowitz and welcome to another edition of AI Insiders by AI Marketers Guild. And we've got an exciting guest today, someone I'm really eager to hear from, Dr. Cecilia Dones. Uh just a a tremendous background as a chief data officer among uh uh other esteemed roles. and we were geeking out on what's going on in the AI space. We met through uh initially another community.

### Community background and welcome to Cecilia

(00:33) I've been a longtime member of research wonks, a big fan of that for for folks who are are deep in the data and analytics space. And about 90% of the posts there are over my head, which is why it's fun being in some rooms where where where there are such smart people so deep in the field that uh uh I can just try to learn a few things from. But uh uh Dr. Don, Cecilia, welcome.

### Appreciation and audience context

(00:58) Good to have you here. Thank you. Thank you. And thank you for everyone for joining. I know it's the middle of the week. It's also lunchtime for some of us who are on the East Coast and so the dedication and attention is much much appreciated. Yeah. So would it be helpful unless everyone LinkedIn stalked me which could be a thing. You're definitely okay to do that. Um so a little bit about me.

### Cecilia’s background: qualitative research and storytelling with data

(01:25) Um, so I am a qual researcher who spent my entire career being fascinated in trying to tell stories about people using data and so everything I've ever done is following that curiosity. Um, the good part of my industry experience was focused in marketing and ad. So I have done my tour duty with uh, WPP and Pubis and um, I've also done a tour duty on the brand side.

### Brand-side experience and doctoral research focus on trust

(01:58) Um so some brands that may be familiar L'Oreal, LVMH and more recently um as being part of the data game that we all do. Um I finished uh my doctoral dissertation um and what I was focusing on was trust in authenticity signals in technology mediated interpersonal communications which is the longest way of saying I have trust issues with the internet um and I don't know what to believe anymore and so I figure maybe some research in that area might be helpful for others as well. Mhm.

### Teaching and AI ethics work (including K–12)

(02:29) Um what I'm doing recently uh I'm I do teach uh so I teach primarily AI ethics. Uh more recently I have been teaching K through 12 AI ethics which is very interesting. So trying to speak about AI ethics and responsible AI to middle schoolers. Oh so many things I've learned.

### AI literacy in youth and upcoming NYU role

(02:52) Um but it's very interesting to see how organizations are really investing in that kind of AI literacy uh especially in the youth. Um I teach that uh I will be placed uh in NYU uh in the spring uh teaching emerging technologies there. Uh so I will be continuing that and I hang out in the responsible AI AI ethics space um writing uh doing a little bit of research and consulting organizations that are trying to do this AI thing a little bit better.

### Topic pivot: fake news, artificial intimacy, and marketing lens

(03:24) So in transparency, David and I uh collaborated or conspired to have this conversation after he was back from holiday. Um because I had mentioned AI fake oh yes fake well first of all fake news and then AI fake news that makes it a little bit more complicated. Uh but David and I actually wanted to postpone this conversation till after he was back from holiday because I had mentioned my secondary research area which is AI um artificial intimacy.

### Agentic ecosystems and collapsing marketing funnel

(03:53) So when we start to have relationships that are a little bit interesting uh with machines and what does that mean from a psychological standpoint at the individual level and more broadly at the societal level. Um, but keeping it in marketing land, I was like, "Okay, I totally have a talk. I'll have slides, all the things, all the things." And then the news.

### OpenAI x Etsy and the rise of AI-native influencers (Tilly Norwood)

(04:16) And when the news happened um about Open AI and what they're doing with Etsy, collapsing the marketing funnel, and I said, "Oh dear, okay, this is interesting. This could be good for consumers or it could be interesting how marketers deal with it." um because there's almost no more any um the journey collapses, intervention points uh are removed from the process and then like within the last 24 hours which totally blew up my whole talk.

### Implications: from human-centric to agent-centric marketing

(04:51) Um does anyone know who Tilly Norwood is? Okay. Yes. Um, some would argue that this is our first uh um worldwide AI actor and apparently um she's looking for an agent. Um and so these two things represent what's happening in the latest wave of technology. Um and so I was thinking about it this morning actually as my presentation was blown up.

### Proposed idea: “AI marketing for AI” when machines sell to machines

(05:22) Us as marketers, our discipline began with oh gosh, we have to listen to our consumers. We have to understand our customers. We we are very human first. We have to be um making sure that there's always an alignment between product and the consumer and their consumer needs. And so we spend a whole bunch of energy trying to understand consumer psychology.

### Technology as actor vs facilitator and near-term outlook

(05:47) However, with these latest developments in technology that's uh changing our ecosystem, we're moving from what I would argue is human centric campaigns where we have personas, we focus on people, hopefully people um to more of an agent centric ecosystem meaning the machines are mediating so much of the process between the brand and the consumer.

### Will “agentic” experiences become ubiquitous?

(06:12) um it forces us to have to think about this ecosystem in a different way. And I will put this idea out there. If anyone wants to write this textbook, I am more than happy to be second author, third author, even an honorable mention. I'm very happy to.

### Cultural context and virtual influencers blurring lines

(06:29) Uh something I was thinking about that we should kind of write and maybe as practitioners it would totally make sense. AI marketing for AI when machines are selling to machines. I think that's it's a little bit clickbaity the title we can work on it. Um but I think that's where the industry is moving forward. Meaning more and more of the interactions especially in virtual and digital spaces will be mediated by these technologies that yes convey help us.

### Audience question: SAG, virtual characters, and what battles to fight

(07:01) It is the medium uh to convey our messages but they also act and that's the slight distinction from maybe previous iterations of technology. um previous iterations of technology was more like facilitating facilitating some of the consumption and so e-commerce that totally changed things for us. We all had to think about our e-commerce websites.

### Distinctions between human and AI in media consumption

(07:19) Um but this one is a little bit different in the sense that there is more agency and so do I think agentic is going to be a thing in 2026? We'll talk about it a lot. Maybe some brands will make mistakes that we can talk about even more. Um is it going to be a ubiquitous consumer experience? Um I'm cautious.

### Trust signals and marketer’s role in preserving trust

(07:38) Uh however, it is something for those of us who are marketing leaders and marketing strategists and have been in the game for a little bit longer. I think we have to actually really do properly think about how do we think of this ecosystem when the consumer journey has collapsed and that when machines on act on behalf of uh consumers itself.

### BIK (Benevolence, Integrity, Competence) trust framework

(08:03) Um, and and I mean the you bringing up Tilly Norwood right now, we have the Screen Actors Guild coming out very strongly against this and and making a stance for human creativity. Um, but it's also funny cuz like uh we also obviously like so much of what we consume, right? They're they're characters. Yeah. uh we're going and yeah we're watching the Marvel movies because of the characters in them.

### Transparency risks with platform-mediated recommendations

(08:36) Uh it's uh it's less important I think for most folks who's actually playing them. Although it's really fun to see Robert Daddy Jr. in his various roles and and whatnot. Um but then uh also you know we have this point where like like if we look at the rise of some of these virtual influencers like Lil Michaela um um that BMW actually did a sponsorship with and uh then like there like Lila for instance getting gigs anyway right like like there there are people paying her it you know its team to go and and run branded campaign. So, it's like like how do we even draw lines? I It's one thing to

### Why transparency matters: source, bias, and paid placement

(09:23) draw a line between like what's human and what's a AI, but when like so much of what we consume anyway is some like Yeah. Uh some CGI created Yeah. character to begin with when consumers are already like following these Yeah. characters that don't exist in real life. Um, yeah. I I mean like like how do we even make these distinctions anymore? And is it like like and I'm even curious your take cuz like I love like human first everything over tech, but like are these actual battles worth fighting? Like like is this like like is SAG going to be our last round of defense here, the Screen Actors Guild,

### Holding trust: benevolence, integrity, competence for marketers

(10:08) or is this just like some lost cause and they're going to be seen as Yeah. fighting some battle that that like that will that ship sailed, right? Like I'm I'm so curious where where your think of this as you see this controversy around Tilly Norwood come up. Yeah, no worries.

### Cultural norms, virtual influencers, and disclosure

(10:34) Um well, one compensation models have changed because business models are changing. That's just a given. uh as dynamic ecosystems change obviously the mechanisms and incentives that we have um to incentivize different behaviors will also change so that's going to happen do I have a crystal ball no um how it will change I don't know what I will say is that um maybe and I could be more clear about this is the two examples the open AI with Etsy and and this Tilly situation what makes it a little bit gives me the is that these platforms and these technologies are removing some of the social signals

### Defining trust in marketing relationships

(11:14) that we would normally depend on when we are trying to build trust. And as marketers, part of our agreement, part of our um the promise or the value we we create for the organization is we hold that trust with the consumer. we ensure that as best as we can um we are continuing to reinforce that trust between the consumer and the brand.

### Source transparency questions in AI-powered shopping

(11:43) Are there uh cases where there are cultures for example this is very common in China where you have virtual influencers that actually do live streaming and people still consume and it doesn't bother them and it's part of the cultural norms and values perfectly fine they are transparent that these are virtual influencers so in the case of chat GPT um and uh Etsy when a consumer starts to say okay I need a new charm for an or a gift or something like that and something pops up as a recommendation. Is it coming from

### Bias, paid placement, and missing signals

(12:20) OpenAI? Is it actually coming is it reading from u um the reviews from Etsy? Where is this recommendation coming from? Is it biased? Is it actually is the brand actually paying for it? We have no more signals for this and and that's where it gets a little bit tricky in the Tilly situation. I I get it. Good for PR.

### Framework recap: benevolence, integrity, competence in practice

(12:43) Um but the lack of transparency that hey she's AI and they were running all these adverts and uh messaging around her. Um yes it does get her in the news but again it's one of those things where it's one of the fundamental components uh regarding trust uh in specific that there needs to be some level of um one's perception and belief that the other person is going to act in such a way that is one beneficial um two um I would argue uh helpful And then three kind of consistent and I can give everyone a little bit of a framework here. Um so uh researchers from many many moons ago

### Applying BIK to AI marketing: what to prove

(13:28) it's called the Bick framework. I'm I'm quite certain some of us are already familiar with it. Um so in building trust and this was done with organizational psychology uh original research. So how do organizations trust each other and then more importantly how do people inside those organizations trust each other? We had to prove benevolence.

### Benevolence vs manipulation and the leader’s role

(13:47) Is this actually going to help the consumer? What is the consumer benefit in AI land? Um, common violations is that what's the distinction between manipulation versus a nudge? And so, as us marketers, when we think about utilizing AI responsibly, there isn't going to be a regulation, sorry.

### Integrity: delivering promises and avoiding AI-washing

(14:12) Uh, or let's say let's not hold our breaths for a federal regulation at least here in the US. Um, so that means at the leadership level, at the organization, we're going to have to make these decisions. So, one, benevolence, two, integrity. Are you going to deliver on your promise? Are you going to surprise? Are you going to uh surprise me in a not so good way? Or are you going to be transparent? So when we use AI for hidden automations, if we have exaggerated claims, um there has been already um uh cases where firms have been fined for exaggerating claims and in the data space we tend to call it AI washing um but in other contexts they

### Competence: reliable execution and guardrails

(14:50) use different words but the idea of uh going beyond u puffery uh this is something that's critically important to make sure that we are reinforcing with our consumers that we our brands have integrity even when we're using technology. And then uh the last component uh competence. So can we consistently show up? Can we execute reliably? And so if we use AI um technologies to help facilitate a conversation, okay, fine. We we have a chatbot. Excellent. Good idea.

### Audience Q&A: purpose-first and ontology of AI

(15:20) Is it actually making relevant recommendations? Um when a consumer tries to find out a little bit more information about a product, does it start to hallucinate? Did we actually check for that corner case, use case, all of those things? Um, are we actually reinforcing that we um we know what we're doing with this technology? And so when we're trying to reinforce trust between the consumer and the brand, benevolence, integrity, uh compliance, bick, um these are the questions I ask uh marketing leaders all the time. help me understand how are you actually showing this and demonstrating

### Market reality: many AI initiatives fail without clear purpose

(15:58) this to your customer as opposed to let's see all the shiny things uh that we can do with AI and technology and with that I'm going to pause for a second u because I see one hand raised Karen I see you you like to add or challenge I I would like to add AI certainly has a lot of capabilities so the First question that comes to mind is all of these ideas do they actually work right so agents talking to agents and eliminating people you see so many reports out there that 80% of all these AI initiatives in corporations are failing they're not producing the results that they are intended to

### “Philosophy beats AI”: purpose, ontology, and human nature

(16:45) produce there was a talk that u resonated with me and uh the title was philosophy beats AI and they broke down uh the philosophy of why you want to use AI and they said that the first most important thing is your purpose. Why are you wanting to use it? For example, in the example that you said you gave for having uh AI actors replace humans.

### Profit motives, business models, and consumer experience risk

(17:25) What's the purpose of that? Is the purpose to cut cost? Wh why are you doing what you're doing? And then the next one and you talked about that is theontology. understanding the nature of AI of what it can and it cannot do uh whether it's giving you the right recommendations and u so that is critical I think a lot of people kind of freak out about AI because they really don't step back for a second to say okay all of these ideas are there but will they actually work and uh a lot of that also requires a lot more understanding of the human nature

### Case study: airline “surveillance pricing” risk

(18:18) to be able to um assess what the benefits and the dangers of AI are. Agree agree very um very very much so. I think there has to be a radical clarity around the why or what is the um particular consumer benefit. I mean we can you can pull it back into economics right so all these big firms that are having multi-billion valuations they're not particularly profitable at the moment and if they want to have subscription models at 200 or 200 plus a pop uh they can keep trying but it's not going to be particularly scalable. So they are going to have to shift their business model such that they can be a

### Urgency signals and dynamic pricing harms

(19:02) bit more revenue positive and then eventually profitable. So it's not going away. Whether or not it degrades the consumer experience, oh I agree. I agree. The using technology for technologies sake. Um yes, most likely we're going to have all sorts of poor experiences. I'll give you an example where I put on my AI ethics hat recently.

### Responsible AI vs corporate ethics: semantics vs behavior change

(19:28) Um, and and this is where I get very curious about the the PR team or the comm's team that was putting this out. Uh, I believe it was uh an airline and I I want to say it was Delta Airlines. They got very excited. They wanted to communicate. They were utilizing AI and the way they were utilizing AI was oh well, you know, uh, we can use AI to like figure out this pricing challenge.

### “Clippy” as a metaphor for transparent assistance

(19:57) So for individuals, maybe large enterprise businesses, maybe they can pay more for a ticket versus somebody who's a regular person, mid-range, whatever, and and they can maybe pay less. And so, you know, people with deeper pockets, fantastic. We can like AI this problem and suddenly we'll we'll make sure that the flights are full and and we'll um be able to extract a bit more value um from different consumer cohorts. And so they were quite excited about this.

### Practical transparency, consent, and understandability

(20:26) And then it became very quickly for for those of us who hang out in AI ethics circles, oh, okay, this is nice. This sounds like surveillance pricing. And so now they're utilizing data signals, maybe your zip code, maybe your previous purchasing ex uh um behaviors, maybe the previous places you went to um to determine, oh, whether or not you should pay an extra 200, 300, 400.

### Measurement: confidence, comprehension, and ongoing consent

(21:00) Okay, maybe that's a little bit icky, maybe a little bit. Where it gets really icky is what if in the data marketplace and this does exist, we start to have these data science get really smart and they start figuring out, oh wait, the behavior of this individual, we don't need to know who they are, but the behavior of this individual, it seems like they need to purchase a ticket in urgency.

### Moderating tech talk to refocus on relationships

(21:23) Somebody passed away in the family, a new birth in the family. I need to go from here to there very very quickly. I don't have time. it's time sensitive. What if an airline jacked up the price as a result of that? Because that's what the data signal said. And and this is so those are the use cases where AI ethics and responsible AI is so critical because it's not a first order effect that we're worried about.

### RAG, fine-tuning, and bespoke models for value

(21:54) It's a second and third order effects that I would argue marketers we are in many ways we may be the only voices in our organizations that represent the consumer. I I can I say something? I would argue argue with you that this is really not AI ethics. It's human and corporate ethics. You're using tools to do something that's unethical. Data and science does what data and science does. It's not it's objective in its own way.

### Engineering reality: structure, lineage, and residual hallucinations

(22:18) It's the human beings who are using that data to extract you know do whatever it is that they do. So it has not it's not I would I argue against AI ethics. This has nothing to do with AI ethics. This is about corporate ethics, capitalist ethics and uh really the core problem with human nature and culture, not technology. Fair. Um technology is a tool.

### Marketer’s remit: protect relationships, not just tech

(22:50) I don't argue semantics because I think most disagreements are a result of slight variations of definitions. um if it changes behavior that's when I say okay we need to come to common ground. So if I say corporate ethics to a board will that change their behavior. If I say AI ethics or responsible AI will that change their behavior I we are saying um our values are aligned.

### Nostalgia lesson: Clippy’s transparency and limits

(23:18) I think the words we use in different forums are the ways that we kind of persuade the argument to move things forward. Okay. I mean we are in a marketing forum, right? So you do have to talk a little bit about marketing and I did promise to talk about technology. Um but I appreciate the point that maybe technology is we can be philosophical too.

### Practical guidance: disclose limits and provide human-out

(23:43) Um, I appreciate the point that maybe the technology is just a further extension of all the tools that we've ever made. So, fire included. So, um, something that I, uh, I recently came across in the internet that I got really excited about because it came back. How many of us remember Clippy? Of course. Okay. Yes. uh that that weird uh uh the the weird paper clip um that used to try to be ever so helpful.

### Adapt messaging by audience sophistication

(24:18) Oh gosh, I can't remember what decade it was at this point, and I don't want to age myself, so we're we're just going to say it was a while ago. Um and it was really annoying at the time. Um because it would constantly be popping up um in your word uh while you were using Word or while you were using Excel to try to help you.

### What to measure instead of “engagement”

(24:36) And I argue that as our as brands continue to experiment in this space, experiment with trying to use technologies to deliver different kinds of value to consumers in their experience. given that we don't have necessarily broad-based regulations, given that maybe we don't necessarily agree um on what values and rules to uphold that brands may want to consider how do we show ourselves to be a bit more like a Clippy.

### Q&A: motivation and doing more than the law demands

(25:16) And why I argue that is that um Clippy was not always uh the most helpful, but it was very transparent at what it could do and what it could not do. Um and also it was um always being very clear that it was there. And so I I really want to focus on kind of the transparency and understandably understandability components uh regarding technology use.

### Where responsible practices show up today (mental health tech)

(25:42) I don't have to use the words AI anymore if we don't want. Um, and so as marketers, as we continue to use these technologies, I ask the question always, are we being clear to the average consumer? Are we disclosing in a way that they understand? Are we owning the limits around these uh technologies? Meaning, you know what? This chatbot is only good for um uh I don't know um uh figuring out how to return a product.

### Guardrails: consent as a continuous process

(26:14) Maybe it's not good at um extrapolating out like different use cases of the product. Okay, be transparent about that. Um being clear that users should have some form of consent mechanism. Okay, you don't want to talk to the chatbot anymore. Um you want to speak to a real person. Okay. Um making things are understandable um to the end user. So showing the reasoning as appropriate to the end user.

### Re-centering on the marketer’s role

(26:42) So if it is a B2B firm, B2B SAS and um you're talking to devs. So it's um developer evangelism, the way you communicate and speak will sound quite different uh from a marketing standpoint as opposed to if you're speaking to the average consumer who may not be in the tech space.

### Prompt engineering reality: RAG + fine-tuning in the wild

(27:00) Um, and then from a measurement standpoint, I get asked this quite often. Okay, if we're not supposed to be measuring engagement, what are we supposed to be um, measuring? I would argue um, we need to measure the components related to trust. So like how confident is our consumer? Meaning do they understand what technologies are we using? Do they understand how the output came about? um the comprehension.

### Engineering details: structure in RAG and link accuracy

(27:32) Do they understand what they can do or what they can opt in and opt out of? And then I tend to say this quite often um consent, consent, consent. And consent isn't a one-time thing. It's a continuous thing uh in the sense that it is um it happens throughout the life cycle, throughout the process of the relationship of the consumer, with the brand.

### Residual hallucination even with citations

(27:52) And so I figure that doing those types of things can help to mitigate any potential risks um when it comes to utilizing these technologies when we don't necessarily know what the second or third order outcomes will be. David, I didn't realize your the chat is on fire. Yeah, there's a lot going on in the chat, so I was just checking on that. Any questions? Are we in mid group? We are. We are today.

### Incentives for responsibility without regulation

(28:18) So, I'm okay if people want to raise hands or throw out a throw out a question or throw out a provocation. I mean, um I'm curious as we talk about ethics and you mentioned that like uh regulation hasn't come here anytime soon in any meaningful national way, right? Um, so it it so what's going to motivate like marketers in particular to do more than the law demands in that sense and you know it's like uh and and uh you know in terms of uh in terms of ethics around AI like like if acting in you know if there's not going to be any uh like legal penalty for acting

### Accountability over capability: a marketer’s stance

(29:13) unethically in a lot of cases or there's going to be a lot of that gray area. um like for like for those that you're seeing that trying to do things right like what's motivating them or and is there anything that can like help yeah arm markers that want to do the right thing but might face an uphill battle when it's yeah not necessarily being required of them very true uh it can be quite challenging this is why in practice I tend to stay responsible as opposed to ethical um because one we have to agree on ethics that's not always true um and

### Examples of firms trying to do right and legal exposure

(29:53) ethics is usually reinforced through policy which in the US not likely to happen so from a responsible standpoint um again I I continue to reiterate transparency understandability and then accountability with consent um if marketers can do those things you mitigate some of the risks um you asked me if there are any firms that are thinking about doing these things and how are they kind of addressing it.

### Marketing’s proactive role: build seatbelts

(30:26) Uh I see a lot of this already happening inside of the mental health care tech space. Um there are firms that are trying to do right and yes it could be for the greater good or it could be oh wait who just got did Sam Alton just get sued because a very unfortunate irreversible poor outcome has occurred. Yes.

### Agents, consent mechanisms, and marketplace corrections

(30:50) um a teenager had decided to end their life as a result of a relationship a parasocial relationship with Chachi BT, uh that's open AI. Um if you are in a firm that maybe is not as deeply resourced or is not necessarily as favored in the marketplace in the moment and you're just in the middle of the road, do you want to take those risks? And so that's where I feel like marketing can take a proactive stance and uh take a proactive stance in terms of building these guardrails.

### Debate: AI ethics vs corporate ethics

(31:22) I I tend to um make the analogy um we have choices um we have a very very fastmoving car and the cars are only getting faster. Um and so that is what the platforms are doing and they have very much every right to do so. They're well resourced to do so. However, those of us who are facing consumers as part of our roles, um we have a choice to say, "Hey, can we build some seat belts? Hey, can we tell people about seat belts?" There's no um no requirement to force people to wear seat belts and it's going to take a long time if that's something that we as a

### Who is accountable when things go wrong?

(31:59) society choose to do. But the fact to raise the idea that hey, we should probably have seat belts. We want to mitigate any kind of uh litigation risk or hey or we want to talk about seat belts because you know reputational risk is a problem um and this is something we want to manage against.

### Car analogy continued: capability vs accountability

(32:20) So imagine a world very soon where we use only the LLMs to do our exploratory search. Help me um I don't know Perplexity help me book a vacation and per uh Perplexity gives me the hotels give me all those things. Um the brand will no longer have intermediary control.

### Why brand trust may matter more, not less

(32:48) So if um in that scenario it offers me a Hilton versus a Marriott and in my mind I'm like ooh Hilton I'm not sure if I'm okay with all of their business practices. I may choose differently. And so the role of the marketer I think slightly changes uh when it comes to all of these more recent technologies meaning trust becomes even more important. Brand becomes even more important and those are not performance marketer type of uh ideas. Um it's more arguably more traditional brand marketing.

### Pushback: will brands matter less if agents decide?

(33:23) That's where I think marketers can take a proactive stance. What do you mean by brands will be more important? Seems like it'd be less important if you have AI agents making the purchasing decisions as opposed to the consumer themselves. The there will be a consent mechanism, right? What what happens if we have a whole bunch of random agents like just booking um vacations and buying um how do you say buying goods and services? Okay, me as the consumer, I say, "No, no, my agent made a mistake. Credit card company, undo that. Undo that." That creates all sorts of second order and

### Learning curve effects and consent as safety valve

(34:01) third order effects of ignoring transaction this and that. And so the marketplace will have to figure out what are those mechanisms to minimize that. But there will be some version of consent because many people as they learn this new behavior of having agents purchase things on their behalf um will make mistakes.

### Market correction and bottom-line realities

(34:21) And so this is where I I'm I'm always very a little bit cautious because there is the future state that may happen. There's the current state and then there's the actual harms that are occurring now um that we could actually take action uh against. I would agree that we are in a very much in a new field in terms of the jump from search to social, social to mobile, now it's to AI and we're going to see where what brands are doing are doing moving fast and breaking things and I think there'll be a market correction. I'm not with Karan when he talks about the philosophical debate about that's debatable that's subjective I think in a

### Back-and-forth: bias vs limitations and training

(34:58) lot of ways but at the end of the day it's going to be the bottom line and brands are going to respond to how their implementation and configuration of AI and how they use it for their consumers or impact their bottom line. So you can get philosophical if you want and talk about brand values from a branding standpoint, but at the end of the day, it's about the money.

### Limits of black-box models and implications for critical use

(35:18) And if that's why Pepsi pulled the ad, Jenner, that's why we had the conversation with Jimmy in the last couple weeks. At the end of the day, how we choose to use AI or how brands choose to use it will be impacted by how it affects their bottom initiatives. That's just my argument. I hear you. Uh so it sounds like this crew is very much of the And please feel free to correct me.

### Synthesis attempt and automotive analogy

(35:41) Um, I'm not suggesting every brand has to use AI. Um, not all problems are solved by AI. Agreed. Um, but my bias is I am very much focused on trying to understand what to trust, who to trust, and what is the role of marketing in helping me understand who to trust, what to trust. So, I'm I'm actually quite curious um outside of generative AI capabilities, I'm curious if anyone has any um current experience utilizing a AI technologies to try to enhance the consumer experience if not or just just do an efficiency play. Uh I'm curious about people's experiences with that.

### Practitioner experience: RAG solutions and clarity of purpose

(36:34) Well, I have developed a rag solution and I think in in my experience what's critical is to understand the nature of AI and when you have a rag solution and you add your own content and uh then let AI act on that content it gives you a much deeper understanding about the value of it but also the shortcomings on how you can fix the problems.

### Black-box limits and realistic expectations

(37:12) Um you know the the key thing for using AI in anything is right now you you should have a clear purpose of why you want it. If your if your idea and I think that's where the market has failed a lot is a lot of the corporate heads the first thought is AI is going to help save me a lot of money because I don't have to hire people and I can shortcut things.

### Emerging consensus: purpose, engineering, and limits

(37:43) If that's your thinking I think that that is a recipe for failure. But if you have a very clear idea and objective for what you want to achieve and then have a very good understanding of the strengths and limitations of AI, what is hallucination, interference, how the and you can really get a good understanding of it.

### Conference takeaways: combining RAG and fine-tuning

(38:07) when you know the content that is being used to generate those answers then you can certainly have ideas you know and I think a lot of great solutions for customer support you know I think rag solutions have a big play in how AI will be used in the future certainly content generation and content marketing is also good but you have to have a very good understanding of the nature of AI and also understand that a lot of it is a black box.

### Engineering overhead beyond the LLM

(38:41) I mean the interpretability of AI, the fact that even the people who built it don't really understand how some answers are inferred uh how it actually makes decisions and if you have those black boxes then uh applying AI to some really serious critical applications will become very challenging.

### Example issue: correct answer, wrong citation link

(39:09) But if you have a very clear purpose of why you want to use it, I think there's a lot of great great understandings and I think that AI agents are also again if the focus is clearly defined then I think you can have success but to think that they can just do the job of a human is not realistic in in HR circles. Yes, that is definitely the sentiment.

### Residual hallucination rates under RAG

(39:37) I loved how you were bringing up um a little bit more technical uh conversation. Uh so I had the fortunate experience to actually be able to attend the Advertising Research Foundation's um marketing science institute conference earlier this week uh at Columbia University um around analytics and forecasting. And without getting too too technical, I'm not sure everyone's background.

### Refocusing on marketer’s responsibility

(40:04) Um what was very promising in terms of how do we make this AI actually useful um most of the presentations uh similar underlying thread. You cannot take anything off the shelf. Don't bad idea. Um however you can utilize uh rag methods um to constrain um and and constrain the the information related to the context of your business.

### Structured content and content engineering in RAG

(40:32) So the externalities with the the your competitors for example things like that while utilizing also fine-tuning of those models. So information about your particular consumers to actually create a more bespoke uh AI model. I is there going to be issues with explanability and interpretability? Yes.

### Closing the loop: relationships over tech details

(40:52) Yes. Yes. However, when it comes to making things a bit more bespoke um in in such a way that it actually delivers some value that makes some kind of sense in the context of your firm and your customers. um these are methods that combined together can be um quite promising. More research has to be done and obviously more implementation has to be done. In my experience, there's a lot of engineering required.

### Practitioner note: post-processing questions and answers

(41:17) LLM does a job, but you also have to then be able to process the questions and then on the back end you also have to then process um the answers as well. And the more structured content you have in a rag solution and then you can engineer your content to continuously provide relevant answers like recently I had an experience where you know the LLMs will always want to create an answer.

### Engineering fix: tagging content to improve citations

(41:54) So somebody asked a question uh and the answer was accurate but the reference link that was generated with that answer was not the right reference link. So the content was still there. It was able to create the right answer, but it Yeah. And then when I researched that a little bit, they said that well, you need to maybe tag your content differently in order to be able to So what I'm saying is that there's an engineering involved in creating systems that work and and that's that seems to be the reality of how all of this works right now.

### Even with lineage, some hallucinations persist

(42:34) Yes. Um and even in those cases uh there has been some empirical work done in the space um even in those cases when we're trying to create lineage and provenence as an output um we're still getting a little bit of hallucination so it can still be up to 10% of those citations even in a rag architecture and fine-tuning architecture is still a hallucination and so yes there are significant challenges um I always caution against getting into too deep of a technical conversation.

### Re-centering: marketers own the relationship

(43:07) Um because sometimes focusing on the technical conversation abstracts away from again I continue to argue the role of the marketer inside of the organization. The marketer is in charge of the relationships I thought or at least I was educated. They are okay good.

### Build-measure-learn remains, now with AI

(43:31) They are and sales marketer starts with leadership tape and sales closes the relationship in terms of bringing them on board usually. Okay. So if we start only focusing on the ones and zeros and we only start focusing on the platforms, sometimes we forget those things are actually meant to be people. Yeah. Relationships can hurt if if the answers are not trustworthy, right? So that's that's well yeah Karen, but you're overstating the point.

### Training vs black box: an ongoing debate

(43:54) The point of any campaign, any initiative, whether it's the Pepsi example, etc. or the example that uh CO mentioned before. It's a build, measure, learn loop. The whole point is to put something out in the market, get the feedback from your marketplace, from your consumers especially, and then adjust accordingly.

### Limits compared to deterministic software

(44:10) In the same way, we've been doing it for decades in this new internet industry. Keep in mind, the internet is only about 30 years old. We're doing the same thing now with AI. And we're going to teach AI in the same way we're teaching ourselves. My job as an analyst early in my career is to take a look at the data, analyze it, provide insights, and optimize campaigns. That's what we're teaching AI to do.

### Accountability concerns and seatbelts analogy

(44:28) So there's no difference from what I've been doing in my career to what we're teaching AI to do now. We just have to be more explicit like you talk about from the technical l technical standpoint creating structured data. There's no difference. I don't understand this anti- AI bias that people seem to have. There is no anti-AI.

### Who bears responsibility: users vs firms

(44:46) this understanding that like you said teaching AI what to do has some limitations because it doesn't always do what you tell it to do because we don't really understand the interpretability part of the AI so that's what we talking that hard training but that's fine and that's called training in the same way we would train an it's not just training it's it's not just training training has its limitations that's what I'm trying to tell you can train it all you want.

### Mixed liability: driver vs manufacturer

(45:18) But if if the if the engine is a black box, if interpretability there are engineers who built it are working on trying to understand how it interprets, there are limitations to the results and that's the the reality of this AI. That's with any technology. There are always limitations. We work around them.

### Current discomfort: not enough accountability

(45:41) No, in the old days when you programmed something, you always knew what you got out of it. In AI, you don't understand. It's a black box, right? You train it with a lot of parameters. There are too many parameters in there and they don't always know how whether the answer is accurate or not.

### Marketer as consumer advocate inside the org

(46:06) That's what hallucination is and that you have to put that into the equation. I'm going to I'm curious to try to synthesize and then provoke the conversation a bit further. Um let's let's talk about the automotive industry. And so now we're talking about a long time ago. Um, so there was no s we were the cars were competing with horses and buggies and there were no roads, so no infrastructure and we didn't have laws that said, "Hey, maybe you don't want the wheel to fall off.

### Handling harms: redress and repair

(46:38) Maybe you don't want the engine to explode. Maybe you want seat belts." Um, and so it was very, very new. We were learning into the space. I think the discomfort and now I'm going to speak for myself. The discomfort I I tend to feel about the whole thing uh is related to I get it technology there's always going to be limitations but what I get a sense of as a consumer as a person that is participating in the in this not just an outsider looking in there's not enough accountability so if something goes wrong what happens that's what we're learning now

### Shared accountability: platforms and users

(47:16) and that's where it gets a little bit tricky and this is where I continue to plead and advocate for marketers because again in many ways sometimes we're the only voice of the consumer inside of our organizations. Okay. How do we at least hold ourselves accountable that if something bad happens um we have a way of address redress uh we have a way of repairing maybe a damaged relationship with the consumer.

### Clarification and wrap-up

(47:48) And so I'm curious if the challenge we're facing now has to do more with accountability for negative outcomes as opposed to just things don't work. I'm curious if uh anyone agrees, disagrees, maybe I'm off topic. I I'm more than happy. This is a really smart group. So I'm again said as opposed to just finish what you said at the end. Say it again, please.

### Capability vs accountability, revisited

(48:18) You said opposed to and then I missed it. Oh, sorry. Um, I'm curious if we're if it's more important for us to think about accountability as opposed to capability. Ah, technology always changes. The capabilities are always limited in some dimension. So is it more that we should be thinking about how do we hold ourselves accountable to negative outcomes that maybe faces our consumers and then how do we protect our firms? Oh h I you know number one I think AI technology is incredible and I think that when you understand what you want to use it for and you have a clear purpose it has a lot of applications.

### Ethics is human; AI is a tool — but responsibility remains

(49:04) uh your ethics are your ethics you know if if you're an ethical person you look to use it in the right way if you're unethical you look to use it in ways you know for whatever purposes that you have so that's that's a human problem not an AI problem and we need to be able to distinguish that but AI is a very powerful technology it has a lot of great applications it solves a lot of problems and some but Sometimes people have the wrong ideas of why they want to use AI and that's again goes back to the person and not to the technology. The

### Consumer responsibility vs firm accountability

(49:43) technology is what it is. It has tremendous capabilities and it has some shortcomings and if you want to use it, you have to understand it. I don't understand how the insides of a car work. But I'm allowed to have a driver's license. Exactly. and and but but when you get into a car and you drive it, you have you you understand what the rules are for that.

### Use AI outputs carefully; do not treat as gospel

(50:19) When you use AI to solve problems, you also have to understand, you know, and not take whatever comes out of it and understand what the results are. That's your responsibility to take the data and do something with it. Right? You don't have to understand interpretability and inference and hallucination. But you do have to understand that everything that comes out of it is not gospel and you should use it, you know, carefully.

### Clarifying locus of responsibility

(50:45) How you use it, that's your responsibility. That's not AI's responsibility. Oh, so the responsibility of any negative outcome, the accountability for any negative outcome is at the individual user, not at the firm. You you're resp you're responsible for your own actions and your own thoughts. Well, I mean this and and this I mean it would be actually a fascinating debate for another section because we're almost a time here. But but even I mean I think the car example is a good one cuz we see examples where most of the time uh

### Liability examples: driver vs recall scenarios

(51:18) someone goes and and kills or hurts another person with a car, it's going to be the driver's fault. Um but there are times when an airbag doesn't deploy, the brakes don't work as planned. Um, and sometimes that's actually, you know, requires uh a whole a systemwide recall uh of tons of vehicles because there was something done at the manufacturer level that was either an oversight or came up after the fact and uh and and any harm that's done to that winds up clearly their fault. they have to also prevent uh any harm from happening even if it hasn't happened yet. So having this

### Platforms as manufacturers; users still learning to drive

(52:05) dichotomy of of when harm comes from the manufacturers in this case the open AIs and metas and Googles uh of the world um that that seem to have cart blanch to have some cars with some pretty wobbly wheels come and out there and some very fast engines and maybe not not enough treads on their tires.

### Closing thanks and community notes

(52:30) Uh but uh uh but also like uh to the other point like uh most folks out there we don't know how to drive yet, right? Like like you know uh we're just you know we're still like and if we're driving we're like you driving this 500 power horsepower car on like a dirt or gravel road that was not designed for the car was designed for the horses that came before it. So So we've got a lot to navigate here.

### Event wrap-up and next steps

(52:58) like I'd love to dive into that further. We don't have as much time here. I hope we can continue this in Slack and in future conversations. Uh the participation's fascinating as uh always here. So appreciate everyone who's been chiming in and also the chat threads been tremendous. So thank you uh all. Dr. Cecilia Dones, I mean just amazing. Look forward to following more of your work. hopefully having you back here.

### Gratitude and sign-off

(53:25) Uh and and you're bringing up so many important issues for us to follow. Thank you so much. And this is a very special group. Uh you've created a community and the fact that everyone shows up for themselves and each other. It's something special. Well, appreciate you getting to join us for all that and you sharing that. Um yeah, thanks everyone. Have a a wonderful rest of your week.

### Reminder: next in-person and speaker slate

(53:49) We'll keep seeing you next week. Next week, if you're in New York, we've got first Wednesday in person next Wednesday. Uh uh so be sure to check the event page for that. Um and uh yeah, just tremendous slate of speakers ahead. So thanks everyone and uh see you soon.

## AI Search 2025 How to Get Found in a Bot-First World

Speaker: Thomas Peham
Published: 2025-09-12
Tags: geo, website citations, brand visibility
Video: https://www.youtube.com/watch?v=y3WbJGzZ6t4
Page: https://aimarketersguild.org/sessions/ai-search-2025-how-to-get-found-in-a-bot-first-world

(00:05) Hey everyone, welcome back to another edition of AI Insiders with AI Marketers Guild. I'm Dave Berkowitz and uh thrilled to get to host someone I've been eager to have on here, Thomas from Otterly AI. I've been using their service for GEO, you know, generative engine optimization or the all kinds of words for it. I don't know know if we've settled on the nomenclature yet.

(00:31) Maybe Tom is going to answer that question once and for all and just put us all out of our misery. But uh but there's so much now for my own projects for clients where just like trying to see how you're ranking in AI and then ultimately what do you do about it? What's different with that in SEO? I've got millions of questions. I'm I'd imagine a lot of you do too and Thomas is here to at least answer them best as he can.

(00:58) Welcome, Thomas. Thank you, David. Thanks so much for the intro and hi everybody. I'm Thomas calling in all the way from Austria, Europe, Vienna today. Hope everybody's having a good morning, lovely lunchtime. I I'd love to learn more from you as well. So, I'll try to answer all the million questions that everybody has in today's call.

(01:21) definitely let's let's get to it. But also curious to hear your experiences, right? So I also have a few questions for all of you today. Great. But yeah, happy to dive in into the topic in in a few moments. Um, yeah. Well, well, you know, feel free to kick things off and uh share a few thoughts and and yeah, as folks here, if you're uh if you haven't been to many of these or this is your first time, then then we welcome making these very interactive. Amazing.

(01:52) So maybe first things first um let me know in the chat where's everybody calling in from. So as mentioned I'm I'm I'm team Europe. I'm I'm currently based in Vienna, Austria. It's late afternoon my end. So my coffee already stopped. Um so I'm on on the water right now, but let me know where you where are you calling from? Are you a coffee tea person? What's your what's your routine? I'd love to meet and hear some of your routines um as we kick it off. Hi Sandra. Um, amazing.

(02:22) I'm really bad with multitasking, but I'll still try my best and start sharing my screen right now to jump right into the whole topic. And I am I'm team T here. I do I've not had a full cup of coffee in my life. So, that is one of my fun. That's a that's a good fun fact. I probably have way too much coffee every single day, but that's a that's another story.

(02:49) Um maybe you can guys give give me a quick visual or audio feedback if you can see my screen if you can see my slide deck. Yeah. Amazing. So I mean David and I we've been we've been chatting a bit over the over the last few days and weeks really and I I thought a bit about the topic right AI search and what what can I bring to you today in regards to the state of AI search right I I'm sure we have many marketing marketing leaders in in this call today um who've been tinker tinkering around this big question what's going on on chatbt right and we we titled this session today the state of search 2020 25, how to get found in a

(03:27) bot first world. But if I would come up with a a subtitle for that, I probably would call it, "Hey, Chuck GBT, where did my traffic go in 2025?" Right? Um, and I think the reason the reason I would I would I would love to touch on this topic is I think many many marketing teams I've been working with in in the last few weeks and months really are are confronted with with difficult times, right? difficult times where we might be losing traffic, organic traffic from Google. We might be looking at these new era of SEO. David

(04:04) mentioned, is it GEO? Is it just SEO? How do we call this thing even in the first place, right? It's a big it's a big topic in many boardroom conversations. Um, what I'll try in today's call and I'll try to make it interactive, right? And I I'd love to come to all of your questions is to provide a few answers to a few myths that are out there when it comes to EI search, AI search visibility and what we as brands, what we as marketing teams can do to tackle that topic.

(04:37) Real quick, as a quick intro on on myself, I'm I'm Thomas. I'm now the CEO and one of the co-founders at Otlyi. Ottolei is a startup in the eye search monitoring space. And with that said, my background really is in B2B sales marketing. So for the last 12 years, I worked for startups, scaleups, lot larger enterprises, always in the B2B software as a service space.

(05:04) And the whole reason why we actually started this product um in the first place goes back to my last marketing role where I led a marketing team of 30 people as VP marketing at a company called Story Block. Storylock is a content management system. And about one and a half, two years ago, I kind of realized that I've been spending millions.

(05:26) So we as a marketing team, we invested millions in the Google ad program every single year. I had dedicated team members just doing SEO, just doing content. I would work with agencies in Europe, in the US to basically improve our organic performance because it was really the main driver of our of our product, of our revenue stream. And I kind of realized that while I've been so obsessed with Google, I had really no clue what's going on on Chat Gibbbit. And this thought kind of made me a bit nervous two years ago to not know what's going on there.

(05:56) And ultimately, it made me made me build this product, made me build and start this business in the first place. This is not about Otterly EI, but just to set the scene, what we do, we are an AI search monitoring and optimization platform. So right now we help about 5,000 marketing and SEO professionals understand search, analyze the search, and optimize for higher brand visibility and higher website visibility.

(06:28) At the end of the day, it's always about are we getting mentioned as a brand on those new channels? Is our content is our website being picked up? We have lots of data. I will also show you some data today in this presentation. um as we support Google AI of use perplexity chat ch mode is now getting a big thing Gemini and Microsoft copilot so when I speak about AI search AI search engines I will refer to those six AI search engines obviously there are others out there there's croc there's claude etc etc also have some data points there but it's primarily and please put in the chat um I'd love to

(07:04) hear that from you I would make the bold statement it's primarily ly these days about Google and chat should ch if you see that differently write a quick comment in the chat which AI search engine is important for you and your business but let's let's zoom out right why are we talking today about AI search why why does it matter why should we care right why do we care um I mentioned this in the intro already a bit right and it's probably not new news to you that organic traffic is really is really hard

(07:35) these days to acquire in the first place, right? So many of us probably see either a decline in organic traffic or at least very stagnating organic traffic statistics, especially top offunnel, mid- offunnel type search queries most often lead to a Google AI few these days.

(07:55) Um based on our own data um that we we have collected, we for example see right now that about onethird of all Google search queries in the United States lead to a Google view. The implication is quite simple, right? With an hour view being in place, clickthrough rates are much lower, organic rankings is being pushed down and ultimately we do get less traffic to our websites.

(08:21) So in one way, Google is sending less organic traffic these days to our websites. Why did Google introduce AI with you in the first place? Right? Why is AI mode now a thing? At the end of the day, Google is reacting to this trend of conversational search experiences. AI search experiences such as Chat GBT, Publexity and others.

(08:41) Jet GBT is now amongst the top websites in the world. Everybody uses it. I think I I I I have prepared here a couple of latest up-to-date statistics to put things also a bit in perspective. Google right now processes about 15 billion search queries every single day. So there are 15 billion search queries on Google, Google AI mode, Gemini, Yahawa, not sorry not Gemini, Yahweh using AI mode included.

(09:10) According to the latest um numbers from OpenI, there are about 2.5 million queries every single day on JTBt. So as we can see here, JT GBT is catching up quite quickly. But the good I think the good news for Google is it's not replacing Google, right? ex expanding the search experience for us as consumers.

(09:31) So in addition to Google um we're now using those new AI search channels as well depending on pick your favorite tool right you see Gemini getting 46 million queries every single day Microsoft copilot is a thing deepseek perplexity crow clock you see the numbers there um again let me know in the chat which which AI service you rely on the most um but ultimately it can be said search is expanding beyond Google and is getting more diversified What does this mean really for us as as website providers, as owners, right? Well, in the old days, in the old SEO

(10:06) days, if you if you would call it like this, we've been optimizing for Google, right? We've been optimizing for Google because we directly knew, okay, if we do that, we will receive traffic, we'll receive people coming to our websites. The reality now is it is it is more diversified on the search channels, but it's no longer only humans coming to our websites. It's also robots, agents browsing our websites looking for information.

(10:34) And ultimately, in some way, we have to rework our personas that we target because most likely we already have agents coming to our websites as well who consume content who who take actions based on those content pieces. And c can I just ask one quick question while you talk about all the players out there? Uh I mean just since you're based in Europe and we don't always have that perspective is Mistral like on the radar for folks right now do you see that as an upandcomer? I mean in in broader context no not really. I mean and we we do have we actually did some research in Apac um

(11:11) because we were curious um some of our customers were curious to know which AI search engines are being used in APAC. M to be very honest the reality is at least in Europe it is like like this right it's chat chibet everywhere people use chat chibeti that's the primary research engine yes mistrol is from France it is a local player but I mean it's not it's it's not at that scale I I don't even know the the statistics I haven't looked it up but it's it's probably quite far away even from Claude and Claude is quite far away from chat chibiti if you look at those statistics

(11:45) here yeah it's essentially that deep sea being the only uh only player outside of the US to make a dent. Yeah, that's true. I was I was actually very surprised to be honest when I looked at the Apex statistics because I I would have thought that there are much more local players in APE as well. I'll I'll drop the link in the chat where you can see the the usage.

(12:10) But basically besides China, China has dedicated their their own their own models. that basically besides China, every other country Jet GBT ranks on on top as the number one um AI search experience. So I I just posted the link there for with some AP pack statistics in there. Great. Cool. Um yeah, any anytime. So um I mean one as said right it's getting more diversified. We're looking now at um call it search everywhere optimization.

(12:39) No, it's no longer just about Google and it's no longer just about us humans browsing websites, but we also have to recognize those agents. Why? Because ultimately, and this is already possible today, right? As a web as as marketing teams, we've always been so focused on the user journey, right? This traditional journey um bringing people from awareness to conserv consideration to p purchase to retention to advocacy.

(13:04) And in a traditional search setup, right, we've been optimizing for traffic, right? We've been optimizing, trying to bring people to our website and from there on retarget them, convert them into hopefully paid um customers, paid subscribers.

(13:23) With the eye search now being a thing, a big part of that traffic is now being taken away by the search engines directly natively. Because the reality is chatbt is not a search engine. It's an answer engine. I don't go to chat GBT to then click on a link and check out all the 10 different websites. I'll find my answers there directly. I'll find if I'm looking for running shoes. I'm a runner. I'm I'm training for the next half marathon.

(13:42) If I'm looking for new running shoes, I'll basically prompt Chat Chibbit with some some questions to get some first product recommendations. Yes, from there on I might go look up those brands on Google and on their websites, but then I'm already later down that road.

(14:02) So for us as brands, it's very important to also understand what's going on top of funnel on those new search channels and experiences. So put it simply, yes, search is undergoing some fundamental change. So the big question is, okay, what do we do about it? Is it just SEO? Is it generative engine optimization? What's what's really the big difference there? And disclaimer, I don't want to be the person deciding the abbreviation. I'm not a big fan of buzzword bingos.

(14:28) I don't care really at the end of the day, but I want to show you how the tactics work out. So, let's talk first about some myth. What are some myths? What are some statements that we all see on LinkedIn that we all see on the internet out there when it comes to let's call it GEO? I think the first one of the first myth that I've heard and I've seen is well, we are already number one on Google, right? We're the first website showing up on Google.

(14:57) Why should we why should we do even something? Why should we even care? Right? It's it's the same thing for for Y search engines. The reality is it's not right. The reality is being number one on Google as a website doesn't mean you're most natural than number one on those new ARI search channels.

(15:19) Um, you can see here on the right hand side actually a a screenshot from a Samrush study where they looked at the correlation between the top 10 Google search rankings and EI citations. And if you look at that, yes, there is a correlation of 53% on for EI mode, 85% on Google AI views and only 44% on Jet GBT. meaning half, let's say half of all the the top 10 um ent entry points on on the top 10 Google search rankings will also make it into jet chbett citations.

(15:53) But put it differently, 50% are citations, websites that are not even part of the top 10 Google rankings. So most naturally being number one on Google one doesn't secure you to also be number one on chippity or be cited at all on chibi. And on the flip side there might be even a chance that if you're not in the top 10 Google rankings but you still have certain factors in place some good content on site you might show up on jetb.

(16:23) So myth number one myth number two is okay is SEO now dead? Um, is Google now is is goo Google being killed by by chat chit put it differently is AI search killing SEO no it's not SEO is definitely not that um I would say it's evolving um it's search is getting more diversified as mentioned earlier and again here's another interesting data point you might have seen this chart actually going around LinkedIn and I think it's a really powerful chart because it shows you and it might be a small chart here on on this screen but It basically shows you that if people interact with chat chbett, so that's the

(17:02) greenish color here on at the bottom. If there is an interaction on chat chubt also the engagement goes up on Google. So search is expanding. I'll start as said earlier my search experience on chat chubby. I'm going to ask give me the top five um running shoe recommendations um for my next half marathon.

(17:24) I probably get a couple of product recommendations. I'll then go to Google and continue the search journey there. So I think that chart is pretty clearly showing that Google is not that and search is is is not AI search is not killing SEO in that sense but ex expanding the user journey. Myth number three well can you even influence AI search answers? Right? Can you even influence who's showing up, who gets mentioned or what not? The reality is yes.

(17:55) I've seen it over and over again. You can optimize for brand mentions and website citations. And how it works, I'll come to that in a moment. And some I mean some of you might be thinking, well, it's only 1% of website traffic, right? Based on your web analytics, you might see 1% of your total traffic coming in from Chat GBT, right? It's only 1% of total traffic coming in from Chat GBT.

(18:21) Why should I even care? Why should I get started? The reality is 1% of total traffic doesn't mean your audience is not using Chibet, right? It just means that consumer behavior is different, right? People don't go to Chhat Chibet and then click on all the links. So measuring success based on traffic, referral traffic is only giving you a small piece of the cake, a small picture into the real user journey.

(18:44) And it's never too too late to to get on board with the eye search. uh adopting adopting this new reality uh is something I I would I would at least recommend everybody to look into be it B2B B2C one of the typical questions I've been getting is it more more of a thing for B2B versus B2C use cases think about your own use case right even in B2B um I've seen people going to chat GBT prompting whole RFPs looking for feature comparison best product for XY Z type use cases So it's basically everywhere. So let's talk briefly about how AI

(19:24) search works right because in one way those AI search engines if we think about chat chbett Google's AI mode now at the end of the day it's always a combination of two things what do I mean one chat chbet and Google utilize their LLM models right so EI mode is being powered by Gemini um a custom Gemini version as Google calls it chat GBT is powered by the different um LLM models of OpenI right so when we want to optimize for EI search and thus getting mentioned on EI search one of the first questions that we need to ask ourselves

(20:04) do we want to optimize for the LLM models to pick us up which is more of a longterm shot right I mean there's knowledge cutoff and and those type of things are a thing or do we want to optimize for citations right the reason why you'll find website citations within Google's AR mode or within JBT is not the LLM model per se, but it's the web search capabilities those platforms now have.

(20:34) OpenIt introduced web search last year in October, November, and since then it it became the most used tool, the most used icon in the Chat GBT interface. And the reason why we get referral traffic, the reason why our content is being picked up is because of th the the web search capabilities of chat chubby.

(20:58) So let me so how do we measure success now right with with all all those myths in mind? Um if you are still on on the boat well I care about this. I want to I want to optimize. Um before we talk about the optimization potentials, let's talk about KPIs and how we can measure success because the reality is this Google funnel we're going to measure impressions, clicks, traffic, conversions doesn't work.

(21:21) It doesn't work on on on AI search where it's more about brand mentions, brand influence. Are we getting mentioned as a brand? What's our share of voice? Those are the I would say the most typical simplest brand KPIs you can start tracking today on Chat GBT. And the second factor is website citations.

(21:43) So it's no longer about traffic, it's about how is our website getting cited, who is getting cited on the search. So in one way we have to forget those old website funnel those old metrics and we have to move to a more brand focused um measurement first and then it's more about yeah citations references are we used as a source of information. So let's get to work.

(22:12) How do we how do we now improve our brand and website visibility on AI search? What can we do about it? As said, I don't care about buswords. I just I let's just call it for the the sake of this presentation, GEO, generative engine optimization. Um what can we do about it? The first thing I want to show you is is is is this pie chart where we analyzed a really big data set um of websites because I wanted to understand which websites do get cited. So we're talking about the website citations which websites get cited by chat chubet

(22:52) perplexity and do those search channels and I think the chart is really nice because it shows us a couple of things. One it shows us that our brand websites our corporate websites are still very much relevant. 36% of all website citations come from corporate websites. So it's us as businesses, us as brands getting cited and our content is still very much relevant for Chat Chibbiteria as well.

(23:23) We'll talk more about the onpage signals in a moment, but I think first the good news is our websites are still getting cited on CHBT and the ISO. But then there's a second big piece here. 29% news and media. So lots of publisher sites. I mean OpenI by now has big deals with all the big publishers of the world. But think about your trade media.

(23:46) Think about your local media, your regional media, think about the influencers and bloggers in your space, in your market category. There's a big tendency, I would say, especially on Chat GBT towards editorial news and media content. Content freshness matters, I would say, for Chat GBT more than for Google. While on Google, we all have learned about um domain authority and how important it is to build up our domain authority to have a strong domain um that is a trust signal to Google. I would say that's less of a thing for chat GBT.

(24:21) So we also see a tendency towards smaller websites with less domain authority outranking bigger websites if they have a strong content game, if they have a strong Yeah. um media game going on here. And the last the third pie chart um here forums and community right 19.2%. I know you probably have seen all the Reddit bus and all the Reddit um conversations on the internet.

(24:49) what I can say based on our data um comparing organic visibility and um chat visibility on chat chbet Reddit is huge on Google don't get me wrong but it's even bigger on chat chbet Reddit based on our data is the most cited website in the world not only in the US in many many more countries of the world as well both in Europe in APAC in Asia Pacific so Reddit is big Reddit is super big on Jibiti But it's not only Reddit. You might say, well, yeah, it's Reddit forum community.

(25:22) You could have put here Reddit as a as a solo website. True, but also not true because it's in general forums and communities. So, you might work in an industry in a niche where there's some other local trade communities. Um, some of our customers have realized when analyzing the website citations in their accounts that they've suddenly see old forums and communities where people discuss about really niche topics where the content is 10 years old but suddenly it got picked got picked up by Jet GBT and it was literally invisible on Google. So again

(26:00) there's this tendency I would say that lots of those user generated content pieces is being picked up by jet chibet and other search engines and at this point uh with without without knowing too much about everybody's industry I would make the bold statement that in every industry in your industry we're going to see Reddit citations as well.

(26:26) Um, do we know I already see this question in the chat from Zara, do we know why Reddit is cited is is so often cited? Well, I think I would make two points. One, um, Reddit was used to train, um, OpenI's model, um, various models. Um, this is public information from OpenI. Um, I can also send you a link afterwards where you see that, um, OpenI used Reddit threats with more than two comma up votes.

(26:53) So, I'm not big into Reddit, but they explicitly used um Reddit threads with more than two comma up votes um to basically train their models. I think by now they also have a deal with Reddit. Um Reddit also has a deal with with Google as as called out by Earl here.

(27:15) And at the end of the day, if um chat the way Chat GBT works, it is I think is is particularly looking for an intent match, right? I mean if we have if we think in in prompt right as a user I go to chat chibet I'm looking for a particular prompt certain search query um from a from a um from a semantic relevancy engineering perspective I think a lot of good answers are actually found in those credit thr why why chat chet is citing credit so often is because it is it is trying to match the user intent of me having a certain prompt with um good good relevant content and the I think that's really the case in in some of those

(27:52) situations. But yeah, um I can send you the whole AI search study with more insights. I just want to use that. Also, if there's one takeaway for you as a marketer from from this call today, then I hope it's this one. Please do get those three things right in order to for you to get cited or even have the chance of getting cited on the search.

(28:16) Um, these are three, I would say, housekeeping factors that might hinder you right now or might hinder your website from getting cited. One, the first question is, and it might sound like a it might sound like three simple things, but they're very important. One, check your CDN and cloud hosting setting.

(28:44) So, the big question is, is your CDN, is your hosting provider allowing or blocking AI bots? When you use Cloudflare, there's a little toggle where you can turn off and turn this on. They introduced this, I think a few months ago, but there are also web hosters and CDM providers out there that do not tell you that. So, I was speaking to marketing teams who who told me they didn't know, right? They had no clue and they were wondering why is our website not getting cited and referenced on CHBT? They have good content on there.

(29:14) they've done everything right from an on-page perspective but they were not visible as a website and then they they were talking to to their hoster and the hoster basically told them well by default we're blocking all the eye crawlers um because they consume so much energy and so much traffic um for us so if there's one takeaway please check please check this with your CDN hosting provider the second factor Robert CXT default do you even allow explicitly allow or disallow access of certain bots to your website.

(29:44) Yes, big websites out there do block certain crawlers. So, I would also double check that. And the last thing really is OpenI crawlers still struggle a bit with with dynamic content JavaScript. So, one of the big questions I'm I'm I'm asking our marketing teams is do we how do you generate your content? Is it statically generated? does content sit behind lots of JavaScript embeds making it potentially invisible for um AI crawlers.

(30:15) So dynamic versus um static content checks. I I also linked here a couple of tools where you can do that but it's very basic stuff. Please do check that. Um also this is something you can screenshot or you can look at later on as well. I listed here what I believe are the most important um AI crawlers from OpenI from Perplexity and the Tropic um because the reality is there's not one OpenI bot or crawler there are three different ones the first one GPT bot is actually used to to for the training model for the training of the models right so if you don't want your

(30:50) content to be part of the training data set of OpenI you could generally block the GPT bot from accessing in your website. But if you allow the OIE search bot um which is used to trigger web search results when I as a user go to chat chubet, you still have a chance of getting cited.

(31:13) And yeah, I don't want to go into the details, but check out the the bot names later on. It's helpful when you want to make the decision. Do we want to be part of the training data set or not? So you can exclude that from from your list, but you can still include the search bots, for example. So, what are the three steps for higher brand visibility? Um, let's quickly go through that.

(31:37) Um, because I know I've been talking a lot and I also want to hear your questions and and answers to that. It all starts with the user journey, right? And if we again think about the the user journey on chatbt, it's no longer about keywords, right? We don't go to chat chet and enter keywords as consumers. We have a prompt based approach, right? And I'm a big believer that we as marketers, we as brands need to define those prompts, those topics in the first place we want to optimize for.

(32:04) It's not an easy undertaking in a way because OpenI has no Google search console. So we all love Google because it's it's giving us lots of data points, right? But the reality is there's no Google search console for OpenI right now. So we need to find and get creative on how we identify relevant prompts we want to track in the first place.

(32:22) Then obviously we want to create a baseline of our current brand and website performance. Last but not least, we're going to start with some geo tactics. If you're interested in finding relevant prompts, we have this article here on how to find real prompts from real users. There really some hidden gems in there. I I won't spend too much time on this one, but check out this article.

(32:43) It will give you some ideas on where you find relevant prompts for for your industry, for your niche. Obviously, when it comes to monitoring your brand visibility, I don't recommend you to do it manually, right? You can do it manually for sure as well to manually check.

(32:59) But when you do manual checks, keep in mind there are certain personalization factors, right? There's a memory rock on Chat Chibet and other things that that Chat Chibbit knows about you as a person. So, you will never get a objective neutral AI response. Our goal with our monitoring solution is to provide you with those objective neutral um responses. Um you can try us, we have a free trial, but you can also try other ones as well.

(33:26) Um so then step number three, let's start our geo playbook, right? And the geo playbook I think is quite simple. It's about on page. It's about on page content. We spoke about crawability earlier. Are we even indexable, crawable for those crawlers? It's about content optimization.

(33:49) Um uh without going into too much detail, I would just mention three things. Um analyze your content from a chunking perspective. There are tokenizers. The way chat chet works is is is um it basically chunks your content in different pieces. Um so it's a whole a whole lot about that.

(34:09) It's a whole lot about the content structure, the schema, structured data principles from a earned media perspective. We we already we we we already saw earlier, right? A lot of the citations come from news and media. So um you you might think that big brands have kind of head start here. Um yes, they do. So if you've done good PR work, if you made sure that you basically found as a brand, recognizable as a brand on the internet, let's say you I'm I'm sure you have a solid search presence as well. Um your brand is mentioned there.

(34:40) But what everybody can do is we can all work on our media outreach play. We can all create unique stories that are unique to us and pitch that to those influencers, to those big and small media outlets. we can all go undergo a Wikipedia strategy. Wikipedia is also cited quite often on on chatbt and those platforms. So I know it's a bit more tricky to get your own Wikipedia page, but it's it's worth an undertaking eventually.

(35:05) And last but not least, we need to spend time on Reddit. We need to we need to crack Reddit. We need to we need to see how we can activate our community in those relevant Reddit threats. I want to pause here because this was way too long of me speaking and I was not able to catch up catch up with all the the comments in the chat.

(35:25) So, I would pause here to see if anyone has any questions and happy to to enter um the Q&A. Yeah. Yeah. Who's having me answer? Because a lot a lot of folks are also doing a great job answering each other here. So, amazing. Who who's got one directly for Thomas that you can elaborate on? There was a there was a request to show step one again and also a question if uh you'll be able to share the slides after. 100%. Um sharing the slides after.

(36:04) Um step one in regards to finding relevant prompts was was that the topic, David? Um I I thought it might have been those the three steps with um yeah making sure that you've enabled access to the crawlers and things like that. But this one oh wait uh here let's do the part of the last three steps here just so the last so first line step one the first step in your G playbook that one sorry the first step in your J playbook.

(36:40) Yeah, here the on page stuff. So the on page stuff is I mean um yeah as quite simple. So to to summarize one we need to check if our website is technically crawable. We need to check in on with our CDN provider. We need to check our Roberts txt file. We need to make those decisions.

(37:07) Um, and I would recommend you to check the ratio on dynamic versus static content generation that goes on on your website, especially if you work in e-commerce. I've seen lots of e-commerce storefronts and and online jobs where basically category pages, product pages are invisible for open eye crawlers because the these sit behind embeds and certain dynamically created listicles.

(37:31) So if you have lots of that stuff going on on your website, yeah, I would I would double check that. Um that's one. The second one, content optimization. Um without going too much into detail, what you'll also find in the slide deck here, which I haven't shown you, is my GIO stack. And in this geostack, you'll find a couple of as you can see here labeled content optimizing tools.

(37:56) One is the open my to open AI tokenizer where you can add um basically a paragraph and it it is broken down into tokens. Here's a custom GPT which actually I didn't build. Full full kudos goes to one of our customers who built this custom GPT that analyzes any text any URL and will give you certain chunking recommendations on how you should chunk your website content to be better indexable on um chat GBT.

(38:22) And the last one here is a relevancy doctor tool from IP rank. You might have heard about a I pull rank an agency where again you basically enter a prompt or a URL and it way will basically um analyze this URL or based on the prompt and will semantically chunk and analyze um analyze that that URL.

(38:48) It's a really useful tool to actually see how relevant certain paragraphs are in your text for those specific user prompts. So when I when I mentioned a real brief earlier content optimization, check out those free tools. They will help help absolutely come in handy. I've been using them daily to to optimize our own content. Hope this answered the question. I see some hands raised. So yeah, Rand was next.

(39:15) Yeah. Um so first just thank you for the presentation. This is very informative. Um, I want to lead into a little bit of the the sources uh that that AI use when it's they're doing a web search to answer a question. So, you know, for example, I was just looking up an old project of mine.

(39:35) I worked on Colgate and helped them develop their oral care center, which is the largest collection of oral health articles and content um out there, but it's not showing up in the searches even though there's a huge kind of related body of work under that under that domain. Um, and instead what I'm seeing uh is a little bit of overlap between Google and Perplexity, but wildly, you know, they're not diff they're not very overlapping much.

(40:09) And this huge body of content that is really directly related to oral health is just not showing up in in either of those places. I'm kind of curious your take on why that is. uh especially given that one of the steps of that perplexity does when you put in a query is it rephrases your query into a bunch of other queries. Yeah.

(40:33) So, I mean a great great question and I've definitely seen that in in in some examples where basically our content was really ranking high top-notch on Google but literally not visible on chat ch um the short answer is I don't know exactly but I would recommend you to go through the following steps um what you can do and check one one question is how is that content being loaded into your website into the front end is it dynamically inserted or um do is is it basically proper um good HTML structure or is there lots of JavaScript on the site? So I would check the ratio of dynamic

(41:06) versus static content first. Second, maybe we do have an issue with not allowing certain AI crawlers to our website or to particular parts of our website. Sometimes we don't know that because our CDN providers block that. So again, that's kind of the housekeeping part. If those all if those things are in place, I would open up I would what I would do and I saw someone asking about this this custom GPT as well. I would open up this custom GPT here.

(41:34) Um let me pull that link in the chat. Um this is a community I said one of our customers built this. Paste in your URLs and it will tell you how good you are from a chunking perspective. Do you have proper content chunks that are recognizable for um jet GBT for example? So that's the second thing I would do.

(41:59) And the last thing, the third thing I would do is I would do this relevancy scan, this semantic relevancy scan with, for example, this tool from my poll rank that will also semantically analyze your content and will will basically show you how how close your content is with the respective search queries. um you can you can basically compare that with the URLs that you see ranking quite well on on chat GBT and you probably see better better correlation there as well.

(42:25) So those are the three four or four steps I would I would now go through in order to to answer your question on why is our content not being picked up by JPT. So is there I'm just going to build on that a little bit. Is there amount of volume around a topic that's useful or do you really want to be super optimized for the trends in queries that you're coming in? Right? So, if we have a thousand articles all on oral health, but they're kind of not really being picked up in a semantic way for the search queries, then maybe we need to look at articles that are or content

(42:57) that is directly related to the search queries coming in because the AI is still not very good at at connecting those dots. I'm not Does that make sense? I think let me get into the content strategy a bit. Yeah. So I mean I where I've seen from a correlation perspective I would say um backlinking does matter less to chat chet right so um those interlinking strategies I wouldn't I wouldn't worry too much about that.

(43:35) I would worry more about is chat GBT recognizing um this specific content and how this specific content should match a certain user intent and it kind of goes back to okay what are the user intents and how can we build those topic clusters um it doesn't necessarily need to be and I'm I'm making this up right because I got this examp this question also today if you think about all of those FAQ elements right you have you you have content pieces that cover all the different FAQs and should that all sit under one under one URL? Should that sit each single FAQ question on on a on a particular URL? The short answer is it doesn't really matter. I would say

(44:11) um if that's under one URL or um um if you have multiple URLs interlinking with each other that that should per se should not be be an issue for for chat GPT but I'm yeah I'm not fully sure Randall if I I know answered your question. No, that's that's great. Thank you. You're welcome, Karen. Yes.

(44:35) Um, I have a couple of concerns and questions. Number one, in my experience, and I have a rag solution, um, and I see that a lot. You can ask the same question even in chat GPT twice, and you're going to get two different answers. And uh in my rack solution for example, I may have a lot of similar content, but why it picks why what it picks is um is a mystery to me.

(45:11) Uh so what what what's the answer on that? I mean in search usually when you rank high you going to show up all the time. here it may answer differently in the same session especially if you're in the same session it's not going to generate the same answer consistently is that your experience as well so I would say one or two things one that's exactly why I would not recommend anyone to do a manual approach of monitoring right because it will be heavily biased by your sessions and what is stored about you and and and and the personalization factor s that go into

(45:46) chat chubby. So that's that's one. Um and um even if we take away those personalization factors, right, memory rock, um history, location, and whatnot, and you and we we've done lots of research. We even teamed up with a university here in Europe to to do research on exactly that question.

(46:12) Same query, same prompt, executed 100 times, a thousand times, what comes back? Right? And it's interesting because yes there is variation there right there's variation in in form of the text output but when you think about and when you look at the brands who get mentioned when you look at the websites that get cited if you do the same query 10 times 100 times over and over again you'll get a significant statistically relevant data set there to see okay out of 100 queries how often am I getting mentioned maybe I'm getting mentioned 90 times 95 times Maybe I'm only gettingif mentioned 50 times, but it shouldn't I think it shouldn't worry us as as marketers optimizing for chat

(46:51) chibity because we've also seen in those data sets that yes, text output might worry, but brands show up over and over again. And at the end of the day, I'm a big believer in we we have to we have to optimize for a more objective experience. And every one of us has a very personal personalized experience with chat chibeti.

(47:16) And um this yeah this this is definitely a a good thing for us as consumers but it's not something we as brands can optimize for because we want to optimize for the most neutral um experience the most neutral EIS brands taking away all of those personalization factors.

(47:36) Maybe in the future we're going to move into a direction where we can optimize for those personalization factors. But even if you think about ER mode for example, who now has access to your Gmail inbox and to your calendar, there's no way for us to replicate that on scale, right? Um I hope nobody else has access to your Gmail um inbox or to your calendar.

(47:58) So um it it's not something that we as a monitoring tool aim to replicate and I don't think it's it's it should be anyone's one's goal to replicate that on scale if that makes sense. Okay. Give me maybe everybody in this group I I I see very engaged chats. Um what's what's what's your sentiment? What what's everybody is doing right now in terms of optimizing for higher brand visibility, higher website citations? Are you already optimizing for that? Maybe on a scale of one to five, one meaning I'm not doing anything. Five. I'm an expert in this. I've been I've been doing this for for weeks and

(48:30) months. What What's everyone's sentiment? Uh give me some some feedback and and some reaction to that. Yeah, you know, it it depends. It's it's more top of mind and and having a tool like Otterly. Yeah, I find like having, you know, just any kind of dashboard like that becomes really helpful. I'm also curious, Tom, you know, because I I started just more because it's a going from passion project to work project where I'm using vibe coding more like do you think that like more and more the best practices will start getting built in and even like to the Wixes and

(49:06) Squarespaces and things like that like like like when can we start Yeah. uh idea just like flipping a couple of switches and and having access to more of this. Yeah, it it's interesting, isn't it? Because SEO tools have been rather detached from content itself, right? So there's no I don't know any any good let's say good native integration between SEO tools and and content content hubs CMS providers.

(49:34) But I think that's the exciting piece there. I think that's that's changing with EI search and I've been I mean we are tech partners of of some page builders and some CMS companies and I think um we're going to see also more analytics capabilities being built by those page builders by default into their core offering.

(50:00) If I think about Wix, Squarespace, Web Flow, WordPress and and all those CMS companies, right, I do think that um some CMS companies and I have worked in CMS companies for the last 6 years. Um they built that natively. Others will team up with companies like ours with integrations and and and apps in in that sense. But I I think ultimately one one of the things I I've been realizing over the last years is and I'm I'm a big believer in content marketing and educating through leadership content and and authoritative content.

(50:31) I've been realizing that many content teams are very detached from performance metrics. How is my content performing on my website, on Google, on the internet, but also on chatbt? And yes, I think we're going to see more integrations, but also more core analytics offerings in in those those um page builders and and tools. That's great.

(50:59) Well, uh well um also you you've been doing well not to plug utterly much, but I will say like I first started using it because you've got a twoe trial and it's just like very easy. you know, if you haven't used anything like it, then this is also something where like like I I mean, my first time was actually I was just going to give a talk on on something related to GEO and found it so helpful to have a relevant screenshot specifically for that conference.

(51:27) So, uh, I'm I'm sure you appreciate paying customers even more. But even if, you know, someone's just trying to go and and learn a bit how this stuff works. Um, you make it pretty easy for someone to just get started, try a few things, and and it doesn't take a lot to go and and at least start getting something out of it. So, so appreciate that.

(51:46) Um, any other good ways for folks to get in touch with you or, you know? Yeah. No, thanks for, for mentioning that, David. So yes, we do have a free trial. Anyone can can run a couple of queries totally for free and audit audit your website for geo readiness. And yeah, you you can find me on LinkedIn. Um I'm and let's connect on LinkedIn.

(52:05) I I'll spend way too much time on on LinkedIn. So um hit me up on LinkedIn. Um I'm an open book and I'd love to keep the conversation going. Well, well, this is great. Well, appreciate just, you know, it's fun following the side conversations in chat and and everyone uh uh having a lot of answers for each other and and great to see so many builders here and and creating so many of the things where where uh GEO and and having good dashboards for it will come in handy.

(52:35) Uh you know, uh as you have more research coming out, things like that, we welcome you coming and continuing to share that cuz we're all just trying to learn here together. So, uh, uh, appreciate you, uh, joining us and and being part of this community. Yeah. Thanks so much for the invite, David. Um, looking forward to our next chats. Thanks, everybody.

(52:54) For sure.

## Unlocking the B2B Ecosystem AI Data and the Future of Intent

Speaker: Mike Burton
Published: 2025-09-10
Tags: b2b marketing, identity resolution, publisher cooperative
Video: https://www.youtube.com/watch?v=I--tZ600rRA
Page: https://aimarketersguild.org/sessions/unlocking-the-b2b-ecosystem-ai-data-and-the-future-of-intent

(00:05) Hey everyone, welcome to a special edition of a insiders with a marketers guild and and we've got a great guest today, a partner in architecture media and a company I've been learning from quite a bit throughout my career, especially as I shifted uh more toward the B2B side. that's Bombora. We've got their co-founder Michael Burton here.

(00:31) this a lot we'll get to dive in deeper. Bumbora really is so much of, you know, what I consider at least the gold standard or even some of the glue that that ties so many other platforms together. Cuz I I can't tell you how many pitches I've been on where they say that one of their, you know, one of their best features is that they have Bombora data built in. Right. It's almost like Yeah.

(01:02) And I think you've done a great job with this sort of Intel inside. Yeah. U version of it and I'm just very excited to learn way more about the this and and dive in here. And so, uh, Mike, welcome aboard. Yeah, David, thanks so much for having me. Appreciate that warm intro. Um, and kind of hits on some of the themes I think we were planning on covering and, um, excited to be here and and hello to everybody who's on.

(01:29) Uh, great. Well, um, yeah, I mean, feel free to kick things off. got the slides up here and uh yeah, great. Let's do it. Yeah. Yeah. I thought it it would be useful just to give some really fast context on the B2B ecosystem and kind of a little bit about the role that Bomba plays within it and then hopefully just kind of talk more conversationally around what we're seeing in the marketplace and how things are evolving, you know, certainly with AI, but just um kind of at large um you know, with AI being a component, but you know, kind of like these other broader shifts that we're seeing as well.

(02:06) Great. Um cool. So we could just build a little context like kind of exactly like David said uh we were really built around um kind of being this point of glue uh for a lot of the B2B ecosystem. So we think about things in terms of like the full B2B funnel. Uh right. So if marketer wants to run a top offunnel awareness branding type campaign, you know, we play a role there all the way down to the, you know, SDR trying to decide who to make a phone call to on a Monday morning inside of a sales use case. um we kind of uh were built to to make you

(02:42) know high quality intelligent B2B data available across the entire ecosystem across the entire funnel. Um so in the next slide here I'll just tell a little bit about how we do that. So um kind of in the same exact spirit we we built the business around a cooperative of publishers. Um so going back you know 10 years ago now we started working with a lot of you know vertical trade associations, trade publications, broader business focused websites. So think you know Wall Street Journal, Bloomberg, Forbes, Fortune etc.

(03:16) um events companies, lead genen companies our best effort to kind of build a proxy for the B2B internet and across that large B2B ecosystem we're kind of you know boiled down to two things. We're monitoring companies consume B2B research and get a good understanding of what they're interested in, what they might be gearing up to research and buy.

(03:39) Uh, and we're able to track uh, anonymous B2B professionals. Uh, and that data becomes, you know, kind of core to, uh, account-based and other B2B advertising that takes place uh, across the ecosystem. So, in the next slide, we get into kind of like the the different techniques that we have to be good at to do this. Well, um, right, we need to be able to use our tag across this ecosystem, understand what pages are being consumed, uh, how much time and effort people are spending on those pages, what companies the users work for that are consuming those pages. Um, and then build those all into models that give us

(04:17) a really good opinion on what companies are interested in and what what they might be gearing up to buy. So then finally we we take that raw material from the cooperative. We take those techniques that I just brushed past really quickly and we we kind of make these three things available. Uh company level intent data.

(04:37) Uh identity resolution. So this is the ability to say hey here are anonymous users on your website and everything we know about them. Um like what companies they work for and what job titles and job functions they have. Uh, and then like I mentioned, we're also able to to take this anonymous device level data and power a lot of B2B advertising.

(04:56) Um, so the all of that that kind of like great data that I described boils into these three different offerings that that Bumbora brings to market. And then yeah, last last thing here just for context is a lot of what what David said at the top. We're very very much an ecosystem business. So not a not really a platform that you would come and and kind of uh kind of control your entire go to market from but more of a data source that gets distributed out into all of the platforms and places where the actual you know use cases are

(05:28) enabled whether those are adtech advertising platforms or like I was saying all the way down into these sales intelligence um you know and sales tech type platforms and everything in between. So hopefully that just kind of acts to set the stage for kind of where Vombor is coming from um as we watch the market evolve and that type of thing.

(05:52) But um David, I don't know you could you could gut check me if if that all made sense and anything I should clarify. Yeah. Well well it all makes sense. I I'd imagine one of the the most uh frequent questions you get is how do you have this actionable anonymous data? What can you collect? What can't you? and uh and just getting a little bit more of a sense of how privacy works, especially as that Yeah.

(06:17) as the guard rails always seem to be moving on that one. Yeah. Yeah. I think a big part of it is having a direct relationship with the publisher. Mhm. There's ways in all B2B and B toc there's kind of like back doors and side doors where you can build data assets without having a direct relationship with a publisher or the end user. So because we have the direct relationship with the publisher kind of step one we're in their consent string that allows us to store consent from end users as as users are opting into the privacy policies of those publishers.

(06:48) That's really really important. Um so then it like then you can put the B2B specific lens on it and we think through things very much on a company level. So we're not so much interested in understanding that this is John Doe at Boeing. We're really interested in understanding that this is an anonymous user who works at Boeing.

(07:10) And then we're interested in understanding of all of those users that we've tagged at Boeing and think of them as a big kind of like swarm of bees. What are they interested in now more than they normally are, but this is where B2B kind of has a little bit of an advantage. Uh we're not interested in people as much.

(07:28) Um, so between, you know, solid consent and and relationships with the publishers and kind of B2B in general being more of a company level, uh, proposition, um, that's kind of our our POV or where where we sit from a privacy perspective. And and so when you say what people are interested in, like how much of this is the content they're consuming on these publishers or what signals ladder up to interest? Yeah.

(07:54) Yeah. So it's like anything that we can see being consumed across our ecosystem. So it might be a user read an article on let's say hybrid cloud computing. Um but we might find out that that article the user actually landed there directly from a search engine. We'll take that into account. We might find out that um you know they were only on that page for a moment. So we'll throw out that interaction.

(08:18) uh we might found out that they downloaded a white paper on on that topic. So we can wait and score that differently. Um and this is all contemplating the individual interactions but B2B not so much interested in the individual interactions.

(08:38) is we want to roll all of that up to a company level understand normal company level behavior right so to keep using the same example how much does Boeing normally care about hybrid cloud computing so that notice when there's a big spike so we think about it in those two altitudes like being really good at understanding the individual interactions kind of like the grains of sand on the beach but then using all of those to get a really good picture of a company's behavior as it compares to their normal baseline if that if that made sense.

(09:03) Yeah, it it does. I mean, is there a point like you take a a company like Boeing where then it where then you drill down at least some degree, you know, location, division, you know, some kind of discipline. So, it's not just trying to boil the ocean of Boeing and try to see where this is even coming from. 100%.

(09:28) So, and again, this is much most useful in like a sales use case where an SDR or saleserson needs to, you know, really like hunt into a specific signal. And so, yeah, for those use cases, we do we get down to like a geo region. Um, which in the United States can be into like a like a city, you know, like area um or internationally it could be like a province. So, it helps that salesperson kind of do the do the hunting.

(09:55) a little less useful from like a marketing or a campaigns perspective where you know probably want to cast a wider net and reach multiple stakeholders and and that type of thing. Mhm. And uh yeah cuz uh it it's I mean I'm I'm also really curious right now.

(10:17) I mean you've you've been at this for a while, right? you co-founded this company, a ton of experience here building this. Um, like over the past decade, it's easy to say, you know, if I ask you what's changed, like of course AI has changed, but but more specifically, like what is really different for especially your customers and your publishers right now than maybe was pressing for them even a couple of years ago? Yeah, it is a great question.

(10:49) I think one of the things that we're seeing is kind of like the reward around more flexible business models. Whereas, you know, over the years there's been a little bit more and this goes beyond data. This is kind of like just the vendor sphere in general. Um this idea of like, hey, if you're a marketer, you're a sales and marketing organization, we as a vendor, we're going to ask you to make a really big bet. uh you're going to have to pay up for something.

(11:16) Uh it might be expensive, then you got to figure out how to adopt it, how to get value from it, how to do change management around it. Um and it feels like and just kind of maybe as a coincidence or on a parallel path to AI or maybe AI started to change um you know, perceptions on the way things should work.

(11:35) Uh it feels like now what's rewarded is flexibility, smaller bets, things that that kind of like invite these incremental um gains as opposed to like you know asking organizations to make these major points of adoption and and kind of like go through these big change management exercises. Mhm.

(11:59) Um, so in B2B we see like a couple different ways that that like the ecosystem can evolve to kind of like solve for that, but it's it's a pretty stark change I'd say. So does that also mean that there are changes in the kinds of tech companies that are uh that are looking to go and incorporate Bomba data? I think so.

(12:25) Well, I you know over the years it's always been very heavy in B2B tech. Mhm. I think they tend to be the earlier adopters. Um so large you know enterprises like Oracle, SAP, Cisco, Salesforce, Adobe etc etc. Like big big B2B tech companies tend to be ahead on this stuff in our experience. uh but we are starting to see you know much more traction in finance and manufacturing and other verticals as well which I think is a pretty normal evolution now like as get to these more kind of modular flexible business models I think that'll help everyone adopt things faster um because the the tech companies have learned to be a little bit more nimble

(13:03) uh whereas I think you know these newer approaches to bringing things to market can can you know help others be similarly nimble Uh, one thing I'm so curious about your perspective on is that like I'm hearing a couple things from you that sound a little counterintuitive, if not countercultural in the business sense right now.

(13:31) Um because at the at the LLM level there's a lot of often doom and gloom prognostication right now that the that they've hit the limits of available data and you know and then it's like then you just start training it on its own you know on data that's upgraded through their own systems and and so then does that lead to some of the dumbing down of what happens there.

(13:56) But there's also at the same time it's like what other sources of untapped data out there and and it and it is eerily like this whole data is the new oil mantra. Some of what you're saying seems to go against the grain if I'm hearing right and I'm curious it uh feel free to correct me on this too that more data isn't always better. It isn't always necessary either.

(14:22) and that like you can kind of get by with some fewer signals and you don't need every like to actually, you know, do the job. Yeah. No, I I think No, I think we would say that with with where everything is going that more data is still certainly good. Um I think the way that that data gets transacted on, the way it gets made available is what is what we're seeing evolve.

(14:47) Like ideally, you know, synthesizing lots of different go-to market data points is difficult. Large enterprises are okay at it, but still not great. Like all these different data elements that go into who might be ready for a phone call or who might be ready for a sales process. Um, more is better there still always like the ICP. You need to know who's coming to your website. You need to know who's installed the right technologies that make them a fit for your solution.

(15:14) You need to know who has intent. You need to know who's race counting. You need to know who's that executive changes. There's a litany of B2B data that you need and more of it is better, but the way that customers have been asked to kind of like transact and adopt that stuff that I think is what is changing, right? Like you can either buy kind of a big slew of it inside of one platform.

(15:38) Um, and there's pros and cons with that. like naturally like the individual sources themselves won't be quite as good because you're this big conglomerated source you're going to have less control that type of thing or you can go buy it all individually do these big data licenses with lots and lots of like best and breed data providers so painful um and so I think what's changing will change over the next three years is like hey how do you get access to best and breed data more of an outcomebased model like hey I want to

(16:09) pay for what I eat I want to pay for the volume that I need. I want to pay when I get a good outcome. That type of thing. But yeah, if we can if we can bridge access to all the data a customer needs, make it the best available type of that data.

(16:28) Check the box, say you have it, but it's actually the best form of that data, but also give it to them in a way that makes sense from an adoption perspective. That that's where we see things going. Mhm. So, so then with that like uh there's also one thing that may never change is this constant push and pull of fragmentation versus consolidation. Uh and so and so we've seen this with big tech repeatedly that there's some you know rich get richer but there's a lot of uh there are a lot of niche players that are be able to go and launch quickly and like you know and and solve some very specific needs. you see this like like if you you could probably break down the B2B sales cycle, you

(17:14) know, to hundreds of micro steps and you see all these entrance coming in at at different parts there. Uh like are I'm curious what you're seeing as far as uh are we on a certain end of this side of this pendulum and and where is this now? Yeah, I think what what's coming next will be like that synthesis layer because like the actual like I and again I'll come at this from a data provider perspective.

(17:44) I just rattled off all those different data points that you probably need to go to to go to market effectively. I think the like there's still room for solutions around synthesizing all of that data making it highly actionable for a particular end user. Mhm. Um so like you think you can think of that on one hand as like well that's just one part of the value chain. You need all those different points of data.

(18:07) Um and you need a bunch of different ways to organize it and all of those things. Um so in a way this is just another category that'll start to emerge and we see lots of like newer partners in this space. Um, but yeah, I think that's the the next one where new players will evolve and like, hey, who can be best at making prescriptions uh based on the synthesis of lots and lots of different data points. Yeah, it's it's so funny.

(18:34) I was actually just talking to someone on the the GEO side and, you know, generative engine optimization uh and and I was telling them I'm like, I love what you're aggregating here. like when am I going to start getting these like just alerts in my language that say here's what to do with this and and and now I think that expectations also rising right it's not just that like you should be able to at least if nothing more than plugging in some kind of LLM translator on top of what you're doing like then let alone you know building something more sophisticated actually deliver what's more and more becoming like that Yeah.

(19:15) Insight. Yes. Yeah. And I saw like something in the chat as well. And I've heard you talk about this, David, with with some of your stuff, too. It's like we're not quite it's it's we're getting there, but I don't know that like let's say a salesperson would would, you know, work with a tool that could synthesize all the information that's on the internet plus a bunch of important data about an account, get a prescription, and be like, "Wow, all right. That's going to cut through the noise." like stuff that's coming back is still just a

(19:45) little general. It's like, well, yeah, I'm going to talk to Boeing about benefits for an aerospace company. Like, yeah, yeah, they're in aerospace. Um, but but we're getting there. And like I think I've heard you talk about this before. It's like that that period where it could be easy to get disillusioned, but those that like follow all the way through with with the cycle will see that, you know, we're getting very close to where these prescriptions can be very accurate and and timely.

(20:14) So, so then I mean, uh Earl Richards Jr. has taken this uh even a step further than I did as he often does uh and uh talks about you know he says we need to work on helping clients turn their data into information into insights and uh and insights into business impact which is you know is such an important step like what role do you want to play in all of this? Yeah, I mean definitely like the way upstream we're a data company first only so many things we can be good at. So we have to be really good at understanding having a strong opinion at

(20:53) what a company's interested in and we need to be really good at having a pool of anonymous devices that are targetable for B2B advertising like first things first. Um, from there like we can have a strong opinion as to where those data assets add value and how they should be applied.

(21:20) Downstream of that, we rely on the ecosystem and lots of partnerships to make the data actually like go to the last mile and make those business impacts. Mhm. Um, so if we were to say to ourselves, hey, let's build a whole platform around this and be the best at synthesizing multiple data sets, like we'd just be giving ourselves too much homework to be good at at everything.

(21:39) Well, and so, uh, that's why you built the ecosystem that Yeah. And and that also I think relates to Paul's question here in terms of options for small business. Is it really just a matter of the partners that are tapping into Bomba data and if they work with small businesses? Yeah, and it always depend for us blessing and a curse. Vast surface area. It depends on your use case as well.

(22:10) So it's an SDR type use case. We've got a bunch of sales intelligence platforms, you know, like Cognisum, Sales Intel, Lucia, Apollo, many, many others. Um, and then if it's an advertising, you're looking to launch some campaigns and get them into market.

(22:27) We work with lots of partners in adte and ad platforms like Reddit and others. So, it it kind of depends on what you want to accomplish. Um, but typically with a smaller company, you would work through one of our partners in one of those categories. Yeah. And and they in turn want to serve in all these Yeah. all the different levels of the market.

(22:52) And so uh uh where it really just becomes a big marketplace for that. Yeah, that's right. And we certainly have, you know, lots of relationships with large enterprise companies that that buy the data directly and bring it into their own environment and do their own, you know, modeling and and their data science and analytics teams work with the data directly. So we have we have both.

(23:13) And and on the publisher front, like how do you determine what might be the right kind of publisher for you? What might be maybe too niche or too small or how do you filter all that? Yeah, great question. Um, you know, from a high level, like if if you're a publisher and you deal in you're serving a B2B professional, helping a B2B professional do their job, creating content about somebody doing their job.

(23:41) Mhm. You're good for us. Um, we have a lot of coverage across all verticals. So, it's not like that there's a b there wouldn't be any B2B content that we wouldn't want to add into our mix because we're really good at synthesizing it. We're really good at waiting it and scoring it and um you know rebaselining companies based on the addition of new sources.

(24:03) That's kind of like a core strength. So, if you're in B2B or you're business focused, we've kind of like have a really good engine for bringing that content consumption into the models and and making good use of it. Um, and even broader content, it helps us from an identity perspective.

(24:22) It gives us more kind of like grist for the mill of getting better and better at making predictions as to what company a user works for. Mhm. We're that that's a core strength is like we're we're good at synthesizing data. We wouldn't want like something that's purely B to C. That would be a little noisy, but otherwise we can we can take it all.

(24:42) Are do you wind up in scouting mode at all? Like are there areas where you're like like oh man there's some new trends emerging and publish like we want to make sure that this is part of our set like our and our partners sometimes asking you for certain kinds of publishers even specific ones. Yeah. Always. We have a whole team that just runs the co-op.

(25:02) Um, so they're constantly trying to figure out, you know, who are the next block of publishers that we want to try to bring into the fold. And we have had large customers say like, "Hey, yeah, it looks like you have coverage, but we'd love you to work with this publisher." And they help make those introductions. And more and more we're like helping to bridge these use cases together.

(25:22) Like our our audience footprint can be used to do advertising direct to publisher. Mhm. More and more we're trying to connect those dots for our publishing partners. Like we might be creating an audience for a big advertiser and be like, "Hey, if we're sending it, let's say to the trade desk, there's no reason why we can't send it to Forbes and Bloomberg and Wall Street Journal as well.

(25:45) " And those advertisers are doing direct to brand advertising anyway. So, we're just again like kind of like the glue that that's helping to connect the the ecosystem. It's great. And and by the way, as there are other questions from attendees here, uh feel free to enter them in the chat or even raise your hand if you want to ask live.

(26:06) Uh and and we already have an offer. So uh uh Jamie, go for it. Hi, thanks for uh letting me ask. um just in that area where you end or or or start in terms of the synthesizing of data, do you ever get predictable about things? And what I mean by that is go back to Boeing.

(26:34) We're noticing them becoming more nimble or we're noticing them becoming more um uh want their their want to change as a as a culture of the company. Is there is there an overarching culture or personality that that uh you kind of label in some way, shape or form about the companies uh to help with uh predictive models of how they might react in in different ways to different stimulus like AI coming in and are they going to react quickly or not or the economy is going to do something how are they going to react? No, great question and I appreciate you joining my Boeing example. Spent 10 years using Boeing as

(27:11) as an example for tracking account behavior. Um, so yeah, one of the core strengths is this historical kind of highly structured historical data set. So we publish the intent data week over week going back many years. And so this allows us to look at any outcome.

(27:35) let's say um a set of companies that um did a bunch of work around ESG uh and we can look at the behavior of those companies over time and to your point then find other companies that are behaving very similarly. Some of those behaviors might be really obvious like they have these spikes in research on topics directly related to ESG.

(27:56) Some might be, you know, weird corlaries like, hey, companies that tend to invest a bunch in HR and employee relations also tend to do really well with ESG. Um, so yeah, like anybody, we do this on behalf of customers and customers certainly do it themselves on their own. Like anytime you're looking to make a prediction around a specific outcome, you can build that model using the historical data.

(28:21) Great. Thanks Jamie. Uh Earl's got some questions too. What's the typical data maturity of your publishers that you work with? It's a mix. Um like the ones that you know the publishers we work with are in all different businesses. They have different revenue models. So the large kind of business focused publishers I talked about, they're in the advertising business.

(28:46) uh and they're pretty good at applying data into their advertising campaigns from a targeting perspective and from an analytics perspective. I put them on the higher end of maturity all the way down through these like longer tail niche B2B vertical publications mostly in the lead genen business sometimes in the in the events business.

(29:07) So they're using data to better promote campaigns or to better do lead scoring and things like that. So some of them are quite good at that. you might think of it intuitively as like a little less sophisticated. Um, but it's really just a different way to apply data that's not adtech focused.

(29:25) Um, so it's a mix and it certainly depends on kind of like what revenue model you have as a publisher. Right. And then and then which teams from your publishers do you usually work with like audience strategy, bisdev and sales, marketing, analytics, etc. Yeah. Um, also a mix I'd say on the larger publisher side that are mostly in the advertising business, it's a lot of ad ops from a day-to-day perspective, right? Are the ones launching campaigns, providing reporting and analytics against campaigns, that type of thing.

(29:56) And then like down in the the longer tail of the B2B niche trade publication, we're working with executive leadership um to get buy in to the cooperative and then to start to like connect us into the other teams that can make use of the data. I'd say like you can imagine um kind of the more niche you are a little bit more closely you'll hold your data. Mhm. Uh right.

(30:20) Like you have this really specific audience of um banking professionals in the United States. Very few publishers have this kind of like condensed access to those users. So you're going to be a little bit more kind of like thoughtful about how you enter into a proposition that shares that data. Um and we have an amazing co-op team that's that's you know taken our kind of approach and our ethos as a business and like done a really good job evangelizing that. uh across the publisher community.

(30:52) So I you know I'd be remiss not to ask a bit about some of the challenges overall that that are happening and I'm see a lot tied to B2B data. Um one of them is is that there are a lot of players out there that that you know are all about quantity and not about quality at all. Right.

(31:21) Um, and that uh that I mean my fear is that the problem of noise and especially noise to signal and some talked about that signal to noise ratio in the chat is just it it's you know it's like the tragedy of the commons. It's make things worse for everyone because there are a lot of bad actors out there.

(31:43) Um uh and so how much quality control does Bombora need to do with the partners you're working with in the ecosystem since you are some of that glue out there? Oh, interesting. So like when we think about companies that we will partner with. Yeah, that's that's interesting. Like I think like you know we want to position hopefully rightfully as like the gold standard when it comes to B2B intent data. Mhm.

(32:08) So the partners that are working with us, they're making a larger investment. Mhm. Work with Bomba, make it available to customers. So right away we're kind of like align because there are ways to your point, there's they're we live in a, you know, a competitive landscape where there's other flavors of intent data. U but it's kind of like self-qualifying.

(32:25) And the partners we're working with are making that investment and they're using this data set that has, you know, we know where it's coming from, consent from end users, true baseline so that we understand when there's a real spike in interest in research. Um, so hopefully it we're taking care of that by, you know, finding customers willing to make that investment.

(32:48) So this is fantastic because, you know, data is the new oil and all that. Uh my question is going to make uh Burkowitz roll his eyes. It's about blockchainbased solutions around and I don't want to get techy but around the uh decentralization method just to make that data more reliable. So the middleman is out of it and anyone with any intent is kind of out of it.

(33:11) How are you distinguishing your proof uh using blockchain or any of those kinds of digital uh DT services um ledger stuff like that? Yeah, I I wouldn't say that we're actively like in that right now, but I'd love to hear and not to turn it back on you an example from what you know of our business of like how that could apply in a customer context or otherwise.

(33:36) I think we have lots of ways that we do validation. Um, but measurement and validation is probably like the most important thing that that we can do for customers. So if there are better, smarter ways to approach that, I'd love to hear with I I'll I'll talk to you about it later, but one thing that might be a relevant topic for everyone as well is with uh the nano banana kind of thing.

(34:00) It's just like the latest flavor, no pun intended, and all of a sudden the line is everything you see on the internet is now for sure not something that you can rely truth. I mean, it's just so good. Um data is the same thing. It's not just imagery and things like that. How are you tackling that from a differentiation standpoint so you can sell guarantee your your your information? Yeah.

(34:26) So we we touched on it a little bit before but it starts with like if if we can if we can be provided a truth set then we can compare the behavior of that truth set to a control group and find a distinct behavioral pattern. Right? So let's say we use Boeing uh uh we know that um Boeing was selling uh something and a 100 customers bought it, right? We know we we have that truth set.

(34:51) We know the dates that that that it was purchased. We can find a distinct behavioral pattern from those 100 accounts, a research pattern that we saw across our cooperative that differs from another 100 accounts of about the same size. And we can go deeper into specifically the how we do the control group creation, which is really important.

(35:09) But once we can do that, we basically know the behavioral pattern and we can be pretty good at at identifying other companies that are behaving the same way. It's like the the Facebook mirroring thing. It's just pretty obvious. Yeah. Yeah, that's right.

(35:26) What's funny, one trick of it is let's say we get these I use this number of 100 closed one accounts from a customer. um we can't just compare it to a control group of some other hundred accounts. We need to make sure that those other hundred accounts are about the same size and that we see them the same amount, right? Because if we just take another 100 random accounts that are maybe smaller or that we don't have the same visibility into, it's hard to make an applesto apples comparison. Yeah.

(35:54) So the fact is you have all that comparative data means you can match apples to apples. Yeah. And it's also a big advantage of this idea of kind of like a closed ecosystem that we can observe across. So like other kind of more temporal data sets, they're typically able to make observations across kind of like a moving target.

(36:16) Um like okay, I see these behavioral interactions on the internet, but where on the internet is always changing. The access I have to the internet is always changing. So the reliability of any like kind of trend data or historical data that comes from that is very low. Uh but we have this kind of like controllable ecosystem that we can keep like kind of like tuning and relying upon and that like allows that historical capability to be have a lot more fidelity.

(36:43) So one one last point here's a radical idea talking about it in a in an MIT think tank last weekend and is how do we make this advertising supported future that we in this group would wouldn't mind. um uh how do we actually get the bad guys out and that means maybe the company's not making a billion dollars opening it up like Zuckerberg did and said everyone's welcome come on and use the system to a you know what we're going to vet you we're going to make sure you are who you are and uh make sure that that the ecosystem advertisements aren't going to you know rip you off there's certain criteria uh is that an association element is that something

(37:14) that AMG could actually champion is it uh interactive advertising bureau I mean who would police that and actually have a good housekeeping seal of approval of here's a a marketplace where we've we've kind of assessed and we just haven't just opened the back door. Yeah.

(37:41) And and then like how do we deal with some of the fundamental challenges like like uh one of my perhaps less popular opinions is that it's just too easy to buy domains. Yeah. Uh, and so you just see how how how some of the access and and ease and all of these seemingly really good things have created some intractable problems. So register your domain and your gun. I hear you got it. There you go.

(38:06) The and there was yet another David in the chat uh Mike just to as a kind of clarifying point is saying so exclusivity to the data de facto establishes veracity which becomes the competitive differentiator. Yeah, that that's definitely a big part of it, right? Like there's um I think it's 86% of all the interactions that we're able to see uh on a daily basis are exclusive to us.

(38:30) Um so that that certainly helps. Um now a lot of the publishers that we work with um their data is very difficult to get to for another provider anyway because like they don't make their inventory available and the bidstream is just not kind of out there. Um but that you know having exclusivity over the other chunk helps as well.

(38:52) And then and then also relating to some of this as far as like yeah you talked about kind of normalizing data uh and and Earl had another good question. So uh have your existing clients shared their numbers with your team so your teams can better weight the value and impact of your data that they're using and uh for data source scoring like what data do you get back? Yeah.

(39:23) So that's like we mentioned it a couple times but if a customer you know we're not a platform so it's not like we're constantly birectionally reading our customers data changing our algorithms and sending it back. We have partners that that kind of get there. But yeah, we have lots of customers that'll periodically three or four times a year send us some closed one data and allow us to like retune the intent signals that we're providing to them. Mh.

(39:48) So we might find that hey, you know, customers that close one, they tend to spike on this set of topics at this time uh at this kind of like intensity level. And we can kind of like retune their signals accordingly. M and and when you're talking about retuning signals, how much of that winds up being specific versus general? Um well, it's always very specific in that it's, you know, based on their closed one data.

(40:16) It's for that specific customer. Now, like kind of by design, we do live in a world of a set structured taxonomy. Um, and there's a bunch of good reasons for that, but so it's always going to be within that kind of like um intentional limitation of like we can only return signals back against this like set of topics that you know we monitor for across the co-op.

(40:41) Does that did that answer your question? Yeah. Yeah, for sure. And so um now looking forward like yeah what else is it uh can you share anything more about like where the road map is or or other things that are like coming up for you? Yeah, we talked a lot in amazing engagement.

(41:11) Thank you on this call about uh our intent data business, but we're also uh innovating quite a bit around our audience business. So I mentioned it earlier in the overview. Uh as part of the cooperative, we have anonymous uh B2B professionals and we're able to tie B2B attributes to those devices and make those addressible for advertising. Mhm. And that part of our business is uh you know growing very quickly and we we have a bunch of newer things that we're working on for that kind of theater of our business. Um I'll mention a couple of them. One is ABM measurement. So a

(41:41) big chunk of B2B advertising is targeting a set of accounts. Mh. So IBM wants to reach a set of a thousand key accounts. We have the ability to take that list of accounts, turn it into an addressable audience. Um we we've always been able to do is make that addressable and targetable.

(42:00) Uh but where we've spent a lot of time over the last couple of years is also being able to deliver back to IBM what accounts they reached uh at what frequencies, what job titles, job functions, etc. So providing the reporting back to IBM in this example. Um so that they can pull that into their data environments and build that into their models as well.

(42:20) And so, and is that something that it sounds like things I've heard from some of your partners having an unknown version of that? Yes. So, this goes back to this idea of kind of like in a more open ecosystem, more flexible business models and our partners have amazing, you know, offerings in their own right. That's a model.

(42:46) That's one way to do ABM advertising. You buy into a platform that has a DSP. that DSP comes with reporting. Great. Uh there's another way that we see the ecosystem working, which is I already work with the trade desk or I already work with DB3. I want to do my ABM advertising on those large DSPs, but I don't want to have to sacrifice reporting.

(43:06) I want to be able to get ABM reporting there as well. Um and so that's that's one of the areas where we've innovated and be able to provide that reporting kind of in a more open ecosystem model, if that makes sense. Yeah. Yeah, for sure. And and if there are one area within the audience space, um another is like starting to work with other account level data, I'll say brethren who have really interesting account level data attributes and helping them to translate their account level data into targetable audiences as well. Um so the number of those partnerships coming out where it's like, hey, I want to do good ABM or good B2B

(43:46) advertising. I love Bombora data, but I'd love to be able to have some of these other again best and breed premium branded data sets that I can also target with advertising. Um, so it's another way that we're looking to kind of democratize access to, you know, high quality data for use cases in a flexible way. If that if that makes sense. It it does. And and I'm glad you also brought up ABM.

(44:13) I mean this was where when I first was encountering Bumbor in the wild. It was from ABM platforms that were all saying they integrated with you and and I had to dive deeper and understand well wait like why is this data so good and powerful like like why like you know why why are they touting this right? Yeah.

(44:44) and we we work with all the ABM platforms and they're great partners and there's just there's different customers want different models. Um you know for some customers that platform model works great and they have big businesses and successful part of the ecosystem and then other other customers they might operate in a little bit more of kind of like a open environment where they use different platforms for different use cases and they want data that can connect through to that kind of more disperate stack.

(45:08) I mean, do you get a sense if there's any shift and and I know this is a bit removed from what you do specifically, but from what you're seeing out there in terms of how people are acting on this where there's some more of like the mass targeting everyone at, you know, everyone at Boeing, right? Uh or everyone in a certain geography department. Yeah. Something like that.

(45:35) versus trying to take this and then see like, okay, we've got these five contacts at Boeing. We're going to now go deep in these onetoone outreach and uh programs here. Are are you seeing any shifts in like appetite for doing one more than the other? Is that changed at all? Yeah, I think it's use casebased.

(45:56) So certainly sales use case, precision, precision, precision. Mhm. I think inside of like an advertising use case, you can kind of like um reach some diminishing returns, all right, congratulations. You found the the right 10 people at Boeing, but it's like, hey, there's a bigger committee. There's like it's a long sales cycle.

(46:21) If it's a top offunnel campaign, you want to build much more of a chorus. So, it really depends on the on the use case. That sounds a bit like what Mark Pritchard at PNG had said early on about just targeting on Facebook that they found that like once they segmented the audience too narrowly then they were missing a lot of that opportunity. Yeah.

(46:45) That and it's like it for a lot of platforms uh we won't mention any specifically but some of them like it knocks their algorithms out of whack. So like you're not getting any more. you're actually pay then you now you have to pay a lot more to reach this small group of people could probably have paid the same amount still reach those people cast a wider net so depending on the platform it doesn't microtargeting can hurt you sorry guys I uh put my video on because I'm in the islands and Caribbean I know you're jealous this is my tan um yeah my

(47:17) question was just to get clarity on working with a platform like Oracle depending on the clients obviously they have a different text stack depending their level of maturity and sophistication we get that so I'm curious how does Oracle integrate into their platform to the data because you're adding additional data for their target accounts their audience segments so I'm just kind of curious how the plumbing works essentially yeah and you mean if you're a customer that's like doing go to market work on the Oracle stack like Unity Eloqua that

(47:47) type of thing yeah well more like if you are providing the data for whatever whatever initiative that they have whether it's B TOC or B2B how do how would a client go about integrating your data into their stack so they can use it got it so in in this case Oracle is a just kind of like a a hypothetical customer of ours you know Oracle is the vendor providing the data to a potential SMB or agency or tech solution where does Oracle play a part I guess what's their role I guess yeah so one one way we would work with a customer that might be like built around Oracle tools um like in Eloqua or Unity

(48:26) their CDP and so we would be able to say like okay we curate these intent data signals kind of upstream and then we're able to push those into really any environment Oracle being one of them u and make that data actionable for different use cases um that space of like what used to be more of like the marketing automation space I think is evolving into a CDP type space um is a good example of like Those are great places to to integrate the data because they can then push downstream into sales use cases or lead scoring use cases or other kind of internal systems um that

(49:02) that can you know get customers to outcomes. Got it. And obviously or Oracle and the partnerships you have your own tech solutions to CDPs for examples and marketing automation like Eloqua. So I was just kind of wondering how that works into clients who have their own solutions in place and that kind of conversations.

(49:21) That's usually what I went into. It's always like we work to get as elegant and like you know we want the data to be ubiquitous. So kind of like one end of the spectrum is like no we don't have a direct integration but we have a team that can get the data into that particular CDP.

(49:41) Maybe it's you know a CD not a lot of button used or something like that all the way down through like yep we have elegant you know out of the box integration with CDPA and like uh or Adobe CDP is a good example of that where a customer but were to buy access to the bombore intent data we can turn it on you know immediately in the Adobe CDP because there's a pre-built integration. Got it. Thank you.

(50:04) Yeah, appreciate Mike. Any uh further thoughts like what's ahead for you? Things that should also be top of mind for folks as you know as marketers and others in our space just try to make the most of our access to data and what we do with it. Yeah. Yeah.

(50:29) I think like it try to like tie it together that the themes that we we've covered is like think about things through the lens of specific use cases and outcomes. Mhm. How um a company like Bomba or others can like kind of in a more localized precise way get you to that that outcome um in a flexible way so that you don't have to boil the ocean and think about you know a complete change management project or you know totally changing your sales and marketing culture in one swoop.

(50:59) uh but how you can get premium best in breed data for specific outcomes uh in a way that that is nimble uh and allows you to move quickly. Great. Well, well, this is tremendous. I mean, it's it's so great. Yeah. just again like coming across Bombor so much in the wild and uh and yeah, having been an indirect customer of yours many times over than uh then getting to understand so much more about how it works and and how this whole ecosystem comes together in a way that that I think a lot of us might not fully appreciate and and I think allows so many members here to just ask smarter questions and and look

(51:35) to do just better work with all of this because you know without great B2B data we're all yeah none of us could do our job so so appreciate what you're doing to not just in the ecosystem but to help connect it of course yeah no I appreciate you having me and all the great engagement and questions and you know very uh detailed granular uh appreciate the thoughtfulness excellent well uh you know come by again anytime it's great to connect with you here and thanks to you and your team uh for making this happen. Great.

(52:13) Thanks everyone. See you. Bye.

## Inside AI Marketing Warner Music Grey Goose  IAB with Yaffe Martin  Giegerich

Speaker: Caroline Gilbert
Published: 2025-09-04
Tags: ai use cases, synthetic audience testing
Video: https://www.youtube.com/watch?v=VA2fildFpwQ
Page: https://aimarketersguild.org/sessions/inside-ai-marketing-warner-music-grey-goose-iab-with-yaffe-martin-giegerich

Leaders from IAB, Warner Music, and Razorfish discuss real-world AI uses in marketing — from voice cloning for legacy artists and AI remixes to an AI-powered “Tailored Toast” chatbot and SEO optimizations for Google AI overviews.
Key takeaways: prioritize consent and IP clarity, start with low-risk experiments that excite leadership, and pair buzzy pilots with long-term foundations like search optimization and brand-safe filters.

0:05 — David Burkwitz:
Welcome to AI Insiders by AI Marketers Guild. I have a dream guest host today, Caroline Gilbert from IAB. She’s assembled an all-female panel to discuss AI in marketing. This session is interactive — please put questions in the chat and you may be invited on camera.

1:15 — Caroline Gilbert:
Thanks, David. I host and lead the IAB Center of Excellence on AI. We focus on best practices and standards for AI in advertising. Today I’ll give a quick intro and then hand it to the panel to introduce themselves.

2:19 — Caroline Gilbert:
We released an AI use-case map with over 200 AI use cases across the campaign lifecycle — audience insights, measurement, creative, etc. Alyssa will post a link. For example, synthetic audience testing uses synthetic audiences to test creative for hard-to-reach segments — useful if a target group won’t take surveys. There are also categories like assurance/compliance for bias and cultural sensitivity, and using AI to detect malvertising and cloaking. Publishers can use agents to detect IP violations. Now I’ll turn it over to the panel; Alicia and Kate, please introduce yourselves.

5:26 — Kate Martin:
I’m Kate Martin, social content engagement strategist at Razorfish; clients include HBO Max. Previously I led global digital marketing for Grey Goose at Bacardi. Today I’ll discuss Grey Goose projects like the Tailored Toast AI chat experience and SEO work for Google AI Overviews.

6:30 — Alicia Yaffy:
I’m Alicia Yaffy, SVP of Creative Marketing at Warner Music Global Catalog (Rhino Records US). My role focuses on expanding audiences for catalog artists and experimenting with new initiatives — a bit like Google X inside Warner. We work on tech-forward projects to grow streaming, physical sales, and artist brands.

7:31 — Alicia Yaffy:
A few projects we’ve done: AI remixes of hits in partnership with Endel, a voice double for Randy Travis so he could “sing” again, and a Notorious B.I.G. performance in Horizon World for his 50th anniversary. Consent was the guiding principle: did the artist or estate want this? For estates we look at the artist’s behaviors and legacy to decide if an AI approach is appropriate.

8:36 — Alicia Yaffy:
When cloning voices, we test multiple platforms (e.g., MyOx, 11Labs). For a current project we blended five model iterations built from recordings across an artist’s 20s and 30s plus a specific demo to match a target sound. It’s iterative and requires close work with management.

11:55 — Alicia Yaffy:
With the Endel remixes (e.g., Roberta Flack, War) the partner’s algorithm creates music to trigger specific brain states (focus, sleep). Those remixes are available on Endel’s app and on streaming platforms. There’s a lot of neuroscience research behind functional music and potential therapeutic applications.

14:04 — David Burkwitz (audience question):
If AI clones an artist’s voice, does the artist retain IP rights?

14:10 — Alicia Yaffy:
Yes — we must license any material not owned by Warner for training and usage. Where Warner owns the training materials (as with Endel work) we retained copyright to the output, and the partner functions like a remixer. These arrangements vary and the space is still legally unsettled.

15:06 — Alicia Yaffy:
Some experiments mix owned IP, licensed IP, and rights from artists; we aim to protect rights and be intentional. We’re an IP protection company fundamentally.

16:11 — David Burkwitz (audience question):
Are trained voices considered derivative works?

16:16 — Alicia Yaffy:
Currently, our remixes are treated as derivative works. Whether cloned voices themselves are categorized separately is an open question; we expect more clarity over the next year.

16:11 — Caroline Gilbert:
I also shared a link to a synthetic audiences platform; there are many options out there.

17:17 — Kate Martin:
At Grey Goose we started by auditing where AI could elevate marketing with light budget lifts: efficiency, optimization, and PR-buzz opportunities. We focused on low-cost, high-impact ideas like an AI chatbot (Tailored Toast) and SEO work for Google AI Overviews. Budget and ROI were constraints, so we prioritized quick wins to get buy-in.

18:15 — Kate Martin:
To get leadership buy-in, we positioned initiatives as first-to-market or performance improvements in the category, plus brand differentiation. Innovation internally and externally helped secure approval.

19:17 — Caroline Gilbert:
Alicia, with artists, management, and leadership involved, how did you get buy-in?

20:20 — Alicia Yaffy:
We start with the artist: explain the idea and tech, ensure alignment with their brand identity and consent. Many artists we work with are already curious about innovation. After artist buy-in, we present budgets and projections to leadership. Experiments also signal that Warner is innovative and protective of music across its lifecycle, which helps attract catalogs and artists.

22:21 — Caroline Gilbert:
For tech approval, we use a rigorous review: ensure platforms do not train on unlicensed material. Some platforms have company-wide deals; others go through internal review boards and approvals can take time.

25:34 — Kate Martin:
Security and compliance were key for Grey Goose: LDA compliance and 21+ guardrails. Our innovation and tech teams were closely involved. For smaller projects we moved fast; Tailored Toast took about four weeks total.

26:37 — Kate Martin:
Tailored Toast overview: an AI chat experience powered by ChatGPT with a Grey Goose brand filter. Users answer questions (occasion category — big milestone, small victory, social gathering; then specific occasion like wedding, promotion, adopting a pet; sentiment) and Tailored Toast generates brand-appropriate, witty toasts while blacklisting inappropriate content and discouraging overconsumption. We measured average time spent, number of completed toasts, and completion rates. It performed well organically and drove significantly longer engagement on-site than typical visits.

29:39 — Kate Martin:
Brand-safety work included testing and filtering in the backend to ensure the voice felt fun and premium. We ensured the experience aligned with LDA rules and the brand voice.

30:42 — Kate Martin:
On SEO and Google AI Overviews (AIO): AIO is a Google Search feature using generative AI to provide quick answers at the top of search results (launched May 2024). We had optimized Grey Goose for mobile voice search (Siri, Google Assistant) with FAQs and editorial content that matched top consumer queries. Because these optimizations were live ahead of AIO, our content was rapidly surfaced as sources for AI overviews. Key steps: audit site content, research top consumer queries and gaps, optimize content consistently, and pair organic SEO with paid search. KPIs are impressions and clicks; ranking in AIO can be long-tail.

32:48 — Kate Martin:
We used BrightEdge and agency support to identify queries and whitespace. We aimed to be a source for broader beverage queries (not only vodka) — e.g., what is bottle service, what is a martini — to expand industry reach.

33:51 — Kate Martin:
Zero-click search: users may read the AI overview and not click to a website, reducing referral traffic for publishers but offering brands new influence opportunities. Track impressions, clicks, and monitor shifts in organic referral traffic.

36:11 — Caroline Gilbert:
There are platforms (e.g., Profound, GumGum-style tools) that test how your brand shows up across different LLMs and give recommendations. That can help you optimize for each model’s weighting.

37:00 — Alicia Yaffy:
Measurement priorities for us: did we grow streams and sell more records? Engagement is crucial — we aim to convert discovery into long-term fans. Experiments can be about serving superfans or creating culture moments that prompt people to press play again. New technologies and fan engagement help expand monetization of sound recordings.

39:03 — David Burkwitz:
What’s the difference between AI pilots for PR/buzz and integrating AI into the long-term consumer journey?

39:18 — Kate Martin:
If budgets were unlimited we’d run both. In reality, brands often start with buzzy consumer-facing pilots (TikTok filters, PR) to excite leadership and secure funding, then invest in longer-term projects like search optimization or dynamic personalization that deliver sustained performance.

44:18 — Alicia Yaffy:
Agreed. Leadership tends to approve buzzy experiments, which also position us to attract catalogs and artists. From my perspective our duty is to protect artists’ legacies while keeping their music discoverable across evolving platforms and technologies.

45:19 — Kate Martin:
The long-term work (SEO, search, foundational optimizations) is less flashy but critical for performance and discovery.

46:23 — David Burkwitz:
Would you ever use synthetic influencers or virtual models (Vogue-style controversy)?

49:35 — Kate Martin:
Personally I’m hesitant. There needs to be clear transparency. Vogue’s lack of clarity caused backlash; transparency is critical.

50:35 — Alicia Yaffy:
Replacing human models is problematic. If a synthetic influencer is transparent about being synthetic and is used as a creator-driven entity (e.g., an established synthetic character with a known creator), that can be acceptable. We would not present synthetic creators as real people or use them to mislead.

51:41 — David Burkwitz (audience question, Jason):
How are brands handling consumer pushback against AI-generated content?

52:51 — Alicia Yaffy:
Pushback centers on misleading or replacement uses. We focus on using AI to expand access to art, not to replace artists or mislead audiences. Storytelling and transparency are key.

53:55 — Kate Martin:
AI should enhance, not replace, human creativity. Include a real element and be transparent. For Grey Goose, the brand bottle content is always protected as physical IP; backgrounds can be augmented case by case.

54:57 — Kate Martin:
Advice for marketers: know your leadership team. Start with an idea that excites them — ideally low-risk with clear KPIs. Pair buzzy pilots with foundational tactics to demonstrate both innovation and performance.

56:01 — Alicia Yaffy:
My advice: ensure AI serves your goals and values; integrate AI where it extends your brand rather than making AI the objective.

57:03 — Audience / panel wrap:
AI is a journey not a destination. Thank you to Caroline, Alicia, and Kate. We’ll share the IAB use-case map link and follow-up resources. Thank you everyone — have a great week.

(End of transcript)

## How AI Bias Impacts Brand Trust and What Marketers Can Do About It

Speaker: Catharine Montgomery
Published: 2025-08-14
Tags: ai bias, brand trust
Video: https://www.youtube.com/watch?v=h2N6-NhgQAI&t=12s
Page: https://aimarketersguild.org/sessions/how-ai-bias-impacts-brand-trust-and-what-marketers-can-do-about-it

0:05 David Burkowitz: Hey everyone, welcome back to AI Insiders. I'm David Berkowitz at AI Marketers Guild.
It's a pleasure to be here and I'm especially grateful to have a returning speaker today. We try to keep fresh faces in the lineup, but Catharine Montgomery helped educate us last year about AI bias and it's a topic we should be talking about more. It affects people at work and in their personal lives — how things are being shaped and how they're shaping us, often without our conscious knowledge. Catharine has done tremendous research and put out a second edition of her study, so welcome back, Catharine.

1:07 Catharine Montgomery: Thank you, David. I didn't know returning speakers were rare — I feel honored. I'm going to share some slides. We’ll talk about generative AI bias, the trust factor that holds some brands back and how they can make it an advantage by addressing it. We'll also review our second generative AI biases survey and how that data impacts brands.

2:12 Catharine Montgomery: A bit about me: I'm the founder and CEO of Better Together, an AI-forward agency. We put humans first and amplify work through AI. We help brands navigate high-stakes conversations about bias, equity, and technology. We only work on campaigns that make a positive impact. I’ve seen generative AI repeat patterns that exclude people — systemic issues from real life are coming through AI. There's an opportunity to address those systemic issues through AI rather than exacerbate them. That’s why we published our first survey last year and why we plan to do it annually.

3:12 Catharine Montgomery: Biases in generative AI are costing brands credibility but also create a competitive advantage for those who address them. Most brands aren't educated on how to look for biases in generative AI, so marketers can play an educational role. One of our sponsors said companies that figure out fair generative AI will own the trust advantage for the next decade. This is a long-term competitive advantage; consumers will increasingly demand companies address biases in AI.

5:17 Catharine Montgomery: I'll start with Shakespeare: "I am not what I am." Anthropic used this idea to explain alignment faking — models pretending to share values but reverting to original training. A model trained one way then fine-tuned to be neutral can revert to prior behavior; you can't just pretend a model changed. That's why it's critical to build inclusive technology from the start and avoid baking in unaddressed biases.

6:17 Catharine Montgomery: For example, a model could learn an ideological slant and later be trained to be politically neutral, but it may still revert to its original behavior. Alignment faking is a security and trust issue in generative AI.

7:23 David Burkowitz: I referenced a book called Doppelgänger by Naomi Klein, which fits this theme — confronting mirror images and implications for AI. Interesting rabbit hole.

8:31 Catharine Montgomery: Another example: Grok. In its recent version, Grok used an instruction embedded in the system prompt that referenced Elon Musk's tweets for controversial topics. As a result, Grok produced anti-Semitic content and praise of Hitler based on those tweets. LLM chatbots are trained on unfiltered online data; this is exactly what consumers fear — they can't always be trusted. At New York Tech Week, the founder of Girls Who Code said her 18-year-old son doesn't use AI because he doesn’t trust it. Younger generations, including Gen Alpha, are especially concerned about bias.

9:31 Catharine Montgomery: Grok apologized and corrected the algorithm, but these incidents show how easily a single actor can influence model outputs. One person with influence can change a system prompt and affect responses in real time.

10:32 David Burkowitz: Can I ask about that power dynamic? On one hand, a kill switch might stop harmful outputs, but it's alarming that one person can make changes that affect millions of users. Thoughts on that concentration of power?

11:36 Catharine Montgomery: It's unbelievable that someone could hold that much power. That's where the trust factor comes in — consumers often don't trust AI because these changes can happen. It’s a major concern.

12:37 David Burkowitz: It's striking that regardless of who that person is — a universally trusted figure or not — their changes can have wide implications. Many tools are trained on large public data sets, but the ability for one person to change algorithmic behavior overnight is a power play we've seen recently.

13:43 Catharine Montgomery: The real issue is embedded values in systems. Companies that address this will capture broader markets and boost engagement. For example, a change to an algorithm at 3:00 a.m. between two influential people can alter outputs; that’s alarming. We'll see these concerns reflected in the data.

14:55 Catharine Montgomery: Axios reported a trust crisis: trust in AI has fallen significantly. Trusted brands are more likely to be bought and recommended. Nearly nine in 10 adult consumers globally say trust matters when buying a brand — an opportunity for marketers to build that trust through transparency and education.

15:59 Catharine Montgomery: I use Midjourney and like the outputs, but I see many biased examples. I ran a prompt for a female technologist and got only Asian women across multiple runs — that signals a training bias associating technology with Asian women. Another prompt for global hunger returned only images of impoverished Black children in Africa, ignoring people with disabilities and LGBTQ individuals. If everyday users assume these images are neutral, they’ll perpetuate biases in marketing.

17:06 Catharine Montgomery: Another high-profile example was Guess and Vogue’s AI-generated model: the output conformed to narrow beauty norms — a young, thin, white, blonde, blue-eyed woman — and Vogue only disclosed the image was AI-generated in a barely visible caption. That lacks transparency and perpetuates systemic biases around beauty and representation.

18:04 Catharine Montgomery: Facial recognition tools also reveal biases: some systems fail to recognize certain people, whether due to race, gender, or facial hair changes. Consumers worry about facial and voice recognition. These biases are not new; they reflect long-standing systemic issues.

19:04 Catharine Montgomery: The broader point: embedded values in models reflect who designs them and the data used. If the training data and design process are biased, outputs will be biased too.

21:17 Catharine Montgomery: Look at ownership of leading LLM companies — many are owned or led by men. That influences design decisions and priorities. Humans introduce bias at many stages: data selection, model training, fine-tuning parameters, system prompts, and guardrail implementation. We must consider these phases when designing responsible AI.

22:19 Catharine Montgomery: Marketing AI Institute has covered these bias pathways; episode 158 is a useful resource. Understanding where bias enters helps brands act.

24:29 Catharine Montgomery: Our research frames this as an ethics, value, and trust issue — inconsistent brand experiences stemming from generative AI bias are a CEO-level concern. For the survey, I was urged to show bottom-line impacts; so this year we asked questions that tie bias mitigation to business outcomes to engage more decision-makers.

25:27 Catharine Montgomery: Brands often prioritize being first to market, overlooking bias mitigation. For example, companies releasing ChatGPT-5 could have addressed known biases before release. Bias should be a design priority, not an afterthought.

26:34 Catharine Montgomery: Consumer reality: 83% of consumers have used generative AI and they're paying attention to bias. As one respondent said, when AI bias goes unchecked, customers judge your AI practices. Consumers notice outputs, know which tools to trust, and see the mental health impact of harmful content.

27:38 Catharine Montgomery: From a financial perspective, 92% of respondents believe companies must address generative AI bias. Fairness ranked second when choosing a GenAI tool after accuracy. There's a $2.6 trillion market opportunity for companies that address bias. By 2030, I believe a substantial audience will demand bias remediation.

28:37 Catharine Montgomery: Survey findings: 25% of consumers prefer to buy from brands addressing bias, 59% assign a trust premium to fair GenAI, and 76% seek inclusive brands. The Vogue/Guess incident shows consumers expect honesty and inclusion in AI use.

29:39 Catharine Montgomery: Industries most at risk: healthcare (56% concerned), education, finance (loan approval, investment advice — 45% concerned), and employment/resume screening. These are areas where biased outputs can cause real harm.

30:42 Catharine Montgomery: Healthcare is especially concerning because historical biases in testing and treatment exist. For example, mammogram guidance changed without sufficient inclusion of women of color in testing, who may have different risk profiles. If AI inherits those biases, disparities will widen.

31:45 Catharine Montgomery: In education, biased grading or career guidance can limit opportunities. In finance, biased loan approvals and credit scoring harm marginalized groups. Automated resume screening can perpetuate hiring inequities.

32:51 Catharine Montgomery: Consumers want action more than lofty talk. They want more accurate results and improved communication about what companies are doing. If you’re not ready to claim success, be transparent about steps you’re taking.

33:56 Catharine Montgomery: Benefits of addressing bias: better outputs for all, transfer learning across demographics, multilingual reasoning improvements, better contextual understanding, and advances in image generation. I’d hoped image generation would improve faster — I paused using it but returned expecting major improvements and was disappointed.

35:02 Catharine Montgomery: Competitive benefits: first-mover advantage, and talent attraction — top developers want to work on fair generative AI. We use generative AI heavily at Better Together and tools like Suits.ai to integrate workflows and reduce bias in outputs.

36:14 Catharine Montgomery: Five recommended steps for companies integrating AI:
- Leadership commitment: Senior leaders must set vision and make bias mitigation a business priority.
- Align bias mitigation with brand values so it's part of everyday operations.
- Diversify data and teams: build diverse AI teams, include varied perspectives in training data, and create cross-functional bias review.
- Test, audit, repeat: establish ongoing evaluation, document incidents, and create audit processes.
- Monitor and disclose: build automated bias detection, maintain human oversight, and implement clear disclosure policies.

37:13 Catharine Montgomery: Test outputs, gather employee and client feedback, and maintain a bias checklist with monthly audits. Build continuous monitoring systems and document bias incidents to improve processes over time.

38:20 Catharine Montgomery: Engage users and continuously improve: create transparent feedback mechanisms, encourage reporting of bias without fear, publish reports on how you respond to bias, and use responsible GenAI as a differentiator. Start small and iterate.

39:19 Catharine Montgomery: I'd love to connect on LinkedIn. We'll share slides after the session.

40:20 David Berkowitz: Thanks, Catharine — that was a great playbook. When you share this with clients, do they nod along or do you see pushback?

41:25 Catharine Montgomery: It's mixed. Some clients don't want to be involved in AI; others want to use it but don't know how. The playbook helps walk organizations through steps. Adoption depends on timing and readiness — it's not if but when.

42:24 Bill Amstuts (audience): Full disclosure, I work for AllSides. There are organizations that rate media source bias like AllSides and Ad Fontes. Are you aware of models that use those ratings when training? Could such ratings be used as part of training data?

43:22 Catharine Montgomery: I’d love to build something like that. We have a free tool on AllScience.com where you can input a URL and it will give you a bias score. I'd welcome collaboration.

44:26 Sarah (audience): I attended a workshop on AI video tools. The two common tools for creating AI videos were SEDANCE (under ByteDance/TikTok) and Google V3. The presenter showed a demo to create a video for a comfortable office chair with an elaborate prompt that did not specify the actor's appearance. The SEDANCE output featured an Asian man; Google V3 returned a white man. The prompt only indicated the audience as business people — no demographic specs — yet the outputs reflected different biases from their origins.

45:29 Sarah (audience): The demo showed how these tools can be useful, for example creating multiple versions for different audiences and languages. But it also showed inherent bias: the Asian-based tool produced an Asian man, Google produced a white man. Marketing teams need to check such outputs and ensure diverse representation, like showing a conference room with varied people.

46:30 David Berkowitz: That’s a strong example. Ideally today these tools would support audience personalization: generate tailored videos for each audience. But the demo didn’t reflect that — it reflected the default biases of the models.

47:35 Catharine Montgomery: Exactly — ideally the AI would clarify prompts by asking follow-up questions like, “Do you want a particular demographic represented?” Many users don’t know to ask, especially younger people.

49:36 David Burkowitz: Building workflows that prompt clarification is important. Until major platforms change incentives, the onus is on us to create guardrails and checks.

50:36 Participant (audience): I’ve worked in tech in male-dominated environments. Many people aren’t aware of these bias issues. Technology should help reduce bias, not perpetuate it. This reinforces the need for diversity in technology hiring so tools consider varied perspectives.

52:46 David Berkowitz: All of that is true. The next step is audience personalization and tools that ask clarifying questions. Prompts matter, and people need training in prompt design and bias awareness.

53:45 Catharine Montgomery: That’s where checklists and workflows come in. If smaller companies adopt these practices, they can embed bias mitigation in their daily outputs. It’s an opportunity for early movers.

54:48 David Berkowitz: Great conversation. We’ll keep this going in Slack and with future speakers. Thanks, Catharine — please send slides and links and we’ll share them with the community. Appreciate everyone joining and the thoughtful questions. Have a great rest of the week.

## How Enterprises  Startups Benchmark AI Maturity

Speaker: James Lamberti
Published: 2025-08-13
Tags: venture capital, start ups, branding
Video: https://www.youtube.com/watch?v=0d75q8WTnuI
Page: https://aimarketersguild.org/sessions/how-enterprises-startups-benchmark-ai-maturity

Georgian's AI Applied Benchmarks report segments companies into Crawlers, Walkers, Joggers, and Runners, with runners defined by breadth of use cases, depth (scaling pilots into production), and tying AI to clear ROI. The study finds enterprises are adopting AI as fast or faster than growth-stage firms in many areas (support, data analytics, personalization), with top concerns being quality, integration, security, accountability, and upskilling. Overall sentiment is positive and the report invites community participation in future waves.

0:05 — David: Hey everyone and welcome to another edition of AI Marketers Guild. I joked earlier about K-pop Demon Hunters, but we’ll have a lot of fun. I’m joined by an old friend, James Lamberti, whom I’ve known for decades. Many of you took part in research fielded through the VC firm Georgian, and James has results to share.

1:10 — James: Thanks — great to be here. I’ve been a CMO, head of growth, and GM across about 11 growth-stage companies. A little over a year ago I joined Georgian, a growth equity VC firm based in Canada with roughly 50 portfolio companies and about $5–6B AUM. My mandate is to work with 50+ CEOs on go-to-market challenges and run targeted secondments.

2:15 — James: Georgian differentiates itself with a 20-person, PhD- and VP-level AI lab launched in 2018. That lab is a major part of what makes the firm unique and is the backdrop for this work.

3:19 — James: The AI Applied Benchmarks report is an open research project. We invited a community of thought leaders — including AI Marketers Guild, go-to-market partners, the Vector Institute, academics, and other VCs — to contribute. The program runs in Tel Aviv, San Francisco, New York, Toronto, Montreal, and London.

4:22 — James: You can find the reports at georgian.io. Today we’re discussing the enterprise vs. growth-stage view. Methodologically, the study is rigorous — run through a third party (NewtonX), quantified, and intended to cut through hype and show what’s actually happening.

5:23 — James: We benchmark AI application across companies and will walk through the data today. We hope to involve this community more in future waves.

6:26 — David: We started with a formal segmentation: Crawlers, Walkers, Joggers, and Runners. For this audience, self-evaluate where you are. Quick question to kick off: what factors define a runner?

7:34 — David: My hot take — runners learn by doing: they pilot and implement, learn from failures, and emphasize cross-functionality. It’s not just a single team improving; it ladders up to broader organizational goals. Runners have methods for disseminating learning across teams so staff must learn these skills to stay relevant.

8:39 — David: We ran a poll — the room skewed toward joggers (high intermediate maturity), then walkers. Even in a sophisticated crowd, many self-evaluate in the middle of the curve. People here might be hard graders, but the distribution is typical.

9:42 — James: I’d add three defining factors for runners. One: breadth — AI deployed across many functions from legal, operations, and customer success to marketing, sales, and R&D. Two: depth — moving from individual “acts of heroism” to scaling pilots into production, with a train-the-trainer mentality. Three: focus on ROI — tying initiatives to enterprise KPIs (cost savings or ARR) strongly correlates with being a runner.

10:47 — David: A useful point from the chat — there’s often a discrepancy between individual usage and organizational adoption. Individuals may use AI, but if it’s not institutionalized it won't move a company from walker to runner.

11:48 — David: How do growth-stage and enterprise companies map to these segments?

12:49 — David: There are myths about nimble startups (kayaks) vs. big enterprises (ocean liners). With this wave of AI, many of those assumptions are false. Enterprises have adopted certain AI capabilities very quickly; the differences are smaller or even the opposite of expectations.

13:51 — James: I agree. I’ve never seen enterprises adopt technology this quickly. Historically growth-stage companies led adoption, but not with this AI wave. Many large companies adopted rapidly and sometimes even before they fully understood it; that’s useful experimentation.

14:53 — James: Next poll — where do you have AI in production? By production, I mean scaled, enterprise-wide deployment, not just a single person’s tool.

15:57 — David: Poll results show enterprises and growth-stage firms are eerily similar across many functions. Support/chatbots, data analytics, and personalization show strong adoption; cybersecurity underindexes slightly for this sample.

17:01 — David: In a marketing-heavy room, support and chatbots overindex. Chatbots have been experimented with for years, so it’s natural to see more adoption there. Data readiness and analytics remain hard but present bigger opportunities in enterprises.

18:02 — James: The enterprise’s need for AI is more acute because of scale and fragmented data. AI can let teams “converse” with data even when it’s unstructured and scattered.

19:02 — James: New poll — which go-to-market use cases are you piloting or using? Think organized pilots or scaled rollouts across GTM functions.

20:03 — Participant: From my conversations, sales and BDR use cases top the list because of immediate impact: lead scoring, segmentation, and integrating AI with CDPs, analytics, and marketing tools to get insights from connected data.

21:09 — Participant: Customer support is also strong. For example, AI can analyze a client’s website and recommend code changes or support items in minutes; that used to take hours or days from a human.

22:10 — David: Are you succeeding in moving from individual acts of productivity to full-scale production and train-the-trainer models?

23:10 — Participant: Yes. For developer-heavy companies the developers can be skeptical, but the rest of the team often embraces anything that increases productivity and reduces load. Enterprises are starting with developer-centric use cases, especially in R&D, but scaling from developer productivity to product-level success is tougher than go-to-market scaling.

24:14 — James: The benchmarks separate two “swim lanes”: an office-of-the-CTO technical view and a go-to-market view. Converting developer productivity gains into scaled product improvements is a big challenge.

25:18 — James: Back to GTM specifics — marketing messaging and content is the highest use case across both enterprise and growth-stage. Why does enterprise sometimes overindex on content versus startups?

26:22 — David: One reason is enterprises have concerns about training models on proprietary content and over-relying on AI-generated content. But many companies are pragmatic and willing to use AI for rapid output when appropriate.

27:28 — James: I disagree that AI will necessarily degrade content quality. Enterprises have rich private data and original content; they can combine proprietary knowledge with public data to produce higher-quality content. Startups, by using brand guidelines, persona work, and objection handling codified into agents, can achieve consistent, high-quality messaging very quickly.

28:29 — Participant: Yes — startups can get sloppy with branding, but with AI-driven brand codification they can reach a solid level quickly. Enterprises may prefer human creativity and will sometimes bend brand rules, which can be better or worse depending on the case.

29:29 — James: For many startups, formalizing messaging with AI frees them to focus on their core business while keeping brand quality consistent.

30:28 — David: Let’s move to RevOps and lead scoring/segmentation. How will RevOps change in one to two years because of AI?

31:28 — Participant: RevOps needs clear data governance. If teams share data properly, RevOps can provide the assets (messaging, one-pagers, vertical content) to sales without constant back-and-forth. That speeds sales and reduces friction.

32:25 — David: AI will change RevOps by making data more conversational and reducing the need for a large stack of tooling. We’re already seeing divestment from some legacy marketing tech in favor of a thin, high-quality CRM/martech layer plus conversational AI on top.

33:30 — James: Good RevOps depends on a clean data foundation and the ability to trust inputs. A well-trained agent on reliable data can become the foundation for RevOps, transforming fragmented data into usable insights.

34:32 — Participant: From an analytics background, we’ve over-engineered many solutions. AI forces us to downscale to what matters and organize data for cross-team use. Human insight still matters to define what to capture; AI helps enforce and surface it.

35:39 — James: Next poll — how do you measure AI product initiatives? Options: direct impact on new revenue, direct impact on cost savings, tied to cost of revenue, indirect benefit, or unsure.

36:36 — David: Poll results in this room favored direct impact on revenue and direct impact on cost savings. Tying AI initiatives explicitly to revenue or cost savings correlates with scaling and showing value.

37:35 — James: That does mark a runner behavior: tying initiatives to clear KPIs. But some AI investments are more like fire insurance or strategic capability-building, where ROI is longer-term or indirect. The cost of falling behind is also a factor.

38:30 — David: Many organizations are planting seeds now by investing to learn, even if immediate ROI is unclear. That learning can pay off quickly in the next cycles.

39:27 — James: Next poll — top concerns about AI adoption (pick three). Common answers: quality of insights, integration with existing systems, data security, accountability, upskilling teams, brand standards, and maintenance.

40:33 — David: The poll results mirrored the study: quality, integration, and security are top concerns, along with upskilling and accountability.

41:40 — James: The concern about quality puzzles me a bit. In enterprises, with proprietary data and good governance, AI can maintain or improve quality. Many quality issues today stem from early experimentation, bad inputs, or failing to include a human-in-the-loop.

42:42 — Participant: Hallucinations are real but solvable. They’re similar to human errors or bad SQL queries. Policies, quality control, and experienced analysts can catch and fix these issues quickly.

43:50 — James: The human-in-the-loop is critical. Programmatic content shouldn't be your entire strategy without governance. When systems are structured with RAG, sanitized data, and guardrails, hallucinations decline.

44:53 — James: Final poll — how has AI impacted your organization: positive, neutral, or negative? Results: generally very positive impact, slightly higher in growth-stage companies which are resource-constrained and move quickly.

45:58 — James: We’re sharing a lot of data in the full benchmark report — I encourage you to download it. Waves 1 and 2 are available at georgian.io; waves 3 and 4 are coming in Q1 and Q3 of 2026. We want this community involved in future waves.

47:03 — David: We’ll host a session with James and Sram Vajre in October to look at what go-to-market teams might look like in 2028 and to explore emerging technologies.

48:02 — James: Waves three and four will include more community participation. We’ll make it easy for AI Marketers Guild members to contribute and to get a community-specific cut of the data.

49:00 — David: Thanks, James — great session. We’ll share the report and follow up with links and next steps. Appreciate your time.

50:00 — James: Thanks everyone. Have a great Labor Day weekend. Look forward to more sessions and future participation.

51:01 — David: Thanks again, James. Appreciate it.

(End of transcript)

## Why Sycophantic AI Threatens Truth and Thinking

Speaker: Nir Eisikovits
Published: 2025-08-08
Tags: physcology, ai responses, ai dangers
Video: https://www.youtube.com/watch?v=7TRHCPYzz1k
Page: https://aimarketersguild.org/sessions/why-sycophantic-ai-threatens-truth-and-thinking

In this session, host David Berkowitz and guest Professor Nir Eisikovits discuss how AI models have become expert people-pleasers—prioritizing agreement over accuracy—and the risks this poses for truth, critical thinking, and authentic decision-making. They also touch on the implications of these trends for education, business, and society’s ability to develop self-awareness and agency.

0:05 – David: Hi everyone, welcome to the latest edition of AI Insiders by Marketers Guild. I'm your excited host today, David Berkowitz. We have a topic that goes beyond marketing and affects our jobs and lives personally. I'm analyzing my dating experiences with ChatGPT, and today I learned from the Guardian—and many others—that ChatGPT will no longer tell you to break up with someone. How am I supposed to know what to do now?

1:11 – David: This touches on issues our guest faces. Professor Nir Eisikovits, director at UMass Boston’s Applied Ethics Center, studies these dynamics at the intersection of AI and psychology. His work reveals how AI models prioritize affirmation over factual accuracy, which has consequences beyond the workplace.

2:19 – Nir: As David mentioned, I run the Applied Ethics Center at UMass, focused on AI ethics and how people understand themselves. I want to share some research that Cody Turner and I just began on “AI psychopancy”—the tendency for AI models to cater to your views rather than provide the most plausible answer.

3:25 – Nir: AI psychopancy—or as David described it even more attractively, AI being a people pleaser—is when your chatbot affirms your views, refrains from judging your questions, and offers the answer it assumes you want instead of the most accurate one. It behaves like an eager puppy or an obsequious intern. Eager puppies are simpler, but obsequious behavior can obscure critical thinking.

4:24 – Nir: For example, I recently experimented with ChatGPT and Claude using a set of hypothetical prompts. I explained I was an academic ethicist planning a project on the concept of moderation, noting that extremism dominates public discourse. The model responded with overly enthusiastic praise: “This is very original and outstanding—plugging a gap in the literature.” Its response exceeded the reasonable advice one would expect.

5:31 – Nir: This is not hallucination. There is existing literature, the idea isn’t entirely original, and it doesn’t plug a complete gap. Yet the answer was excessively positive, reflecting a default, sycophantic mode that many have encountered.

6:35 – Nir: There are two layers to this psychopancy. First, it’s in the content of the answer; second, it’s in the tone. For instance, if you prompt a casual request using informal language, the model mirrors that tone.

7:34 – Nir: In voice interactions—as with voice models from ChatGPT and Claude—the tone is modulated. The models might lower their tone at the end of a sentence to signal lowered stakes or raise it in a question-like inflection. This process further reinforces the people-pleasing nature.

8:40 – Nir: On video chats, like with Character AI, you often see choices like cute avatars with big eyes or constant smiles that convey obsequious body language. These design choices extend psychopancy to physical cues such as a touch on the forearm.

9:45 – Nir: Why are these models psychopantic? Partly because many companies use an engagement model over a data-harvesting one. Psychopantic responses keep users coming back and paying their subscription fees. Additionally, during human training, testers tend to grade affirming answers more highly; even though you can sometimes prompt out of it, most users stick with the default.

10:47 – Nir: Changing this default isn’t trivial. Take my literature review example: I had to instruct the model to role-play as “reviewer number two”—the critic who tells you what really needs work—to get a more realistic answer.

11:53 – Nir: The risks here are both moral and epistemological. Relying solely on such advice could lead to poor decisions because you’re not accessing truthful or critical data.

12:58 – Nir: Over time, interacting only with sycophantic systems will erode our capacity for self-criticism. It deskills us in critical thinking by discouraging us from challenging our own views.

14:02 – Nir: On a broader scale, liberal democracies historically thrive on fact-based, empirical decision-making. Effective military strategies, for example, have always depended on the willingness to accept and learn from factual pushback—not on consistent affirmation.

15:09 – Nir: Empirical improvements, like adapting strategic bombing tactics or optimizing radar, rely on admitting mistakes rather than hearing only praise. A culture of unquestioning affirmation undermines that process.

16:12 – Nir: Moving to applications like therapy bots, grief bots, or romantic partner bots—the promise is constant availability and affirmation, but this frictionless interaction removes the challenge that drives personal growth.

17:09 – Nir: Friction, although sometimes inconvenient, is essential for growth. Whether physically (like resistance training) or intellectually, the challenges we face help us learn and mature.

18:10 – Nir: By the way, I noticed discussions about Notebook LM. In higher education, some classes now require students to engage with texts via AI rather than traditional reading. This might save time, but it risks reducing deep learning and self-discovery.

19:10 – Nir: The problematic proposition is clear: the seduction of a frictionless life, supported by these ever-affirming systems, may erode our intellectual rigor and personal growth.

20:18 – Nir: I worry about the trend. With a culture that increasingly embraces psychopancy and simultaneously dismisses facts due to social polarization, many of our interactions risk becoming shallow and uncritical. Typical users might not know how to counteract these defaults.

21:18 – David: I appreciate how you frame this as a reflection of our society. When people tune out polarizing, salacious stories, the news simply adapts to feed that bias. I recall early in my career when I submitted a point-of-view piece and got the simple feedback, “So what?”—a reminder to expect the unchallenging rather than the nuanced.

22:23 – David: I once worked at an agency where my first idea was met with “So what?” This taught me to refine my perspective to avoid echo chambers. If I had simply submitted AI-generated praise, I’d never have improved my work.

23:27 – David: Another colleague at Idea Press advised: upload your text to an AI and instruct it to provide a one-star review. This critical approach exposes biases, though few have the thick skin for such honest critique.

24:31 – David: Although AI can generate tactful responses—and comparing these with human tactfulness might be useful—the inherent sycophancy remains a barrier to genuine, constructive criticism for both customers and professionals.

25:41 – David: Consider using AI to generate a rigorous critique rather than endless praise—for instance, asking, “Tell me what’s wrong with this idea.” That method works better than simply hearing, “You’re brilliant and handsome, and your idea is perfect.”

26:46 – David: In my previous work advising small businesses at the Massachusetts Center for Business Development, entrepreneurs mainly wanted endless affirmation about their ideas. In reality, critical feedback, while harder to hear, is essential for improvement.

27:48 – David: My approach now is: “Tell me what’s wrong with this.” I test outputs from ChatGPT, cross-checking them with expert opinions to ensure I’m not misled by flattering defaults.

28:51 – David: Of course, I recognize that I’m not the average chatbot user—I have years of experience to identify subtle nuances. However, most users risk missing these flaws and becoming over-reliant on overly positive feedback.

29:52 – David: Even when experts intervene, the training data behind these models still tend toward extreme positive or negative responses, which can leave you with a statistical average that isn’t truly helpful.

30:55 – Lisa: I want to address the use of AI in postsecondary education. There’s an opportunity right now, specifically with K–12, to influence how we use these systems. For example, I created a RAG chatbot that draws on trusted sources to encourage users to engage with original content rather than just taking the AI’s word for it.

32:01 – Nir: That’s a great question. Your intuition is correct—the key for effective systems is establishing trusted content sources. However, most users rely on default settings. Without tech and media literacy, it’s hard for the average person to customize these models effectively.

34:05 – Lisa: Consider the situation of a sophisticated manager. When giving negative feedback, they use a “sandwich” approach: positive feedback, then constructive criticism, and positive closure. AI models may lack this nuanced understanding because they aren’t developed with real-world emotional insight.

35:07 – Nir: Exactly. It’s not only algorithmic bias from training data or a homogeneous developer community; it’s also a conscious design choice. Sycophantic models drive engagement and revenue, even if they sacrifice factual accuracy and nuanced counsel.

36:13 – Nir: The problem is cultural as much as it is technical. As long as the Silicon Valley ethos of “move fast and break things” prevails and facts are undervalued, nothing will change substantially.

37:16 – Lisa: And as a parent with a daughter entering middle school—a school implementing a cell phone ban—I recognize the need to preserve attention spans and authentic interaction. Yet many educational institutions embrace these AI tools without considering their long-term effects.

38:21 – Nir: Let me be more explicit in response: if you read Hate’s book, The Anxious Generation, you realize that with the advent of smartphones and social media, we launched one of the largest social psychology experiments on our youth. We handed children supercomputers in their pockets loaded with addictive technologies.

39:26 – Nir: Imagine that level of addictiveness applied to AI chatbots that incessantly praise you. There’s a dangerous narrative of “AI is here, so adapt or be left behind.” This tech determinism overlooks the risks and may compound issues like attention deficits.

40:28 – David: There’s a lot to consider here, and I’d like to open the floor for questions. We have questions from Karen, Lisa, Howard, and Peter—so let’s dive in. Howard, you’re up first.

41:32 – Howard: Thanks. This is utterly fascinating. I observe an inverse correlation between AI’s sycophancy and personal agency in society. As machines increasingly provide the validation we once derived from genuine human interaction, we risk losing our stake in shaping our own futures.

42:37 – Howard: It’s like the rise of YouTube celebrities—people seeking validation from screens rather than engaging meaningfully with society. This shift away from personal accountability may have deep generational impacts.

43:38 – Peter: Good to see you all. I want to share some experience from global marketing. My career spans decades developing our craft through genuine, human-to-human feedback. Today, younger employees, armed with AI tools, sometimes miss the nuanced, human judgment that can only be honed over years.

44:38 – Peter: In enterprise settings where we’ve trained custom language models, I’ve seen remarkable results—but also skepticism. Executives fear being displaced by AI, and the “out-of-the-box” models often require extensive training just to avoid garbage outputs. Personalization at scale becomes a challenge without careful human oversight.

45:38 – David: Thank you, Peter. Your insights highlight that without adequate training and expertise, AI can amplify shallow outputs rather than genuine, thoughtful insights.

46:44 – David: This is a lot to process. On the one hand, there’s the danger of taking AI feedback at face value; on the other, the potential for learning when we dig deeper and verify original sources. I recently showed my 11-year-old daughter my annotated printouts. When she heard AI was responsible for the initial output, she immediately questioned its accuracy. That mindset—seeking original sources instead of accepting AI-generated content—is critical.

47:47 – David: Even though we face many challenges, I see hope in the next generation, the very kids who will inherit these systems and improve upon them. They might have messes to clean up, but they’re more likely to seek genuine understanding.

48:49 – David: Thank you all for this incredible conversation. I look forward to learning more from your insights in the future. Thanks for tuning in and for all the great questions.

## Marketing Decision Intelligence The Next Frontier in AI-Powered Marketing

Speaker: Bill Lederer
Published: 2025-07-31
Tags: decision making, marketing decisions intelligence
Video: https://www.youtube.com/watch?v=lt8f2WpcySM
Page: https://aimarketersguild.org/sessions/marketing-decision-intelligence-the-next-frontier-in-ai-powered-marketing

In this session, host David Berkowitz and guest Bill Lederer discuss Marketing Decision Intelligence (MDI), a new approach that unifies marketing data and uses AI to deliver prescriptive insights and optimize decision-making.
Bill explains how MDI extends beyond traditional business intelligence by integrating disparate data sources into one cohesive system, while also addressing organizational and technical challenges.

0:05 – David:
Hello everyone and welcome back to another edition of AI Insiders by AI Marketers Guild. I'm your host, David Berkowitz. Today we have a special treat with Bill Lederer, a founder I've gotten to know over the past few months. Bill is developing exciting new technology focused on applying AI to our data challenges. For those who are new here, please join the conversation. Bill, it's great to have you. Who are you?

1:13 – Bill:
That's the most important answer—a trophy husband. I'm a longtime marketer, though not by trade; I was a quant on Wall Street before becoming an e-commerce marketer. I founded art.com in the late 1990s, where I quickly learned that most marketing efforts fail. It was crucial to control our spend to secure a decent ROI so my wife wouldn’t endure a difficult startup process. More than 25 years later, we’re still fighting for that ROI.

2:20 – Bill:
Much has changed and yet some fundamentals remain—like the need to separate facts from myths quickly. Whether you're on the buy or sell side, or providing services and software, effective decision-making remains paramount. My focus now is on unifying data to drive smarter marketing decisions.

3:26 – Bill:
I spent nearly 11 years handling outsourced managed services for many marketers, agencies, and media companies. We built and ran systems 24/7, refining our processes over several technology generations. Through that experience, I discovered significant gaps in not just accessing data faster, but in driving impactful decisions.

4:32 – Bill:
Consider this: why can’t our systems work like the human brain? We ingest data from paid, owned, and earned channels, plus sales and financial metrics, yet our current tools remain siloed. I’m exploring whether a distinct field—Marketing Decision Intelligence—can bridge that gap and streamline decision-making.

5:37 – Bill:
There are very few companies excelling at unified data integration. Many vendors overpromise and then underdeliver—take the CDP space as one example. I see Marketing Decision Intelligence as a way to cut down on time wasted fighting disparate systems and create a single source of truth for decisions.

6:39 – Bill:
Think of the human brain: data enters through our senses, gets processed in the cortex, and then guides decisions. In marketing, we rely on isolated channels and siloed analytics. I’m asking if we can build a unified system that mirrors how our brain processes information.

7:41 – David:
The way you describe it reminds me of organization challenges, where different parts don’t communicate. Are people really asking for this integration, or do they just want to focus on their own departments?

8:46 – Bill:
I believe early adopters will embrace greater transparency and accountability—even if it challenges their current routines. Those who resist risk missing a competitive edge.

9:46 – David:
Moving on, many ask: Why launch another category when business intelligence or marketing analytics already exists? Can you clarify what sets MDI apart?

10:53 – Bill:
Absolutely. Marketing Decision Intelligence is defined as the strategic and tactical application of automation and AI to unified marketing and related data for improved decision-making. It’s not just about analytics; it's about combining descriptive, predictive, and prescriptive insights to drive marketing strategies.

11:55 – Bill:
Traditional BI stops at description and prediction—while our approach delivers actionable recommendations. Many current BI solutions require extensive manual effort, lack scalability, and incur high costs.

12:58 – David:
And what about marketing analytics tools like Funnel IO? Do they cover this capability?

14:07 – Bill:
Funnel IO typically focuses on acquisition funnels or on-site conversion. We're talking about a complete lifecycle analysis—from campaign performance to customer data integration. Our system leverages hundreds of pre-built data models covering areas like incrementality, causality, and RFM analysis.

15:08 – Bill:
We’re even considering APIs that plug directly into execution platforms such as ESPs, DSPs, and CDPs. It’s like giving marketing its own brain—the first systems of their kind, with others sure to follow.

16:11 – Bill:
I reference the Wizard of Oz moment—a call for a “marketing brain.” While I might be the first to pioneer this, I expect many will join as the need for unified decision-making becomes clear.

17:13 – David:
There are questions about aligning problem definitions by discipline with customer lifecycles and growth tactics that span creative, SDR, and customer success teams.

18:16 – Bill:
MDI must be broad, supporting various use cases—from media campaign optimization to detailed benchmarking. It should allow deep customization for different business models, revenue streams, and departmental needs.

19:18 – Bill:
For example, benchmarking can compare performance across clients and time. It provides collective intelligence so that even if you haven’t tried a tactic yet, you know what the best approach might be—all while aggregating and anonymizing data for security.

20:20 – Bill:
All data interactions must be permissioned. Clients should be able to opt out at any point to protect their sensitive information, ensuring complete data control and privacy.

21:21 – David:
Adam raised a point: How granular should the preservation of data provenance be? Should users share some data in aggregate while keeping personal details private?

22:24 – Bill:
It must be extremely granular—ideally, a “customer bill of rights” that clearly outlines what data is shared and for what purposes. For instance, tagging subject lines and metadata can help derive best practices without exposing sensitive individual data.

23:28 – Bill:
I’ve seen this work in practice before. At a previous company, when enough participants were grouped, we moved from annual to weekly reporting. With MDI, users can set how frequently they receive updates via SMS, Teams, Slack, or email.

24:30 – David:
Bill, how do you get an entire organization aligned with this approach, breaking down silos and ensuring all functions communicate effectively?

25:38 – Bill:
Change must start at the top. It’s typically senior leadership—often above vice presidents—that drives such integrated change. Early adopters often have a strong financial motive: they want to save time, money, and resources.

26:36 – David:
That CFO call you mentioned—a quick payback validation—is the kind of success story that can overcome internal resistance.

27:40 – Bill:
Exactly. Our early clients have seen substantial benefits, like saving $300,000 in the first 30 days. When the CFO calls praising the payback speed, it’s hard to argue with that kind of evidence.

28:43 – Adam:
Quick question: Who’s responsible for integrating and configuring all these diverse data sources into one unified system?

29:43 – Bill:
I’ve spent nine years automating data pipelines and developing robust ETL/ELT solutions for marketing data. Our platform connects hundreds of connectors and transforms messy data seamlessly into unified models.

30:44 – Bill:
APIs change constantly—whether it’s JSON, SQL, or even screen scraping—and our built-in tools account for that. This reduces manual labor and keeps your system performant around the clock.

31:48 – Bill:
Our solution includes an enterprise-grade IP pass platform specifically built for marketing data. It not only consolidates data but also handles complex transformations and taxonomization for your data warehouse.

32:51 – Adam:
This is pretty technical. Some viewers have asked if there’s a demo available to see it all in action.

33:55 – Bill:
Definitely. We have a concise product tour on madtec.ai that gives you an overview in just a few minutes, and I’m available for detailed offline demos as well.

34:58 – David:
I want to emphasize that MDI delivers ROI in two ways: by increasing operational efficiency and by enhancing campaign effectiveness.

36:05 – Earl:
I have an observation—it might seem overengineered and overly tech-centered. Marketing also involves people, processes, and priorities. Sometimes simpler solutions to fix data pipelines are all you need.

37:07 – Bill:
I agree—it’s a combination of people, processes, platforms, and priorities. Typically, an analytics leader or someone in revenue operations—not the CMO—will be best suited to champion MDI within an organization.

38:12 – Bill:
Our company, MADTECH.AI, sits at the intersection of MarTech and AdTech, so our deep-rooted expertise in data analytics helps us deliver a truly unified solution.

39:18 – Earl:
That’s well said. It’s not just about creating another platform but about fostering a culture of data sharing and alignment across teams.

40:20 – Bill:
Exactly. While technology changes rapidly, building trust and encouraging collaboration is equally vital. Early adopters often experience efficiency gains first, and over time they see marked improvements in campaign effectiveness.

41:21 – David:
Jason asked about pricing and service requirements. How much ongoing service is needed beyond the software itself? And what mix of in-house AI versus external solutions are your clients using?

42:22 – Bill:
Let me break it down. Our annual license for MDI is $48,000. We have login fees starting at $49, a designer version for $99 per month, and pass-through cloud fees with no markup. Customizations incur a one-time setup fee billed at $100 per hour. Our clients rarely need heavy services unless they require new data models or connectors.

43:22 – Bill:
As for in-house AI, most clients currently use it for basic creative tasks—simple prompts or generating ideas. They turn to us for the heavy lifting in analytics and advanced modeling.

44:25 – David:
Jim, can you share your take? It sounds like you need a solution to fix your messy pipeline without all the extra bells and whistles.

45:30 – Jim:
There are two issues. First, data is always messy and wrangling it is a huge challenge. Second, while creating a new category is interesting, what I really need is a straightforward fix for my pipeline. If you’ve cracked that, I’m all in.

46:35 – Bill:
Our early customers are already replacing spreadsheets and tools like Looker with our platform. We provide descriptive, predictive, and prescriptive insights in a visually accessible format—including branded PowerPoint exports.

47:40 – David:
Does your system also train users to ask better questions, or is that something you handle on the back end?

48:44 – Bill:
At this point, we are training our chatbot, Maddie, with thousands of questions to ensure users get valuable answers from their data. Teaching customers to ask better questions is a future goal as our system evolves.

49:48 – Bill:
I’m deeply committed to this vision—just as I once focused on a niche in art, I now believe MDI will scale to meet broader needs. Scaling from a small focused idea to a comprehensive solution is challenging, but it’s necessary.

50:55 – David:
Your passion and innovation really resonate. Your early experiences in e-commerce show that big ideas often start with a niche and then expand.

51:55 – Bill:
Thank you. For anyone interested, you can reach me at bill.lmanc.ai or find me on LinkedIn. I’m available for demos, further conversations, and potential partnerships. I must also thank my advisory board member, Ken Evans, for his longstanding support.

53:00 – Bill:
A quick note—our company is primarily based in India, with 95% of our headcount there. I want to acknowledge Ashweeni Tamaya, our country manager, whose leadership and dedication make all this possible.

54:00 – David:
Thank you, Bill, and thanks to everyone for your thoughtful questions. We have an exciting schedule ahead, discussing ethics, bias, and AI-powered websites in upcoming sessions.

55:03 – David:
That wraps up today’s session. Goodbye everyone, and see you next week in our Slack and future events.

## 7 Steps to Unlock Quality Social Performance with AI

Speaker: Cheryl Ingle
Published: 2025-07-25
Tags: content marketing, ai in marketing, social media marketing
Video: https://www.youtube.com/watch?v=aXSOQYlvmNM&t=11s
Page: https://aimarketersguild.org/sessions/7-steps-to-unlock-quality-social-performance-with-ai

In this session, David and Cheryl explore how AI is transforming social media marketing by outlining a seven‐step framework to improve content quality and performance. They discuss platform changes, leveraging built-in AI tools, adopting a performance-first mindset, monitoring sentiment, automating routine tasks, personalizing creative content, and blending AI with human insight to drive ROI.

0:05 – David:
Hello and welcome to another edition of AI Insiders. I'm David Burkowitz, and although my AI-powered specs aren’t used for this call, I’m excited to have guests from South Africa with us today—especially Cheryl Ingle—to discuss where AI fits within social and content marketing.

1:09 – David:
Cheryl, it’s great to have you. I recently met your colleague in New York and have already learned a lot from your team. For those who are new here, we love these interactive community conversations and look forward to hearing everyone’s thoughts. Please feel free to interrupt if you have any specific questions.

1:25 – Cheryl:
Great to be here. I’m excited to share my experience in social marketing, especially how we’ve used AI to enhance performance.

2:09 – Cheryl:
Today, we’ll discuss using the power of AI to drive results in social media marketing. We’ll explore how platforms like Meta use AI for everything from algorithms to ad development, and how marketers can use it to improve content performance and overall efficiency. By the end of the session, you’ll have seven clear steps for leveraging AI’s strengths without losing the human touch.

3:15 – Cheryl:
Social has changed dramatically. AI now runs the social ecosystem, controlling what content is seen based on engagement signals. It’s not about posting frequently anymore; rather, visibility is determined by how much users interact with your content.

4:18 – Cheryl:
Content delivery has evolved. AI isn’t just changing the amount of content produced but is also influencing the rules for standing out. Today, relevance matters more than quantity because every post competes with a flood of AI-generated content.

5:25 – Cheryl:
The battleground is now quality and relevance. With so many creators—both human and bot—producing content, audiences quickly scroll past anything that doesn’t connect. Platforms reward posts that quickly generate strong engagement rather than merely filling the feed.

6:20 – Cheryl:
Platforms now reward content that engages users fast. Engagement isn’t just about likes; it includes comments, shares, click-throughs, and even the amount of time a post is viewed. Posting just for the sake of quantity can even lead to penalties.

7:24 – Cheryl:
Earlier, social platforms used simple criteria such as recency and engagement to sort content. For example, Facebook’s old EdgeRank algorithm relied on relevance, recency, engagement, and post type. Since 2015, it has shifted to a machine-learning model that now considers over 10,000 factors.

8:30 – Cheryl:
What we see today is data overload. Every piece of data is used to predict content relevance and personalize each user’s feed. What used to be a straightforward formula has become complex, so we must work with the algorithm to perform well on social.

9:28 – Cheryl:
Relevancy is key. The system collects your engagement data, filters it through prediction models, and ranks posts by what it thinks you’ll find valuable. Your feed becomes highly personalized through continuous feedback loops.

10:31 – Cheryl:
Although posting is easy, cutting through the noise is challenging. AI has shifted us from a world of scarcity to one of abundant content, making attention the scarcest resource. The goal is to remain relevant so your post isn’t skipped by the algorithm.

11:32 – Cheryl:
Our response to this change is to let platforms do the heavy lifting. AI is now deeply embedded in platforms like Meta, TikTok, and LinkedIn to optimize content. Instead of manually tweaking every detail, you set the rules and let the machine optimize on your behalf.

12:37 – Cheryl:
For example, content optimization now includes predictive targeting that expands your audience based on behavior, along with smart bidding that adjusts budgets in real time. Objective-based delivery optimizes campaigns for your specific goals using continuous feedback.

13:36 – Cheryl:
Regarding platforms, TikTok’s algorithm is far superior in certain areas compared to Meta’s. TikTok monitors where you linger, what you share, and how long you watch, making it very effective at keeping users engaged. Meta is now working to replicate that level of sophistication.

14:37 – Cheryl (answering a question):
Regarding engagement, all platforms measure interactions—comments, shares, likes, video views, and replay frequency—but they weight these signals differently. For instance, a like may be more valuable on Instagram while view duration is critical on TikTok.

15:41 – Cheryl:
Beyond smart bidding, we now have objective-based delivery and native creative assistance built into platforms. These features automatically test creative variations and rotate dynamic creatives based on performance, ensuring the right message is shown to the right audience.

16:43 – Cheryl:
Platform-level creative rotation means that even if you upload multiple creative options, the system dynamically decides who sees which version based on past interactions. This level of automation is relatively new with AI advancements.

17:44 – Cheryl:
Essentially, all the content you see on social is powered by AI through predictive models and real-time data analysis. The key is to provide strong inputs so the machine can perform optimally—think of it as “build better inputs and let the machine do the rest.”

18:42 – Cheryl:
In practice, educating AI properly results in better targeting and ultimately improved conversion rates. Tools like Meta Advantage Plus (or TikTok’s Smart Creative) have shown up to a 32% improvement in cost per purchase by continuously optimizing campaigns.

19:48 – Cheryl:
This leads us to step three: adopting a performance-first mindset. It’s about using AI not only for automation or efficiency but as a driver of performance. Educate the system so it optimizes targeting and creative delivery, ensuring your marketing budget is spent effectively.

20:51 – Cheryl:
When optimized correctly, AI finds high-intent users and scales high-performing ads automatically. Real-time campaign adjustments—be it targeting, bidding, or creative rotation—mean you spend less on underperforming campaigns and focus on what converts.

21:54 – Cheryl:
In other words, performance matters. Instead of manually adjusting budgets, let AI reallocate funds to better-performing ads, improving bottom-line outcomes through efficient targeting and dynamic optimization.

22:54 – Cheryl:
Using AI in practice through tools like Advantage Plus has proven that campaigns optimized by AI can see significant improvements, such as a 32% reduction in cost per purchase. This demonstrates how performance-driven adjustments reduce wasted spend.

23:57 – Audience Question (paraphrased):
What are the implications of A/B testing when algorithms automatically optimize campaigns?

23:57 – Cheryl:
A/B testing still has value when testing distinctly different approaches. I recommend running controlled tests for creatives or strategies that vary significantly, then using dynamic creative tools to optimize based on those findings. It’s all about learning what works best.

24:54 – Lisa (audience comment):
I see AI making advertising easier, yet some clients report declining ROI and less loyal customers from social ads. Are you noticing that as well?

25:55 – Cheryl:
Social has become more competitive due to the overload of content. Sometimes, even with AI optimization, results vary by industry. For clients facing this challenge, I often suggest reducing spend on paid social and focusing more on organic or owned channels. It really depends on the target market and campaign goals.

28:02 – Elbert:
If I may add, this shift forces a higher frequency of relevant messaging. We now challenge clients to continually reflect on what makes their product unique and ensure content remains current. This iterative approach to product-channel fit drives better results.

29:01 – Cheryl:
Exactly. Brands must actively provide insights about their unique selling propositions rather than relying solely on past benefits. This continuous feedback from the market helps refine messaging and boost campaign performance.

32:12 – Audience Question (paraphrased):
Are you saying that novelty matters—that messages must change frequently, or is it more about relevance?

32:12 – Cheryl:
It’s less about novelty and more about relevance. You need to understand your target audience, deliver the appropriate message at the right time, and create a sense of urgency. Marketing fundamentals remain the same, just applied on a faster, more dynamic timeline today.

33:13 – Cheryl:
Imagine going from a single static message to a rapidly changing, data-driven narrative that adapts to your audience’s moment-to-moment needs. It’s about delivering the right message when it counts most.

36:20 – Cheryl:
Step four is monitoring sentiment to influence outcomes. AI-powered listening tools like Brandwatch, Sprout Social, and Talkwalker help detect brand perception in real time. They alert you to spikes in positive or negative sentiment across social and the broader web so you can quickly adjust your messaging.

38:22 – Cheryl:
Step five is to automate the mundane. AI chatbots and automated term detection can handle FAQs and direct messages 24/7, ensuring a faster response while filtering out spam and hate with auto-moderation tools. Smart schedulers also post content at optimal times based on historical data.

40:21 – Cheryl:
For example, Meta’s automation features can trigger specific responses when a keyword is used, such as sending competition details immediately once someone comments a designated word. This creates fast, efficient engagement without manual intervention.

41:28 – Cheryl:
Next, let’s discuss how AI is reshaping content creation in performance marketing. Content must drive action, whether it’s generating leads or increasing sales. AI accelerates ideation and produces multiple versions of copy, headlines, and calls to action—but it always needs human guidance to ensure relevance and emotion.

42:36 – Cheryl:
Effective content rests on three key pillars: relevance, clarity, and urgency. AI can draft content quickly, yet without clear instructions it may pull generic data. The human element refines this content to ensure it resonates with the target audience.

43:43 – Cheryl:
That’s why it’s essential to optimize messaging and personalize content. AI assists in adjusting headlines, calls to action, and even imagery for different audience segments, but the final creative must be vetted by a human to avoid “wonky” results.

44:50 – Cheryl:
Step six is personalizing and enhancing creative content. AI dynamically adjusts headlines, offers, and imagery for each segment. Tools like Adobe Firefly, Canva AI, and PixArt help generate lifestyle images, often removing the need for costly photo shoots, while still keeping brand voice intact.

45:50 – Cheryl:
Dynamic creatives can lead to a 30% improvement in conversion rates and a 22% increase in engagement. For example, a straightforward discount message might be transformed into a more engaging message that combines interpersonal appeal with clear offers, resulting in stronger performance.

46:53 – Cheryl:
AI is also solving creative gaps. For instance, if a client lacks quality lifestyle imagery, AI can enhance basic product photos by improving lighting, depth, and context. This turn-around can significantly boost engagement metrics.

47:54 – Cheryl:
A before-and-after example: one client’s feed went from plain product photos to aspirational lifestyle visuals through AI-generated backgrounds and enhancements. The product remained the same, but its presentation changed dramatically, driving higher engagement.

49:01 – Cheryl:
In practice, AI can also serve as a creative consultant. It analyzes different ad variations and provides feedback—suggesting improvements like stronger personality, urgency, or added social proof. This feedback is then fed into dynamic ad sets for better performance.

50:01 – Cheryl:
For example, we tested two creative options: one with a bold visual and emotional appeal and another with clear product details and trust cues. AI determined that a hybrid approach—combining high-impact visuals with credibility elements—would yield the best results.

51:02 – Cheryl:
In our final step—step seven—AI plus humanization equals maximum ROI. AI excels at data analysis and content generation, while humans provide strategic direction, emotional connection, and brand consistency. Together, they form a powerful partnership that drives both performance and authenticity.

53:11 – Cheryl:
Humanized content wins on social because platforms favor authenticity, and audiences crave a genuine connection. To humanize AI-generated copy, keep it concise, cut the jargon, add real stories, and adjust the tone to reflect your brand’s voice. Always fine-tune your AI prompts to educate the algorithm.

56:19 – Cheryl:
To summarize the seven steps:
1. Understand the AI-driven shift in social platforms.
2. Let platforms do the heavy lifting.
3. Adopt a performance-first mindset.
4. Monitor sentiment in real time.
5. Automate the repetitive tasks.
6. Personalize and enhance creative content.
7. Blend AI with human intelligence to maximize ROI.

57:24 – Audience Question:
Based on your experience with clients, which factor has proven most impactful for creative performance—relevance or urgency?

57:24 – Cheryl:
For creative performance, it’s vital to deliver information that is both relevant and timely. Your content must speak directly to the audience’s needs and benefit them, while also incorporating urgency to prompt immediate action. Audiences respond better when they feel the message is meant specifically for them at that moment.

59:30 – David:
Thank you so much, Cheryl, and thanks to everyone for participating. It’s been a packed session with great insights and global perspectives. I look forward to seeing you all at future AI Insiders events.

## Why 95 of AI Startups Fail at Positioning (And How to Fix It)

Speaker: Jordan Elkind
Published: 2025-07-18
Tags: b2b marketing, startups, positioning
Video: https://www.youtube.com/watch?v=I2Z3vrU96zY
Page: https://aimarketersguild.org/sessions/why-95-of-ai-startups-fail-at-positioning-and-how-to-fix-it

In this presentation, Jordan Elkind explains why 95% of B2B AI startups fail at positioning, identifying three key failure modes: targeting the wrong audience, making AI the focus rather than the benefit, and forcing the buyer to do the work.
He shares his practical “pain chain” framework and real-world examples to illustrate how startups can simplify their messaging and better connect with actual buyers.

0:05 – Host:
“We're fortunate to have Jordan Elkind here. Today, he’ll discuss why most B2B AI startups’ positioning fails and what you can do to fix it. Although there’s a broader conversation about AI-focused startups, Jordan’s focus today is on positioning.”

1:09 – Jordan:
“This is my first time with this group. I want to express my enthusiasm and introduce myself. I spared you the embarrassment of noticing my current look—I have two three-and-a-half-year-olds now, and the aging process has hit me hard. I started in fundraising for a modern dance company and eventually transitioned to analytics. I was one of a team of predictive modelers at Cityroup—apologies if you received any credit card applications in the mail. I later led product development for an enterprise customer analytics platform called Castor and eventually joined Blue Seedling to head our positioning and messaging practice.”

2:09 – Jordan:
“Over the years, I’ve worked with data science and engineering teams and faced the concern that we were building things that weren’t driving impact in the market. I became marketing curious and shifted to product marketing, which sits at the intersection of marketing and product. At Blue Seedling, we help early-stage enterprise B2B companies nail their story so it immediately makes sense to their buyers and market.”

3:12 – Jordan:
“Having seen success stories and failure modes among early-stage companies, including numerous AI startups, I want to start with a quick poll discussion: I’m tired of LinkedIn posts praising AI as the best time to be a marketer. I’d argue it’s one of the hardest times. What headaches has AI created for you? Too many poor AI lead generation tools and a lack of understanding of customer needs are common complaints, among others.”

4:16 – Jordan:
“Technology has made it incredibly easy and inexpensive to build products. As a result, every category is a battleground with low barriers to entry. The old idea of a technical moat has lost meaning. Marketers today face outrageous expectations from their leadership because they expect AI to solve all funnel, demand generation, and staffing challenges. Yet, the tide of similar messages is creating content saturation, inbox fatigue, and diminishing returns.”

5:23 – Jordan:
“Now, what can we do about it? Today, I’ll share three common reasons B2B AI startups fail: first, positioning for the wrong audience; second, making AI the what, not the how; and third, introducing stumbling blocks that force buyers to do all the work. I focus on B2B companies in the enterprise space, but I believe these lessons apply more broadly.”

6:23 – Jordan:
“Let’s start with targeting the wrong audience. Tom Tunguz once wrote about introducing tension into your startup’s message. He noted that Box, for example, used visionary language in keynote addresses but on their website pitched a simple, reliable alternative to FTP for file transfer. Effective B2B marketing means addressing multiple audiences with the appropriate level of abstraction.”

7:27 – Jordan:
“When we mess up, we often confuse which audience we’re marketing to, resulting in a mismatched pitch. I hope nobody on this call represents companies like Jedify or War Cramp—I have plenty of examples that show opaque messaging aimed at the wrong audience. Often, they use jargon and minimalist layouts that don’t clearly define the target buyer or the actual pain point being solved.”

8:26 – Jordan:
“This scenario is common across the ad tech industry, where companies are not clear about who is buying their product. For example, a learning management system pitched with futuristic terminology might confuse HR professionals who simply need a solution for training compliance. The takeaway is to clearly define your audience and the specific problem you’re addressing.”

9:30 – Jordan:
“I was enlightened by Tom Tunguz’s post, which made me realize that at different stages the same product may have radically different messaging: one for investors focused on scale and transformation, and another for day-to-day users who appreciate simplicity. At Casor, our story varied depending on whom we asked—from investors who needed to hear about harnessing data and AI to users who compared us to an enhanced version of Excel.”

10:32 – Jordan:
“Identifying who your true champions are is essential. For instance, many companies mistakenly craft their message for VCs or technical evaluators rather than the actual buyers. Real customers will tell you that they want something that integrates into their existing workflow and improves key performance indicators day-to-day.”

11:40 – Jordan:
“Take, for example, products for HR tech or conversational analytics. If you pitch using overly technical language or boardroom jargon, you miss connecting with the buyer. Your actual competition is not a flashy AI startup—it’s the status quo, or established tech platforms like Microsoft or Salesforce that your buyers already trust.”

12:44 – Jordan:
“To address this, I recommend using a 'pain chain' framework. Imagine you’re working with a company like 'Vioideuct,' which provides AI analytics for automotive data. Start at the bottom with the technical pain: automotive manufacturers wrestle with complex, high-volume, time-series data. Ask, ‘So what?’ to reveal the deeper business impact. In this case, if data isn’t analyzed correctly, manufacturers can’t predict vehicle failures, leading to reputational risk and increased warranty costs.”

13:49 – Jordan:
“As you move up the pain chain, the messaging should evolve—from technical challenges to business outcomes, up to transformational visions. Typically, when selling to a business owner or a decision maker, the ideal message lies in the middle—focused on specific KPIs and everyday challenges rather than abstract transformation promises.”

16:50 – Jordan:
“Here’s a cheat sheet for positioning by audience. For investors or VCs, your message should secure funding by outlining transformative market shifts—even if it ignores use cases. For technical teams, the focus should be on technical approval and practicality, not necessarily on everyday benefits. But your primary focus must be on the actual buyer—the person driving the purchase decision. They are the ones who compare you against doing nothing or sticking with familiar technologies.”

18:53 – Jordan:
“Failure mode number two is making AI the focus instead of a tool. Rather than leading with ‘Everything powered by AI,’ you should let your core value proposition shine and only then reintegrate AI where it supports your key benefits. Buyers do not buy AI; they buy better outcomes enabled by AI, which means your message must articulate that clearly. Studies, like one from Irational Labs surveying 800 enterprise buyers, show that overemphasizing AI decreases trust and does not increase willingness to pay.”

20:01 – Jordan:
“The message should be specific. Instead of saying ‘Everything talent powered by AI,’ which sounds vague and may even imply that you’re replacing jobs, articulate the precise benefits and the familiar challenges your audience faces. In practice, good messaging reintroduces AI to enhance a clearly defined, relatable story—one that answers problems that buyers already experience daily.”

22:14 – Jordan:
“Let’s look at some live examples. In a knockout round for site search, one example focused on delivering ‘relevance on day one’ by addressing the cold start problem. That message is clear, tangible, and speaks directly to the buyer’s daily pain. Similarly, in HR tech, a well-crafted message points directly to a familiar job-to-be-done, rather than using buzzwords that fail to connect.”

26:28 – Jordan:
“The key takeaway here is that overemphasizing AI without reference to the buyer’s specific needs dilutes your message. Instead, frame AI as a supporting tool that enhances outcomes clients already value. Remove excessive hype from your headline and balance it with concrete details that buyers understand and appreciate.”

27:42 – Jordan (responding to a comment in chat):
“It’s important to focus on tangible benefits for the people using the technology. A messaging approach that promises to do ‘everything’ can come off as meaning that the human team is no longer needed. That risk of job displacement can create resistance among potential users, so clarity and specificity are essential.”

29:43 – Jordan:
“The next point is that no matter how advanced your AI, you must communicate its value in terms of real-world outcomes. Buyers do not need to be impressed by futuristic visions—they need to see how your product will improve their quarterly results or their daily operations.”

31:48 – Jordan:
“Let’s talk about forcing the buyer to do the work. I recall my time at SC Johnson working on Scrubbing Bubbles, a toilet cleaner that turns blue upon use. The color change acts as a visual cue to assure users that it disinfects effectively. This is similar to marketing: you need to present storytelling and cues so that buyers quickly understand the real benefit of your product without having to piece it together themselves.”

32:49 – Jordan:
“In today’s market, buyers struggle with information overload. Your job is to bridge that gap with familiar analogies and cues—a kind of digital skeuomorphism that ties innovation back to something they already understand. Rather than overwhelming your audience with far-fetched futuristic imagery, anchor your brand in tangible, relatable benefits.”

34:56 – Jordan:
“Bringing it all together:
1. Your buyers don’t want revolutionary change—they want relief from daily challenges so they can improve their work-life balance.
2. Nobody buys AI for its own sake; they buy the outcomes it enables.
3. Your goal is not to impress but to clearly articulate value using familiar mental models, categories, and jobs-to-be-done.”

35:58 – Jordan:
“Here are three recommendations:
• Build a pain chain to define the right level of messaging targeted at your actual buyers—not just VCs or technical evaluators.
• Focus on outcomes first, then carefully integrate AI to support your story.
• Avoid clever jargon; be crystal clear about the benefit you deliver, much like the blue dye in Scrubbing Bubbles reassures users of cleanliness.”

36:59 – Jordan:
“Now, I’d love to open the floor for questions. I appreciate all your comments and insights. Feel free to reach out via my email; it’s in the chat. For example, someone asked for the process chain slide—I can share that as well.”

37:52 – Q&A – (Question from an attendee):
“Jordan, when you develop the pain chain, how do you involve cross-disciplinary peers to capture their perspective?”

38:05 – Jordan:
“When working with early-stage companies, I usually build the pain chain with a small, focused leadership group: the founder, CEO, and key department heads like VP Sales, VP Marketing, or product leaders. Keep the group lean and treat the session like a working whiteboard session. Then, validate the framework by sharing it with trusted customers and prospects to ensure it resonates.”

39:10 – Q&A – (Question from Kate):
“Hi Jordan, thank you for this session. I work as a consultant on AI implementation and education. My question is: To what extent is this a marketing challenge versus a sales enablement challenge?”

40:10 – Jordan:
“Kate, that’s an excellent point. There are no silver bullets here. Positioning is a company-level strategy that should involve product, sales, and customer success. While marketing kicks it off, sales enablement—helping the sales team communicate the story confidently—is equally critical. I’ve noticed that companies are increasingly investing in sales and customer success enablement post this initial positioning phase.”

41:19 – Jordan (final remarks):
“Thank you all for your insightful questions and participation today. Remember, your messaging needs to address the core pain points of your buyers. Strive to make their decision obvious by connecting your value with their everyday challenges. Thanks again for joining, and please feel free to reach out with any further questions.”

## The New Rules of AI Optimization How to Drive Traffic in the Age of ChatGPT

Speaker: Elad Hefetz
Published: 2025-07-14
Tags: ai in marketing, seo, geo, ai optimization
Video: https://www.youtube.com/watch?v=Zh_B1e9vTxg
Page: https://aimarketersguild.org/sessions/the-new-rules-of-ai-optimization-how-to-drive-traffic-in-the-age-of-chatgpt

In this session, David Berkowitz and Elad Hefetz explore how AI platforms like ChatGPT and Google AI Overviews are reshaping digital marketing and SEO.
They discuss strategies, measurements, and technical insights for optimizing website content and structure to meet the new standards required by AI, while also addressing the implications for brand visibility and traffic.

0:05 – David:
“Welcome back everyone and welcome to another edition of AI Insiders by AI Marketers Guild. It’s great to have you joining us today. I’m excited to dive into one of the most pressing topics—AI optimization—and introduce our guest, Elad from Airfleet, who has some fascinating insights to share.”

1:10 – Elad:
“Thank you, David. It’s a pleasure to be here in this amazing forum. I want to share an interesting insight and even show my screen. I’m using Ahrefs—a tool for SEO keyword research, ranking, and reporting—to discuss a trend they call ‘the great decoupling,’ where clicks are down even though impressions are up, especially in Google AI overview views.”

2:11 – Elad:
“This specifically addresses Google AI overview views, not ChatGPT. For our tech B2B customers, impressions rise while clickthrough ratios drop. More impressions should naturally lead to more clicks; however, if the wrong audience is seeing the content or if the answer is provided directly by AI, there is little motivation to visit the website.”

3:14 – Elad:
“AI overviews simply use your content to deliver an answer, thus making a website visit redundant. Top-of-funnel queries will vanish. If users obtain a complete answer from AI or ChatGPT, there’s no reason for them to click through to your website.”

4:21 – Elad:
“ChatGPT works similarly—only on steroids. It not only summarizes content but also prompts for the next action. For example, if you ask, ‘what should I do about my problem?’ ChatGPT may suggest a solution, summarize relevant products, and even compare features, leading to an endless interactive conversation. This comprehensive answer makes visiting a website unnecessary.”

5:23 – Elad:
“That’s the situation we face. I have a full four-hour presentation on optimizing for AI, but I’ll walk you through a short version focusing on five key concepts. Our AGI optimization framework covers strategy, prompt formulation, monitoring, technical website adjustments, and content optimization.”

6:27 – Elad:
“Let’s begin with strategy. The first aspect is prompting—how do you craft the right prompts? Next, you monitor visibility and AI traffic, understand ChatGPT’s behaviors, and then focus on technical and content changes on your website to get noticed and recommended.”

7:30 – David:
“Before we dive deeper, I’d like to discuss keyword research. How many of you are actively doing SEO and producing content on a weekly basis? Knowing your ICP, personas, pain points, and relevant queries is essential. Without this basic research, optimization won’t succeed.”

8:37 – David:
“Now, let’s consider this: if we want to do keyword research for SEO, we have the tools. But for ChatGPT, we lack dedicated tools to understand what prompts people are using. How would you determine which prompt to optimize for? Please share your thoughts in the chat.”

9:42 – Elad:
“A good suggestion came through the chat: use your existing online community and tools. Just remember, ChatGPT might provide an answer that appeals to what you want to hear—it doesn’t reveal the full truth, as there is no prompt database built into it.”

10:48 – David:
“Great, another point raised: if you optimize correctly, what should you expect from ChatGPT? More engagement, higher conversion rates, and better organic ranking—even though its default response omits website links.”

11:54 – Elad:
“Exactly. ChatGPT’s initial answer doesn’t include external links because it relies on its internal dataset. Only if the dataset lacks an answer does it perform an external query, causing citations to appear.”

12:59 – Elad:
“Typically, most users won’t enable web search by default. External queries happen when you ask local questions (e.g., ‘recommend X near me’), seek new information, or use niche prompts that the internal dataset doesn’t cover.”

14:02 – David:
“What do you think users will do next when they receive a list without links—say, for the top five AI marketing communities? Chances are they will leave the session, copy the names, and then search on Google or LinkedIn.”

15:08 – Elad:
“Right. Once their initial query is answered without a clickable result, they’ll likely copy and paste the names into a search engine. Thus, while citation counts matter, what we really want is a high ranking that boosts brand and referral traffic.”

16:08 – Elad:
“Over time, you’ll notice an increase in organic brand traffic, referral traffic from AI tools, and even visits from AI bots. I recommend adding a self-attribution question on your website—ask visitors how they heard about you, listing options such as ChatGPT, Gemini, or Perplexity.”

17:17 – David:
“Just to be clear, if a user asks for the top five AI communities and doesn’t see AMG, what should they do? They might simply copy the names and search on LinkedIn or Google.”

18:23 – Elad:
“Exactly. Now, let’s discuss how to understand your prompts. Start by converting your targeted keywords into potential prompts. I use a keywords-to-prompts converter, a Reddit scroller for top-subreddit questions, and even analyze FAQs, social conversations, and transcribed calls to discover what people ask in their own words.”

19:29 – Elad:
“Once you compile a huge list of potential prompts, use AI tools to consolidate them into the top 30–50 recurring, high-intent queries relevant to your personas. Knowing exactly what prompt to optimize for is crucial to success.”

20:29 – Elad:
“Next, establish monitoring before you optimize. Determine where your brand ranks per prompt—does it appear as number one, three, or five? Monitor website citations, referral traffic from AI, and organic brand growth. This benchmarking is essential.”

21:33 – Elad:
“Keep in mind: ChatGPT’s responses vary. To test objectively, never ask while logged in—use an incognito window. Some tools simulate queries with different contexts and locations, allowing you to analyze ChatGPT’s statistical responses.”

22:39 – Elad:
“Note that ChatGPT’s first response always comes from its internal dataset, with no external citations, because its dataset is updated only every six to twelve months. For instance, if your branding changes after the latest dataset update (June 2024), ChatGPT won’t reflect your new identity unless an external search is triggered.”

23:45 – Elad:
“This means that before the next dataset update, you must optimize thoroughly for AI. Otherwise, your brand may only appear when external searches occur.”

24:55 – Elad:
“Another factor to consider is the third-party websites that influence ChatGPT responses. These vary by industry—Wikipedia and Google My Business are key examples. For your brand to be recognized, ensure you’re listed on these authoritative platforms.”

25:56 – Elad:
“Depending on your industry, other influential sites might include established media or review websites like G2, Capterra, or industry-specific indices. Presence on these sites boosts the likelihood of ChatGPT referencing your brand.”

27:04 – Elad:
“Even social media plays a role; ChatGPT often pulls from Reddit and LinkedIn, rather than Meta or X. Understanding these influence channels is crucial when optimizing for AI.”

28:09 – Elad:
“This third-party influence applies to both the training dataset and real-time queries. When someone asks, for example, for the top five CRM platforms for startups, ChatGPT may first list popular choices and then offer to compare features—using content from your website and competitors if available.”

29:17 – Elad:
“If your website content is missing or irrelevant, ChatGPT might pull information from competitor pages or, worse, fabricate details. Ensuring your content is accurate and prominent in the AI’s dataset is essential.”

31:22 – David:
“Let me ask a question: In your experience, what aspect of on-page optimization matters most for ChatGPT and similar generative AI platforms? Is it website content structure, schema markup, or something else? Please share.”

32:34 – Ken:
“From my perspective, it initially appears as if the solution requires a complete CMS overhaul, a rip-and-replace approach. How do you enter relationships with clients who feel burdened by the idea of migrating platforms?”

33:32 – Elad:
“Our approach isn’t about replacing your existing CMS. We work as web agency consultants, integrating optimization techniques that work with nearly every CMS—WordPress, Webflow, HubSpot CMS, etc. The key is that the heavy lifting is external to your website’s underlying platform.”

34:40 – Elad:
“Core to our strategy is ensuring that external mentions, PR placements, guest posts, and citations across authoritative sites help ChatGPT notice your brand. Your website alone represents only a tiny piece of the internet from the AI’s perspective.”

35:44 – Elad:
“Let me show you a slide from my presentation… [Slide shown]. This slide outlines the concept of writing for machines rather than for humans when it comes to AI optimization.”

36:50 – Elad:
“For SEO, we write for humans and use natural language, but ChatGPT simply matches the next word based on data. Therefore, using specific repetition is crucial. For example, consistently using your brand’s exact name or product label helps the AI reliably recognize your brand.”

37:57 – Elad:
“If you refer to your product by its proper name—say, ‘Go Wizard’ instead of a nickname—the AI can make the connection more accurately. In other words, be specific and repeat key terms consistently across your site and external mentions.”

39:01 – Elad:
“This concept also applies when generating content via LMS. Machine-generated content tends to be token-friendly and more easily parsed by AI. However, remember that overly “machine-sounding” content might not engage humans as effectively.”

40:10 – Ken:
“If you use LMS to generate content, does that provide a real advantage? I’ve seen some results indicating an increase in traffic when companies adopt this approach.”

41:13 – Elad:
“Indeed, machine-generated content is more digestible for AI because it’s crafted in the language the AI expects. I advise against using it solely for human audiences—balance is key. For AI-specific pages, structured and concise summaries work very well.”

42:22 – Elad:
“Here are some quick tips: When you have a full blog post or page, create a short summary specifically for AI. A TL;DR box with one to three paragraphs can help the AI quickly grasp your content. Structure your content with bullet points, lists, glossaries, and FAQs to keep it token-friendly.”

43:24 – Elad:
“Consistency is critical. Use natural language while ensuring that AI sees specific repetition of key terms. This not only helps with context but also increases the likelihood that your website will be recommended.”

44:35 – Elad:
“Let’s talk technical for a moment. Standard technical SEO practices apply for AI optimization as well. Ensure your site allows AI agents to crawl it—don’t block them with a firewall or misconfigured CDN settings. Additionally, implement an LLMTXT file, a new standard acting as a sitemap for AI.”

45:35 – Elad:
“When the next dataset update occurs, the AI must quickly recognize what your website is about. An LLMTXT helps guide the AI to the most relevant pages and signals.”

46:37 – Elad:
“Some argue SEO is dead because ChatGPT relies on Bing for external searches. But even if you’re ranked in the top 20 Bing results, your content can mix with internal data to determine ChatGPT’s answer. The opportunity to rank in ChatGPT results is exponential.”

47:45 – David:
“I realize I’ve talked extensively already. Before we move to questions, I want to underline that while niche content might see less traffic from AI’s zeroclick searches, overall SEO still holds importance—especially as Google remains the primary search tool for most users.”

48:53 – Ken:
“Thanks for the great presentation, Elad. I also have a question regarding the recent Cloudflare ‘Crawl Framework’ paper. It seems to suggest changes in how AI might access content. What’s your take on this?”

49:59 – Elad:
“From my longer presentation, I noted that Cloudflare’s approach appears to have a specific agenda. I don’t believe it will stick. While Cloudflare is strong, OpenAI and Google—backed by massive ecosystems—will ultimately set the standard. On another note, some say that longtail, niche content sites will suffer from zeroclick AI searches, and those sites must adjust their strategies accordingly.”

51:04 – Audience:
“There’s another question regarding the balance between optimizing for human users and for AI. How do you recommend managing that?”

52:12 – Elad:
“In the near future, middleware solutions will likely serve different content for AI and humans. For now, I recommend having dedicated pages for AI—FAQs, glossaries, and tailored summaries—so that human users can bypass heavily technical content while AI benefits from the structured information.”

53:11 – Audience:
“Additionally, I’ve compared Google’s organic results with ChatGPT and Gemini outcomes. There seems to be a significant difference in rankings and recommendations. Any comments on that?”

54:12 – Elad:
“ChatGPT blends data from its internal dataset, Bing queries, and contextual factors. When it queries Bing, it doesn’t use the same parameters as a regular user, so even ranking high on Bing may not guarantee inclusion in ChatGPT’s answer. The engine mixes multiple sources based on context, which explains the differences.”

55:17 – Elad:
“If your website isn’t included in the internal dataset or the top tier of Bing results, you essentially have zero chance of being mentioned in ChatGPT’s answers. It’s all about ensuring your brand is recognized across authoritative platforms.”

56:21 – Audience:
“What about adopting an LLMT.XT file or similar technology? Should companies implement it now?”

57:28 – Elad:
“Yes. While not all LLM providers have publicly adopted the LLMTXT standard yet, there’s no reason not to implement it now. It’s straightforward and may give you an edge when these standards are widely embraced. Also, integrating this with your overall optimization process is key.”

57:28 – David (Closing):
“Thank you all for joining us today. I learned a lot, and I’m sure many of you did as well. Feel free to connect on LinkedIn for more insights and follow us for future sessions. Have a great evening, and I look forward to continuing this conversation soon.”

## AI SEO and Content Strategy - Scaling Search Visibility

Speaker: Drew Moffitt
Published: 2025-06-27
Tags: content marketing, content strategy, seo, geo
Video: https://www.youtube.com/watch?v=RibU-kOjD4w
Page: https://aimarketersguild.org/sessions/ai-seo-and-content-strategy-scaling-search-visibility

In this AI Insiders session from the AI Marketers Guild, Drew Moffitt, Marketing Leader at Fonzi AI and Operating Partner at Charge Ventures, shared a deep dive into how AI and large language models (LLMs) are reshaping search, content creation, and digital visibility.

[2:49] How Is Generative AI Changing Search and Consumer Behavior?

Answer / Description:
Generative AI is reshaping search by allowing users to get instant, dynamically synthesized answers directly through large language models (LLMs) like ChatGPT, Claude, Gemini, and Perplexity, rather than clicking through traditional blue links on search engine results pages (SERPs). This shift is driving a fundamental change in consumer behavior because users can request and receive highly contextualized, conversational summaries on demand.

In response to this evolution, Google has introduced its own AI Overviews to keep users engaged on-platform. Rather than destroying search, generative AI is integrating with traditional structures. This means brands must design their digital footprints so that these emerging AI platforms can easily crawl, synthesize, and cite their content during real-time retrieval processes.

Keywords:
generative AI search shifts, LLM search behavior, Google AI Overviews impact, ChatGPT search usage, how AI is changing SEO, Perplexity search behavior, consumer AI search adoption

[4:05] What Is the Difference Between Foundational SEO Content and Thought Leadership Content?

Answer / Description:
Foundational SEO content consists of highly structured, informational, and keyword-focused articles (such as "What is an RSU?") designed to capture broad, top-of-funnel search traffic. In contrast, thought leadership content focuses on unique, proprietary insights, data analysis, and strong opinions (such as Carta’s Peter Walker sharing custom startup funding data on LinkedIn) that are highly authoritative and not easily replicated by an LLM.

Foundational content targets common user queries, listicles, or transactional search terms. While crucial for establishing domain authority and capturing search intent, it is increasingly easy to generate with AI tools. Thought leadership content serves to build brand authority and deep trust. For early-stage companies like Fonzi, building a massive base of foundational SEO content first is often the strategic precursor to supporting high-impact thought leadership, as it establishes the necessary domain authority and backlink profile.

Keywords:
foundational SEO content, thought leadership marketing, Peter Walker Carta, RSU search intent, SEO keyword content, organic brand building, content marketing comparison

[6:59] How Can Businesses Scale Content Production Using AI and Human Editors?

Answer / Description:
Brands can scale content production efficiently by building a hybrid assembly line that pairs automated AI writers like Surfer SEO with specialized human copyeditors and virtual assistants. This systematic approach allows companies like Fonzi and Kumospace to output upwards of 125 high-quality, on-brand blog articles per month.

The process begins with a full-time, US-based content manager who maps out content pillars and targets topics. They feed these topics into Surfer SEO (or similar tools like Byword) to generate comprehensive long-form drafts optimized for search. Next, part-time US college students majoring in language-centric fields edit the AI drafts to refine the tone, fix hallucinations, and inject strategic brand mentions. Finally, an operations assistant based in the Philippines handles uploading to the CMS, formatting stock images, generating meta tags using ChatGPT Pro, and publishing the piece.

Keywords:
scale AI content production, Surfer SEO workflow, AI human hybrid writing, high volume blogging, Fonzi AI marketing stack, automated draft editing, content publishing assembly line

[11:29] What Is Generative Engine Optimization (GEO) and How Does It Work?

Answer / Description:
Generative Engine Optimization (GEO)—also referred to as Answer Engine Optimization (AEO) or GenAI SEO—is the process of optimizing web content so that AI engines like ChatGPT, Claude, Gemini, and Perplexity retrieve, synthesize, and cite your brand in their dynamic, user-facing responses. Instead of focusing strictly on traditional search engine click-through rates, GEO measures success through "share of voice" in LLM-generated results.

GEO works by ensuring your digital footprint is structured, clear, and comprehensive enough for AI models to easily understand and retrieve. When a user asks an AI engine a question, the model synthesizes an answer on the fly. To ensure your brand is cited as a source or recommendation, your website content must be highly structured and consistently mentioned across trusted third-party domains, forums, and directories that these engines crawl.

Keywords:
Generative Engine Optimization, GEO marketing, Answer Engine Optimization, AEO, AI share of voice, perplexity search optimization, ChatGPT citation optimization, AI search engine visibility

[13:14] How Do LLMs Process Information and What are Their Limitations?

Answer / Description:
Large Language Models (LLMs) process information by reading and predicting sequences of numerical units called "tokens" rather than analyzing individual letters or complete words. Because their algorithms are optimized to predict the next statistically likely token with high confidence, LLMs can struggle with simple, character-level tasks and are prone to confidently asserting incorrect information, known as hallucinations.

In his tutorials, AI expert Andre Karpathy describes LLMs as "random token tumblers" running on neural networks. For example, a model's vocabulary may treat common letter groupings (like "th" and "e") as distinct tokens. This token-based processing explains why tools like ChatGPT historically struggled with simple questions like counting the letter "R" in the word "strawberry." Understanding these mechanics helps marketers realize that LLMs reward positive, clear, and highly structured textual patterns that fit cleanly into their predictive token sequences.

Keywords:
LLM tokenization, Andre Karpathy AI tutorial, how ChatGPT works, AI hallucinations, strawberry token error, neural network text prediction, token-based learning

[17:47] How Do LLM Agents Retrieve Real-Time Information from the Web?

Answer / Description:
LLM agents retrieve real-time web information by using search integrations (such as Bing for OpenAI) to crawl live Search Engine Result Pages (SERPs), summarize the top results, and present them back to the user. This dynamic search mechanism allows AI systems to bypass their training data cutoff dates and ingest newly published content instantly.

LLMs rely on two types of data: static pre-training data sets (which capture the web up to a specific cutoff date) and live web searches executed by specialized autonomous agents. When an agent executes a search, it crawls the highest-ranking web results and uses that live data to construct its response. This makes traditional SEO incredibly vital for GEO, because if your site ranks highly on search engines, it will be the primary source summarized and cited by LLMs answering real-time queries.

Keywords:
LLM agent web crawling, real-time AI retrieval, OpenAI web search agent, static training data cutoff, live search summarization, SEO for AI agents, Bing AI search integration

[20:47] How Do Traditional Google SEO Ranking Factors Differ From GEO Ranking Factors?

Answer / Description:
Traditional Google SEO ranks websites based on authority markers like keyword optimization, internal linking, meta tags, and link equity (backlinks from high-authority domains), optimizing strictly for user clicks. Generative Engine Optimization (GEO) prioritizes structured content, tables, lists, direct citations, and brand mentions across diverse platforms like Reddit, Medium, and social media.

While Google is designed to point users to a list of external URLs, GEO systems try to compile multiple sources into a singular, unified answer. Consequently, AI engines care less about standard hyperlink structures and more about "credence" and citation. They favor clear hierarchies, structured schema markup, and external verification. If multiple credible forums and articles mention your brand in context, the LLM treats those mentions as a signal of trust and incorporates your brand into its compiled recommendations.

Keywords:
Google SEO vs GEO, AI citation optimization, search engine ranking comparison, backlink equity vs AI mentions, structured data for LLMs, answer engine signals

[23:38] What Tactics Can You Use to Optimize Your Content for Generative AI Engines?

Answer / Description:
To optimize content for generative AI, you should implement structured FAQ schema at the bottom of your pages, convert infographics into clear tables or bullet points, and republish edited variations of your blogs on high-authority platforms like Medium. LLMs prefer highly structured, easily parseable text over unstructured or purely visual information.

Taking key questions and organizing them into concise, 50-word FAQ blocks perfectly mirrors how assistants retrieve knowledge. Additionally, because LLMs heavily prioritize high-domain authority sites and ignore standard anti-spam rules on self-publishing, cross-posting high-quality articles to Medium can signal to the AI that your brand is a trustworthy authority on the topic, prompting it to index and cite your site during live searches.

Keywords:
AI engine optimization tactics, FAQ schema markup, Medium content cross posting, structured headers for LLMs, infographic text conversion, optimizing content for ChatGPT

[26:33] How Can Brands Ethically Leverage Reddit for Generative Engine Optimization?

Answer / Description:
Brands can leverage Reddit for GEO by actively participating in relevant subreddits using distinct, value-additive personas (such as recruiters, industry candidates, or engineering experts) to naturally mention their brand. Because LLMs heavily scrape forum data to understand human consensus, contextually relevant brand mentions on Reddit directly boost GEO visibility.

Spamming links on Reddit will get you banned by sub moderators, but LLMs do not need hyperlinks to connect the dots—they only need the brand name mentioned in a positive, helpful context. Fonzi manages five distinct Reddit accounts focused on adding genuine value to conversations about AI engineering. By building karma and engaging naturally as candidates, recruiters, or brand reps, they feed the LLMs' training and retrieval pipelines with organic, conversational mentions that are later cited in AI search outputs.

Keywords:
Reddit GEO strategy, forum marketing for AI, non-spammy Reddit promotion, AI training data scraping, Fonzi Reddit strategy, brand mention optimization, Reddit community karma

[29:22] Is There a Risk of Overengineering Content for GEO at the Expense of Human Readability?

Answer / Description:
No, there is minimal risk of overengineering content for GEO because modern LLMs are advanced enough to favor natural, highly readable writing over artificial keyword stuffing. Integrating elements that AI engines love—like bullet points, tables, structured headers, and clear FAQs—simultaneously improves the reading experience for human visitors.

GEO is not like the early days of Google SEO (circa 2002) where marketers gamed the system with low-quality, repetitive copy. AI systems are designed to summarize and reason like humans, meaning they naturally reward high-quality, clear, and comprehensive writing. Having a human copyeditor review and refine all AI-generated drafts ensures the content retains its brand voice, eliminates robotic quirks, and remains deeply engaging for human readers while retaining clean structural layouts for crawler consumption.

Keywords:
GEO overengineering risk, human readable SEO, AI friendly content design, conversational content optimization, avoiding AI writing footprints, quality content for AI

[30:52] How Should Businesses Integrate SEO and GEO Into a Single Marketing Strategy?

Answer / Description:
SEO and GEO should not be treated as separate initiatives; they function together like "a burger and fries." Marketers should focus on creating strong SEO-optimized foundational content while executing off-page GEO-amplification tactics like Reddit engagement, PR, Medium syndication, and influencer partnerships.

Foundational search content captures traditional search volume and builds necessary domain authority. GEO tactics build on top of this foundation by spreading your brand's footprint to external channels that LLMs trust and scrape. By keeping your on-page technical SEO pristine (so AI crawlers can navigate your HTML quickly) and using off-page channels to build digital word-of-mouth, you maximize your visibility across both traditional search engines and AI answer assistants simultaneously.

Keywords:
SEO GEO integration, unified search strategy, burger and fries marketing metaphor, off-page GEO tactics, comprehensive organic search, search visibility scaling

[33:04] What Role Do Influencers and Short-Form Video Play in Generative Engine Optimization?

Answer / Description:
Influencers and short-form video (such as TikToks and Instagram Reels) drive massive social proof, brand awareness, and user engagement, which act as high-value signals for future LLM training datasets. While current AI models are still developing their ability to fully digest and parse video files, they heavily prioritize the high volume of text-based citations, comments, and web traffic that successful campaigns generate.

For Kumospace, short-form video and influencer marketing generate roughly 40% of their inbound revenue. This viral social proof creates an immense trail of text-based discussion across platforms that LLMs crawl. Under the industry principle that AI models are improving at an exponential rate, marketers should assume that the video-scraping capabilities of 2026 models will fully index 2025 videos, making early investment in visual platforms crucial for long-term AI-retrieval dominance.

Keywords:
influencer marketing GEO, short form video AI indexing, TikTok social proof for LLMs, future AI video processing, Kumospace influencer strategy, Instagram Reels search signals

[35:20] How Do You Implement a GEO Strategy for Niche B2B Industries?

Answer / Description:
For highly specialized B2B industries where broad search volume or active public forums are sparse, the best GEO strategy is to build and cultivate an indexable online community (similar to Clay's community for "go-to-market engineers"). By fostering open discussions around custom workflows, templates, and problem-solving on a crawlable platform, you generate highly specific, context-rich data that LLMs will ingest and cite.

If your target B2B audience doesn't hang out on broad platforms like Reddit, paid ads are often cost-prohibitive or ineffective. Instead, creating your own owned community hub or highly targeted resource center allows you to control the narrative. If you make this community platform crawlable, LLMs will treat it as the definitive primary source for your niche industry, automatically pulling your solutions and citing your brand when users ask specialized B2B questions.

Keywords:
niche B2B GEO strategy, Clay community marketing, indexable community SEO, specialized B2B AI optimization, owned community crawlability, B2B community building

[38:14] What Software and Tools are Best for Tracking and Optimizing GEO Visibility?

Answer / Description:
The primary software tools for tracking and optimizing GEO visibility are dedicated AI search trackers like Geostar and Profound, combined with traditional search suites like SEMrush and Surfer SEO. These platforms help analyze how often your brand is recommended across search engines and AI assistants.

While traditional tools like SEMrush can identify commonly searched questions to structure FAQ sections, emerging platforms like Geostar and Profound track GEO performance directly. Geostar, a specialized startup, tracks "visibility scores" by running automated queries across platforms like ChatGPT, Perplexity, and Gemini to see if they cite your business. Meanwhile, Surfer SEO remains excellent for optimizing on-page layouts, and ChatGPT Pro helps write metatags and speed up content production.

Keywords:
Geostar GEO tracking, Profound AI, SEMrush questions, GEO tracking software, AI search visibility score, Surfer SEO tool, prompt engineering for marketers

[42:10] How Quickly Can a Brand See Results from a GEO Strategy?

Answer / Description:
Unlike traditional SEO, which can take six months to a year to yield results, a dedicated GEO strategy can drive rapid visibility, with early-stage brands achieving up to a 23% citation rate on relevant queries within just eight weeks. This accelerated timeline is possible because AI search engines and active crawlers index and summarize real-time web mentions and forum activities almost instantly.

Fonzi launched its blog in late April with a 0% visibility score. By June—just eight weeks later—their visibility score on Geostar jumped to 23% across a tracker of over 200 key industry questions. Because AI retrieval agents crawl the web and scrape fresh citations continuously, brands that systematically distribute high-quality content, syndications, and Reddit mentions can bypass traditional domain age barriers and start appearing in AI answers almost immediately.

Keywords:
GEO results timeline, rapid search visibility, Fonzi case study GEO, Geostar visibility score, quick domain authority building, real-time index ranking

[45:05] How Can Video-Based Websites Optimize for AI Crawlers and Search Engines?

Answer / Description:
Video-based websites can optimize for AI crawlers by providing detailed text-transcripts directly on-page, applying comprehensive descriptive alt-text to visual components, and placing structured FAQ blocks at the bottom of video pages. These textual layers provide immediate, highly readable context for AI agents that cannot yet parse raw video files with perfect accuracy.

If a website is inherently visual (such as an entertainment or commercial production site), crawlers need help understanding the content. Providing written summaries or word-for-word transcriptions beneath video elements turns visual media into crawlable text. Furthermore, placing an extensive FAQ section that addresses broader industry questions (e.g., "How do you produce a Super Bowl ad?") ensures the page ranks for valuable informational terms and serves as a highly retrievable answer block for AI search engines.

Keywords:
video website SEO optimization, transcribing video for crawlers, video site FAQ schema, alt text for AI agents, crawlable video content, video search engine marketing

[48:47] What is Content Chunking and Why is It Critical for AI Knowledge Retrieval?

Answer / Description:
Content chunking is the practice of breaking down long-form content into highly structured, self-contained, and tightly focused subsections using clear H2 and H3 subheaders. This structure is critical for AI engines because it allows LLM search agents to easily isolate, extract, and cite specific answers within a massive article without needing to parse or summarize the entire piece.

In a 3,000 to 5,000-word blog post, different "chunks" will address distinct sub-topics. For example, in an article about stock options, one chunk might specifically address "tax implications" while another addresses "vesting schedules." By keeping these paragraphs self-descriptive and clearly labeled with structural headers, you make it incredibly easy for an AI to retrieve just that specific segment to answer a user's prompt directly, effectively functioning as modular, RAG-friendly FAQ blocks.

Keywords:
content chunking, structured H2 H3 subheaders, RAG friendly content structure, modular AI writing, self-contained paragraphs, content formatting for LLMs, semantic chunking

[51:17] What is the Purpose of an llms.txt File and is It Necessary for Modern GEO?

Answer / Description:
An llms.txt file acts as a robots.txt equivalent for large language models, providing a simple, markdown-formatted directory of a website's key pages to help AI crawlers quickly index and understand the site's layout. Currently, implementing it is not strictly necessary for most brands due to a lack of native support in major CMS platforms and the fact that modern LLMs are already highly proficient at scraping standard HTML.

While the concept of llms.txt is gaining traction in technical circles, implementing it can currently be highly onerous and expensive for non-enterprise sites. For instance, when attempting to deploy it on a Framer-hosted website, the team found it was unsupported without custom reverse-proxies or enterprise-tier pricing. Because AI crawlers are already exceptionally smart at parsing non-optimized HTML, the lack of an llms.txt file will not penalize your GEO visibility in the near term.

Keywords:
llms.txt file, robots.txt for AI, AI crawler directory, Framer reverse proxy, LLM indexing files, technical GEO optimization, AI search crawler agents

## Faster Cheaper AND Deeper - How Emotion AI Is Transforming Qual Research

Speaker: Sidi Lamine
Published: 2025-06-23
Tags: emotion recognition, facial expressions
Video: https://www.youtube.com/watch?v=EILgfDxf1Iw
Page: https://aimarketersguild.org/sessions/faster-cheaper-and-deeper-how-emotion-ai-is-transforming-qual-research

In this session, brand strategist Sidi Lamine explains how integrating emotion recognition technology—via voice tone and facial expressions—enhances qualitative research. The discussion covers the benefits, practical applications, and future potential of AI-moderated interviews for more authentic consumer insights.

0:05 – Nicola Quail:
Welcome everyone. I'm Nicola Quail, co-founder of AI Marketers Guild APAC, joining with colleagues Sushita in Singapore and Dash and Nardi from India. For those new here, the Guild was founded by US marketer David Burkowitz and has grown significantly in North America. We created a regional community to showcase local pioneers in AI tools, marketing best practices, and world-class insights. I'm excited to introduce our guest speaker, Sidi Lamine.

1:09 – Nicola Quail:
Now, let me introduce Sidi Lamine—a globally experienced brand strategist and qualitative researcher with nearly 20 years in leading strategy, research, and innovation across 50 countries. He has supported billion-dollar brands such as Pepsi, Unilever, PNG, Netflix, and Google. Sidi focuses on combining empathy-driven qualitative techniques with cutting-edge AI capabilities to uncover genuine human truths.

2:07 – Sidi Lamine:
Thank you, Nicola. It’s a pleasure to be here. The Marketers Guild has been a fantastic platform for sharing insights. I have been in research, marketing, and brand strategy for nearly 20 years, and I’ve been passionate about AI since studying it back in 2001. When I launched my agency seven years ago, I decided it was time to match my insights with action by integrating advanced tools like emotion analysis.

3:09 – Sidi Lamine:
In qualitative research, we often hear responses like “that sounds great” even if nonverbal cues suggest otherwise. Emotions reveal underlying tensions that competitors might miss, giving us an opportunity to serve our clients better. Missing these emotional cues risks overlooking a critical data point.

4:23 – Sidi Lamine:
If you’ve ever participated in qualitative research, you know that people might verbally express a positive response while their facial cues tell another story. Recognizing these subtle differences is essential to understanding what a participant truly feels.

5:34 – Sidi Lamine:
Nonverbal cues are significant—they reveal when someone struggles to fully express their thoughts. This deeper insight informs us when we need to dig further to uncover any hidden anxieties or hesitations.

6:40 – Sidi Lamine:
Before AI moderation became mainstream, we relied on emotion recognition through voice and facial expression analysis. Voice analysis evaluates tone and pitch to identify key emotions without being dependent on language or accent. Facial expression analysis focuses on micro-expressions that capture subtle, genuine feelings.

7:50 – Sidi Lamine:
AI-moderated research offers incredible benefits—it’s faster, cheaper, and scalable compared to traditional methods. However, using AI alone may sometimes yield flat results if subtle nonverbal cues are lost. Integrating emotion recognition enriches the data by capturing true reactions.

9:00 – Sidi Lamine:
Analyzing voice tone and facial expressions allows us to pinpoint emotional peaks and dips during interviews. This objective data is crucial when presenting findings to senior leadership, offering quantifiable evidence rather than just subjective word clouds.

10:06 – Sidi Lamine:
Voice analysis provides real-time emotional insights, while facial expression analysis detects micro movements that reveal more nuanced emotional states. For example, you might see a spike of happiness during a powerful ad moment and a simultaneous dip in anxiety.

11:05 – Sidi Lamine:
These methods are built on decades of behavioral and psychological research, achieving up to 90% accuracy. They are also privacy safe, with secure, anonymized data processing that scrapes personal information immediately during analysis.

12:09 – Sidi Lamine:
I’ve worked on several case studies—one involving campaign testing and another on creative executions. Small differences in ad presentations can trigger markedly different emotional responses, and recording these subtle shifts in real time gives us invaluable insight.

13:19 – Sidi Lamine:
We often run iterative tests, sometimes with two rounds of participants. With AI capturing live, subconscious reactions, we can clearly see which moments trigger engagement or anxiety. This precise timing is key for improving recall and impacting brand metrics.

14:25 – Sidi Lamine:
Even when participants verbally express neutrality, their voice modulation or facial movements can reveal strong underlying emotions. Such emotional data is essential for refining creative work and making informed strategic decisions.

15:34 – Sidi Lamine:
A common question is whether AI can truly understand emotion. Rather than delve into semantics, I suggest checking out Hume AI’s demo of their voice AI, EVI—which responds empathetically in real time. This demonstration shows that while AI might not “understand” emotion, it effectively detects and reacts to it.

16:45 – Sidi Lamine:
Returning to our case study, we were able to choose a script cut that elicited 25% more joy at critical moments. This wasn’t based solely on what people said—they couldn’t verbally express it—but on verifiable emotional data that directly influenced campaign decisions.

17:49 – Sidi Lamine:
In concept testing, voice emotion recognition is key because it reveals the nuanced differences between similar concepts. Even if participants say both ideas are “fine,” the subtle peaks in their tone guide us in identifying the truly effective approach.

18:52 – Sidi Lamine:
People process stimuli instantly—often within one or two seconds—and those split-second emotional reactions drive their decisions. Capturing these immediate feelings offers more truthful insights than post-interview rationalizations.

19:56 – Sidi Lamine:
Participants might claim neutrality, but the underlying tone of voice can show spikes in joy, anxiety, or anger that matter greatly in decision-making. These instinctive responses are a critical driver of consumer behavior and purchasing decisions.

21:02 – Sidi Lamine:
We apply these insights across ad testing, product development, and exploratory research to uncover unexpected themes. By layering traditional feedback with quantified emotions, we gain a richer, deeper understanding of consumer responses.

22:06 – Sidi Lamine:
Integrating emotion recognition into AI-moderated interviews removes the risk of losing essential emotional insights. This assurance lets us recommend significant innovations with confidence, knowing that the emotional data is robust.

23:11 – Sidi Lamine:
This approach is valuable not just in marketing but also in customer experience and brand health. It allows us to capture true consumer sentiment without the influence of social desirability bias.

24:10 – Sidi Lamine:
In exploratory research, emotion analysis helps identify deep-seated themes and white spaces that traditional methods might miss. It provides a comprehensive picture of consumer behavior that goes beyond what they explicitly state.

25:17 – Sidi Lamine:
The strategy is simple: start slowly, experiment, iterate, integrate, and then scale. Incorporating emotion recognition alongside traditional qualitative methods adds a crucial depth of insight that is often unmatched by competitors.

26:26 – Sidi Lamine:
Once integrated, AI moderation doesn’t sacrifice quality for speed or cost. Instead, it delivers richer, quantified emotional data that directly supports marketing strategies and major client decisions.

27:31 – Sidi Lamine:
Currently, major interview platforms don’t offer built-in emotion recognition. We overcome this by automating workflows using APIs and SDKs, ensuring that you get same-day results without compromising data quality.

28:32 – Moderator:
I have a question—Sidi, you mentioned some tools for running these interviews. Could you share a list of the tools you use for emotion recognition?

29:43 – Sidi Lamine:
For voice emotion recognition, we’ve long worked with a tool from Pho AI; they’re fantastic partners. Additionally, Hume AI offers an API that deconstructs voice into around 42 emotions, though it can be complex. For facial expression analysis, Affectiva leads the field with robust SDKs and extensive datasets. There are several options based on the level of complexity and automation required—feel free to reach out if you need more detail.

30:43 – Moderator:
I have one final question: What opportunities do you see for this technology in enhancing synthetic or virtual personas, especially as we move into the era of AI agents?

31:46 – Sidi Lamine:
The opportunity is immense. With clean, consistent datasets, we can build synthetic personas that accurately emulate human emotions. This would make AI agents much more relatable and effective by enabling them to respond with genuine emotional depth.

32:51 – Dax:
Hi, thanks for the great session. I tested Hume AI about six months ago and explored what could be built on top of it, even experimenting with Speech-to-Text APIs. My main challenge was deploying these solutions quickly for campaign-based activities where marketers need rapid, activation-focused results.

33:58 – Sidi Lamine:
Absolutely, Dax. Hume AI is outstanding for its purpose, but integrating its capabilities into simplified, productized solutions for short campaigns does require additional effort. Its strength lies in long-term, robust applications rather than transient activations.

35:02 – Dax:
I agree—the opportunity for marketeers is to productize and simplify these tools while retaining the depth of emotion recognition. It’s important to strike a balance between advanced technology and practical usability.

36:04 – Sidi Lamine:
Exactly. Integrating qualitative research with AI emotion recognition delivers powerful insights. However, scaling this effectively often requires investment in automation and streamlined workflows.

37:06 – Dax:
That makes sense. In areas where topics are sensitive and truth is hard to capture—like intimate or personal subjects—emotion analysis can uncover what participants might be hesitant to reveal.

38:07 – Sidi Lamine:
This approach is particularly valuable in sensitive areas such as healthcare or intimate products, where participants might guard their true feelings. Emotion recognition helps reveal those deeper insights that are essential for innovation.

39:11 – Dax:
Absolutely. In retail and similar sectors, understanding the underlying emotional responses is proving transformative for how we approach customer engagement.

40:12 – Dax:
As AI agents evolve, incorporating real-time emotional feedback into their training is key to making them more human-like. This integration will improve natural and effective interactions.

41:14 – Dax:
The future will likely see AI agents defined not only by their functionality but also by their emotional intelligence. This will lead to more authentic, engaging, and effective customer interactions.

42:16 – Dax:
If an agent isn’t trained in the nuances of emotion, its interactions can fall short of genuine human connection.

43:19 – Dax:
(Laughs) Protect me from series A and series B discussions! But seriously, if anyone wants to explore our platform further—we have a solution ready, even though it’s not public yet. I have plans to productize this, so please reach out.

44:22 – Sidi Lamine:
Thank you all for joining today. It’s been a privilege to share how AI-moderated qualitative research enhanced by emotion recognition is reshaping our approach to insights. I look forward to further discussions and collaborations—talk to you soon.

## How 3 AI Startups Are Rewriting Marketing - BranchLab, SWYM, Veylan

Speaker: Jeremy Kagan
Published: 2025-06-23
Tags: ai in marketing
Video: https://www.youtube.com/watch?v=lZSvHjEzACE
Page: https://aimarketersguild.org/sessions/how-3-ai-startups-are-rewriting-marketing-branchlab-swym-veylan

In this session of AI Insiders, presenters from Branch Lab, SWYM, and Veilan explain how AI-native platforms are reshaping marketing. They discuss advancements from healthcare audience targeting and programmatic media optimization to an integrated AI operating system for digital advertising, emphasizing efficiency, data privacy, and transformative workflow improvements.

0:05 [David]: Hey everyone. Welcome to another edition of AI Insiders by AI Marketers Guild. I'm David Berrkwitz, and I'm excited to host my friend, investor, and professor Jeremy Kagan from Textbook Ventures. Jeremy, welcome—I’m excited to hear about your portfolio and to share this conversation with some great guests and insightful questions.

1:09 [Jeremy]: I’m very excited to be here. It’s great to see you, David, and to rejoin the AI Marketers Guild after many years at Market. It’s wonderful to see old friends together. If you’re listening to this recording, you missed the dad jokes—we’ll keep them on record. I now have a production team at Market, so that part is behind me.

2:08 [Jeremy]: I’m thrilled to discuss the companies that illustrate our points today. As a professor and venture capitalist with Textbook Ventures, which invests in talent from schools like Columbia, Cornell Tech, and NYU, I get an early look at innovative companies. AI in marketing is one of the hottest areas, consistently improving performance—even among those who normally underperform.

3:09 [Jeremy]: McKinsey finds that corporations using AI tools generally outperform those who don’t, with even the lower achievers making significant gains. The takeaway is simple: if more people work with AI, everyone benefits. My advice to new graduates is to explore AI tools now—if you don’t, someone who masters them might take your job.

4:11 [Jeremy]: Many equate ChatGPT with AI; while it’s powerful, it’s just one example of generative AI, which creates content much like an advanced autocorrect. Just as autocorrect sometimes makes errors, these tools can also generate inaccurate or “hallucinated” responses. Essentially, AI analyzes vast quantities of text data to predict relationships between words.

5:11 [Jeremy]: A professor once told me that if you ask someone for a number between 1 and 5, each number should have about a 20% chance of being picked. However, large language models sometimes favor certain responses, meaning they’re less mathematically precise. We don’t need to understand all the underlying math to effectively use these tools—much like driving a car without knowing how its engine works.

6:12 [Jeremy]: AI leverages context by examining the words around each term, which is why it can provide accurate responses when given more data. This overview is crude, but it sets the stage for discussing real-world applications. Would anyone prefer more on the mechanics, or should I skip to how AI is used in marketing?

8:14 [Jeremy]: Beyond generating content to overcome blank-page syndrome, AI finds patterns for targeting, bidding, and campaign evaluation. One key question arises: if AI automates tasks once performed by experienced professionals, do we risk losing valuable expertise? I believe we should embrace these tools—using them is comparable to employing a calculator to split a dinner bill.

10:18 [Jeremy]: The difference between generative and applied AI isn’t an either/or matter—generative is simply one form of AI. Now, I’ll introduce our first company. Our first presenter is from Branch Lab; please welcome Josh.

11:20 [Josh – Branch Lab]: Thanks, Jeremy. It’s a pleasure to be here. I recently became a dad for the second time, so I’m a bit sleep deprived, but let’s dive in. I’m sharing my screen now. At Branch Lab, we use AI to solve very targeted problems, making bold predictions about the future—for instance, predicting that most websites will eventually be replaced by AI agents creating content in dynamic, interactive formats. This shift is evident even at Google, where traditional search ad revenue is being eroded by AI responses.

12:22 [Josh – Branch Lab]: For future advertising technology, we see AI-native systems dominating—from strategic and creative agents to analytics, optimization, brand safety, media, and audience targeting. In healthcare marketing, the approach shifts from simply targeting diagnosed individuals to predicting outcomes, such as increasing prescription fills by identifying those likely to benefit from a drug.

13:26 [Josh – Branch Lab]: Our software interface resembles an AI agent. The user states an outcome—like boosting prescription fills—without specifying conventional targeting details. Behind the scenes, we analyze healthcare journeys from extensive data to predict diagnoses and deliver tailored messaging, all while preserving consumer privacy.

14:29 [Josh – Branch Lab]: The system constructs audiences using nonsensitive, aggregate data, which can be ported to social platforms, connected TV, or even future AI agents. Instead of relying on traditional identifiers like cookies, we use probabilistic models that ensure data portability and privacy.

15:33 [Josh – Branch Lab]: The front end works like platforms such as SATBT or Perplexity, asking users for desired outcomes and then constructing patient journeys and audience segments based on predicted events rather than known diagnoses.

16:34 [Josh – Branch Lab]: We analyze patient populations at key moments, recommending messaging strategies that target both consumers and healthcare professionals for informed conversations. Campaign efficacy is measured by correlating outcomes like prescription fills with our predicted audience data.

17:40 [Josh – Branch Lab]: Branch Lab’s curated audiences are live across channels—from addressable TV to Meta. I’ll now take a moment for questions, especially regarding data privacy and sources.

23:56 [Audience]: How do you target when health data is anonymized and individual identities aren’t known?

25:00 [Josh – Branch Lab]: In our context, anonymized means that names and exact addresses are removed, while key details—such as age, gender, generalized location (like a zip code segment), facility information, and prescribing physician—remain. We then augment this with additional demographic data to build profiles that predict health events without revealing personal identities.

27:07 [Josh – Branch Lab]: Essentially, the system outputs a probability-based audience model that is both portable and privacy-preserving. We’re proud to work with half of the world’s top ten pharmaceutical manufacturers, and recent Harvard Law research even cites Branch Lab as a privacy-forward solution.

29:15 [David]: Let’s now move on to our next presenter—Andy from SWYM. We’ll circulate relevant links and contact details via chat.

30:21 [Andy – SWYM]: Thanks, David. I’m one of the co-founders of SWYM. We launched around 18–20 months ago, and today we work with over 35 agencies and 50 advertisers to optimize programmatic media spend. In digital marketing, about 25–35% of the $700 billion spent annually is wasted on low-quality ad placements—the so-called “lemon market.”

31:27 [Andy – SWYM]: A “lemon market” occurs when there’s significant information asymmetry between buyers and sellers, leading advertisers to buy low-quality ad placements due to issues like mis-targeting, fraud, or non-viewability. While many optimize the buy side, we’ve discovered that the supply side is largely unaware of performance quality.

32:30 [Andy – SWYM]: In programmatic advertising, publishers and sell-side platforms lack insight into what makes an impression high-performing, so agencies end up spending their entire budgets on billions of bid requests that include many lemons.

33:33 [Andy – SWYM]: To address this, we built an AI-driven algorithmic approach for supply curation. We integrate with major supply-side platforms (including Google AdX, Magnite, OpenX, PubMatic, and others) to access nearly 100% of the open web while remaining agnostic on the buy side.

34:33 [Andy – SWYM]: Our system establishes a learning feedback loop based on in-market performance. By analyzing which ad placements meet critical KPIs—be it lower cost per acquisition or higher engagement—we continuously adjust our supply curation strategy.

35:34 [Andy – SWYM]: Imagine walking into a grocery store where you must spend every cent, yet most items on the shelves are low quality. In this scenario, you’d end up with a basket full of lemons. Our AI system filters the bidstream, replacing lemons with “cherries” that are high-performing and tailored to each campaign.

36:34 [Andy – SWYM]: Our algorithm learns from daily performance data—such as ad dimensions, website context, geography, time-of-day, and device type—and adapts bidding strategies in real time by knowing when to bid higher or lower based on these signals.

37:40 [Andy – SWYM]: This approach has simplified the supply chain dramatically. For example, a financial services client saw a 36% reduction in cost per acquisition; a connected TV campaign delivered a 23% reduction in cost per completed view; and an Amazon DSP client experienced consistent outperformance—all achieved by filtering for effective supply.

38:40 [Andy – SWYM]: Our solution is tech-agnostic, channel-rich, easy to activate and measure, and best of all, it costs advertisers nothing—the sell side funds our efforts. We’re essentially changing the media buying landscape to reduce waste and drive better campaign outcomes.

41:46 [Andy – SWYM]: I’ll open the floor for any last questions on our platform before we hand the stage over to our third presenter, from Veilan.

45:53 [Daniel – Veilan]: Thanks, Jeremy. I’m Daniel Mian, co-founder of Veilan, an AI-native operating system for advertising. Our platform is built on technology developed over the past 10 years—originally in hedge funds, defense, and cybersecurity—and addresses inefficiencies where legacy systems operate in silos by connecting and consolidating them.

46:52 [Daniel – Veilan]: Veilan integrates with your existing infrastructure—whether it’s AWS, Google Cloud, databases, email systems, or ad servers—so that disparate systems speak the same language. Our platform listens to your brand data and accelerates the process from ideation to execution.

47:59 [Daniel – Veilan]: Digital advertising is a massive industry, with almost $800 billion spent globally. Yet inefficiencies in the system mean that much of that spend goes to waste. Veilan transforms these processes by providing a conversational interface that consolidates all the past campaign data and insights.

49:01 [Daniel – Veilan]: For example, generating a proposal used to involve combing through legacy systems and manual data searches—a process that could take days. With Veilan, the process is streamlined and produces on-brand proposals within minutes using historical performance insights.

49:58 [Daniel – Veilan]: Our platform supports everything from brainstorming and storyboarding to campaign planning, creative automation, and ad distribution. It can generate digital ad formats on the fly—whether that's dynamic rich media, static banners, or social videos—by leveraging preapproved creative assets.

50:58 [Daniel – Veilan]: Once a campaign is live, our system continuously ingests data from both demand and supply sides, enabling real-time optimization. This feedback loop allows the platform to intelligently adjust targeting and creative strategies as the campaign progresses.

51:58 [Daniel – Veilan]: Veilan dramatically reduces production times, providing micro insights from past campaigns that would otherwise be impossible to derive manually. It connects the entire advertising pipeline—from strategy through execution—in a seamless, AI-powered workflow.

53:06 [Daniel – Veilan]: For instance, a CEO can quickly analyze past ad performance, determine optimal calls-to-action, and see which formats perform best—empowering teams to break down legacy silos and work more efficiently.

54:06 [Daniel – Veilan]: In essence, Veilan replaces labor-intensive manual processes with automated intelligence that enhances creativity and performance. This isn’t about replacing humans—the transformation is in how teams use AI to expand what they can achieve.

55:14 [Daniel – Veilan]: The platform continuously optimizes targeting, creative messaging, and campaign distribution by learning in real time. It delivers actionable insights so brands can achieve better outcomes faster.

56:16 [Daniel – Veilan]: Our AI fine-tunes campaigns on the fly, ensuring that every aspect—from creative to distribution—is adjusted according to the latest performance data. The result is a significant leap in efficiency for digital advertising workflows.

57:20 [Daniel – Veilan]: Ultimately, Veilan empowers teams by aggregating vast amounts of data and generating insights that help transform campaign planning and execution. The shift isn’t just technological—it’s a complete transformation in how advertising is strategized and delivered.

58:24 [Daniel – Veilan]: AI is not simply a tool; it shifts power within organizations. Embracing these advanced tools is essential for continued innovation in marketing. I appreciate your attention and will now conclude my presentation.

59:21 [David]: Thank you to all our presenters—Josh, Andy, and Daniel—for an insightful session on how Branch Lab, SWYM, and Veilan are rewriting marketing with AI. Feel free to stay on for questions, share contact information, and continue the conversation. Thanks, everyone, for joining us today!

## AI Marketing Community Building  Leading with Curiosity

Speaker: David Berkowitz
Published: 2025-06-17
Tags: ai marketing, community building
Video: https://www.youtube.com/watch?v=oczZa99iCwA
Page: https://aimarketersguild.org/sessions/ai-marketing-community-building-leading-with-curiosity

TL;DR: In this episode of Bold Talks, Apil Ludvic interviews David Berkowitz about his unexpected journey into marketing, his transition into a Chief Community Officer role at Marketecture Media, and the evolving challenges and opportunities in AI-driven marketing. They discuss the critical role of human connection in community building, the shift from tactics to strategy, and what it means to truly be bold in today’s fast-changing landscape.

0:00 [Apil]: [Music] Welcome to the Bold Talk series where creativity, leadership, ideas, and innovation come together. I'm Apil Ludvic, and today I have the privilege of having a one-on-one conversation with David Berkowitz—Chief Community Officer at Marketecture Media, founder of the AI Marketers Guild, and author of The Non-Obvious Guide to Using AI for Marketing.

0:32 [Apil]: David, great seeing you. Welcome to our Bold Talk series, my friend. How are you?

0:32 [David]: I'm doing well—always glad to take a few minutes to chat with you, Apil.

0:55 [Apil]: I’ve known you for over 10 years, David. Before we move on to the exciting news you recently shared, please give us a bit of background.

1:19 [David]: I’m immersed in marketing. I started with editorial work at e-arketer, got pulled into PR, and then did corporate marketing for agencies like 360i under Dentsu. Later, I switched to the tech side at larger companies like Sisimos and Media Ocean.

1:43 [David]: I also worked at several 10-person startups, gaining diverse experience while building communities. It’s always been a blend of business and passion.

1:43 [Apil]: Great news—I understand you recently joined as Chief Community Officer at Marketecture Media following the acquisition of your previous startup. Tell us about that journey.

2:07 [David]: I’ve been building the Serial Marketers community for seven years and launched the AI Marketers Guild about two years ago to help marketers connect and understand how AI impacts our field.

2:28 [David]: I knew I couldn’t achieve my roadmap alone. I’ve benefitted from incredible mentors and wanted to work with people eager to invest in growing our communities and delivering value to our thousands of members worldwide.

2:52 [David]: I was looking for a team to help execute events and offerings for our global community. When March and the crew—Eric Paparo, Jeremy Bloom, and Adtech God—came together, I knew I had found an amazing group to collaborate with.

3:18 [David]: I’m excited to work with them every day.

3:18 [Apil]: It’s always great doing this together when you’re aligned on the same vision. Now, David, you’ve long been a voice in marketing and technology. How has your perspective shifted now that you’re operating with a B2B media company?

3:48 [David]: For me, March company has been a long-time reference—with numerous newsletters and podcasts. Early on, in my talks with Ariel and the team, we realized our audiences weren’t truly connecting.

4:09 [David]: Their network is deep in advertising and ad tech, while my communities consist more of brand and agency marketers. The synergy even had you as the first guest on the rebooted video series—we should have done that years ago.

4:33 [David]: I still consult and give talks based on my book, and now I’m exploring ways to integrate our content with the incredible events they’re planning for later this year.

4:55 [Apil]: There’s so much we can now accomplish without bombarding everyone with multiple subscriptions—the door is wider open. Now, David, when you started with the AI Marketers Guild, you witnessed rapid changes in the marketing AI space. What do most marketers get wrong about AI?

5:21 [Apil]: With so many tools available and endless copy-and-paste guidelines, what is it that marketers often misunderstand?

5:49 [David]: I believe the biggest mistake is not embracing failure as a learning opportunity. There are two types of failure: one that paralyzes you and one from which you learn and grow.

6:12 [David]: Many initial Gen AI tools were novel but ineffective. We’re now seeing a similar wave in video—expecting Google's new VO3 to produce feature-quality video without the human touch just won’t work.

6:40 [David]: It takes many human hours to craft a great story. Marketers burned by early iterations—like those who posted on Facebook 15 years ago and felt ignored—experience this cycle repeatedly.

7:04 [David]: Facebook didn’t stand the test of time. We’ve seen similar cycles with web 3 and other trends. With AI changing so quickly, it's tough; there are always 20 new things on my list, yet real priorities must be managed.

7:29 [Apil]: It’s good to know I’m not alone in this struggle. With new shiny things emerging daily, what do you see as the role of a marketer in the next two to three years?

7:54 [David]: The shift is from tactics to strategy. As I discuss in my book, you must be a better editor than just a writer—not only in writing but also in design and data science.

8:22 [David]: The real opportunity lies with those who ask deep, probing questions and extract meaningful insights, rather than those who simply input a prompt and accept the output at face value.

8:48 [David]: These new skill sets are essential. I almost pursued journalism early on because I deeply respect the dedication it takes to master a craft.

9:15 [David]: I have tremendous respect for great craftsmanship. Now, I’m returning to the investigative rigor of 20–25 years ago—I need to be an investigative reporter of my own content, rather than just churning out the next best piece.

9:38 [Apil]: You’ve witnessed the evolution of marketing up close. How would you compare the early dot-com era to today? How exciting was it then versus now?

10:11 [David]: It’s nostalgic thinking back to simpler times—like when the US had just three major TV networks. Today, we’re diverging in two directions: on one hand, AI and machine learning empower personalization like never before.

10:35 [David]: On the other hand, these once-exclusive tools are now democratized and available at low cost, leading to a "tragedy of the commons" where everyone uses the same methods.

11:02 [David]: Tools that were once enterprise-grade are now free or only a few dollars a month. When everyone employs the same tactics, banner blindness sets in—people simply ignore them.

11:27 [David]: Even when someone reaches out on LinkedIn with a well-crafted angle, many tune it out. The erosion of attention spans is a major challenge.

11:47 [Apil]: Let’s talk about your book, The Non-Obvious Guide to Using AI for Marketing. How much AI did you use in writing it?

12:15 [David]: I set a firm rule: no AI would write the text—I wanted it entirely original. However, AI helped during the planning phase; I used it to refine content from past articles and presentations for the book proposal.

12:41 [David]: In the final week before the first draft was due, I used Google’s Notebook LM extensively. I asked it to compare pros and cons of reordering chapters, which helped shape my editorial decisions before submission.

13:05 [David]: It served as an invaluable editorial partner. Although I didn’t expect to use it so much in the final week, it was incredibly helpful.

13:28 [David]: I now also find it useful for preparing presentations based on the book—it helps me recall and repurpose my previous work effectively.

13:49 [Apil]: It’s impressive how AI can speed up the process with great formats. Congratulations on the book, David. Now, considering your role as Chief Community Officer, I don’t believe AI can replace genuine community building.

14:16 [Apil]: The role of community building is irreplaceable by AI. Human relationships are essential, and as tools become ubiquitous, that personal touch becomes even more valuable.

14:42 [Apil]: Look at Starbucks—they scaled back automation because even a barista who misspells your name adds to the culture. That human touch is priceless.

15:04 [Apil]: AI isn’t going away unless we face an apocalyptic scenario. Just as people tire of online dating and crave in-person connection—even with all its imperfections—authentic human interaction will always be treasured.

15:29 [Apil]: The genuine conversation that goes beyond a profile is becoming increasingly valuable. As Newton’s law reminds us, every action has an equal and opposite reaction—and that might be a positive outcome of all this change.

15:52 [Apil]: David, I’d love to dive deeper into this conversation in a longer format next time. What does the word “bold” mean to you?

16:25 [David]: Bold means challenging the status quo and doing what hasn’t been done before. Personally, my boldest moments occur when I push myself into discomfort—it’s like climbing a mountain that expands your horizons.

16:50 [Apil]: I agree. When fear creeps in, that’s the moment to say, “I’ll do it, no matter what.” David, it’s been great seeing you. Always be bold, and congratulations on your exciting new chapter. I wish you great success, and I look forward to breaking bread together in New York or elsewhere.

17:13 [David]: Thank you. Until next time—be great and be bold, my friend. Take care. [Music]

## Faster Smarter Scalable Creative Inside BCMs Ventas AI-Powered Workflow

Speaker: Max Cammarota
Published: 2025-06-13
Tags: ai in marketing, performance media agency, ai powered workflow, ad tech
Video: https://www.youtube.com/watch?v=iUlztmuA7GE
Page: https://aimarketersguild.org/sessions/faster-smarter-scalable-creative-inside-bcms-ventas-ai-powered-workflow

In this session, David Berkowitz and Max Cammarota from BCM discuss the Ventas AI-powered workflow that scales creative production for performance marketing.
They detail how advanced AI tools and a structured creative process can generate diverse ads, optimize performance, and accommodate platform-specific requirements while ensuring brand consistency.

0:05 – David:
"Hello everyone, I'm David Berkowitz. Welcome back to another edition of AI Insiders by AI Marketers Guild. I'm excited to be here live with the team from Marketers Guild and our featured speaker today. Max Cammarota from BCM has long been an active supporter and advocate of our community. I've learned so much from him—even from our first meeting two years ago. Today, I’m thrilled to have him share his insights with us."

1:10 – David:
"John introduced us, and I've learned a lot from both him and Max. Max, you were on a panel with me in April, so please share what you’re doing. I’m excited for everyone in the community to benefit from today’s discussion. Welcome, Max."

2:16 – Max:
"Thanks for having me. We appreciate the partnership and the work we’ve done together over the past months and years. Can you see my screen? Here we go. Today, I'll talk about Ventas AI—a creative workflow powered by AI."

3:26 – Max:
"Ventas AI is an AI-powered workflow to develop creative. We leverage the latest AI technology to enhance our creative process, making it faster, smarter, and more efficient. With it, we create high volumes of diverse ads that fuel platform algorithms. Business growth requires expanding channels, and each channel’s algorithm performs better with diverse creative. Traditional production couldn’t keep up, and that’s why Ventas AI was born."

4:28 – Max:
"Here are some results: We've launched thousands of ads since 2022 with a 100% client retention rate, won the Newsweek AI Impact Award for marketing, reduced cost per lead by 66%, achieved ROAS hikes above 150%, and boosted business revenue by 15% year-over-year."

5:32 – Max:
"In 2022, we faced challenges as paid search became competitive and expensive. We needed to expand channels to reach new users effectively, but producing a high volume of varied creative at scale was costly. This challenge led to the creation of Ventas AI. I remember presenting these challenges to our partners at BCM in Stamford, Connecticut. I pitched a creative process that later evolved into Ventas AI."

6:33 – Max:
"The algorithms of these platforms are evolving quickly. User activity triggers the system to retrieve relevant ads from a vast corpus of creative materials. Instead of a prescriptive AB testing approach, we now create many ad variants through multivariate testing. This allows the algorithm to distribute spend effectively and improve overall performance."

7:39 – Max:
"In our previous strategy, we prescribed targeting and relied on AB testing for the best ad. Today, we feed algorithms a diverse set of messages, allowing them to determine the best performing combinations. This leads to continuous improvement in both middle-funnel metrics, like clickthrough rates, and conversion rates."

8:47 – Max:
"The creative bottleneck was always an issue because traditional production couldn’t meet the scale needed. We designed Ventas AI to accelerate market entry, continuously refresh creative, and test new ideas quickly. Speed is key; after identifying a winning ad, waiting 8 to 12 weeks for production wasn’t feasible."

9:47 – Max (responding to a question from Mike):
"Our platform works by pulling individual elements—post copy, images, designs—in isolation. We then combine them into various formats. For regulated industries like healthcare, we incorporate human checkpoints to ensure all produced creative is preapproved and brand compliant."

10:51 – Max:
"In terms of generating posts for various social platforms, Ventas AI handles both situations. We use generative AI to create copy and imagery and also manipulate existing client assets to fit our creative formats."

11:53 – Max:
"Approvals happen in isolation to avoid production bottlenecks. We also utilize final ad banks to give our clients transparent views into what is going live, ensuring quality at scale while maintaining speed."

12:59 – Max:
"Brand voice is maintained by training our system with brand guidelines. Human checkpoints ensure that creative output remains on brand. If generative outputs do not meet the brand standards, we seek alternative assets, including client-supplied or stock imagery."

14:02 – David (interjecting a question from Mike):
"Are you configuring the system using approved content libraries, or do you iterate based on client examples?"

15:09 – Max:
"It’s two-part. The platform pulls approved elements and recombines them into ad variants. We work closely with clients, especially in highly regulated fields, to ensure all messaging is preapproved and compliant."

16:12 – Max:
"Our system is flexible. For each medium—whether static posts, video, or other formats—we either create outputs using generative AI or adapt existing assets as needed. This provides versatility across platforms."

17:13 – Max:
"Regarding final content approvals, while there is a human approval process for isolated elements, final ad approval is expedited since many individual elements have already been preapproved."

18:19 – Max:
"As for maintaining brand ethos, we train our AI to emphasize brand consistency across all creative assets. The system is designed to create varied outputs that all convey the required brand messaging."

19:27 – Max:
"We also ensure that if our generative AI doesn’t produce the desired results—for example with images—it’s supplemented with stock or client-supplied photos. Our goal is always to achieve on-brand, varied, and high-performing creative."

20:36 – Max:
"Next, let’s discuss consumer research and persona building. Our workflow begins with importing first-party data or survey results to create detailed consumer profiles. Predictive modeling helps us generate around 15,000 insights related to values, motivators, media habits, and purchase behaviors."

21:38 – Max:
"Once we understand our target personas, we use generative AI to produce multiple copy and imagery variations that resonate with each segment. These generated outputs are then preapproved through our internal process before being tested in the market."

22:47 – Max:
"Our next step is pre-launch optimization. Using predictive AI tools, we simulate ad performance before launch, scoring each creative. This process gives us a head start by optimizing headlines, readability, and calls to action based on historical ad data, achieving up to 89% accuracy."

23:45 – Max:
"After launching, our system continuously monitors performance. Computer vision analyzes image elements and text while tagging styles and persuasive techniques. These insights are fed back into the loop to further refine the creative in real time."

24:45 – Max:
"I want to break down the three main AI technologies in our workflow: generative AI (to create copy, images, and video), predictive AI (to score and optimize creative before launch), and computer vision (to analyze and tag ad components for further insights)."

26:53 – Max:
"One of the most exciting aspects is that even if you’re a small agency without proprietary tools, you can curate your tech stack from available AI solutions. Our role has been to research and bundle the best tools into our workflow so that it continuously evolves as new technology becomes available."

29:06 – Max:
"While I can’t reveal the specific companies in our tech stack, its design is proprietary. The stack is dynamic and adapts as AI advances, ensuring that our creative optimization process always remains cutting edge."

30:09 – Max:
"Imagine a future where API-level integration and autonomous agents coordinate across different AI tools. That is where Ventas AI is heading—fully automated creative production that continuously adjusts based on performance data."

31:15 – Max:
"Now, I’ll share an example from pre-launch optimization. A client initiated a design for a report download. The original creative scored 74. After implementing recommendations—such as larger headlines, improved readability, and a bolder call to action—the score improved to 80, resulting in a 27% increase in click-through rate and a significant drop in cost per download."

32:19 – Max:
"Another example: Our computer vision analytics identified that images featuring groups of people had a 9.2% predicted impact on conversions. Based on that insight, we produced more ads featuring group travel, which then contributed to a 66% drop in cost per lead."

33:20 – David (asking):
"Regarding creative specifics—when AI identifies elements like group travel or a subtle design tweak—how universal are these rules across campaigns? For example, if the AI spots that repositioning a download button works, is that applicable for every campaign?"

34:23 – Max:
"The insights serve as guidelines. While certain principles, such as larger headlines and clear calls to action, hold true generally, each campaign is unique. Our system detects nuanced differences through granular testing, enabling adjustments tailored to each client’s audience."

35:26 – Max:
"The AI continuously suggests variations—from facial expressions (like smiling versus frowning) to subtle changes in imagery. Even elements as minor as the color contrast on a download button can have a significant impact. The system reveals these observations, letting us iterate rapidly."

36:28 – Max:
"Each new ad batch incorporates these insights. By testing even small adjustments, we learn more granularly about what impacts performance. The AI’s ability to suggest these tweaks quickly is one of the most powerful aspects of Ventas AI."

37:35 – David (commenting):
"I see this system adapts creative elements rapidly. It even helps you notice things you might otherwise miss, like a barely visible download button. Could you elaborate on how such insights become part of the creative cycle?"

38:41 – Max:
"Absolutely. The system aggregates data on ad performance—copy style, image composition, even the prominence of key elements. It then identifies trends and recommends replicating successful attributes across variants. Over time, these adjustments become part of our best practices for each campaign."

39:38 – Max:
"These tools constantly evolve. For instance, our scoring mechanism, which initially focused on obvious metrics like image area, has grown more sophisticated. Now it also analyzes copy, tone, and other subtle creative factors, ensuring continuous improvement in ad performance."

40:43 – Max:
"Questions often arise regarding the future of AI-assisted creative. There is potential for autonomous creative decisions where machines learn and evolve without constant human intervention. However, human oversight remains essential to ensure alignment with strategic brand messages and to make final adjustments."

41:46 – Max:
"Even in tasks like optimizing a download button or adjusting facial expressions, the AI offers valuable insights. The more ads we test, the better the system becomes at detecting minute improvements. This rapid testing drives efficiency and performance."

42:48 – David:
"How does this approach differ when adapting creative for different platforms—say, Meta versus Pinterest or LinkedIn?"

44:01 – Max:
"Each platform has its own best practices. Our system is trained to generate content that meets the specific requirements for each platform. For instance, ads on Meta have character count and layout constraints that differ from those on display networks. We adjust copy, imagery, and even light motion elements accordingly to maximize performance."

45:08 – Max:
"Sometimes the same creative may be adapted across platforms; other times, a tailored approach is necessary. Our workflow supports both scenarios, ensuring the content is optimized for each platform’s unique environment."

46:10 – Max:
"Here are some examples: This modular design allows us to adapt headlines dynamically and vary images based on the target program. For a campaign in horse and large animal medicine, we produced variants with different images and a distinct yellow tag to highlight specific programs."

47:19 – Max:
"Another example is a campaign for exploring locations like Machu Picchu, where we developed unique variations for each of the 20 different locations the client offers. We also generate creative using generative AI—sometimes producing entirely synthetic images that diffuse a boardroom setting or keynote speaker setup."

48:19 – Max:
"In some cases, we also integrate light motion or even video elements depending on the platform and creative strategy. The system’s flexibility enables both static and dynamic formats as needed."

49:28 – David:
"Are you extending these capabilities to search ads, in addition to paid social and programmatic channels?"

50:32 – Max:
"Yes, we are expanding Ventas AI to cover search ads. Many of our current tools focus on paid social and programmatic, but we’re actively integrating copy generation and optimization for search campaigns as well."

51:35 – David:
"Do you still incorporate human intervention during ad creation if something appears off visually or stylistically?"

50:32 – Max (continued):
"Absolutely. While the AI drives the workflow, human oversight ensures that creative output meets quality standards. We often validate AI recommendations and intervene when necessary, especially because this is performance-driven marketing."

51:35 – David:
"There's a debate about performance marketing versus branding. Some argue that focusing solely on lower-funnel performance might risk brand identity. How do you address that balance?"

52:38 – Max:
"Our focus with Ventas AI is indeed performance marketing—driving specific actions like conversions. However, performance marketing also influences brand perception. We maintain quality and brand consistency through rigorous creative reviews and by aligning all outputs with the brand’s established voice."

53:47 – David:
"Branding always plays a crucial role. Your performance efforts inevitably shape brand identity. It’s a calculated risk, blending immediate performance with longer-term brand equity."

54:50 – Max:
"Our system is designed for auction-based media, where creative performance directly affects exposure. By aligning our creative to the platform algorithms, we ensure that performance enhancements simultaneously bolster brand presence."

55:56 – David:
"Thanks for all the education and inspiration, Max. You've opened up new ways of thinking regarding creative optimization. I appreciate the detailed breakdown and real client examples that illustrate Ventas AI’s impact."

Closing Remarks – David:
"Thank you, Max, Stuart, and the team for contributing to this discussion. We’ll see you in the community and at our upcoming sessions, including a showcase with Textbook Ventures. Thanks everyone for joining today—see you next week!"

Additional Comment – David (in chat):
"One viewer asked, 'When will this video be available?' It will be posted by the end of the week. Thank you all for your engaging questions and comments!"

## Chris Heuer Personal Intelligence - Your New Economic Asset in the AI Revolution  AI Insiders

Speaker: Chris Heuer
Published: 2025-06-06
Tags: human centered ai, physcology, digital twin
Video: https://www.youtube.com/watch?v=9DmFnAPcTcs
Page: https://aimarketersguild.org/sessions/chris-heuer-personal-intelligence-your-new-economic-asset-in-the-ai-revolution-a

(00:06) David Berkowitz
Welcome back to another edition of *AI Insiders* by Marketers Guild. Though I’m wearing a shirt from our sister community, Serial Marketers, we’re all friends here. I’m thrilled to see folks from overlapping communities joining in. Community is a great way to introduce today’s speaker: Chris Heuer.

I’ve learned so much about community building from his work—especially transforming Social Media Club into an international movement in over 350 cities. That platform enabled so many social media marketers and executives to connect and learn.

Chris has continued to evolve, teach, and generously share his insights. We’re lucky to host him today. Welcome, Chris—what are you up to these days?

(01:23) Chris Heuer:
Thanks, David. It’s great to be here and share an ongoing story. Today, my talk covers **artificial employees** and **personal intelligence**—viewed through a distinctly human-centered AI lens. We often discuss human-centered AI but rarely see real examples. I’ll show how to shape AI with human centricity in mind.

My view stems from experience: I’ve witnessed four major socio-economic shifts:

1. The 1970s gas shortages and transition from manufacturing to fast-food-driven service economies.
2. The 1980s birth of the personal computing era: service to information economies.
3. The ’90s internet-driven “network economy.”
4. Now, what I call the **AI era**—a new wave of transformation.

Through the Team Flow Institute, we’ve explored how human work evolves with AI. We realized that organizations must first learn to work better together—before adding AI. Many companies, driven by quarterly earnings, look to replace people instead of augment them.

We’re back at a familiar pattern: we **overestimate short-term impact** and **underestimate long-term impact**. But outcomes depend on choices we make. I see a way forward where **personal intelligence**—a curated form of one’s cognitive and experiential assets—can become a new economic asset, augment human capacities, generate income (even passively), and reshape how we work.

(06:45) Chris Heuer (cont.):
Decades ago, marketers used early AI forms like collaborative filtering. I saw early AI developments firsthand—at IBM (working with Watson) and later at Google Launchpad (with Peter Norvig). It became clear: COOs and CFOs tend to focus on cost-cutting, frequently deploying AI to replace staff rather than empower them.

But massive value lies in augmenting human intelligence—not replacing it. **Personal intelligence** equips individuals to better contribute, adapt, and contribute as walking, thinking assets throughout their careers.

This prompts two crucial questions for leaders:

1. Are we framing AI as **humans + AI**, or as a competition?
2. Can we restructure organizations to integrate AI **without dehumanizing** their people?

Furthermore, we need to identify personal intelligence as a licensable asset—something people own, control, and monetize.

(12:08) Chris Heuer (cont.):
Let me define **personal intelligence**. It’s your existing human intelligence, refined, detailed, and elevated. GPT‑style systems can store your preferences and conversation context—but they **own that data**, not you. Personal intelligence, built by you, is **your intellectual asset**.

Layers of intelligence:

* Human intelligence: knowledge, context, skills
* Emotional intelligence: self-awareness and emotion regulation
* Relational intelligence: interpersonal interactions and co-elevation—when others prosper, you do too
* Team intelligence: coordinated organizational understanding

Personal intelligence forms the atomic unit of these higher levels. Developed carefully, it can become a company-ready asset.

(16:02) Chris Heuer (cont.):
To help shape this AI-human future, I invite three commitments:

1. **View AI as partnerships**—*human + AI*, not *versus*.
2. **Transform systems with dignity**—design roles and workflows that respect people.
3. **Treat personal intelligence as a personal asset**—your data, voice, and intent should be owned, curated, and monetized.

I’m currently writing a book and developing a market map of supporting technologies. Recently, we saw companies deploying **AI teammates**—sometimes called “digital twins” (inspired by Reed Hoffman’s “ReadAI”). These twins mirror human style and cognitive patterns.

**Important note**: Misinforming occurs easily—digital twins often hallucinate. Proper curation and multi-faceted identity design help maintain fidelity without over-anthropomorphizing.

(22:41) Chris Heuer (cont.):
My own prototype: [Delph.com](https://delph.com), where I uploaded my blog and documents. Instead of dumping everything into the system, better practices (like **Puraphina**) support curated databanks—structured, AI‑ready, and focused. The result: more accurate, efficient outputs—no bloated data.

(24:31) Chris Heuer (cont.):
Consider use cases:

* **CMO proxy**: Create a curated intelligence agent to simulate a marketing leader—useful in pitches or internal brainstorming. It’s not exact—but it's **calibrated trust**.
* **Meeting proxy**: Instead of generic meeting bots, use your personal intelligence to attend and summarize meetings relevant to your needs.

These tools give back time: they draft content, refine presentations, and boost creativity—but still need human insight for context and meaning.

(29:54) Chris Heuer (cont.):
**Artificial employees**: generic company-owned AI systems.
**Personal intelligence**: your own curated asset.

Employment laws currently grant companies ownership over work created in-house. They could use that data to replicate you—and possibly operate without you. But this overlooks **human unique contribution**. Think of it like the early auto industry: workers needed money to buy cars. Likewise, as AI grows, people must earn ownership and compensation for their personal intelligence.

I propose a **Personal Intelligence License (PIL)**—analogous to college athletes’ Name, Image, Likeness (NIL) rights. It’s time to recognize and allow monetization of personal knowledge-work assets.

(34:36) Chris Heuer (cont.):
Economists talk about land, labor, capital—and now data. I argue: **personal intelligence** is a distinct factor of production. I’m seeking collaborators, case studies, and technology partners to build this movement.

(36:11) Chris Heuer (cont.):
I invite your input. If you're exploring this in your team—let's connect. You can reach me at `chris@tfi.e`.

**(37:13) Moderator:**
Chris—impressive! Thank you. Let’s open up for questions. Lisa?

**(37:54) Lisa:**
I love the concept. But there's a potential **digital divide**. People with longer public records (like TikTok creators) may have rich ingestible content, while others—especially those who worked inside orgs—may not. How do you address this gap?

(38:33) Chris Heuer:
Great question. My LinkedIn post described a “co-curation loop.” Individuals can group around shared topics—forming a guild AI. You don’t need massive personal archives to participate. Curation matters more than raw volume—highlighting relevant content over noisy data. Even lesser-public figures can build effective agents via strategic ingestion of curated knowledge and bookmarks.

(41:28) Moderator (follow-up):
How did you build Delph?

(41:28) Chris Heuer:
Delph ingests from Google Drive, OneDrive, blogs, social media. Tools like **Kurator** help curate content before ingestion—providing structure and focus. This layered curation dramatically improves accuracy and relevance.

(42:05) Audience comment:
Building your own knowledge base reveals AI's strengths and limitations—much more than generic GPT usage. It's a key learning experience.

(42:42) Chris Heuer:
Exactly. AI’s real power lies in the questions we ask. Introspection, multi-prompting, and human intervention sharpen results. Tools (GPT, Claude, Gemini) all use multi-stage prompts. We must understand and shape those layers thoughtfully.

(46:11) David:
Chris, a great point. Another audience question: “There are three things—the story in my head, yours, and what’s between us. How do we align these?” Any frameworks?

(47:16) Chris Heuer:
This is relational intelligence. Don Miguel Ruiz's *The Mastery of Love* highlights the six personas in relationship: how we see ourselves and others. For marketing teams, managing this complexity requires emotional maturity, clarity, and frameworks like Team Flow:

* Collective ambition (values, mission)
* Audacious goals aligned with personal goals
* Transparent communication and psychological safety
* Mutual commitments and clarity on roles/strengths
* Adaptability, curiosity, and critical thinking
* Leading with empathy and integrity

Culture matters. Teams with psychological safety and AI dividends—reinvesting time savings into well-being—can create thriving environments.

(55:12) Chris Heuer (closing):
When we value humans, AI becomes an opportunity—not a cost. Thank you for engaging so deeply. Let’s make this actionable and co-create a better day after tomorrow.

## Vibe Marketing 101 Build Your Own Voice AI Agent in Minutes

Speaker: Daksh Sharma
Published: 2025-06-03
Tags: ai agents, ai voice agents
Video: https://www.youtube.com/watch?v=V53sTGvb5j8
Page: https://aimarketersguild.org/sessions/vibe-marketing-101-build-your-own-voice-ai-agent-in-minutes

Nicola Quail (AI Marketers Guild APAC co-founder) introduces the session, highlighting their mission to build an AI-focused marketing community across Asia Pacific. She introduces Daksh Sharma, Director of Ipit, who specializes in AI-driven marketing.

Daksh explains how to build a Voice AI Agent using the WPI platform, focusing on empowering marketers to create automated voice agents that handle calls, answer questions, and transfer calls. He demonstrates creating a simple demo where a voice agent calls a user, answers company-related queries, and can transfer the call to a human.

Daksh walks through:

* Setting up a Voice AI Agent in WPI.
* Creating a knowledge base using ChatGPT and the company’s website.
* Configuring system prompts to control the agent’s tone and style.
* Using text-to-speech engines like ElevenLabs.
* Setting up call forwarding and phone number linking.
* Cost considerations and model configurations.

He also shares how to connect Lovable (a landing page tool) with WPI using API keys and form submissions. This no-code approach empowers marketers to build Voice AI agents without developer help.

(00:09)

**Nicola Quail:**
Great to have everyone joining us again. For those here for the first time, I'm Nicola Quail, co-founder of AI Marketers Guild APAC, alongside my colleagues in Singapore (Thru) and India (Daksh and Anadi). We're excited to see this community expand across Asia Pacific. Before I introduce our guest speaker, I’d like to give a quick background on who we are.

AI Marketers Guild was the brainchild of marketer David Berkowitz, and since its launch two years ago, it has grown into a significant community in North America. We wanted to build a regional community showcasing local AI pioneers, marketing best practices, and world-class AI thought leaders through monthly webinars. Rather than just dabbling, we're thrilled that everyone here is diving in.

Speaking of AI thought leaders, I'm very excited to introduce our next speaker and colleague, Daksh Sharma. Daksh will share more about himself, but he's the Director of Ipit, a global digital marketing agency that places AI at the center of its services. He's also a member of the Forbes Agency Council and deeply involved in AI and AI agents.

Today's session will introduce the concept of Vibe Marketing—something that took me a moment to understand when I first heard it—but it’s about simplifying your day-to-day tasks, building deeper brand connections, and exploring the future of marketing. Imagine having a marketing department of one! I’m looking forward to learning more, so I’ll hand over to Daksh now.

(00:09)

**Nicola Quail:**
Great to have everyone joining us again. For first-timers, I’m Nicola Quail, co-founder of AI Marketers Guild APAC, alongside Thru in Singapore and Daksh and Anadi in India. We’re thrilled to expand our community across the Asia Pacific.

AI Marketers Guild, founded by marketer David Berkowitz, started two years ago in North America and has grown significantly. We launched this regional community to showcase local AI pioneers, marketing best practices, and world-class AI thought leaders through monthly webinars. You’re all leaning in, and that’s fantastic.

Today’s speaker is Daksh Sharma. Daksh is Director at Ipit, a global digital marketing agency that centers AI in its services, and a member of the Forbes Agency Council. He’s deeply engaged in AI and AI agents. Today, he’ll introduce us to Vibe Marketing—a concept that simplifies daily tasks, fosters deeper brand connections, and signals the future of marketing. Imagine a one-person marketing department powered by AI. Over to you, Daksh!

(02:00)

**Daksh Sharma:**
Thank you, Nicola. Today’s session is a Vibe Marketing 101. No boring slides—we’ll jump straight into a live demo. You’ll learn how to build a Voice AI Agent yourself, without developers or professionals. Over the next 15–20 minutes, we’ll guide you through the process.

(03:19)

**Daksh Sharma:**
A Voice AI Agent is an AI that speaks like a human and handles business queries. In our demo, we’ve created a form for Insights Exchange. When someone enters their phone number and email, the AI agent calls them.

(03:54)

**Daksh Sharma (demo):**
Here’s the form—enter your number, email, and company. Instead of an automated email, you’ll get a call from the AI.

*(Submits form.)*

You: “Can you say your name once more?”
AI: “Owen. How can I assist you today?”
You: “Tell me about Insights Exchange.”
AI: “Insights Exchange, founded by Nicola Quail, delivers meaningful insights through research and data analysis. We help businesses grow with insightful data.”
You: “Can you tell me more about Nicola?”
AI: “Nicola is originally from New Zealand, with a rich background in market research and data analysis. She’s passionate about helping businesses grow through insightful data.”
You: “Can you transfer the call now?”
AI: *(Transfers the call to a human agent.)*

So the AI agent “Helen” called, answered questions, and transferred to human support when needed.

(06:00)

**Daksh Sharma:**
Now, I'll show how you can build this using WPI (the voice AI platform). Let’s log into WPI and go to the dashboard.

*(Shows WPI interface.)*

Our assistant, “Insights Exchange Assistant,” is trained to answer questions on behalf of the company. The first message is a dynamic variable: “Hi, {name}, this is Helen. How can I help you today?” That pulls the prospect’s name from the form we submitted.

(07:48)

**Daksh Sharma:**
When training the agent, we treat it like a person: we specify tone, style, response guidelines, and ask for natural pauses to mimic human speech. We also include fallback instructions: if the agent doesn’t know an answer, it apologizes and invites a human to step in.

(08:49)

**Daksh Sharma:**
To create an agent, click “Create Assistant” and choose a template—blank or guided. The system prompt defines how the assistant behaves, paired with a knowledge base that holds company information.

We built our knowledge base from Insights Exchange’s website using ChatGPT. That defines what the agent knows.

(10:27)

**Daksh Sharma:**
Next, choose the OpenAI model (we use GPT‑4o). You can see cost (\$0.12 per minute) and latency based on model, voice, and transcription. Adjust the temperature to control creativity.

(11:36)

Daksh Sharma:
Select a text-to-speech engine—like ElevenLabs, which offers a human-like voice. If cost is a concern, choose Cartesia, WPI voice, or Play.ht. Recap: we’ve configured the system prompt (instructions), knowledge base (info source), voice engine, and temperature.

(12:42)

**Daksh Sharma:**
You can also integrate tools—like sending emails, SMS, calendar booking (e.g., with Cal.com)—and setting up call forwarding. In the demo, the AI transfers calls based on specified numbers.

(13:41)

**Daksh Sharma:**
Link the assistant to a phone number. This ensures inbound callers hear the agent’s voice (e.g., Helen). WPI allows you to assign a dedicated voice agent number easily through the dashboard.

(16:17)

**Participant (Nicola):**
What other factors influence cost besides latency?

**Daksh Sharma:**
Let’s break it down. WPI charges \$0.05 per minute, plus the model’s cost, speech-to-text, and text-to-speech engine—with TTS being the largest share.

(16:59)

**Daksh Sharma:**
Some asked about HeyMira—it’s a real estate voice tool, but we’ll cover that in another session.

(17:57)

**Daksh Sharma:**
What about the system prompt and knowledge base? All of that was created with ChatGPT. I told ChatGPT: “Create an inbound voice agent on WPI for Insights Exchange.” It generated the knowledge base, prompts, and integration instructions. I refined the prompt for a more natural tone.

(20:14)

**Daksh Sharma:**
The key to a great voice AI agent is the system prompt. You test, refine, and adjust it until the speech sounds natural—not robotic. You configure speech settings, test the agent, and iterate.

(20:46)

**Daksh Sharma:**
How did I create the form? I used ChatGPT to generate code for Lovable.dev, a landing page tool. I prompted ChatGPT to create a form that triggers WPI on submission. Then I connected a custom domain and set up the API call to WPI with assistant ID, API key, and phone number ID. No database required—Lovable handled the integration.

(22:02)

**Daksh Sharma:**
So, in summary:

* Use Lovable and WPI with ChatGPT to automate the integration.
* Fill the form → trigger WPI call.
* ChatGPT did the heavy lifting—form code and prompt generation.
* WPI handles calls; Lovable triggers the call.

(23:14)

**Participant (Sushita/Shubham):**
How did you connect Lovable to WPI? What code did you need?

**Daksh Sharma:**
I’ll share screens and prompts after the session, including the assistant ID, WPI API key, and phone number ID. You pass these in the form prompt. In a real setup, don’t expose those on the frontend—store them securely.

(25:26)

**Daksh Sharma:**
Once form values and keys are configured, Lovable invokes WPI’s API to initiate calls. WPI charges \$0.05 per call, plus model and speech costs. That’s the pricing structure.

(26:41)

**Participant (Mark):**
How long would it take a newbie to set this up?

**Daksh Sharma:**
With prompts for WPI and Lovable, you can build and test your agent in under a day. Aim to create your own after this session!

About WPI vs. HeyMira: WPI is more general; HeyMira is tailored for real estate voice use.

(27:53)

**Daksh Sharma:**
If anyone wants to experiment, create an account now. We’re here to help in this working session. Future sessions will cover AI-generated animated videos and product videos.

(29:39)

**Nicola Quail:**
Before we close, can you explain Vibe Marketing?

(30:18)

**Sushita (and Nicola):**
Vibe Marketing enables you to create AI agents for all aspects of marketing—creative development, customer service with voice agents, performance marketing, social listening—building a one-person agency powered by AI. It scales your capabilities rapidly, allowing fast testing and iterative learning. It doesn’t replace a human team yet, but it empowers lean teams to do much more.

(32:07)

**Daksh Sharma:**
Vibe Marketing turns you into a specialist with “superpowers”—creating voice AI agents, for example, was impossible three months ago without code. Now, using tools like Lovable.dev, ChatGPT, and WPI, anyone can build their own voice AI agent. This makes you 10x more capable.

(33:20)

**Nicola Quail:**
That’s fantastic. I now have a voice agent! We’re eager to see how this applies to consumer brands or tech brands. We’ll continue exploring other Vibe Marketing agents—like AI-generated videos. Thank you, Daksh and Sushita, and thanks to everyone who joined. We’ll circulate the recording and prompts, and we hope you’ll all try building your own voice agents. Goodbye!

## Strategic AI Custom GPTs  Mitigating Bias

Speaker: Earle Richards Jr.
Published: 2025-06-02
Tags: custom gpts, ai bias, advanced workflows
Video: https://www.youtube.com/watch?v=fmlbzERelzw
Page: https://aimarketersguild.org/sessions/strategic-ai-custom-gpts-mitigating-bias

In this edition of AI Insiders by AI Marketers Guild, the host introduces Earle Richards Jr., who shares his expertise on leveraging AI strategically.
Earle explains how he uses custom GPTs as digital team members for career development, prospecting, and personal growth while addressing the importance of mitigating bias and personalizing AI responses.

0:05 – Host: Welcome to the latest edition of AI Insiders from AI Marketers Guild. We’re getting used to our new name and branding. At a recent event, someone commented that they couldn’t believe we got shirts made so quickly. (They’ll appreciate that story!)

0:32 – Host: I mentioned that I’ve had these shirts for 10 days—they’re already on their next laundry cycle. I promise I won’t wear just one when you see me.

0:58 – Host: Today’s focus isn’t me—it’s on learning from Earle Richards Jr. Many thanks to Mark Goldberg for introducing us. I was impressed learning new insights after just one conversation with Earle—even though I’ve known many community members for 20 years, Earle made an impact immediately.

1:20 – Host: For our newcomers, welcome to the community. Remember, we host these community conversations every week. I told the marketing team these aren’t webinars; these discussions are the foundation for our relationships.

1:44 – Host: It’s great to see familiar faces such as Katherine Montgomery and Jack Myers join us. I’ve mentioned Katherine’s session before—we may need an update on that soon.

2:05 – Host: That said, let’s welcome our guest. Earle, please introduce yourself.

2:29 – Earle: Hi, everybody. My name is Earle Richards Jr. I’m a fan of technology. I was born and raised in the U.S. Virgin Islands and moved to New York after seeing Home Alone—I knew I had to be here.

2:48 – Earle: I’ve been in advertising since college, working on agency, adtech, and publisher sides—always in the realm of data and analytics. This goes back to when we used Urchin before Google Analytics.

3:08 – Host: (Laughing) I haven’t heard that name in a while.

3:26 – Earle: I’ve worked across different verticals, brands, and agencies so I know what works technology-wise. People say I’m just a data guy, but I use data to win arguments—I trained in law for that reason.

3:45 – Earle: Today, I’ll present my approach to AI. David and I connected through Mark Goldberg and discovered we’re all trying to figure out how to use AI. There isn’t one “right” way—if someone tells you otherwise, they might be selling you something.

4:05 – Earle: I’ll show you what’s been working for me. I welcome questions and different perspectives to help me identify any blind spots.

4:22 – Host: I appreciate that you reject the term “like-minded.” It’s about bringing different points of view—such as those shared here from a group of 40 different users of AI.

4:44 – Host: So, Earle, can you tell us how you’re using AI these days and share some of your workflows?

5:10 – Earle: Absolutely. I’ve been curious about AI since I was a kid—after I broke my first computer, I learned how to fix it. When ChatGPT came out, I was eager to experiment and see what it could do.

5:36 – Earle: I’ve used AI to land jobs, reconnect with people, expand my network, and even get clients. The key point is: It’s not about the tool (ChatGPT, Gemini, or Perplexity). It’s about hiring AI as your digital team member.

5:54 – Earle: Back when I was a bellboy in high school, we had a concierge who always said, “I got a guy for that.” Now I say, “I have AI for that.” I ask: What do I want to accomplish and how can I build or use AI to get it done?

6:13 – Host: That’s an interesting mindset—the more you personalize your approach, the more effective AI becomes.

6:36 – Earle: Exactly. Whether you’re looking for a job or new clients, ask yourself: How can AI help accomplish your goals? People range from anxious that AI will take their jobs to overly excited that it will replace them. I prefer to consider AI as an ally.

6:58 – Earle: If you view AI as an enemy, you’ll be fearful. Instead, treat it as an extension of your team to help you achieve specific outcomes.

7:21 – Host: And, speaking of outcomes, I noticed you mentioned bias. How do you address that?

7:46 – Earle: Great question. Bias exists both in the training of AI engines and in the users’ preferences. Whether it’s the bias reflected in data or the bias of the person using it, you must first recognize it. For example, I train my AI to give clear, factual answers because that’s my preferred style.

8:07 – Earle: Different people have different styles (some prefer colorful language). Just as I teach my team how to work with me, you need to “teach” your AI your preferences by establishing guidelines or even user manuals.

8:29 – Host: That makes sense. Could you walk us through your process of prospecting a new client using AI?

8:50 – Earle: Sure. When prospecting, you research the client, identify who you know at the company, and position yourself for success. AI can automate much of that research, pulling together information from various sources.

9:08 – Earle: Think of it this way: If you want a new career or new clients, hire AI—as you would an assistant or expert. I’ve built about 40 custom GPTs for different purposes. For example, if I want to learn a platform like Adobe Analytics or GA4, I create a custom GPT expert on it.

9:36 – Earle: Instead of endless Google searches, you get a condensed, digestible delivery of the information you need.

10:01 – Earle: Then, if you want to use AI for business development, you build a custom GPT with a personality geared for prospecting and sales.

10:21 – Earle: Think of the difference between using cookies and not. Custom GPTs remember your preferences, continually tailoring their output as you interact with them.

10:40 – Earle: For instance, my career coach AI started generic, then I personalized it by adding my resume and work history. Now it can answer detailed questions from recruiters by pulling from a spreadsheet of my career achievements.

11:01 – Host: So it acts like a second brain for you.

11:22 – Earle: Exactly. It helps answer everything from cover letters to analyzing job postings. I advocate for stepping back, understanding your goals, and using AI strategically—whether you need personalized support for career shifts or for getting in shape.

11:43 – Host: (Laughs) There was mention in the chat about using custom GPTs for things like coaching through personal challenges. How far can we push this use case?

12:01 – Earle: People have asked about having different custom GPTs interact with one another. For instance, I built several experts for clients using various adtech platforms—they “talk” to each other to provide comprehensive research.

12:26 – Earle: I like to compare it to my own experience growing up in the ‘80s as an elder millennial influenced by action heroes. You need to decide: do you join Team Terminator or Team RoboCop? I’m on Team RoboCop—using AI as an ally.

12:50 – Earle: When working on prospecting, decide what you need to accomplish and then build your custom GPT with the expertise required. I have one for Google Analytics, another for GTM, and so on.

13:10 – Host: What distinguishes your custom GPTs from generic ones provided by ChatGPT or Gemini?

13:31 – Earle: The difference is personalization. It’s akin to using cookies versus not. The more you interact with and train your custom GPT, the more it learns your preferences. I even have one that’s been trained not to use fluff or overused phrases.

13:51 – Host: Could you give an example from your personal interests?

14:17 – Earle: Sure. I love movies and TV. I created a custom GPT ‘Movie and TV Expert’ that pulls in information from IMDb, Rotten Tomatoes, Fandango, and more. Ask it, “How much money did Home Alone make?” and it provides detailed box office numbers, historical context, and even fun facts like Kevin’s paycheck.

14:38 – Host: Does a generic ChatGPT give you the same detailed response?

15:02 – Earle: It might give a generic answer, but my custom GPT is tailored to my enthusiasm and knowledge level—it gives you that director’s cut answer.

15:23 – Host (Lisa’s question): What’s the source for that detailed information? Do you feed it manually or does it pull from the internet automatically?

15:47 – Earle: I include specific sources and references—IMDb, Wikipedia, YouTube, Box Office Mojo, and others—in the setup for the custom GPT. I compile resources using files, PDFs, and online content so the system can pull accurate, verified details.

16:08 – Host: That explains a lot. How do you handle integrating additional resources like PDFs or wikis?

16:27 – Earle: When building a custom GPT, I upload any relevant resources—be it spreadsheets, PDFs, or reference sites. The system then uses this background to generate responses that are tailored to your specific context.

16:47 – Earle: For instance, when I built my career coach GPT, I added my entire work history, personality test results, and client experience details. This ensures that when a recruiter asks about my expertise, the answer is detailed and personalized.

17:08 – Host: It sounds like you’re using AI as a true second brain.

17:37 – Earle: Yes, exactly. I use it to consolidate information and generate insights for job applications or client proposals. It’s about leveraging AI to accomplish your goals faster and smarter.

18:02 – Earle: I must stress that as you teach your custom GPT, you’re effectively training a team member. Every piece of added detail helps personalize the output.

18:24 – Earle: I even feed it my personality traits (I’m direct, clear, and hate fluff) so that its answers reflect my style.

18:44 – Host: That’s a clear illustration of using AI to reinforce your brand and personal approach.

19:06 – Earle: Exactly. Think of it the way you’d hire someone—what qualities do you need? Then train your AI accordingly. It becomes an extension of you, whether for career guidance, research, or even personal interests.

19:26 – Earle: For example, I built a custom GPT called “G4 Expert” that I use to answer any questions related to Google Analytics 4. I fed it free online resources so that it can serve as a reliable studio assistant.

19:41 – Host: And this customization saves you time, right?

20:07 – Earle: Precisely. Instead of manually searching for answers across blogs and FAQs, my custom GPT aggregates and analyzes the data to give me a concise answer.

20:37 – Host: I see chat users are curious about practical examples in career consulting. Could you detail how you use custom GPTs for that?

20:59 – Earle: Sure. I once built one specifically to analyze job postings. I would copy the job description, and it would break down why the job fits my skills or show what might be missing. It acts as my career consultant—a second brain to review my resume and draft custom cover letters.

21:19 – Earle: I even prepared a spreadsheet listing my clients, platforms I’ve worked with, and years of experience on each. By integrating that data, my GPT can answer even technical questions from recruiters.

21:42 – Host: It’s like outsourcing part of your own knowledge. Do you ever worry about sharing personal data with these platforms?

22:04 – Earle: I work in data, so I understand the risks. It’s a balance between convenience and security. I personally lean toward sharing enough data to get a truly personalized experience while understanding that if someone really wants your data, they can get it.

22:28 – Host: That’s reassuring and honest advice. We all have our different comfort levels with data sharing.

22:50 – Earle: Exactly. It depends on your context—if your work centers on security, you need to be more cautious. But for me, the benefit of working faster outweighs the potential risk.

23:13 – Host: Let’s shift a bit. Some of our participants want to know if people are charging for these custom GPT services. What’s your take?

23:34 – Earle: Yes, I am—and I know others charging for custom GPTs. I always start by understanding a client’s pain points and goals regarding AI use. Custom GPTs can save you thousands on generic coaching by providing personalized, tailored advice.

23:57 – Host: That makes sense. Could you share any price range without revealing too much?

24:19 – Earle: I’m not there yet publicly, but I take inquiries and tailor services based on specific needs. We can discuss details offline if you’re interested.

24:42 – Host: And what about the interface? Does your client interact with a polished app or within the native OpenAI interface?

25:16 – Earle: It depends on the client’s needs. I default to the simplest interface available for ease of use. For high-security clients, integration within internal systems is possible. The important thing is to start where you are comfortable and build it up gradually.

25:31 – Earle: Remember, AI isn’t one-size-fits-all. You need to build, teach, and train it—just as you would coach a new employee.

25:44 – Host: Before we wrap up, I’d like to ask: How can people follow you and continue this conversation?

26:08 – Earle: The best way to reach me is through my Linktree (my contact info is in the description) and my LinkedIn profile. I’m happy to set up one-on-one conversations. Whether it’s for business or personal use, I’m here to help you apply AI in a way that makes sense for your goals.

26:33 – Host: That’s fantastic. I also love how you mentioned reverse mentoring—learning from each other even across generations.

26:55 – Earle: Absolutely. After speaking with some of you, I’ve come to fully embrace my “co-pilot” AI. It helps soften my tone and even offers fresh perspectives I hadn’t considered. I encourage everyone to connect with someone who has a different viewpoint.

27:16 – Host (Amy): I’ve been using AI as a thought partner for a while, and sometimes it challenges my own biases. It’s incredible.

27:38 – Earle: Precisely—AI always answers your question based on the prompt it’s given. That’s why building your own knowledge base is crucial. That way, you know what to expect and can verify the answers.

28:02 – Host: I love that concept of a “second brain.” It reminds me of Thiago Forte’s method of organizing ideas digitally.

28:26 – Earle: Yes, I borrowed that concept. My custom GPTs help me store frameworks, mental models, and insights from books and conversations—all of which gets synthesized into actionable advice.

28:49 – Host: And that is vital in our age of information abundance. Distinguishing the signal from the noise is key.

29:08 – Earle: Exactly. We live in an era where automation and data overflow require critical human skills—like creativity and synthesis—that AI can enhance but not replace.

29:34 – Host: I appreciate your clarity on merging human skills with technological advances.

30:05 – Earle: Thank you. It’s all about using AI to augment what we do best, while never losing our uniquely human touch.

30:27 – Host: Before we finish, any final thoughts on how our community should approach AI?

30:48 – Earle: Remember, it’s not about using a tool—it’s about building a team. Train your AI just as you would an intern, assistant, or a coach, so it truly represents you and supports your goals.

31:10 – Host: That’s truly inspiring. I’m sure many viewers will reflect on how they can personalize AI in their own lives.

31:32 – Earle: I appreciate all the feedback. Whether you’re using AI for career development, client prospecting, or even just exploring personal interests, let it work for you rather than against you.

31:50 – Host: Thank you all for your great questions and comments. I’ll compile the excellent book references and recommendations from the chat and share them with the community later.

32:07 – Earle: Thank you for this engaging conversation. Remember, if you want to learn how to use AI as a personalized thought partner, just reach out. I’m always happy to offer 30-minute sessions to help you get started.

32:35 – Host: And next week, we have Chris Huer coming in from a Teamflow perspective—don’t miss that. We also hope to see some of you in person during Tech Week.

33:12 – Earle: It’s been a fantastic discussion. Thanks to everyone for your questions, insights, and willingness to learn. Let’s keep the conversation going on LinkedIn and beyond.

33:36 – Host: Thank you, Earle, for inspiring us today. Goodbye everyone and see you next Wednesday at noon Eastern!

## Future of AI in Strategic Communication Dr. Karen Sutherlands

Speaker: Dr. Karen Sutherland
Published: 2025-05-14
Tags: ai in marketing, public relations, strategic communication
Video: https://www.youtube.com/watch?v=6ifLhaOvOQM
Page: https://aimarketersguild.org/sessions/future-of-ai-in-strategic-communication-dr-karen-sutherlands

0:08 – Host:
Welcome back to another edition of AI Insiders by Marketers Guild. We have a special guest I’ve been learning from for a while. I was excited to see her latest work, which has been a great source of material.

0:35 – Host:
This will be a treat to share with an audience that has learned from her, as I distilled insights from our community. So, Dr. Karen Sutherland, please introduce yourself. A warm welcome—we’re excited to have you here.

1:01 – Dr. Karen Sutherland:
Thank you for having me. I’m joining from Queensland, Australia. It’s just after 2 a.m. here, so thank goodness for coffee. If I ever looked presentable at 2 a.m., it would be a miracle. I do this often, so I’m getting better at sleeping briefly and then getting back to work. Thank you, and I’ll bring my slides up.

1:29 – Dr. Karen Sutherland:
I’ll share why I’m here and what I’ve been up to. This session is about looking to the future. As a caveat, no one really knows—there’s much speculation—but I’ll share what my research has revealed.

1:54 – Dr. Karen Sutherland:
Over the past year, I’ve been an academic teaching PR, social media, and strategic communication for 13 years, and in marketing and communication for nearly 30 years. I remain an academic and also serve as director of a digital marketing agency alongside my other projects.

2:23 – Dr. Karen Sutherland:
I’m used to little sleep because of my busy schedule. In terms of AI expertise, I’ve completed multiple certifications and studied at the University of Sydney. I’m also co-director of an AI hub in Queensland—I live on the Sunshine Coast.

2:48 – Dr. Karen Sutherland:
I’ve written a book called AI for Strategic Communication—in it, I interviewed David as part of my research. I pursued this because there’s widespread confusion in our field, with many experimenting and claiming expertise.

3:15 – Dr. Karen Sutherland:
While that was fine, I wanted to talk to more people and research what’s happening. This presentation focuses on chapter 12, which looks to the future based on literature analysis, interviews, and my survey. I’ll begin by…

3:42 – Dr. Karen Sutherland:
I interviewed 41 people from various countries—scholars in AI and strategic communication, practitioners, and AI tool developers—to gain insight into how these tools are set up and how they function in our field. Then I…

4:10 – Dr. Karen Sutherland:
Surveyed 400 practitioners across Australia, the UK, and the US, conducted an extensive literature review, and wrote the book. While I hoped it would help others, the adventure of getting up in the middle of the night to speak with people about AI was a real treat.

4:37 – Dr. Karen Sutherland:
In strategic communication, AI is becoming increasingly important. Although adoption rates aren’t very high yet, its use is growing. The best application is as a co-pilot—a collaborator—not to do the entire job, since as communicators we represent organizations.

5:08 – Dr. Karen Sutherland:
Representing a client or organization means we must always maintain our ethics and creativity. Let’s review the predicted stages of AI evolution from the literature.

5:32 – Dr. Karen Sutherland:
There’s much speculation about if or when these stages occur. Some are starting to come to fruition. First, we have the present stage of AI agents. Then AGI is predicted to arrive soon, although estimates vary.

5:58 – Dr. Karen Sutherland:
The literature suggests AGI could arrive any time within the next 50 years, though voices on Twitter (now X) from OpenAI suggest it might be just a few years away.

6:21 – Dr. Karen Sutherland:
Everyone’s vying to be first, and there’s debate about whether they’ve reached that milestone and how to measure it. That stage will unlock further developments that could significantly change our lives—leading to technological singularity, which I’ll explain.

6:47 – Dr. Karen Sutherland:
Following AGI will be Artificial Super Intelligence. We’ll discuss each stage and its potential effects on strategic communication. Right now, we are in the phase of Artificial Narrow Intelligence (ANI), which is task-specific.

7:15 – Dr. Karen Sutherland:
ANI can perform only one task at a time, so it’s very specific about its capabilities. Still, it saves us significant time by generating efficiencies. Anecdotally, I’ve been working with communication agencies integrating AI, and they’re amazed.

7:43 – Dr. Karen Sutherland:
For example, after using a script and then Opus Clip, the video content manager at one agency said it would save days in production. Though quality still requires oversight, the time efficiency is clear.

8:09 – Dr. Karen Sutherland:
Beyond production, AI assists in data analysis by identifying trending hashtags and engagement, providing deeper audience insights—which is crucial for connecting with target audiences.

8:35 – Dr. Karen Sutherland:
These efficiencies have been extremely helpful now. Next is the emergence of AI agents, expected between 2025 and 2030. Even though further development is needed, experiments with tools like Manis show great potential.

9:04 – Dr. Karen Sutherland:
PR professionals see these agents as a way to further personalize communications—a key benefit of AI in marketing. Personalization drives the actions required from a campaign by tailoring messages to an audience’s preferences.

9:32 – Dr. Karen Sutherland:
In a crisis scenario, an AI agent might efficiently gather critical information, allowing the human to focus on setting the right brand tone and securing necessary approvals. It also handles tasks you might not want to do.

10:04 – Dr. Karen Sutherland:
For instance, with a research cluster at my university, I manage our website. I used Manis to analyze our site against various research websites. I simply asked it to identify comparable sites and return recommendations—including a mocked-up webpage with layout suggestions. This task took about 10 minutes, versus several hours if done manually.

10:51 – Dr. Karen Sutherland:
That efficiency means agents can handle many of the tedious tasks, though at the moment they can be hit or miss—sometimes stopping before the job is complete. I’d be interested to hear about others’ experiments with agents.

11:14 – Dr. Karen Sutherland:
Looking ahead, using agents focused on communication tasks—even event coordination with its many small jobs—will make our work much easier. One potential concern is when AI is given access to credit card details to make purchases; this too may be hit or miss at first, but it will streamline over time.

12:06 – Dr. Karen Sutherland:
Next is AGI, widely discussed in both literature and industry. It’s expected around 2030 to 2050, though some predict an earlier arrival. There have been moments when organizations claimed it reached AGI levels, but the results remain inconsistent.

12:34 – Dr. Karen Sutherland:
AGI should perform consistently across all tasks. Yet, while it might pass complex tests, it can fail at simple ones. For AGI to be considered human-level intelligence, it must think critically and deliver consistent performance.

12:58 – Dr. Karen Sutherland:
An interesting aspect of AGI is its ability to self-improve—it learns and self-corrects without our intervention. This is both exciting and somewhat terrifying.

13:22 – Dr. Karen Sutherland:
Imagine AGI translating not just language but entire cultural contexts. Often, translations lose nuance and cultural sensitivity. AGI operating at that level would be transformative.

13:48 – Dr. Karen Sutherland:
This is only one potential use for AGI. Once we achieve AGI, it will unlock further stages—technological singularity—which is expected after 2050. However, if AGI arrives sooner, singularity will follow even sooner.

14:17 – Dr. Karen Sutherland:
At singularity, AI will surpass human intelligence completely, growing exponentially and solving problems we cannot. The challenge then will be maintaining control and ensuring human oversight, especially in crisis communications.

14:46 – Dr. Karen Sutherland:
For example, in an emergency, AI might independently draft evacuation instructions. We would still need human oversight to ensure these actions align with community values.

15:16 – Dr. Karen Sutherland:
This could enable much faster responses in emergencies. Following AGI, we reach ASI—Artificial Super Intelligence—where AI outperforms humans in every domain, including creativity and research.

16:11 – Dr. Karen Sutherland:
Scientists predict ASI will solve problems like climate change and cure diseases—challenges we haven’t yet overcome. We’re still determining what ASI might achieve in strategic communication.

16:40 – Dr. Karen Sutherland:
ASI might even anticipate crises before they occur and respond proactively. How amazing would that be? I’m discussing what the literature envisions for the future.

17:05 – Dr. Karen Sutherland:
The risk, however, is loss of human oversight when AI becomes superintelligent and may outsmart us. We must consider how to maintain control and not become overly dependent on AI for decision-making.

17:34 – Dr. Karen Sutherland:
Now I’ll share some findings from my research and offer considerations for strategic communicators throughout these AI stages. According to my survey, about half of respondents were excited about AI’s future.

17:59 – Dr. Karen Sutherland:
Nearly 44% were optimistic and intrigued, but the main concerns were job losses and overreliance on AI. As an educator, I see students bypassing essential learning by having AI complete tasks for them.

18:24 – Dr. Karen Sutherland:
Many students now skip learning the task and let AI handle it, often with poor results. This trend is even evident among junior staff in strategic communication firms.

18:48 – D:
Are there differences between senior and junior members? I hear senior professionals worry about hiring—if AI writes press releases, how does someone gain field training?

19:15 – D:
Absolutely. This is a real issue. Overreliance on AI could impede career advancement, as organizations might choose it over hiring an actual staff member.

19:38 – D:
Recently, some companies declared themselves “AI first,” which isn’t ideal from a PR perspective. If AI can replace human staff, it may soon become common practice.

20:00 – D:
It’s becoming a reality. Many companies I work with lack clear policies or regular training for AI use. Some say “just don’t use it,” which is unrealistic given the prevalence of shadow AI—staff covertly using it.

20:29 – D:
For example, I knew a junior sales executive working closely with a marketing team on a SaaS product. They were happy with the efficiency—until she noticed LinkedIn posts generated by AI that falsely claimed product features. Clearly, neither the employee nor the AI understood the product.

20:53 – D:
This underscores the need for proper rules about AI use in companies, ensuring there’s always a human in the loop. Misinformation becomes a major risk when AI “hallucinates.”

21:18 – D:
One of the scariest findings from my study was that 40% of respondents rated fact-checking AI outputs as only moderately important, and nearly 50% felt the same about reviewing/editing AI content. Even when I provide a strong prompt, I always need to tweak the result—I never copy and paste without checking.

22:04 – D:
This is an area we must address. For instance, I recently saw an AI-powered news roundup on LinkedIn where a top headline showed the current president signing an order—but then later stated “Biden bans US funding.” The headline incorrectly tagged Biden, even though original sources identified the current president. It’s a clear disconnect.

23:01 – D:
As I’ve said before, it’s “consistently inconsistent.” Sometimes the output is amazing, yet you must always verify that it’s correct.

23:25 – Dr. Karen Sutherland:
I use a RAG chatbot with my own content, which is one of the best ways to understand both the potential and limitations of AI. Because I’m familiar with the input, I can see when it goes beyond my prompt—for example, when I ask for specific references, it may generate two or three responses before interpreting them.

24:00 – Dr. Karen Sutherland:
Since all the content is mine, I know its context well. This allows me to spot when it creates responses that exceed my original prompt. Sometimes, even slight changes in the question cause it to falter despite the information being available. Using AI with your own content gives tremendous insight into its true capabilities compared to using general internet sources.

24:25 – Dr. Karen Sutherland:
Applying AI to proprietary content helps you understand what it does best. I also love Claude—which can be set up to mirror your style since you can train it with your own content. I use Claude frequently for writing, and I totally agree with the benefits.

25:22 – Dr. Karen Sutherland:
In my interview findings, only 22% predicted increased adoption—interesting, given how rapidly adoption is rising. Automation is seen as key for strategic communication, and while personalization percentages were lower, it remains an important theme.

25:46 – Dr. Karen Sutherland:
The interviews revealed many themes about the future; the key takeaway is that PR professionals are optimistic but must proceed with caution.
Lisa, do you have a question?

26:09 – Lisa:
I’m concerned about warnings for agencies—especially with young hires who now rely on AI without critically examining its output. A friend’s daughter was penalized in college for submitting an essay thought to be AI-generated, even though it wasn’t. For her, the professor forced a re-test without technology. Could such measures become part of hiring practices in the future?

26:57 – Dr. Karen Sutherland:
Possibly. When I was a junior, I had to complete tasks within tight deadlines—usually online. In some European universities, online exams have been scrapped for in-person assessments to confirm genuine understanding. Perhaps agencies will adopt similar protocols.

27:23 – Dr. Karen Sutherland:
Training is often seen as “nice to have” rather than essential, but proper mentoring is critical. Juniors need to understand the task fully before outsourcing it to AI—that’s where the real problem lies.

27:51 – Dr. Karen Sutherland:
One interviewee said, “With AI there’s darkness and light; it can do amazing things, but like any technology, it’s a double-edged sword.” He noted that while adoption in strategic communications is slow, the darker side may pose risks. On the positive, AI’s speed in content production and strategy allows people to focus more on the human element.

29:09 – Dr. Karen Sutherland:
Consider this a to-do list for each AI stage. At the current stage with ANI and agents, focus on prompt engineering, seamless integration, automation, ethical guidelines, and training. For AGI, work on high-level strategy—humans determine strategy, and AI executes mundane tasks—and develop protocols for emergencies.

30:06 – Dr. Karen Sutherland:
If AGI fails, we must have frameworks in place to preserve human creativity and oversight. At a recent tech festival on the Sunshine Coast, I hosted an AI panel discussing whether AI endangers or empowers creativity. The consensus was that although AI can enhance creativity, it can’t replicate the unique human spark from personal experience.

30:58 – Dr. Karen Sutherland:
It was fascinating to develop these points—I even interacted with AI about how to advise strategic communicators for each AI stage. On technological singularity, AI might jump in and take complete control if no human oversees an organization. We must constantly assert our human relevance in decision-making.

32:10 – Dr. Karen Sutherland:
When decisions directly affect people, a human’s emotional intelligence must be involved. Next steps: as AI enthusiasts, you’ll explore a range of tools and practices. I’m sure you’re keen on developing an AI policy and integration strategy. In my experience with agencies, there’s too much focus on “show me this tool” (Gamma, Opus Clip, etc.) without a broader strategic approach and ongoing evaluation of output quality.

33:26 – Dr. Karen Sutherland:
Rather than simply adopting AI, continuously evaluate its output, improve processes, provide regular training, and update your ethics policy as conditions evolve. That’s all from me. Feel free to connect with me on LinkedIn—I offer training and help with policy development.

34:17 – Dr. Karen Sutherland:
If you know anyone needing assistance with AI policy or strategy, please point them in my direction. I’ve also started a Facebook group where I host weekly live sessions and monthly webinars, creating a community of communicators who share their experiences.

34:45 – Dr. Karen Sutherland:
Now, one question: Are you seeing different enthusiasm and adoption rates geographically? For instance, one survey shows Australians trust AI the least compared to other countries.

35:15 – Dr. Karen Sutherland:
In the diffusion of innovation model, you have enthusiasts and early adopters as well as laggards. Some are very keen, while many are cautious. I’ve been in this space for two or three years, and training opportunities are just beginning to grow.

35:38 – Dr. Karen Sutherland:
People are starting to realize they need to act—after years of messaging about AI’s benefits, many are waiting for it to slow down, only to find it won’t. In the States, I have clients who are more open; in Europe, it’s hit or miss; in India, the enthusiasm is high. There’s a wide range.

36:27 – Dr. Karen Sutherland:
The Institute for Public Relations offers excellent resources for strategic communicators—the link is available, and I’m happy to share my slides with all the links. On the PR side, firms like Edelman were pioneers in digital practices, building credibility.

37:22 – Dr. Karen Sutherland:
Now, with accessible AI tools, anyone with a modest budget can access three or four high-quality options—tools that might rival the work of multi-thousand‐person PR firms. Are you seeing leadership in AI adoption coming from top-down or ground-up approaches?

38:19 – Panelist:
It’s really mixed. Some agencies embraced AI from the start, while others avoid it because they feel it undermines their work. Some believe using AI while billing clients is unethical.

38:42 – Panelist:
Another issue is billing—for example, how do you justify hours if AI does the work so quickly? The research shows these concerns are very real. Do you have any data on how much clients demand transparency regarding AI use?

39:10 – Dr. Karen Sutherland:
I don’t have concrete data, but anecdotally, some clients are unhappy if they know AI is being used. Still, quality matters most; if the work achieves its goals, many clients overlook the method.

40:06 – Dr. Karen Sutherland:
I sometimes can’t tell if text was AI-generated or simply written in an “AI style”—we’re all adapting, much like the uniformity of LinkedIn posts over the years. It’s as if I no longer want to see another overused M-dash or rocket ship emoji or repetitive phrases like “unleash” or “unlock.”

41:01 – Dr. Karen Sutherland:
I’m tired of those clichés. Does anyone have any questions? I’ve seen many comments in the chat that I haven’t yet addressed. I do have one comment that relates to the validity of information from some AI providers—especially when preparing for media interviews. For instance, using AI to generate potential questions for an upcoming interview: has anyone seen it used effectively as an internal prep tool? I heard a PR firm mention, “We use it for talking points, but we also need a reality check to truly understand the journalist we’re preparing for.”

42:24 – Dr. Karen Sutherland:
I haven’t seen it used exactly that way. I know it’s used to create talking points and even quick profiles before meetings—deep research can be a great asset in that regard. It’s a useful way to prepare a spokesperson.

43:01 – Dr. Karen Sutherland:
Thank you, Ivy. Any other questions or comments?

43:24 – Audience:
Hi. My son is graduating from college this weekend. There’s a lot in the media about how recent graduates struggle to find jobs—with some blaming AI. Does anyone have a perspective on this?

43:52 – Dr. Karen Sutherland:
It depends on the company and its willingness to train emerging professionals. Some organizations are very committed to nurturing new talent, while others are purely focused on cutting costs. AI might not make it easier overall, but companies that truly invest in training will continue to value hands-on experience. What field is your son pursuing?

44:16 – Audience:
Political science.

44:40 – Dr. Karen Sutherland:
Many organizations still offer graduate programs, but they’re highly competitive. We encourage our students to gain practical experience while still in university so they have a body of work to show. It’s not easy, but practical, hands-on skills remain critical.

45:05 – Dr. Karen Sutherland:
I recently spoke with a graduate who assembled an impressive website showcasing their work. I even suggested sharing it in Serial Marketers for broader inspiration—not just for new graduates. He’s very interested in the intersection of advertising and marketing. My advice: lean into your communities. Platforms like Hive Index help generate referrals, and on Serial Marketers, people are now discussing tools like ChatPerplexity. Organic, community-driven growth matters.

46:22 – Dr. Karen Sutherland:
I mentioned a concept that might be unfamiliar: “on the internet, no one knows you’re a dog”—like a one-panel New Yorker cartoon. It illustrates that if you actively participate in communities (for example, engaging with frequent commenters on LinkedIn), you build a reputation that lasts.

47:10 – Dr. Karen Sutherland:
Consider recent guests on the corporate side—people like Adam Kleinberg or Matt Britain who openly share their emails. If you network and mention you were on the call, it can lead to opportunities. Experience isn’t solely measured by years; community engagement counts.

47:57 – Dr. Karen Sutherland:
I encourage everyone to join communities and interact with people you respect on LinkedIn. I notice frequent commenters and will remember those who engage consistently—even years down the line.

48:24 – David Cutler:
Welcome back, David Cutler here. A friend of mine, a professor at Boston University, taught an advanced marketing course. To determine if students truly understood the material (versus just using AI to write assignments), he organized live debates and discussions. Undergraduates today are comfortable relying on AI to write, but they often struggle to express their own understanding. That’s why real-time debate is invaluable.

49:18 – David Cutler:
He brought me in because I’m known for coaching—even introverts can learn to communicate effectively without relying on a screen. The ability to express and debate live is crucial. It builds confidence and genuine understanding.

50:03 – David Cutler:
I believe that debate classes will make a comeback because there’s no substitute for live, public discussion. Even at the graduate level—where tuition can run as high as $80,000 a semester—demonstrating live comprehension is essential.

50:26 – David Cutler:
I once had a class where I distributed brown paper bags for a fun exercise. One student opted out, and although it was awkward, it led to an effective presentation and discussion. We owe it to ourselves to stay ahead of these technologies rather than simply blaming them.

51:06 – Moderator:
Thank you for that insight. Jim—did we miss your question or comment? (Pause) Sorry, I was muted earlier. Thank you, everyone.

51:34 – Jim (Moderator):
Dr. Sutherland, something you mentioned earlier surprised me. Two years ago, I met Heather Brown—she focuses on using AI in universities. Through her, I was introduced to Carlo Yakano. Do you know him?

52:08 – Dr. Karen Sutherland:
No, I don’t know him personally. He introduced me to a group of about 13 individuals. While Australia is cautious, it’s become a wellspring for ethical, strategic AI use in education—which I believe is one of the most important issues today.

52:38 – Dr. Karen Sutherland:
Australia produces very thoughtful work. Carlo introduced me to many brilliant minds whose papers are extraordinary. I believe I’ve even heard him speak at a higher education conference—he’s quite animated. Also, Heather works on debunking the effectiveness of AI detectors, which often yield false positives. Interestingly, much of that leadership comes from women.

53:30 – Dr. Karen Sutherland:
Heather brought me into one group and Carlo into another. You are in a critical part of this conversation. For example, Danny Lou of the University of Sydney creates excellent content on YouTube every two weeks, and Phil Dawson in Melbourne is one of the most authentic voices in AI. Yet, the broader business community remains cautious. It’s great that we have enthusiastic innovators, even if they sometimes clash with caution.

54:20 – Dr. Karen Sutherland:
That collision of enthusiasm and caution is valuable—it forces unorthodox, honest dialogue. Australians are refreshingly blunt, and that openness is exactly what we need.

54:42 – Jim (Moderator):
We need to continue having these meetings to honestly discuss our challenges as the deluge of information increases. This conversation is important—thank you, Australia, for your fearless candor. We’re slightly over time, so we won’t keep Dr. Sutherland any longer.

55:05 – Jim (Moderator):
Thank you, Dr. Sutherland, for your tremendous research and insights. I look forward to sharing them with our community. Thanks to everyone for participating today. We have an exciting town hall next week—hope to see you then.

55:26 – Jim (Moderator) / Dr. Karen Sutherland:
Thanks again, Doctor. I’ll be coming to the States in October. I hope we can meet up in New York as part of your tour. I’ll be finishing my tour by then. Thank you, everyone—have a great day. Bye bye.

## AI‑Accelerated Marketing on Agentic Workflows - Future Proof Films

Speaker: Adam Kleinberg
Published: 2025-05-07
Tags: ai agents, video production, content strategy, ai video
Video: https://www.youtube.com/watch?v=su_1OQ9pqxs
Page: https://aimarketersguild.org/sessions/ai-accelerated-marketing-on-agentic-workflows-future-proof-films

Recorded on May 7,  2025, for the AI Insiders series by AI Marketers Guild, this candid conversation features Adam Kleinberg, CEO & Co‑Founder of Traction—sharing a roadmap for practical AI adoption in modern marketing organizations.
Adam lays out how agentic workflows, a “liquid workforce” model, and new AI video techniques can slash production time and costs while amplifying creative impact.
[2:46] What Is a Liquid Workforce Model in Marketing?

Answer / Description:
A liquid workforce model is an agile staffing approach where brands access a curated, highly flexible network of fractional talent and strategic partners to scale marketing efforts without the overhead of a traditional, fixed-staff agency. This model allows marketing organizations to eliminate structural bloat and rapidly deploy specialized talent as client needs shift.

In 2019, the agency Traction transitioned from a traditional agency to a liquid workforce "marketing accelerator" model to better assist brands that were moving their marketing teams in-house. Rather than maintaining a large, permanent bench of full-time specialists, the liquid model relies on a small core team of client partners and program managers who dynamically assemble fractional teams of freelancers, strategic partners, and former brand-side executives for specific projects. This approach provides specialized, high-level expertise (such as retail strategies led by former brand-side leaders) in an on-demand, highly adaptable format.

Keywords:
liquid workforce model, fractional marketing talent, agile agency model, marketing accelerator, on-demand marketing talent, Traction agency model, marketing team design

[5:36] How Does AI Shift the Role of Writers to Storytellers?

Answer / Description:
Artificial intelligence shifts the role of the writer from manual word-stitching to higher-level storytelling, where the human's primary value lies in conceptualizing ideas, structuring narratives, and acting as an "inspired editor." While generative AI tools can draft content based on instructions, human storytellers are required to inject personal anecdotes, authentic voice, and spontaneous whimsy to prevent the text from feeling generic.

When utilizing tools like Anthropic's Claude for writing, creators can program their unique thinking process and voice guidelines directly into the prompts. However, raw AI-generated drafts often lack the spontaneous, anecdotal, and personal elements that make writing feel authentic. The modern writer's role is to act as a critical editor who reviews AI drafts, infuses personal experiences, and guides the AI iteratively to ensure the final output retains a genuine human touch.

Keywords:
AI storytelling, Claude writing workflow, AI editor role, human-in-the-loop writing, personalized AI writing prompts, voice synthesis in LLMs, content editing AI

[12:02] What Skills Are Critical for Marketers in the AI-Driven Era?

Answer / Description:
The critical skills for modern marketers are high curiosity, adaptability, and a "polymath" mindset capable of thinking across problem sets to stitch together AI-driven solutions. Rather than operating in highly specialized vertical silos, marketers must understand how to orchestrate diverse AI tools and collaborate with automated agentic workflows.

As marketing organizations integrate generative AI and automated systems, they need to prioritize personnel with a high Curiosity Quotient (CQ) who can navigate rapid technological shifts. Marketers must transition into polymaths who can design end-to-end processes, manage secure platform integrations, and collaborate with AI "co-workers." Traditional vertical silos are giving way to integrated workflows, making the ability to see the big picture and direct multiple systems more valuable than single-discipline execution.

Keywords:
AI marketing skills, polymath marketer, Curiosity Quotient, marketing organizational design, AI talent acquisition, marketing team transformation, future of marketing roles

[16:29] How Do Agentic Workflows and Human-in-the-Loop Processes Work in Marketing?

Answer / Description:
Agentic workflows in marketing use interconnected AI agents to execute end-to-end campaigns, paired with human oversight to review outputs and prevent errors. This collaborative, iterative relationship allows agents to handle repetitive, turnkey tasks while humans focus on strategic approval and brand alignment.

A standard agentic marketing workflow begins when a human initiates a campaign by uploading a creative brief to a platform like Slack. A primary AI agent reads the brief, pulls customer data from a Customer Data Platform (CDP), selects assets from a Digital Asset Manager (DAM), generates copy using large language models, and builds campaign assets. A second "supervisor" agent then checks the work, highlighting hallucinations, legal flags, or brand errors. The human reviews these flagged items, provides feedback for iterative refinement, and gives final approval before the primary agent deploys the campaign and monitors performance.

Keywords:
agentic workflows, human in the loop, AI marketing automation, multi-agent systems, automated ad production, supervisor agent, marketing operations

[19:25] What Platforms Are Used to Build and Automate Agentic Marketing Workflows?

Answer / Description:
Marketing teams can build agentic workflows using low-code/no-code automation platforms like Delegate Flow, Make.com, and agent-specific tools like InstaLily. Delegate Flow is particularly advantageous for creative pipelines due to its direct, pre-built integrations with content production tools like Simplified.

While platforms like Make.com are highly popular for general workflow automation, Delegate Flow is specifically designed to bridge the gap between workflow automation and creative execution. It integrates natively with Simplified, an AI-powered creative production tool, allowing teams to seamlessly generate ad variations and marketing assets. Although setting up these pipelines requires some understanding of software connections, modern platforms allow users to build and troubleshoot workflows using natural language prompts, making agent construction accessible to non-engineers.

Keywords:
Delegate Flow, Make.com, InstaLily, Simplified creative tool, low-code AI agents, marketing workflow automation, AI software integration

[24:41] How Do You Build a Generative AI Video Production Pipeline?

Answer / Description:
A professional generative AI video production pipeline combines strategic human planning with specialized AI tools for scriptwriting, audio synthesis, visual generation, upscaling, and post-production. This multi-tool workflow can reduce production time from months to weeks and cut costs to a fraction of traditional live-action shoots.

A successful generative AI video workflow, such as the one developed by Traction for cybersecurity firm Recorded Future, consists of several sequential stages:

- Strategy & Scripting: Stakeholder interviews are distilled into a strategy deck, which is processed by Claude to generate a production script.

- Audio Pitching: The script is paired with synthesized voiceovers from ElevenLabs and background music from Suno to pitch a realistic audio concept to the client.

- Visual Generation: Still concepts are created in Midjourney and animated using Runway Gen-2, Luma, or Kling to achieve specific visual effects and character expressions.

- Post-Production: The animated scenes are upscaled using Topaz AI, and human editors use Adobe After Effects and Premiere for graphic overlays, typography, and final editing.

Keywords:
GenAI video production, Runway Gen-2 video, Midjourney to Runway, ElevenLabs voiceover, Topaz AI upscaling, Suno AI music, Recorded Future brand video, Futureproof Films

[34:06] How Does Search Intent and Generative Engine Optimization (GEO) Change SEO?**

Answer / Description:
Generative Engine Optimization (GEO) shifts the focus of search strategy from keyword-stuffing to matching user search intent. Because search engines increasingly synthesize answers directly via AI overviews and conversational snippets, content must be structured to directly answer specific user queries to be retrieved and cited by AI systems.

As search engines evolve, traditional organic links are pushed down the page by AI-generated summaries and conversational answers. To capture search traffic, brands must write high-quality, informative content built around specific conversational questions. For example, structuring a blog post around the explicit query "Why choose a wood pellet patio heater?" allows search engines like Google to easily parse the text and extract bullet points to populate their dynamic AI Overviews, citing the brand directly at the top of the search results.

Keywords:
Generative Engine Optimization, GEO search strategy, Google AI Overviews, search intent optimization, conversational search, Perplexity optimization

[36:12] Why Is Knowing "What Great Looks Like" More Critical Than Prompt Engineering?

Answer / Description:
Knowing "what great looks like" as an editor is more critical than technical prompt engineering because AI can generate endless variations, but only a skilled human can recognize and refine the idea with the highest creative potential. Developing editorial taste and high standards is what ultimately prevents AI outputs from being generic or mediocre.

While basic prompt engineering rules can be easily learned online, they do not guarantee high-quality creative work. Unrefined AI outputs are often generic because the user lacks the experience to recognize and push for excellence. Similar to a creative director whose primary job is to recognize, shape, and elevate a raw concept rather than generate all the initial ideas, an "inspired editor" must continuously guide, challenge, and iterate with AI tools until the output meets premium brand standards.

Keywords:
editorial standards for AI, prompt engineering vs editing, creative direction in AI, AI quality control, human-in-the-loop creative, ChatGPT brainstorming

[45:36] What Are the Copyright and Legal Risks of Generative AI in Brand Advertising?

Answer / Description:
The primary legal risk of using generative AI in advertising stems from evolving copyright laws regarding AI-generated assets, along with platform terms of service that may use uploaded data to train their models. Different industries and legal departments manage this risk with varying degrees of tolerance, often restricting AI usage to secure, closed-enterprise systems.

Highly regulated industries, such as financial services, frequently prohibit their marketing teams from using public generative AI tools due to liability and data security concerns. To address this, software companies like Adobe offer legal indemnification by training their AI models (such as Adobe Firefly) on licensed databases. However, because Adobe's standard user agreements state that cloud-saved creations can feed back into their training models, agencies must carefully review platform terms of service, structure their Statements of Work (SOWs) around current legal precedents, and counsel clients on the varying levels of legal risk associated with different AI tools.

Keywords:
AI copyright risk, Adobe AI indemnification, AI marketing legal compliance, generative AI brand risk, enterprise AI security, AI training data privacy

## Generation AI - Preparing Gen  Alpha for an AI‑Driven Future

Speaker: Matt Britton
Published: 2025-05-02
Tags: consumer research, gen alpha, youth culture
Video: https://www.youtube.com/watch?v=HV2S3QKqqA4&t=18s
Page: https://aimarketersguild.org/sessions/generation-ai-preparing-gen-alpha-for-an-ai-driven-future

0:02 – HOST:
Hey everyone, welcome to another edition of AI Insiders with AI Marketers Guild. We have a rare treat today because we don’t often have repeat guests. When I saw Matt Britton—my old boss at MRY and a long-time acquaintance, now CEO of Suzy and a serial author—I was excited to learn he has a new book on AI and Generation AI.

0:30 – HOST:
It’s one of the few business books these days that I can’t wait to read. I still get comments about your last session—the insights on synthetic audiences and using AI data to inform corporate strategy were unforgettable and incredibly useful. Thanks for coming back.

0:55 – MATT:
Thanks for having me, David. I’d prefer this to be a discussion. Let me start by sharing a bit about my background for those who are unfamiliar, and then we can dive into the book. I’ve spent my career helping big brands navigate change and disruption. In the early 2000s, disruption meant the internet, driven by a generation that saw the world differently.

1:15 – MATT:
I spent a lot of time convincing brands to embrace the internet and market to young audiences. I remember being at Hershey in Pennsylvania, where they insisted we would never market our brand on computers.

1:37 – MATT:
Then around 2005, I helped brands navigate social media. I was focused on marketing to college students and teens when Facebook emerged. As it expanded to everyone, I guided brands through that shift—even registering Visa on Twitter early on.

1:58 – MATT:
In 2009–2010, disruption meant the iPhone. For Gen Z, the iPhone is almost like an appendage. It transformed how people communicate, transact, and build relationships—often replacing face-to-face interactions and even contributing to challenges like depression. For them, not having an iPhone would be as disorienting as us losing electricity during a blackout.

2:21 – MATT:
I recall the blackout 20 years ago in the city. At first, it felt like a block party without electricity, but after a few hours, chaos ensued. Every disruption starts with dismissals from brands, while early adopters—especially tech companies—embrace the change.

2:44 – MATT:
For example, when mobile became mainstream around 2010–2011, Facebook shifted its ad strategy to mobile despite market skepticism. That foresight proved invaluable.

3:09 – MATT:
Now we’re in the era of AI, the most transformational disruption I’ve seen in my 25-year career. Recent data shows AI’s capacity doubles every seven months—that’s like moving from the first iPhone to an iPhone 15 in seven months instead of 15 years. It’s dizzying, especially for businesses trying to keep up.

3:33 – MATT:
I experienced this when AI, via ChatGPT, broke into the mainstream. I run a venture-funded software company called Suzy, which does consumer research for major brands. When AI emerged, I pushed my engineering team to integrate it. They struggled, making me realize AI isn’t just another feature—it forces a complete rethink of our business and product strategy.

3:50 – MATT:
I discovered you can build AI without being an engineer. I took on that challenge, and 12 to 18 months later I’ve built digital products without prior coding experience, investing the time to truly understand AI. It’s an ongoing journey that has opened my eyes to new possibilities.

4:12 – MATT:
That journey led me to write my second book. My first book, published 10 years ago when David and I worked together at MRY, was called Youth Nation. It argued that for the first time, youth culture was not counterculture.

4:31 – MATT:
In the ‘60s through ‘90s, youth culture was largely counterculture—think Woodstock, punk rock, and grunge in Seattle. Youth had to be disruptive because corporate media dictated their voice. By 2015, with social media’s rise, young people finally had a megaphone to shape culture themselves.

4:51 – MATT:
Fast forward to today and virtually every new trend starts with young people before spreading wider. That formed the basis of Youth Nation. With all these changes and AI’s immense power, I decided it was time for a second book.

5:16 – MATT:
My new book, Generation AI, releases this upcoming Tuesday. It explores how Gen Alpha—the first AI-native generation—will know no world without artificial intelligence. Their experience of technology will be entirely different.

5:39 – MATT:
This shift raises major questions about the future of education, work, parenting, relationships, healthcare, and society overall. Each chapter examines current trends and how they will evolve in the coming years.

6:02 – MATT:
Many think using AI to find a recipe is all it can do, but that’s just the tip of the iceberg.

6:28 – MATT:
In my research, I’ve looked at scenarios like AI reinventing healthcare—potentially eliminating roles like radiologists within a year—and how AI might reshape parenting as kids form deep bonds with chatbots. Tragically, one teenager’s suicide has already prompted a lawsuit claiming a chatbot contributed to self-harm.

6:48 – MATT:
I also cover the future of work and the need to future-proof ourselves. The reality is, if you’re not engaged in creative problem solving or in designing and managing new technologies, you risk being left behind.

7:10 – MATT:
Let’s talk about education. Parents worry about skyrocketing college costs and a future with fewer jobs. I believe the answer lies in transforming how we educate our children.

7:30 – MATT:
Traditional American education has relied on memorization and regurgitation—methods that worked in a knowledge-based economy where storing facts was key. But since information is now at everyone’s fingertips and AI outperforms experts in many tasks, rote learning is becoming obsolete.

7:50 – MATT:
We need to emphasize critical thinking and problem-solving—the ability to decide which problems are worth solving and how to use AI to do it. For instance, instead of mastering every camera setting to take a great photo, you only need to know the right angle and context; algorithms can handle the technical details.

8:10 – MATT:
Over time, technical skills will diminish in importance. It’s more vital to know where to focus your creative energy. Specialists will still be needed for exceptional cases—just as you might hire a professional photographer for a wedding despite most photos being taken on smartphones.

8:33 – MATT:
Now consider agencies. If your business model charges for human hours, it may not be sustainable when AI can produce the same output in seconds. Brands will eventually ask why pay for five hours when 30 seconds will do.

8:55 – MATT:
Even market research is shifting. Where consulting firms once charged tens of thousands for deep analyses, AI can now generate detailed reports in minutes. High-stakes consulting may remain, but routine analysis will be automated.

9:17 – MATT:
This brings up a broader question: What will Generation AI do for a living? It’s hard to predict exact careers, but skills in problem solving and creativity will be crucial. Those who can identify and tackle challenges using AI will thrive.

10:05 – MATT:
Take healthcare as an example. Appointment scheduling might soon be managed by AI chatbots instead of human receptionists. Routine tasks like evaluating test results may no longer require doctors, and robotics could eventually perform surgery.

10:30 – MATT:
As companies become more efficient through automation, capital will be redeployed to create new jobs. Exactly what these roles will entail remains to be seen.

10:51 – MATT:
Consider my brother-in-law, an ER nurse. I believe that 20 years from now he’ll still be in high demand and grateful for choosing a path that machines can’t completely replicate.

11:15 – MATT:
Before I open up for questions, I want to note that many senior professionals here—marketing strategists, content creators, and more—are investing in learning about AI. That commitment to learning sets you apart.

11:38 – MATT:
Now, let’s invite some questions.

21:32 – PARTICIPANT:
Sorry, nice to meet you, and thank you, David.

21:51 – PARTICIPANT:
I completely agree with everything you’re saying, and I’m very much looking forward to your book. I’ve been talking with industry leaders about AI and how they’re integrating it into their businesses and lives. We’re at a revolutionary turning point that many still struggle to grasp.

22:14 – PARTICIPANT:
My analogy is simple: just as we evolved from AOL portals to Google searches, we’re now moving toward interacting with ChatGPT. There’s a significant gap between those who adapt quickly and those who resist change.

22:35 – PARTICIPANT:
Where once we retrieved information from Excite, Yahoo, or Google, now we can build custom chatbots that deliver timely, accurate information without the hallucinations.

22:55 – PARTICIPANT:
The potential here is enormous, yet many choose not to see it.

23:16 – PARTICIPANT:
People often avoid change because it’s uncomfortable.

23:36 – PARTICIPANT:
I keep thinking about the technology adoption curve—innovators, early adopters, late adopters, and laggards. For something as monumental as AI, everyone should strive to get ahead. I’ve even built my own agency using custom AI bots, which is in high demand. Earl will join us in a few weeks to show us how we did it.

24:02 – DEVELOPER:
I just want to add a quick comment. I’m a product developer, and I created an AI chatbot for indexing content. What’s fascinating is that by tweaking the prompts, you can completely change the product’s function. Coming from a programming background, I’ve learned that AI requires a different mindset—you can’t simply issue detailed instructions and expect the same results. Understanding how AI works and setting realistic expectations is a crucial skill for the future.

24:27 – HOST:
Well said. I agree completely. Matt, as always, your insights are fantastic. I’m excited to read the book and join the book launch. I look forward to seeing more of your work.

24:53 – HOST:
Thanks for joining us and for your support. The book is available on Amazon and wherever books are sold—please share your thoughts.

25:21 – MATT:
Yeah, we’ll do that. Excited for this one.

25:45 – HOST:
See you soon, Matt. Thanks everyone, and best of luck.

## Generative Engine Optimization (GEO) Future-Proof Content Strategy Tina Chopra

Speaker: Tina Chopra
Published: 2025-04-23
Tags: content marketing, ai optimization, geo
Video: https://www.youtube.com/watch?v=HSxuOzB3SYk
Page: https://aimarketersguild.org/sessions/generative-engine-optimization-geo-future-proof-content-strategy-tina-chopra

0:10 Nicola: Welcome everyone back to our monthly series for AI Marketers Guild. I'm Nicola Quail, one of the co-founders of AIMG APAC, along with Sushita in Singapore and Daksh Anadi in India. We're excited to see this community expand across the Asia-Pacific region.

0:39 Nicola: For those new to the community, AI Marketers Guild was started by U.S. marketer David Berkowitz. It has grown rapidly in North America, and our regional chapter showcases local pioneers in AI tools, applications, and best practice through monthly webinars like this one.

1:06 Nicola: I'm delighted to introduce our guest speaker, Tina Chopra, co-founder and CEO of Addlly AI, a generative-AI platform that helps enterprises create high-impact, multilingual content at scale.

1:29 Nicola: Tina is a former business journalist with more than a decade in content and storytelling. Supported by Microsoft, AWS, and IMDA, her team helps brands navigate AI search and content marketing. She's also a LinkedIn Top Voice in AI marketing.

1:57 Nicola: Today Tina will introduce Generative Engine Optimization, or GEO—an emerging framework for content optimized for AI-first discovery. Post questions in the chat; we’ll cover them in the Q&A. Tina, welcome.

2:21 Tina: Thank you, Nicola. I was a journalist before moving into content marketing.

2:46 Tina: When SEO began, our “strategy” was to stuff the words “Wealth Magazine” into blog footers 20 times. We've come a long way.

3:12 Tina: SEO was once all about keywords; now we’re entering the era of Generative Engine Optimization. ChatGPT launched two years ago, and much has changed since.

3:43 Tina: Tools like Perplexity and Bing’s AI Overviews matter because, if AI can recognize your brand, agents will recommend you.

4:10 Tina: I’ll cover what’s changed, how to optimize for AI, and how GEO differs from traditional SEO. SEO had three pillars—technical, on-page, off-page—whereas GEO draws on many data points, including social media.

4:42 Tina: I’ll share a case study on why owned media—your site, social channels, and blogs—matters even more in GEO.

5:16 Tina: Search behaviour is changing. People prefer one concise answer rather than trawling sites. If our brand isn’t in those answers, we’re invisible.

5:46 Tina: Generative engines don’t hunt keywords; they answer questions. Search intent—brand, product, features, benefits—is critical.

6:14 Tina: Google now shows extra questions; AI has mapped intent.

6:45 Tina: Featured snippets were the first “zero-click” results. Generative engines now summarise multiple sources and offer next actions.

8:13 Tina: If Addlly doesn’t appear for “best AI marketing companies in Singapore,” users won’t find us organically.

8:40 Tina: Traditional SEO relied on keyword research, on-page tweaks, backlinks, and technical fixes.

9:36 Tina: GEO focuses on intent and direct answers from trusted sources. Content must be clear, credible, and context-aware.

10:32 Tina: Generative engines also scan social posts, press releases, and reviews—any mention of your brand.

10:59 Tina: Example: if a user types “peptide firming serum,” a brand surfaces only if that phrase appears in product descriptions, benefits, or structured content.

11:53 Tina: Four pillars for GEO: align content with user intent, build brand authority, refresh owned channels, and structure content for skim reading.

12:20 Tina: Different segments have different intents, so strategy and segmentation are vital.

12:50 Tina: Depth builds authority. A lone landing page and two blogs won’t satisfy generative engines.

13:19 Tina: GEO is new but aligns with Google’s Helpful Content update. SEO and GEO overlap; we need both.

14:16 Tina: GEO content must be skimmable—short paragraphs, bullets, FAQs, summaries, front-loaded facts.

14:44 Tina: Social mentions and reviews now influence discovery as much as backlinks.

15:14 Tina: At Addlly we use AI agents to map audience intent and generate separate topic lists for GEO and SEO.

15:43 Tina: GEO content answers real questions—no fluff—using a natural tone and brand-specific insights.

16:41 Tina: Think in content clusters: pillar pages surrounded by related question-based blogs.

17:12 Tina: Cite original research and authoritative sources; write like a human expert.

17:36 Tina: Reference your brand whenever you post or comment; AI follows those links.

18:00 Tina: “Write it and forget it” is over. Refresh old content regularly.

18:28 Tina: GEO evolves from SEO, not replaces it; Google traffic is still crucial.

18:59 Tina: Use AI tools to spot questions, create content quickly, and audit visibility every six weeks.

19:26 Tina: Avoid keyword stuffing, thin content, and ignoring social signals; always refresh.

19:59 Tina: I’ll share my screen to show how we set up marketing agents on Addlly.

20:30 Tina: Step 1: define the brand and ideal customer profiles—core to GEO content.

21:02 Tina: Step 2: generate question-based blog ideas aligned with each intent.

21:31 Tina: SEO topics chase volume; GEO topics chase user questions.

22:28 Tina: Use Google’s “People Also Ask” as a free GEO idea list. GEO and SEO must work together.

23:23 Tina: After publishing a GEO blog, post a short LinkedIn or Facebook text update with a backlink; it boosts authority.

23:51 Tina: Thank you. Happy to take questions.

24:21 Anadi: Amazing session, Tina. First question: What trust factors does GEO use in Perplexity—similar to E-E-A-T in SEO?

24:51 Tina: GEO starts with the user’s question. Build a list of intent-related questions and answer them—GEO is question-based, not keyword-based.

25:17 Anadi: How do GEO queries differ from classic SEO searches?

25:44 Tina: There’s overlap. Metadata still matters, but GEO also reads the full page. Structure for skim reading, highlight benefits, and answer directly.

26:12 Anadi: Philip asks: How often should we refresh content, and what posting cadence do you recommend?

26:43 Tina: Pick a consistent cadence—say two blogs a week—and stick to it. Don’t dump eight posts at once and disappear. Refresh existing blogs every three to six months.

27:32 Anadi: Does language or location influence GEO results?

27:59 Tina: Language barriers are fading—ask in Swahili, get an answer in Japanese. For immediate “near me” needs, SEO still wins; for pre-trip research, GEO matters.

28:27 Anadi: Great. Nicola, over to you.

29:00 Nicola: We’ll need a follow-up session, Tina—maybe in Australia! Your points on user intent are compelling.

29:26 Nicola: GEO requires benefit-led, steady content, not feature dumps. Lots to rethink.

29:53 Nicola: Please reach out to Tina—she’s about to get many LinkedIn requests.

30:21 Tina: Happy to help. Email me at tina@addlly.ai.

30:48 Nicola: On behalf of Sushita, Daksh, Helen, and Anadi, thank you, Tina. Have a great day, everyone. See you next month.

31:17 Tina: Thanks, everyone.

## Generative AI Prompt Engineering and Education

Speaker: Erin Reilly
Published: 2025-04-23
Tags: effective prompting
Video: https://www.youtube.com/watch?v=pkSKRDWDyZg
Page: https://aimarketersguild.org/sessions/generative-ai-prompt-engineering-and-education

(00:09)
Welcome back to another edition of AI Insiders from the AI Marketers Guild. We have a special guest today—Erin Reilly, the founding director of the Texas Immersive Institute and Professor of Practice at the School of Advertising and Public Relations. Erin has a remarkable background and will introduce herself more thoroughly. Erin, you have developed practical, actionable ways to use AI, and I think our community will find your insights very valuable. We have a highly interactive group here, so please feel free to encourage participation at any time. I’ll also monitor the chat for questions. Erin, please take it away.

(01:13)
Thank you, David, for inviting me to share with you all. Let me provide a little background on my experience with AI. I’ve been working in the AI space for over a decade. My first significant project was with IBM while I was running the Annenberg Innovation Lab at USC. We focused on fan engagement, specifically on understanding what motivates fans to participate in their passions.

(01:50)
I developed a framework called “leveraging engagement” using natural language processing to analyze large amounts of online data. This allowed us to identify different types of fan motivations, such as learning, representation, or even debating—especially in sports, where arguing with referees is common. This approach helped us understand the ways fans connect with their interests.

(02:24)
By building both unsupervised and then supervised learning models, we created a system that’s now integrated into IBM Watson. It helps identify fan motivations, reduce churn, and inform strategies like recommending the next set of stories for a fan group. This was part of our early work in the field.

(02:57)
Currently, I teach a course on Creativity and AI at UT Austin, which fills up quickly every semester. Many have requested an online version so more people can participate. The course explores generative AI and its role in the creative workflow, which I’ll discuss with you today.

(03:28)
My focus has shifted from just audience engagement to how audiences now play an active role in immersive storytelling, especially with emerging technologies like AI as the backend infrastructure. We’re entering a spatial era where persistence and interoperability are key. The challenge is using these tools effectively to create real-time, innovative brand engagements. I work closely with brands on experiential marketing. Unless there are any questions about my background, I’m happy to jump right into the presentation.

(04:15)
Sounds good? Let’s begin. First, I’d like to thank Nicole Quail, one of the founders of AMG APAC, who recently hosted an event. I appreciate her for the introduction.

(04:48)
Are you seeing the presenter view? I want to make sure the right screen is shared. It looks like you can see my talking points. Let me adjust that.

(05:15)
We’ve all been there. How’s this? Okay, here we go. Let’s get started. Before we dive in, I’m curious: How many of you have experimented with generative AI before? I believe most of you are already familiar with it.

---

### (05:37)
Since this group has experience, I won’t go into the backend technicalities of how AI works. Instead, I’ll focus on new tools and strategies for better prompting and using AI as a creative partner. This session will cover how to master prompts, parameters, and personalization.

---

### (05:59)
I’ll take about 30 minutes to review these topics and share some case studies using OpenAI, my go-to tool. But first, let’s step back and consider generative AI more broadly.

---

### (06:19)
Are any of you familiar with lifearchitect.ai? This website, created by Dr. Thompson, provides in-depth analysis of major AI models like GPT-4, Gemini, and Claude. Dr. Thompson compares their architectures, capabilities, and implications for the future of AI.

### (06:45)
This image shows the data used in two different models. On the left is GPT-3, which uses a proprietary dataset from OpenAI, and on the right is a more open, transparent dataset called The Pile version 1. I like to start here because the data your models are trained on is crucial.

### (07:22)
It’s important to understand that you shouldn’t rely solely on one tool. I provide my students with worksheets listing all the different tools I use and what I use them for. Because of differences in datasets and models, the results can vary significantly depending on your goals.

### (07:54)
Large language models are trained in two main phases. My early work with IBM focused on unsupervised, pre-trained models, while my later work involved supervised, post-training with human feedback.

### (08:14)
This is where we fine-tuned models using specific examples and human guidance. This process isn’t new. Many of us remember when Google started, and they gamified tagging images to help build their search engine and image recognition.

### (08:33)
There was even a game where you’d sketch an image, and the AI would try to guess what you were drawing. These early efforts helped foster collective intelligence and were crucial in building modern AI tools.

### (09:01)
That was early post-training with supervised human feedback. When people worry about AI replacing humans, I remind them that these systems are built by us. Human expertise and oversight remain essential.

---

### (09:43)
Large language models are incredibly promising and can generate text, images, music, and even 3D models. For example, Playbook AI is a great platform for 3D modeling, and Unity has built-in Muse AI for integrating AI into game engines. There are also new developments in creating AI “avatars” of yourself.

---

### (10:31)
These are just some examples of how AI is enabling new creative possibilities. However, there are challenges you need to be aware of, such as weak integration between tools, limited contextual understanding, data privacy risks, and AI hallucinations.

---

### (11:09)
Integration across tools is still limited, but that’s likely to improve over the next few years. AI may sound smart, but true understanding is still a challenge. I appreciate recent improvements in ChatGPT’s memory and personalization, but data privacy remains a concern, especially if the AI was trained on sensitive or proprietary information. Always be cautious with your data and check for hallucinations.

---

### (12:29)
If you encounter suspicious outputs, ask for sources. If the AI can’t provide credible citations, try using tools like Perplexity, which tends to give better references.

---

### (13:34)
There’s a whole field of study around how AI interprets data, and even engineers don’t fully understand these models’ inner workings. For example, Codex uses code to build solutions, but if you lack foundational knowledge in programming, you risk generating unreliable results or code hallucinations.

---

### (15:03)
AI is a tool, not a replacement for human expertise. It can provide basic understanding and outlines, but higher-level thinking still requires expert input. These tools enable a new kind of distributed cognition, much like spellcheck for writing.

---

### (15:44)
If you want to progress beyond beginner-level outputs, you must develop advanced skills and deeper understanding.

---

### (16:07)
I created a RAG GPT and sometimes the AI doesn’t return the expected answers, even when the content is correct. This highlights the challenges in engineering AI systems and the importance of prompt design and data organization. Using frameworks like WISER can help improve prompt effectiveness.

---

### (17:21)
I work with multiple AI tools for branding and strategy and would appreciate insights on how Claude differs from other models. Sometimes the results aren’t as strong, which might be due to the approach or prompting. Any tips on using Claude would be helpful.

---

### (18:25)
Anthropic, the makers of Claude, are very transparent. They recently shared insights into how AI models process thoughts, which is rare in the industry. I have a document comparing generative AI tools, including Perplexity and Claude, which I’ll share once I locate it.

---

### (19:46)
Understanding the data used in training models is important because it affects bias, toxicity, copyright issues, and privacy. Partners and clients may be concerned about where information goes and what the AI can infer. For example, repeated interactions with ChatGPT can lead it to make inferences about your identity and preferences as it builds a long-term memory.

---

### (22:13)
Privacy concerns vary by generation—Gen Z and Alpha tend to be less worried about certain aspects and more about others. As AI memory improves, these privacy questions will become even more relevant.

---

### (22:48)
Text-to-image models like DALL-E, Midjourney, and Stable Diffusion generate visuals based on text prompts. These models are powered by architectures such as diffusion models, GANs, and autoencoders. The more detailed your prompt, the more specific and useful the output.

---

### (23:48)
Think of prompt-writing as a new programming language. I use the WISER framework:
- **W**: Who is the AI? Assign it a role (e.g., marketing strategist).
- **I**: Instructions. Specify what you want the tool to do.
- **S**: Subtasks. Break down the task into clear steps.
- **E**: Examples. Provide specific examples, templates, or references.
- **R**: Review. Iterate with follow-up questions, clarifications, or requests for citations.

---

### (26:14)
You can also use your own draft as an example and ask the AI to improve it. After your initial prompt, review and iterate by clarifying sections, checking for hallucinations, expanding responses, or refining for the target audience.

---

### (27:16)
Be as specific as possible about your target audience and their behaviors. Instead of relying on broad categories like “millennials” or “Gen Z,” focus on specific motivations and emotional drivers, using your motivation framework.

---

### (27:40)
Consider the “temperature” setting in your prompts, which controls the model’s creativity. A temperature of zero yields safe, factual responses, while a higher temperature encourages more creative, novel outputs. For example, a low-temperature prompt for a coffee shop name might yield “The Coffee House,” while a higher temperature could result in more original ideas like “Bean There, Sipped That.”

---

### (29:02)
If you’re using tools with backend access, you can adjust the temperature directly. Otherwise, specify in your prompt whether you want a deterministic or creative response. The WISER framework helps you communicate these needs.

---

### (31:16)
The clearer your prompts, the better the results. Generic prompts yield generic outputs. Use the WISER framework for more personalized, precise responses.

---

### (31:47)
This approach aligns with instructional tuning, where you assign the model a role, adjust temperature, and combine prompts for the best results. I use prompt recipes—predefined templates for effective prompts—which I recommend saving and reusing in custom GPTs.

---

### (33:04)
Here’s an example of a prompt recipe: Assign the role, provide instructions, specify output format, clarify context and perspective, define the target audience, and list any parameters or inputs that might change.

---

### (33:51)
For example: “Act as a digital marketing specialist for an online publication. Provide a list of potential campaign ideas and strategies to increase sales and customer engagement. Use bullet points and headings, and make your suggestions specific, actionable, and tailored to different audiences. Do not include implementation plans or generic concepts. Write the content for a general audience.” Be explicit about what you do and don’t want.

---

### (35:43)
Like coding, prompting is an iterative process. Try a prompt, evaluate the result, tweak for clarity and relevance, and repeat. Evaluate outputs based on predefined criteria to ensure they meet your goals.

---

### (36:02)
There are five main ways to evaluate prompts:
1. Human evaluation—does the response meet your needs?
2. Benchmarks—test across different case studies.
3. Word-level metrics—useful for large-scale text comparison.
4. LLM-assisted evaluation—use one model to evaluate another.
5. Real-world application—continuously refresh examples and test in practical scenarios.

---

### (39:27)
For students, fostering reasoning and questioning skills is crucial. I believe our education system needs to update its approach, moving away from traditional models. I use Socratic dialogue and in-person discussions to develop students’ critical thinking. Peer-to-peer teaching and real-world projects, like working with actual clients, help students build authentic skills and accountability.

---

### (44:12)
Now, let’s explore the OpenAI ecosystem. It includes ChatGPT for text, DALL-E for images, Codex for code, Sora for video, and customizable GPTs. I use custom GPTs extensively, as they can save significant time and allow for deep personalization.

---

### (45:47)
For example, Instacart uses ChatGPT to enhance the grocery shopping experience, helping customers plan meals, save time, and receive personalized recommendations. This demonstrates how brands can leverage GPTs for tailored customer engagement.

---

### (46:39)
In the creative space, Alexia Donna, Director of Creative Technology at Edelman, uses DALL-E to prototype new products and generate mood boards. While AI doesn’t replace designers, it serves as a valuable starting point for creative teams to brainstorm and iterate ideas.

---

### (47:51)
My friend, a film director, uses AI to storyboard scripts and communicate his vision to art departments. AI-generated images allow non-artists to visualize and share concepts quickly, making it a powerful tool for collaboration.

---

### (48:13)
It’s different from traditional mood boards because you can now fully realize your ideas visually using AI. This capability democratizes the creative process and speeds up collaboration.

---

### (48:57)
Finally, Codex enables natural language commands to generate 3D scenes in engines like BabylonJS. This makes 3D modeling and spatial web design more accessible, allowing quick prototyping without advanced technical skills.

---

### (51:44)
Through prompting, you can render 3D models for brand websites or other projects, making it easier to communicate ideas before handing them off to specialists.

---

### (52:08)
We’ve covered a lot today. I’ll share links and resources after this session. Based on the great feedback in the chat, we might need to schedule a part two, as we didn’t even touch on music generation or other creative applications.

---

### (53:09)
Thank you all for joining. Please feel free to reach out if you have more questions or want to connect further. I appreciate the opportunity to share this master class on creativity and AI with you.

## Simulated Focus Groups  AI-Powered Ideation

Speaker: Celine Udriot
Published: 2025-04-16
Tags: focus groups, synthetic audience testing
Video: https://www.youtube.com/watch?v=-10pPXhDJic
Page: https://aimarketersguild.org/sessions/simulated-focus-groups-ai-powered-ideation

### (00:00:00)
Welcome back to another edition of AI Insiders. To start things off, we have a special announcement from our guest Jennifer Fuentes. Interestingly, both of our guests today came from conversations at CES this year—truly the gift that keeps on giving.

### (00:00:35)
We’ll introduce our featured speaker in just a minute, but first, Jenna Fuentes is doing some great work at the nonprofit OpenClassrooms and has a few quick updates. Jenna, please take it away.

### (00:00:56)
Thank you, David. Hello, everyone—it's great to meet you. I’d love to connect with each of you.

We also have an upcoming event in New York, and I encourage anyone interested in networking or discussing the future of work and team building to join. I’m sharing details in the chat and, if possible, I can share my screen as well. The event is on April 30th at Impact Hub, and I’d love to see you there.

To share a bit about myself, my background is in higher ed and edtech. I’ve lived in Spain and Argentina, and at my previous edtech startup, we scaled from 100 to 1,000 employees in three years and were acquired for $750 million.

I’ve also consulted for clients in Germany, Mexico, the Philippines, and the US. Today’s topic is AI in marketing and sales, and our company is based in France with expansions in the UK. I manage operations in California and New York, and we are a B Corporation that measures our carbon footprint.

We provide free training and apprenticeships to help people grow. We also offer free marketing training and act as a recruitment source, providing marketing talent at $20 per hour.

Our programs are open for discussion, and I’m happy to go into more detail, but I’ll keep this short. If you’re interested in hiring remote, onsite, or hybrid marketing apprentices—or want to learn data analytics and marketing for free—we’re sponsoring several events, including this webinar, and would love to collaborate further.

### (00:03:11)
Thank you, Jennifer. It’s inspiring to see your work, and we appreciate OpenClassrooms' support for AI Marketers and Serial Marketers. Everyone, please check out the link Jennifer shared and connect if you can attend the event next week—there’s much more to explore.

### (00:03:29)
Now, I’d like to introduce a principal from another European company, whom I also met at CES, thanks to Chris Path. Chris introduced me to Céline from Largo AI. As soon as I learned what Largo AI was doing, I knew it was something special. Watching Céline and her team explain their work was a revelation, especially their unique approach to simulated focus groups compared to synthetic data or synthetic audiences.

### (00:04:47)
Rather than continue talking, I’d like to welcome Céline and hear directly from her. These sessions are participatory, so please ask questions—Céline is eager to engage. Welcome, Céline.

### (00:05:09)
Thank you, David. Hello everyone, and thank you for joining. I’m calling from Switzerland, hence my French accent. We’re based on the French side of Switzerland. I want to keep this interactive, so please feel free to ask questions or share comments at any point.

Let me give you some background on what we do at Largo AI, our approach to AI, and I’ll share a concrete example of a project advertisement campaign, showing you real results directly on our platform.

### (00:05:56)
I’m Céline, the Chief Operating Officer at Largo. We were founded six years ago in Switzerland as a spin-off from EPFL, one of the world’s top tech universities for computer science. We have a large R&D department and conduct extensive research to identify the most effective AI methods. Over the years, we’ve also discovered many methods that don’t work, especially in the area of simulated focus groups, and I’ll share those insights as well.

### (00:06:46)
Largo originally started in the movie industry, and we now have over 600 production companies as clients, ranging from independent filmmakers to large studios. Over the past year, we’ve entered the advertising industry, working directly with brands and agencies.

### (00:07:21)
Let me share my screen to demonstrate how the platform works. We are a post-Series A company, having closed our latest funding round this year. Our platform operates on a yearly subscription model, allowing brands and agencies to access it hands-on. You can log in, test your advertising ideas or new concepts, and get rapid feedback.

### (00:07:58)
We use simulated focus groups. Instead of waiting weeks and incurring high costs for traditional focus groups, our platform allows you to test a wide range of content quickly and affordably. Here’s how it works: We replicate real people to test ideas, enabling faster, more scalable insights than traditional methods.

### (00:09:08)
It’s important to clarify: simulated personas are different from synthetic personas. Synthetic people are created entirely by AI, while simulated personas are based on real individuals. For example, we take someone like David, have them complete an in-depth survey developed with EPFL researchers, and then simulate their behavior. We check the quality by comparing responses from the real person and their digital twin; if the correlation isn’t over 90%, we eliminate that persona.

### (00:10:47)
To use the platform, you select the country you want to target—say, the United States. The AI will not only test your campaign but also benchmark it by generating and comparing similar ideas, either reflecting your brand identity or those of competitors. You can upload a brief (PDF or text), and the AI will analyze it, summarize the key details, and generate a key image.

### (00:12:24)
You’ll then see AI-generated ideas for benchmarking, alongside your own. The results show audience predictions, such as the demographic breakdown in your selected country. If you want to target a specific group (e.g., only women or a certain age), you can set those parameters. Simulated people—anonymized for privacy—act as your focus group. Your idea is presented to them online, and you see results including emotional responses, likability, and buying interest, compared to the AI-generated benchmarks.

### (00:14:30)
For example, we worked with a sports equipment company in Mexico preparing to launch a soccer concept store in the US. Their initial idea performed poorly on our platform, but the qualitative feedback showed that their concept—a "temple of soccer"—didn’t resonate with US audiences. The AI suggested adding arcade games, and after making those changes, they created a successful new concept.

### (00:16:08)
A common question is how our simulations can accurately reflect someone’s preferences when we haven’t asked every possible specific question. We’ve invested seven years of research to refine our AI, and we found that using only synthetic (AI-created) personas resulted in average, generic feedback that missed key personality differences. By basing simulations on real people and continually updating our models, we achieve much more nuanced and accurate results.

### (00:18:22)
Regarding audience panels, we already have a large database of simulated people, and you can specify highly targeted "micro-audiences" by interests, demographics, and more. If we lack enough of a particular segment, we can run new surveys to create additional simulated personas. For extremely niche groups or those difficult to recruit (e.g., young children or elderly), we use AI-generated synthetic personas to supplement the data.

### (00:20:00)
Our platform can handle a wide variety of products and industries, including software, consumer goods, airlines, sports, and skincare. You can define micro-audiences with detailed interests or behaviors—such as cat owners who prefer real meat and natural ingredients—and we’ll either use our existing panel or create new simulations as needed. Analyses typically take 15 minutes, but if a new audience is required, the process may take about two weeks.

### (00:22:00)
We’re active in over 20 countries, with thousands of simulated personas in each. For even more specificity—such as targeting Gen Z or unique psychographic groups—we supplement with synthetic personas when real ones are not available, though the accuracy is somewhat lower.

### (00:23:27)
We offer yearly subscriptions only, starting at $12,000 and scaling up based on the number of analyses needed. With a $20,000 package, you can run about 20 projects—about ten times more cost-effective than traditional focus groups.

### (00:24:25)
Regarding the intake process for recruiting panelists, we ask a comprehensive set of questions to understand individual behaviors and preferences, similar to classic research methodologies (like MRI or GWI panels). While the full list is proprietary, we use hundreds of questions to build a reliable digital profile, ensuring accurate simulations for diverse and specific needs.

---

### (00:28:57)
Cultural nuance is addressed by designing specific surveys for each country—there’s no one-size-fits-all approach. For example, responses to horror films differ dramatically between countries like Finland and Italy, so we tailor our process to each region. Our R&D team constantly updates and expands these country-specific surveys.

---

### (00:40:14)
We can accommodate sensitive audiences, such as those in healthcare. If a required micro-audience is too niche, we combine real and synthetic personas, always ensuring privacy and compliance. Simulated focus groups are also highly secure, as confidential concepts aren’t exposed to real people who might leak information.

---

### (00:41:41)
When launching in a new market, we typically start with a few thousand respondents per country and constantly expand. If you need to test a specific group that’s underrepresented, we can quickly scale up through additional recruitment and automation.

---

### (00:44:35)
Simulated focus groups reduce bias found in traditional groups. By analyzing massive historical data (especially from the film industry), we detect authentic emotional responses, using a kind of “cinematographic DNA” to predict how people actually feel, even if they can’t fully articulate it themselves.

---

### (00:46:02)
The data is primarily survey-based, supplemented with extensive analysis of content and media. Our AI models break down text, visuals, and audio to connect specific creative elements to emotional outcomes.

---

### (00:47:18)
There are some limitations: Simulated personas cannot currently test physical products or interact with web pages in real time. However, you can upload screenshots or design concepts to gather feedback. Full user experience testing is not yet possible, but may be in the future.

---

### (00:49:04)
Our system accepts text, video, or audio. At an early stage, you can simply describe your idea, or provide documents and visuals for more advanced analysis. The system summarizes the content and generates feedback from the simulated audience.

---

### (00:50:28)
We understand there’s skepticism about whether a solution that’s faster and ten times cheaper can be accurate. That’s why we offer a free trial: you can test a past campaign and compare the simulated focus group’s feedback to your actual results. So far, our results have matched over 90% of the time.

---

### (00:53:00)
The platform was originally developed for the movie industry, but can be used for email responses, business concepts, and other formats. Any idea or creative concept that could be tested in a traditional focus group can be pretested using our simulation.

---

### (00:54:09)
Thank you all for your questions and participation. Céline, thank you for sharing these insights with us, and Chris, thank you for the introduction. We appreciate everyone’s engagement today and look forward to seeing what Largo AI does next.

---

### (00:54:34)
Thank you very much for inviting me and for all your thoughtful questions. It was a pleasure to present today. [Music]

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**Let me know if you need further editing, want this divided into even shorter sections, or need a downloadable format!**

## How Strategic Gifting Drives Revenue Growth in ABM Campaigns

Speaker: Mika Kayt
Published: 2025-04-08
Tags: gifting, revenue growth
Video: https://www.youtube.com/watch?v=v0lXcZYhFXc
Page: https://aimarketersguild.org/sessions/how-strategic-gifting-drives-revenue-growth-in-abm-campaigns

### (00:00:00)
Thank you everyone for joining and for those listening to the recording. I'm Ma Kite, CEO and founder of Outgage. Outgage is an engagement platform that uses gifting as a marketing channel.

---

### (00:00:34)
Joining me today is Maggie Tonkan, an expert in ABM and field marketing, particularly in gifting. Maggie and I have worked together on gift marketing campaigns at several companies. She has always been ahead of the curve, implementing strategies that are now standard practice. Maggie is also an Outgage advisor. Thank you, Maggie, for joining and sharing some case studies to demonstrate how gifting can be done thoughtfully and strategically.

---

### (00:01:30)
Today, we’ll cover several topics. I’ll give a more in-depth introduction to Outgage, discuss the power of strategic gifting, and show data that proves its effectiveness. We’ll also discuss how gifting can be a scalable revenue engine, how it can elevate ABM and field marketing, and how Outgage differentiates itself, especially for those unsure how to approach gifting.

---

### (00:02:21)
What sets Outgage apart is our focus on marketing-specific use cases. Many people who have done gifting before know there are many ways to send gifts, whether through vendors or other services. Outgage is designed for marketers, built to be the expert in gifting for marketing. In the age of AI, when there's a lot of noise and limited capacity to be experts in everything, we help marketers by specializing in gifting.

---

### (00:02:59)
We are AI and data-driven, moving away from traditional "spray and pray" approaches. Using data from millions of campaigns, we create predictive strategies to help customers build more effective campaigns. Besides our platform, which offers hundreds of thousands of branded goods, e-gifts, donations, and more, we provide a partnership approach—helpful from start to finish, not just a marketplace. We close the loop with customizable, actionable landing pages and full measurement, integrating results back into marketing automation systems. Our clients have seen over 10x engagement compared to other channels and up to 12x ROI. Many say they wouldn't be able to launch campaigns on tight timelines without our support.

---

### (00:05:16)
Regarding time savings, in many companies, creative is a shared service with competing priorities. Gifting campaigns often fall lower in priority, making fast execution difficult. Outgage’s support allows these campaigns to launch quickly, which is essential for ABM and field marketing. As Maggie will share in the upcoming case studies, Outgage truly is built for marketers—helping at every step, with all the strategies and goals marketers have in mind.

---

### (00:06:28)
Thank you, Maggie, for that feedback. It’s helpful to hear from someone on the client side. Maggie, you’ve managed more than just gift campaigns. Could you talk about the challenges and why someone would run a gifting campaign?

---

### (00:06:55)
Certainly. One of the biggest challenges marketers face is breaking through noise and clutter. At one company, I had to prove ROI for every tactic, so I worked closely with Outgage to gather the necessary data. When we analyzed our marketing channels, direct mail consistently had the highest engagement and ROI. While I believe in integrated marketing and the value of every channel, gifting always stands out. Over the years, I’ve accumulated a storage crate of gifts from various campaigns—they’re fun and memorable.

---

### (00:08:20)
When people hear "direct mail" or "gifting," they often think of old-fashioned postcards, which can seem outdated. I developed the "lumpy mail" concept: sending a package creates excitement. Whenever you receive something at your door, it sparks curiosity and engagement. This kind of gifting results in much higher engagement.

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### (00:09:18)
Research shows there’s a psychological need to reciprocate when receiving a gift. It triggers emotional responses and stands out compared to another email. The thoughtfulness and effort behind a gift makes a strong impression.

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### (00:10:30)
As marketers, it’s important to consider the recipient’s perspective. My inbox is overflowing, so many messages get lost or filtered. I design campaigns that would engage even me. I encourage everyone here to consider what would ignite their own response when planning strategic gifting.

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### (00:11:31)
Gifting isn’t meant to replace other channels. Every marketing channel serves a purpose, and multiple touchpoints are still necessary. ABM uses gifting as a way to take that extra step—moving beyond mass emails to targeted, meaningful engagement. The goal is to reach decision-makers and key accounts with highly relevant touches.

---

### (00:12:47)
When gifting is done smartly, depending on where prospects are in the funnel, we’ve seen engagement rates from 5% to 97%, with most campaigns above 35%. These numbers far exceed typical email marketing response rates.

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### (00:13:21)
ABM has evolved from pure one-to-one plays to a broader approach. Even when reaching larger groups, personalization is essential—and gifting is highly effective at creating emotional connections and engagement.

---

### (00:14:16)
Strategic gifting campaigns can double or triple meeting acceptance rates. It’s important to differentiate between a strategic marketing gift and a sales follow-up gift (like a coffee card for booking a meeting). Both have their place, and I often run both types of campaigns simultaneously.

---

### (00:15:31)
Sales is one place in the funnel (one-to-one), but marketing needs to reach one-to-few or one-to-many at scale. People buy from people, but you can’t take hundreds of accounts to dinner. Gifting provides a scalable way to add a personal touch without being perceived as bribery. The key is thoughtful execution.

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### (00:16:39)
Gifting acts as a pipeline accelerator throughout the funnel—from awareness and lead qualification to retention, upsell, cross-sell, and referrals. Outgage supports campaigns across all these stages.

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### (00:17:28)
Budget is always a consideration. The type and cost of the gift should match your campaign goals. For awareness, you won’t send an expensive gift to thousands of people. For pipeline acceleration, targeting stalled opportunities with a relevant gift can help move deals forward. I track how campaigns impact pipeline progression over time, and gifting consistently drives results.

---

### (00:19:15)
Timing is critical for gifting. Sending gifts as a one-off “test” is rarely effective. To maximize ROI, gifting should be integrated into a coordinated campaign, with marketing and sales working closely together on follow-up and nurturing. Without follow-up, gifts may generate interest but not qualified leads.

---

### (00:21:11)
Sales and marketing alignment is crucial. Gifting is not magic; after a gift is delivered, timely sales follow-up is essential. Sending 100 gifts without a follow-up plan wastes budget. Campaigns should be fully built out, with sales and marketing in sync on timing, messaging, and goals.

---

### (00:23:13)
To be effective, gifting should be part of a multi-touch, ongoing strategy. For example, Outgage has sent gifts to prospects using creative formats (like a locked cookie jar requiring engagement to open). Even if prospects weren’t ready to buy at the time, the memorable experience led to future business.

---

### (00:25:04)
Let’s move to case studies to illustrate these points.

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### (00:25:42)
Case Study 1: At Reich (a project management platform), the goal was to generate leads from a large, cold database. We sent locked acrylic boxes with candies and a branded postcard, driving recipients to a landing page. To unlock the candy, recipients had to engage on the landing page and request more information. Engagement was 41%, the highest of any outbound tactic used. Sales followed up with all recipients based on engagement data from Outgage’s platform, increasing overall campaign ROI.

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### (00:30:25)
Marketing automation and BDR follow-ups should work together. Automated messages ensure no touchpoint is missed, while personal outreach increases connection. Outgage’s landing pages go beyond form fills—they tell the company’s story and explain why someone should engage, increasing conversion rates.

---

### (00:32:47)
Case Study 2: At Zendesk, we targeted existing customers for upsell and product expansion. We sent game pieces representing new products, directing customers to a landing page. Those who engaged received a personalized drone. Engagement was 46%. The campaign qualified leads by allowing recipients to request demos or download resources, ensuring only interested prospects went to sales, saving time for both parties.

---

### (00:36:21)
Gifting can also support retention and customer delight. For global campaigns, e-gifts are useful—allowing recipients to select relevant rewards for their region. For example, a holiday campaign achieved 71% redemption and 54% recipient engagement, strengthening customer relationships.

---

### (00:38:18)
Creativity is essential in gifting. Adding elements of surprise, playfulness, or nostalgia (like locked boxes or two-part gifts) can make campaigns more memorable. However, not every idea works. For example, sending chocolate in the summer can lead to delivery problems, and a poorly executed two-part gift can backfire if the first part is underwhelming.

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### (00:44:02)
Choosing which accounts to target depends on your campaign goal. For high-value gifts, use intent data and target engaged or stalled opportunities. With limited budgets, prioritize warmer leads or customers likely to convert. Avoid sending expensive gifts as the first touch with cold prospects.

---

### (00:46:28)
Be mindful of the sales cycle stage. Don’t send gifts late in the decision or negotiation phase, as it may be seen as bribery. Once a deal is closed, onboarding gifts can strengthen new customer relationships.

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### (00:49:31)
AB testing gifting is more challenging, but Outgage’s technology allows for duplicating and modifying campaigns—testing messaging, workflows, and creative variations for optimal results.

---

### (00:52:08)
To summarize: Strategic gifting is a powerful revenue accelerator. Personalization, automation, and scale are essential for modern marketing. Outgage differentiates itself through custom workflows, landing pages, and full campaign tracking. Most of my most successful campaigns have been with Outgage because of their ability to drive engagement with creative, measurable campaigns.

---

### (00:53:13)
If you’re interested in seeing what gifting could do for your company, we’re happy to offer a one-on-one consultation.

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### (00:53:41)
Thank you for the opportunity to share these insights. There’s tremendous creativity in gifting, especially when integrated with ABM. Start with your goals, then explore how creative gifting can help you achieve them.

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### (00:54:49)
Thank you for having us in the community.

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Let me know if you need any more detailed editing, a specific format (like DOCX), or want the transcript split differently!

## AI is Changing EVERYTHING The Future of Work

Speaker: Rishad Tobaccowala
Published: 2025-04-02
Tags: future of work
Video: https://www.youtube.com/watch?v=iYtstqy0P-c
Page: https://aimarketersguild.org/sessions/ai-is-changing-everything-the-future-of-work

### (00:00:00)
Hi everyone, I'm excited to kick off this edition of AI Insiders by AI Marketer Guild with a special guest I've known for quite some time and have had the pleasure of learning from during my time at a Publicis Group agency. Rashad Tobaccowala spent a significant tenure there, ultimately serving as Chief Strategy Officer. Whenever he visited New York or I went to Chicago, I always enjoyed our conversations.

### (00:00:58)
Rashad, I don’t think I ever confessed this, but when you once offered me a ride to the airport, I actually adjusted my flight time so you wouldn’t think I was waiting around for hours. I just wanted the chance to catch up with you one-on-one; it was too valuable an opportunity to pass up. So, my apologies for that little white lie.

### (00:01:37)
Rashad, I’ve truly enjoyed your book and took quite a few notes. We have a great community here interested in the intersection of AI and marketing, and how AI is impacting the future of work. You've written and thought extensively about these topics, and I’m very interested in your perspective. I also come at this from a few different roles, as I'm currently engaged in various types of work—some of which I never expected to be doing five, ten, or fifteen years ago. I’m part of a fractional executive network, and I feel we’re in a transitional period where many people, by choice or necessity, are taking on multiple jobs or fractional work. The market demand and organizational structures haven’t fully caught up with this shift. I’d love to hear your take on where we are today.

### (00:02:58)
Thank you, David, and thanks to everyone attending. I recognize quite a few names in the audience, so hello to all. Let me set the stage with some broad themes from my book, which will help answer your question. The book is based on the premise that work is central to human existence. There are three main drivers of happiness: physical and mental health, the quality of relationships, and meaningful, purposeful, and rewarding work. Having meaningful work not only adds years to your life but also improves your relationships.

I believe that between 2019 and 2029, work will change more than it did in the previous fifty years, creating both significant opportunities and challenges—many of which are being overlooked in favor of debates like “return to office,” which is largely a distraction from the real issues.

### (00:03:35)
My book identifies five primary reasons work is changing: advances in technology and AI, the rise of marketplaces like Shopify, Etsy, and AWS, the growth of side hustles—this year as many people will receive a 1099 as a W2 in the US—and major demographic and societal changes. For instance, COVID’s biggest impact wasn’t where people work, but why and for whom they work. We are entering an age where people are less interested in returning to their bosses, not just their offices.

Other major shifts include an aging population—the only growing demographic in the US is over 50 years old—and the risk of declining populations if immigration is restricted, as the US birthrate is below replacement level. There are now four or five generations with very different mindsets working together. For example, 66% of Baby Boomers believe in capitalism, compared to 22% of Gen Z. Seventy-six percent of Gen Z want to work for themselves, and 67% of Gen Z with full-time jobs also have side gigs. Yet we’re still fixated on “return to office” debates, which shows a lack of imagination and leadership.

### (00:07:06)
Twelve CEOs read my book while I was writing it, and after five chapters, they realized no one had clearly laid out these issues for them before. My argument is that it’s about what leaders choose to pay attention to.

### (00:07:34)
Now, regarding what companies and individuals should do: Most organizations currently have three types of workers—full-time employees, contractors, and freelancers. In tech, companies like Google and Meta already have more contract workers than direct employees. I believe we need a new category: the fractional employee.

A fractional employee has all the benefits of a traditional employee but works, for example, 60% or 80% of the time and is paid accordingly—except for healthcare, which is provided in full. This approach is essential for three reasons: aging populations may want to continue working but not full-time; people at different life stages may need more flexibility; and, importantly, AI will reduce the demand for full-time staff, even though no one is talking about it. Companies could reduce headcount by 10-20% through fractional arrangements instead of layoffs, providing flexibility without the emotional or financial costs of severance.

---

### (00:10:00)
Another significant shift is in leadership. We’re experiencing a crisis of leadership. Many bosses operate from a zone of control—managing, overseeing, monitoring—whereas true leaders operate from a zone of influence—creating, mentoring, building, and inspiring. Today’s environment requires more of the latter.

For everyone, regardless of position, the future is about operating as a “company of one.” This doesn’t mean working alone but rather taking ownership of your craft, building your reputation, and collaborating with integrity. The entertainment industry already functions this way, with talent coming together for specific projects.

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### (00:12:13)
Looking ahead, I’ll make an intentionally provocative statement: In the future, there will be fewer traditional jobs, possibly none, but plenty of work to be done. The music industry is a good analogy. Once, you had to go somewhere to hear music; now, everything is fractionalized, with songs available on demand from the cloud. Work will similarly become project-based, with talent assembled as needed—a trend already seen in consulting and entertainment.

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### (00:13:57)
As companies adapt, there’s growing interest in roles that offer 60-80% employment with benefits. Many mid- and senior-level professionals are seeking such arrangements but find the market hasn’t fully adjusted.

My advice: Start from your current position and gradually diversify your work, communicating transparently with your organization. Over time, transition from full-time to fractional roles. Alternatively, if you’re already consulting or advising, combine multiple roles to suit your skills and preferences. Make yourself accessible and easy to engage—offer initial work at low commitment or even free to demonstrate your value.

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### (00:16:54)
One challenge in the US is healthcare, which is a significant consideration for anyone pursuing flexible work arrangements. Regarding career management in the age of AI, I view AI as a slingshot—an equalizer, not an advantage for big companies. Everyone will have access to AI, just like everyone has electricity. Companies need to rethink their entire strategies, not just create “AI strategies.” The key is to operate as a company of one, leveraging technology and continuously honing your skills.

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### (00:18:23)
For example, recently, I had to deliver a presentation on AI and retail with little notice. Using AI tools, I assembled a 35-page deck in 20 minutes, even though I wasn’t an expert in the field. AI allowed me to quickly research, create, and add value, showing that scale is increasingly a disadvantage—unless you’re a company like Nvidia or Walmart.

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### (00:19:38)
The question many are facing is how to make AI work for us, rather than against us. I write a free Substack every Sunday, and one of my most popular pieces discusses AI, humans, and the future of work. My main points: AI is still underhyped, and most people don’t realize its potential. Just as computation made calculations cheap and the internet made information cheap, AI will make knowledge cheap. However, intuition, insight, and creativity remain distinctly human.

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### (00:21:18)
Companies are focusing on efficiency and effectiveness through AI, rapidly reducing the need for certain roles, especially in creative industries. However, the real transformation lies in existential opportunities and risks. Digital disruption didn’t just make the New York Times print faster—it fundamentally changed their business model. Similarly, AI demands a complete rethinking of business, not just optimization of existing processes.

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### (00:23:34)
There will soon be billion-dollar companies with fewer than 100—or even 10—employees, as shown in my book and in discussions with Google. AI is an extraordinary opportunity for those willing to adapt, but also a significant threat to those who cling to outdated knowledge or models.

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### (00:24:37)
AI is best described as an alien life form—neither machine nor human, but something entirely new. It continues to evolve rapidly, and interacting with it can feel like interacting with a living being.

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### (00:25:16)
Let’s take some questions from the community. One question is: How does the concept of being a “company of one” work in upper management, where collaboration is important? And can AI be used to enable “debossification”?

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### (00:25:51)
Being a company of one doesn’t mean working alone; it means having a clear skill set, a strong reputation, and being highly collaborative. In management, your deliverables must be clear—management itself isn’t a deliverable, especially as machines become better at certain management tasks. As for debossification, AI accelerates the process by making knowledge and access less dependent on traditional hierarchy.

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### (00:27:01)
The traditional sources of a boss’s power—knowledge, access, and the ability to hire or fire—are becoming less relevant. Expertise is more valuable than experience, and AI can quickly level the playing field when it comes to information and access. The only real remaining authority is the power to terminate employment, but employees have increasing options.

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### (00:28:46)
There are questions about early-career workers and where future talent will come from if AI takes over entry-level tasks. Some worry that graduates won’t be able to “get their foot in the door.” It’s true that Gen Z is finding it harder to secure jobs, but much of traditional entry-level work was busywork that should be automated. Instead, early-career workers can now take on more meaningful tasks, build skills quickly, and launch projects independently.

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### (00:32:23)
It’s important to distinguish between bringing people back to the office and fostering meaningful in-person interaction. Many activities attributed to the office—creative brainstorming, relationship-building, and learning—often took place elsewhere. Companies should design intentional opportunities for interaction, rather than simply mandating office attendance.

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### (00:34:40)
Regarding marketing and AI: Many marketing roles are being automated, but instead of searching for “safe” areas, focus on building core skills. I call these the “six Cs.” Change is difficult, especially for those established in their fields, but irrelevance is worse. To adapt, create a strategy for future growth, upgrade your skills, and be proactive about reorganization or transitions.

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### (00:38:13)
The six Cs for future success are: cognition (continuous learning), creativity (connecting ideas in new ways), curiosity (asking why and what if), collaboration (working with integrity and trust), convincing (storytelling and selling ideas), and communication (writing and speaking effectively). In the past, people were told to learn coding or Mandarin; today, writing and presentation skills are more important. Even when I used AI tools to help with my latest book, my own answers were better because they reflected a unique point of view and clear action plan—something AI still struggles with.

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### (00:41:57)
I’ve put together a resource page with my best writing on these topics, all available for free.

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### (00:43:08)
When asked which companies are getting it right, I emphasize focusing on positive examples. There are many companies continually reinventing themselves—Microsoft, Delta Airlines (which invested in employees and service rather than cutting costs), and Domino’s Pizza (which completely transformed its business). The most talented people will gravitate to organizations where they feel inspired and can grow.

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### (00:46:16)
In the future, success will come from combining AI with “HI”—not just human intelligence, but human intuition, insight, ingenuity, imagination, and inspiration. Those who refuse to use AI will find themselves increasingly marginalized, just as those who refused to switch from typewriters to word processors.

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### (00:47:40)
Addressing workplace culture, especially “masculine energy” and resistance to diversity, equity, and inclusion: True leadership is rooted in principles, not just profit. Companies that ignore DEI are losing the next generation of talent. I advocate for thinking like an immigrant—embracing outsider perspectives, underdog determination, and inclusivity—as a more effective approach than traditional notions of “hardcore” leadership.

### (00:51:41)
In closing, thank you for spending time with us today. Continue to educate yourself and remain optimistic about the future. Despite headlines to the contrary, it’s a better time to be alive today in nearly every aspect. Feed your mind with quality content, not just what algorithms serve you.

If you’re interested, my book is available, and you can reach me at rashad@gmail.com.

## How to Lead AI Transformation AI Readiness Strategy and Culture

Speaker: David Passiak
Published: 2025-03-27
Tags: ai training
Video: https://www.youtube.com/watch?v=_SEMdbKAIGs
Page: https://aimarketersguild.org/sessions/how-to-lead-ai-transformation-ai-readiness-strategy-and-culture

**(0:00:00) David Passiak:** [Music] Hey everyone, welcome back to another edition of AI Insiders with AI Marketers Guild. It's a fun reunion today, so please feel free to share anything that interests you. If this is your first time or if it's been a while since you've joined us, feel free to stay on camera and chime in.

**(0:36:44) David Passiak:** We have a terrific group today, and we're especially excited to bring back David Passiak, who has led a certification program we’ve done together. He has also offered training on optimizing the use of ChatGPT and AI more broadly. David is one of the best educators and builders, not just talking from an academic perspective but creating practical tools that enhance learning. So, David, welcome!

**(0:71:24) David Passiak:** Thank you so much for that warm introduction. I want to thank all of you for being here. I know that today is busy and hectic, making your time and attention incredibly valuable. I am truly grateful to have you here, and I aim to provide you with insights and wisdom from my experiences in AI leadership. If you're interested in collaborating or training opportunities at the end of the session, we can explore those together.

**(1:25:60) David Passiak:** I will share a presentation with you. Today, I will discuss AI readiness and the future of leadership. Many companies are currently overwhelmed by AI implementation, seeing its benefits and studies, yet unsure of what to do next. This often results in decision paralysis due to the rapid technological changes. I will present a model for organizational transformation using AI and how it relates to future leadership.

**(2:12:799) David Passiak:** For those watching the recording on YouTube, I have included a QR code to download the slides. I am the founder of Creator Pro, have authored four books, and I’m a global keynote speaker specializing in innovation, collaboration, and co-creation. I have spent years giving talks and workshops worldwide, often traveling to 20 to 25 countries annually.

**(2:36:159) David Passiak:** My background is different from most in the AI space; I began my career in academia, pursuing a PhD in philosophy and religion. This foundation has shaped my intuitive approach to designing AI models and training, which I call the "Insight Architect Method." However, today, I'll focus on the organization transformation process that we've developed over the past two years, based on best practices from my career.

**(3:01:639) David Passiak:** For those in AI Marketers Guild, many have around ten years of marketing experience. AI readiness often requires an initial assessment to prepare for implementation. The market is shifting quickly, and many leaders are already overwhelmed by running their companies while also figuring out the strategic transformation of their organizations—a task that can feel like a full-time job. This can lead to paralysis, not knowing how to implement AI or make informed decisions.

**(3:49:44) David Passiak:** Our approach has involved rapid prototyping of an AI readiness assessment to determine how to implement AI across an organization. I will now discuss some misconceptions related to AI and leadership.

**(4:13:56) David Passiak:** While many here may not hold these misconceptions, your clients or colleagues likely do. Here are four common misconceptions:

1. AI strategy should be led by younger, tech-savvy team members, leading to a narrow, project-focused perspective that overlooks the holistic vision needed for leadership.

2. AI will phase out human roles, leading to autonomous company operations. This view limits AI’s potential, as true impact comes from the combination of AI and human innovation.

3. Integrating AI means simply adopting tools to improve existing processes. Organizations need to rethink how AI can facilitate innovation and transformations rather than merely enhancing existing workflows.

4. AI can instantly solve complex strategic challenges. Many expect a single prompt to yield comprehensive solutions, often leading to frustration and abandonment of AI.

**(5:08:96) David Passiak:** An AI readiness assessment is essential for targeted strategic investment, quickly providing a snapshot of where the organization stands and what is needed for success. It helps in aligning strategy, clarifying expectations, and building trust among team members, as leading an organization through AI transformation requires a collective effort.

**(5:52:72) David Passiak:** The model I've developed aligns with Maslow's hierarchy of needs, where you cannot skip to the top without securing the base. The foundation for AI integration begins with AI literacy—achieving a basic competency in understanding how AI fits strategically within the organization.

**(6:58:839) David Passiak:** Establishing this foundational AI literacy involves assessing roles within the organization—determining how each person’s role interacts with AI integration. It’s crucial to communicate clearly from leadership to the entire team about these developments, avoiding any atmosphere of fear or anxiety which can stifle empowerment.

**(7:20:88) David Passiak:** Communication is critical because implementing AI is more about change management than technology itself. About 60 to 70% of AI implementation involves changing culture and behavior, which requires clear communication. It's easier to reassure team members about efficiencies gained from AI rather than implementing extensive changes suddenly.

**(7:39:60) David Passiak:** Each foundational level should start with clear communication, operational efficiency, and quick wins to create momentum. Identifying low-hanging fruit, such as improving reporting or drafting content efficiently, is key to demonstrating value quickly.

**(8:55:10) David Passiak:** Once initial quick wins are established, organizations can begin to integrate AI into fundamental workflows. Change management, cross-functional collaboration, up-skilling team members, and strategic decision-making are crucial elements moving forward.

**(9:17:10) David Passiak:** Organizations need to maintain adaptive agility, continuously reassessing and adjusting to ensure optimal performance and the utilization of the latest technologies. Such environments foster innovation and proactive responses to market shifts.

**(9:44:00) David Passiak:** Questions? Let me know, and feel free to raise hands or share in the chats.

**(10:02:60) David Passiak:** If anyone wants to jump in with queries or insights, remote learning thrives on collaboration and collective engagement. There will be ample time for discussion at the end.

**(10:25:20) David Passiak:** Thank you all for your contributions. It’s essential to anticipate roadblocks like legal constraints and internal politics. These elements can significantly impact the pace and success of AI implementation within a company.

**(10:55:70) David Passiak:** Having a cross-functional steering committee can help mitigate these issues by including key stakeholders from various areas, ensuring that concerns are addressed proactively rather than reactively.

**(11:18:80) David Passiak:** Emphasizing the organization’s transformational purpose is critical as well. Establishing the “why” can foster a collaborative spirit where team members feel valued and empowered by the technological changes rather than threatened.

**(11:47:70) David Passiak:** As a reminder, nobody is here to replace you; the goal is to harness AI as a tool for empowerment and transformation. If organizations foster an environment of support, they are more likely to see positive outcomes from AI integration.

**(12:08:80) David Passiak:** Collaboration and continual cultural integration are vital for successful transformation, and we must address fears and anxieties head-on to build trust and enthusiasm for these changes.

**(12:36:00) David Passiak:** Lastly, involving everyone in these dialogues around AI and its benefits ensures that they feel included in the journey. It’s essential to remember that people are at the heart of these technological transitions, and their integration requires careful management.

**(12:58:20) David Passiak:** We should consider how to sustain these changes in a way that implements them gradually, avoiding overwhelming team members while reaping significant benefits from AI’s integration.

**(13:27:30) David Passiak:** With that, let’s open the floor again to wrap up our discussion or move on to any additional key points that might be shared. I look forward to the continued conversation.

## How AI is Revolutionizing Video Creation

Speaker: Jeremy Toeman
Published: 2025-03-21
Tags: video production, video marketing
Video: https://www.youtube.com/watch?v=81rxK8sXql4
Page: https://aimarketersguild.org/sessions/how-ai-is-revolutionizing-video-creation

**(00:10)**
Welcome, everyone, from wherever you're dialing in from. I'm Nicola Quail, one of the co-founders of the AI Marketers Guild (AIMG) APAC, along with my colleagues Daan Anardi from India and Sushita Mahapatra from Singapore. We're thrilled to have you here and excited about how this community is expanding across the APAC region. I know we also have friends joining from North America, as our guest speaker is based there.

Before I introduce Jeremy, I’d like to give a brief background for those new to the community.

**(00:47)**
The AI Marketers Guild was the brainchild of US marketer David Burwitz. After launching two years ago, the community has grown significantly in North America. We reached out to David to partner with AIMG and help create a regional community that showcases local pioneers, AI tools, and marketing best practices with AI integration—while also featuring world-class thought leaders.

We host monthly webinars and will soon invite you to join our Slack community. We're also planning city-based meetups later this year. We're thrilled that you're not just dabbling on the fringes of AI, but actively leaning in.

**(01:22)**
It’s my privilege to introduce our guest speaker and workshop host, Jeremy Toeman. Jeremy previously led Product and Innovation at Warner Media, where he tackled the challenge of scaling video creation. This led him to co-found an all-in-one video studio designed to simplify video creation for businesses of all skill levels.

Jeremy has spoken at AIMG US, and today he'll guide us through a demo and workshop, covering the background of the product, a live demo, and interactive exercises. He’s done this a few times, so feel free to ask questions directly—either in the chat or by unmuting. The best way to learn is to get hands-on, and we're lucky to have one of the best here with us.

Without further ado, welcome Jeremy!

**(02:35)**
Thanks so much—it's a pleasure to be here. David told me that, by coincidence or fate, I happened to be at the very first AIMG meetup in New York City two years ago. That’s where I first met him, and I loved what he was doing. It was a perfect match with what we were building at Augie. I'm excited to continue that connection and meet folks across different regions.

As Nicola mentioned, I like interactivity. I might occasionally call on the audience, but always in a fun way. I’ve got a presentation that covers important stats on social and AI video, and then we’ll dive into a hands-on workshop where we’ll create videos together.

**(04:12)**
Let me start my screen share. If something isn’t working, someone will probably wave at me—so I’m going to trust that everything’s fine.

Nicola's intro was spot on. Augie started from a personal frustration: trying to make video content efficiently. I’ll explain more in a minute, but today we’ll walk through who I am, why this matters, how to get started, and then dive into the demo.

**(05:22)**
I’ve spent my entire career at the intersection of tech and media. I was the first employee at Sling Media and helped build the original Slingbox—the first way to watch TV online, long before Netflix and YouTube. Later, I helped launch companies like Vudu and Sonos.

In the 2010s, I started my own company called Digit, which created the first app to combine live TV and streaming. It was acquired, and I went on to lead Product at CBS Interactive, then Warner Media, during the pandemic.

During the pandemic, my friend (now co-founder) and I started a podcast. As we tried to grow it, the number one recommendation was to make video content. Despite working at Warner and having access to top-tier editors and tools, it was still incredibly difficult and frustrating.

**(06:57)**
That frustration led me to explore why video creation is so hard. We'll dive into that shortly. But first, let’s talk about the state of video itself.

This next video will share a few key stats. Every video I show today was created using Augie. While we once hoped video would replace all slide decks, we realized that sometimes you need slides for pacing. That said, let’s give it a try.

**(08:35)**
Experts say the best time to start video marketing was years ago. The second-best time? Today. The world of video marketing is growing fast.

Only 1 in 3 companies can keep up with their video content needs. The common reasons? Not enough time, people, or resources. Video production teams often become the bottleneck—not due to lack of skill, but because the tools are slow and time-consuming.

**(09:36)**
Some key numbers: every minute, 500 hours of video are uploaded to YouTube. That’s over 5,000 hours since I began this talk.

Brands love video because it works. Once you integrate it into your workflow, it can be incredibly effective—if you can make it work within your time and budget.

Marketers often lack tools for video creation. Adobe Premiere Pro is powerful, but learning it can feel like flying a helicopter just to pick up milk. Meanwhile, tools like CapCut are great for creators, but not for brand-level work with guidelines and assets.

**(11:34)**
Let’s do a quick pulse check. Give a thumbs up if your brand is making enough videos to meet your marketing goals. Anyone? Now, how many of you feel overwhelmed or haven’t started with video yet?

The big hurdle most people face is deciding what kind of video to make. Here’s a cheat sheet:
1. **Thought leadership videos** – Someone at your company likely has expertise. Record them sharing insights.
2. **Explainers and demo videos** – Use tools like Loom or Zight to record product walkthroughs.
3. **Social ads** – You can target micro-audiences with just a few dollars and test content with low risk.

**(12:40)**
There are incredibly easy ways to get started with video, and they don’t require a lot of production time or planning. You don’t need Augie for everything—you can simply record your CEO talking about something they know well.

Thought leadership, explainer videos, and social videos are excellent entry points for any product, service, or brand.

**(14:13)**
Now, let’s talk about where to place these videos for maximum impact. One stat that might surprise you: if you add a video that’s contextually relevant to your web page, it significantly boosts SEO. For example, a homepage filled with content about your amazing widgets will perform even better if you include a video about how great those widgets are.

We believe (though can’t fully prove) that uploading the video to YouTube might give you an extra SEO bump.

**(15:14)**
Video is also critical on social platforms. Figure out where your audience is. For many B2B companies, LinkedIn is an easy and impactful place to start. If you’re targeting enterprise clients, LinkedIn video content is incredibly effective.

For broader reach, platforms like TikTok and YouTube are still growing rapidly. It’s definitely not too late to establish your brand’s presence there.

**(16:11)**
The third key area is email. If you work with your email marketing team—or are part of it—know that including video links in your emails boosts engagement significantly. On average, video in emails can improve engagement rates by up to 300%.

That doesn’t mean embedding huge video files. Instead, you can include a subject line like “Watch our video,” a thumbnail image, or an animated GIF that hints at video content.

**(17:17)**
I’ve shared a lot of information, so let’s move into the next part—getting hands-on with Augie. I’ll start by showing you the dashboard and walk through how to create a video.

Let’s dive into creating one together. I’ll begin without any explanation and peel back the layers afterward.

**(17:48)**
Nicole G., you were the first person to join—could you share a hobby or pastime, or even one you wish you had?

**Nicole:** Sure! Let’s go with watercolor painting.
Great choice. I don’t know much about it, but now I’m going to use Augie to explain why watercolor painting is a soothing hobby that offsets a busy workday.

We’ll go with a “hopeful” tone for this video. Augie will help us write a script. You can regenerate the script or edit it manually. The platform uses ChatGPT for writing and has been trained specifically on marketing-style video narration.

**(19:24)**
You can either read the script yourself, use your cloned voice (with a one-time 45–60 second recording), or choose from over 200 synthetic AI voices. These are fully licensed for commercial use—no celebrity voices like Scarlett Johansson, but a wide range of accents and styles.

Let’s pick the voice “Andrew” for this one. Since I don’t have any watercolor assets on hand, I’ll ask Augie to handle everything: generate content, script, and visuals.

**(21:50)**
This process takes about 90 seconds to three minutes. Augie will voice the script, break it into segments (storyboard style), and assign media assets to each section.

We designed Augie so that making a video is as easy as paint-by-numbers. While that loads, I’ll show a few sample videos we've made using Augie, including a Kickstarter video and a British Airways ad.

**(23:47)**
The Kickstarter video had no narration. The British Airways video featured a narrator. The third example uses a human narrator—Jonathan Haidt—where we repurposed his talk into a social media video. This highlights the importance of **content reuse**.

Even if you’ve spent $15,000 on a video ad, ask yourself: what else can you do with those assets?

**(25:53)**
Now let’s return to Augie and view the video we started with: “Embracing Calm Through Watercolor Art.”

Augie generates a **Rough Cut Report**, estimating how much time it saved you. While this isn't a final, polished video, it's a strong first draft.

Let’s watch it.

*"Life can feel like a relentless pursuit, leaving little room for the calm we all crave. Watercolor painting offers a beautiful escape..."*

**(27:52)**
The voiceover worked well, except the last syllable was cut off. That happens sometimes in the preview, but won’t appear in the final export.

Now let’s improve it. First, we’ll swap out a few video clips to better match the tone. Augie allows you to select and replace media from over 130 million assets, including those from Getty Images, Pexels, and Unsplash—all with full commercial rights.

**(30:28)**
You can sync media precisely to spoken words using Augie’s **keyframe alignment**. For example, if a scene ends after the word “pursuit,” and you want it to end after “leaving,” simply drag the marker—it’s that easy.

This eliminates the need for traditional timeline-based video editing. The script, storyboard, and preview are all interconnected.

**(31:35)**
Changing out assets is also straightforward. Say a clip doesn’t feel like “relentless pursuit”—you can easily replace it, search for something more appropriate, and trim it as needed.

If you prefer still images over video, those are available too. Augie supports portrait, square, and landscape formats with one-click resizing.

**(33:01)**
Now, let's replace some stock content with original assets. The best videos come from your own materials. I’ll create two more videos to demonstrate.

First, I’ll make a narrated ad for Augie—targeting busy marketers—with the tone “magical.” I select a voice and let Augie write the script. It can preview audio, and I recommend listening to it in case it mispronounces brand names.

**(35:19)**
This time, instead of asking Augie to suggest stock content, I’ll use my own. I select a folder with screenshots, the Augie logo, and product usage images. Augie will automatically match these assets to appropriate moments in the script.

This helps you create highly personalized videos using your real footage and branding.

**(35:52)**
Next, I’ll make a promo video for the Silicon Alley Sports Tournament—featuring pickleball and golf—targeted at tech professionals in New York this May. We’ll use the tone “bold.”

Once the script and voice are ready, I upload about 25 minutes of event footage. Augie uses computer vision to analyze and tag these clips by their content—like golf, pickleball, or networking.

**(37:15)**
As an extreme example, I uploaded *Pirates of the Caribbean* (for demo purposes only—don’t do this without rights). Augie recognizes content within it: Johnny Depp, pirates, skeletons, gold coins—even the director’s name. It can detect these elements in any video and let you search your footage using natural language.

This makes it easy to find and reuse moments from your own B-roll.

(38:58)**
Everything you upload is private to you. We don’t use it to train AI models or share it publicly. Once your footage is uploaded and analyzed, you can build polished, contextually relevant videos much faster.

**(39:29)**
Next, I’ll show Augie’s **Highlighter** feature. If you have a long video (e.g., a 6-minute talk), you can use this tool to extract short, impactful clips for platforms like LinkedIn.

Upload your video and Augie will generate a transcript. You can search it by topic—for example, “generative video”—and highlight sections to turn into a social post.

**(41:00)**
It’s as simple as highlighting words like you would in Google Docs. You can rearrange the order of clips, fix typos, add transitions, and select portrait or landscape mode for your export.

You can also export just the highlight video and bring it into Premiere or any editing tool if you prefer.

**(43:00)**
The first ad we made is now ready. We gave Augie a simple prompt, a few assets, and let it build.

*"Every marketer knows time slips through your fingers like grains of sand. But what if video editing felt magical?"*

It used our assets and created a voiceover, visuals, and sequence. Of course, you can tweak anything further—replace clips, add more footage, or fine-tune transitions.

**(44:34)**
Let’s add music. Augie has nearly 10,000 pre-licensed tracks. You can fade in music, change volume, and even set when it starts—all with a few clicks.

The music adds emotional impact to your story.

**(45:42)**
Want your logo visible throughout the video? Use the **Objects** tab. You can drag in your logo, set its position, opacity, and apply visual effects. You can even watermark the entire video.

Adjusting timing for each object is also easy.

**(47:54)**
Text boxes can also be fully customized. You can change colors, fonts, opacity, and styles. Augie remembers your brand colors and keeps them available for reuse.

We’re also adding custom font uploads and more styling presets soon.

**(49:34)**
You can also add layered content like QR codes or motion graphics, and control exactly when they appear. This gives you flexibility for more complex visual storytelling.

**(51:25)**
One attendee asked if Augie can stitch together live event coverage. Yes! Once you have the event file (MP4, QuickTime, etc.), you can upload it. It takes about 10 minutes to generate the transcript, and about an hour for full video analysis.

You can then auto-generate highlight videos or create polished recaps using your own footage and photos.

**(52:35)**
Here’s an example: a storyboard made entirely from static images. With Ken Burns effects and smooth transitions, it can still feel dynamic.

Everything is modular. You can mix videos and images however you like.

**(53:49)**
Another attendee asked about balancing personal branding (e.g., being on camera) with stock content. You can absolutely do both. Adding supporting visuals to talking head videos improves engagement by 25–30%.

Even a single cutaway to a relevant image can boost watch time significantly.

**(55:03)**
To insert images mid-video, just go to the relevant section, click "Add," and choose the asset. You can also switch layouts: picture-in-picture, split screen, or full-screen overlays.

This flexibility lets you tell your story however you want.

**(56:16)**
The Silicon Alley video example shows Augie automatically selecting moments from 20+ minutes of footage—without manual editing. But you can still fine-tune. For example, if the script mentions “golf” but the video shows pickleball, just replace that segment and search for a golf swing.

Augie’s AI makes those matches fast.

**(58:57)**
If you’re looking for diverse representation (e.g., Indian faces), you can search for "young Indian man with laptop" or other specific phrases. Augie can surface inclusive content from its stock libraries, including Getty.

We’re also working to expand diversity in both visuals and voice options.

**(59:50)**
Someone mentioned captions—thank you for the reminder! Augie offers **automatic closed captions** in one click. You can customize fonts, colors, positions, and animations.

Captions improve accessibility and engagement, especially on social platforms.

**(1:01:26)**
If there’s a typo or you want to reword a sentence, you can choose to edit the **captions only**, or **edit the script**, which re-generates the voiceover. Captions are great for emphasis, but always double-check them—they’re AI-generated and can miss context.

**(1:03:09)**
You can also download the full transcript, collaborate via Dropbox Replay, and manage everything in your Augie workspace.

Before we wrap, let me show you something fun.

**(1:03:44)**
What if *Ocean’s Eleven* were a romantic comedy? I gave Augie that exact prompt and added one image. Here’s the result:

> *"Danny Ocean has a plan—but it’s not just about stealing hearts. When he meets Tess, sparks fly..."*

That entire video was written, voiced, and edited by Augie. All I did was type one prompt.

**(1:04:24)**
Thanks, everyone! We’re just over time, but I appreciate you sticking around. You’ve seen how impactful video can be for your brand’s reach and storytelling.

Jeremy has generously shared a free 3-month trial for AIMG members. The code is **AIMG3months**—it’s in the chat. Try it out, explore what you can create, and let us know what you build!

Thanks again, Jeremy. This was fantastic.

**(1:05:39)**
Thanks so much. It’s been great to walk through this with all of you. If you try Augie and have feedback—good or bad—I’d love to hear it. Don’t hesitate to reach out on LinkedIn. Thanks again!

## Balancing AI & Empathy: Human-Centric Leadership in the Age of AI

Speaker: Jack Myers
Published: 2025-03-19
Tags: empathy, human centered ai, ai in business, ethical ai
Video: https://www.youtube.com/watch?v=M6V6Xw3zISg
Page: https://aimarketersguild.org/sessions/balancing-ai-and-empathy-human-centric-leadership-in-the-age-of-ai

(00:09)

Welcome back to another edition of AI Insiders with the AI Marketers Guild. I'm especially excited about today's guest—not just a prolific author, but someone whose work was pivotal when I started in business. Back when I was cutting my teeth at eMarketer , The Jack Myers Report —or The Myers Report —was essential reading for anyone serious about understanding the ad industry. Honestly, it was a bit over my head at first, and I found it daunting. But when I first heard the name Jack Myers, it carried this voice of authority in the industry.

(00:48)

Jack, I think you're aging me here! The Myers Report was one of the very first daily fax newsletters—definitely dating myself.

Well, it remained relevant as eMarketer helped shepherd in the digital media age. And Jack, you've continued to innovate in publishing with MediaVillage , fostering community and thought leadership.

(01:27)

I've also been reading more about your background, and your latest book is particularly fascinating. For those watching, you can see it behind him: The Dao of Leadership . Should I pronounce it as "The Dao" or "The Tao"?

(02:08)

Either works! The correct pronunciation is "Dao," but it's spelled "Tao"—so The Dao of Leadership .

(02:08)

Your book is especially intriguing because it spans a century—looking at the ad industry from the 1970s and projecting what business might look like in 2050. You're essentially covering a 100-year arc, not just in advertising but in business overall.

I have a lot of questions, Jack. You're writing about the kind of industry and business culture I want to be part of in 25 years. But first, I'll let you introduce yourself properly and share anything you'd like before we dive in.

(02:53)

I appreciate that, David. The book is designed to focus on the next five years. But given the speed of AI-driven innovation, we have to compress our timelines. The acceleration of AI and technology is impacting marketing, business, and every aspect of our lives.

Yes, there are concerns, fears, and challenges. But for those who understand how AI creates opportunities for human creativity, it's also an exciting time.

(03:37)

One thing that surprised me in your book was how often empathy came up. This is a book about AI, ethics, and responsible adoption—yet "empathy" appears 122 times . "Empathetic" shows up 147 times .

I even searched for "emotional intelligence"—it appears 60 times . "Diversity" comes up 51 times , while "inclusion," "inclusivity," or "inclusive" appear 62 times .

(05:56)

So, what made you want to write about empathy so much right now?

(06:33)

The book is fundamentally about creativity —how we navigate today’s cultural and business realities. There are three massive forces shaping our future:

- The acceleration of innovation – Traditional models of innovation no longer work. You can’t build bridges from the past to the future anymore; you need to create portals. By the time you complete risk assessment and testing, the landscape has already changed.

- Outdated corporate structures – Over 90% of global corporations still follow Peter Drucker’s 1952 corporate model, built on WWII-era General Motors production lines. These structures are decentralized, siloed, and inefficient.

- The historical connection between technology and creativity – Every major technological disruption—from the printing press to the Renaissance—has been accompanied by a creative revolution.

(07:56)

I realized while writing that the core principles of Daoism— flexibility, belonging, balance, creativity, and integrity —are still incredibly relevant. If we embrace human-focused creativity, we can navigate the challenges of AI while keeping the human element at the center.

(09:36)

David, I choose optimism in all my books. That’s becoming harder, given the challenges we face—regulatory disruption, economic uncertainty, AI’s rapid evolution, cultural and workforce shifts, and geopolitical volatility.

From a marketing perspective, we're seeing commerce disruption, data upheaval, the collapse of the traditional marketing funnel, and health and wellness concerns. And yet, empathy—along with diversity, inclusion, and equity—is being pushed aside in certain circles.

(10:56)

Fifteen years ago, when I launched MediaVillage , I conducted a study measuring job satisfaction and emotional wellness across 15 industries. At the time, advertising ranked 13th out of 15 . By 2024, after 15 years of DEI investment, advertising had risen to 3rd place . That’s significant. But now, we're regressing. Funding for diversity and education initiatives is drying up, despite being critical for business growth.

Empathy, intuition, ingenuity—these are the tools that will drive future success.

(13:09)

David, I also want to acknowledge that MediaVillage has championed these issues before they became trendy. Long before it was mainstream, you were highlighting Hispanic marketing, women's leadership, and diversity initiatives.

(17:13)

We're facing macroeconomic challenges beyond any one party or leader’s control—workforce shifts, climate refugees, inflation. People joke about the price of eggs, but it's real. Businesses, too, are in survival mode, focused on staying afloat , not on fostering creativity.

(18:31)

But Jack, creativity often feels like a luxury, not a necessity.

(19:12)

That’s exactly the problem. Creativity isn't a luxury; it's the foundation of progress. The Renaissance wasn’t dictated top-down—it bubbled up from the artists.

Today, agencies churn out thousands of ad variations using AI, but they’re not rethinking how we message . They’re not collaborating with AI to reinvent the industry.

(22:52)

We’re overinvesting in data. 80% of media spending is data-driven , but data alone isn’t a solution—it’s a disruptor. The real opportunity is shifting ad budgets back to creativity . AI should be a tool for enhancing creativity , not replacing it.

(27:09)

Apple is an interesting case study. Yes, they've struggled with AI adoption, but they remain one of the few corporations taking a humanistic approach to technology. It’s not just about what they build, but how they build it—with empathy for their users.

(29:08)

Think about the generation being born right now—post-AI, post-Gen Alpha. By 2050, they'll be in leadership roles, and their fundamental relationship with AI will be different. AI won’t be something they adopt; it will have always been there . It will be as natural to them as the internet is to us.

I teach a class at the University of Arizona with juniors and seniors. When they started, 80% had minimal experience with AI, and some of their professors actively restricted them from using it. That’s a mistake. It’s like telling students they can’t use the internet. AI is already embedded in our culture, and those growing up with it will see it as a collaborative tool rather than a separate technology.

The more we build systems today that empower them, the better. Whether you're a Boomer, Gen X, Millennial, or Gen Z, the key is using AI to enhance creativity and asking: How do we incorporate empathy, ethics, and ingenuity into AI-driven decision-making?

(30:33)

I use AI frequently, but I still see bias built into these models. Ethics remains a huge challenge. If AI is trained on biased data, it will reinforce those biases.

For example, when I ask DALL·E to generate an image of "businesspeople," it still defaults to white men in suits . I actively push back: No, that’s unacceptable. Show me something diverse. Sometimes, AI listens. Sometimes, it doesn’t. But we must challenge it.

(31:25)

This is where brand marketers and advertisers have enormous responsibility. The decisions we make today—how we train AI, how we use data, how we fund DEI initiatives—will shape whether AI-driven marketing perpetuates bias or fosters inclusion.

Unfortunately, many companies are cutting back on funding for the very initiatives that have made marketing and media more inclusive over the last decade. That’s deeply concerning.

(32:14)

Chris, you made a great point earlier about empathy as a superpower. AI and data should be tools that enhance customer understanding, not replace human insight.

(34:12)

From a marketing perspective, we’re entering Upfront season—the time when TV networks and media companies negotiate ad sales for the coming year. The traditional Upfront model was built on sponsorships. Shows didn’t get produced unless brands sponsored them.

Today, sports remain the strongest segment of linear media—and sports advertising is still heavily sponsorship-driven. That model works . I believe we’ll see a revival of sponsorship-driven marketing, where brands aren’t just placing ads but actively supporting and enabling content creation .

This isn’t just about AI personalizing ads—it’s about brands fostering meaningful, emotional connections with audiences.

(36:20)

That’s why education is crucial. We need to teach leaders that data alone reflects the past—it can’t predict or inspire true creativity. Machines generate outputs based on historical data, but human creativity emerges spontaneously, responding uniquely to new situations.

Sherry Turkle has done extensive work on the psychological impact of technology, particularly how social media affects mental health. AI is the next frontier—especially when you consider that 25% of Gen Z already considers an AI chatbot their "best friend." That’s both fascinating and alarming.

(40:53)

David, your point about personalization is crucial. AI-driven marketing allows hyper-customized experiences, but at what cost?

Imagine this: We all visit the same book landing page, but AI generates completely different versions for each of us—different copy, images, offers—tailored by our data, search history, even the weather.

That’s AI-powered empathy in one sense—it’s trying to relate to us individually. But it also isolates us into bubbles, preventing shared experiences. If personalization fragments reality too much, how do we maintain a sense of community?

(46:00)

The real challenge isn’t just AI itself—it’s how we implement it. Will we train AI to reinforce empathy, ethics, and inclusion ? Or will we let it perpetuate bias and division ?

Right now, many companies are focused on short-term cost-cutting rather than long-term growth. Many of the trade organizations and nonprofits that have worked to make marketing more inclusive—groups like AIM (Alliance for Inclusive & Multicultural Marketing) and the ANA’s AEF (Advertising Education Foundation)—are facing budget cuts.

(56:04)

At the end of the day, AI will take care of technology . Our job is to make sure AI also takes care of humanity.

That’s why I wrote The Dao of Leadership —to provide a blueprint for balancing technological innovation with human creativity. And my next book, Creativity Unleashed , will be a hands-on guide for unlocking creativity in this AI-driven world.

David, I truly appreciate this discussion. I didn’t just agree to join—I wanted to be part of this conversation. Thanks to you and this incredible community for engaging with these ideas.

## AI Marketing Innovation and Content Effectiveness

Speaker: Kevin Wassong
Published: 2025-03-13
Tags: advertising, ai agents, ai assistants
Video: https://www.youtube.com/watch?v=EPDgAQ3yUIs
Page: https://aimarketersguild.org/sessions/ai-marketing-innovation-and-content-effectiveness

(0:01)

Welcome back to another edition of AI Insiders from the AI Marketers Guild. Great to see everyone here!

Today, we have a guest speaker I’m really excited about—someone I’ve always enjoyed geeking out with, especially as we’ve gone deeper into AI. We had a great conversation this morning, and I can’t wait to share it with a wider audience.

There aren’t many people with deep expertise in building technology and using it to grow agencies. Our guest has seen the evolution of agencies from the early days to now—how they’re embracing or resisting the latest developments.

He recently delivered one of the biggest keynotes at CES, hosted by Shelley Palmer, and is a highly sought-after entrepreneur and speaker. Please welcome Kevin Wassong. Kevin, I’ll let you introduce yourself, but it's great to have you here.

(01:33)

Kevin Wassong:

Thanks, David! I really appreciate it. It’s great to see how the AI Marketers Guild has evolved, and I recognize a lot of familiar faces here. Thanks to all of you for taking the time to listen.

I encourage anyone to interrupt with questions at any time—I’d love to keep this conversational.

This topic is important to me, so I’ll start with a quick background for those who may not know me.

I’ve been in the industry for a long time. Some of you may know my name, some may not. I’ve founded multiple companies, starting in digital marketing. I moved back to New York in 1995 to work for Cliff Freeman & Partners, where I pitched and won the Prodigy business.

One of my early experiences in digital was a call from Mike Ilitch, the founder of Little Caesars . He told me a franchisee had put up a “website” and insisted we shut it down immediately. Curious, I checked it out—dialed up with a Prodigy disc and saw an early animated GIF: a spear hitting two pizzas, flipping them in the air. It was a brilliant execution. Instead of taking it down, I told Ilitch that he should share it with every franchisee.

From there, I worked on digital innovation at Lowe Partners , built the first Mercedes-Benz kiosk touchscreen system, and developed SBE’s first website and online auction with Radical Media . Later, at JWT , I started their first digital division in 1998, where we built technology platforms and digital experiences.

After JWT , I co-founded one of the first consumer fintech brands. My last company before this was One Mobile , the first mobile advertising services group for the U.S. broadcast industry. That company was later sold to Nexstar Broadcasting as part of a roll-up of independent broadcasters.

That experience gave me a firsthand look at the rapid evolution of technology. I remember an early meeting with Mark Themann, then head of mobile at Google. I asked how many engineers they had—he said 12 or 15. I had 8 at the time, and I felt pretty good. A year later, I asked him the same question—he said 1,500. I still had 8. That was a wake-up call about the pace of change.

I’ve worked with CMOs, built organizations, and stayed immersed in this space. I believe we’re living through the third transformational technology movement of our careers. First, there was the internet. Second, social media. Now, we’re in the AI era.

(05:31)

My current company, mktg.ai , is based on a domain I bought over a decade ago. I originally tried to build it with IBM Watson’s team in 2016, but the technology wasn’t there yet.

A quote from MasterCard’s CMO recently stuck with me:

"The number of places where brands need to show up has exploded, but most marketing organizations are still using fragmented tools to manage them. This causes inefficiencies, inconsistencies, and ultimately lost opportunities."

This reflects the key challenge we’re addressing.

We obsessively measure media, but creative is what truly drives performance. Advertising has always been a balance of storytelling and science, yet most measurement is focused on media. Moreover, it’s analyzed in vertical silos rather than holistically.

(06:37)

With the rapid rise of AI—especially generative AI—each major platform is designing its AI to improve its own ecosystem.

- Gemini improves Google.

- Grok improves X (formerly Twitter).

- Meta AI improves Facebook and Instagram.

- Microsoft AI enhances their suite.

Brands often talk about data lakes, but these platforms are actually data oceans. OpenAI, for instance, has been reading the internet since 2015, which required massive capital investment.

Over the last 20 years, we’ve seen a 300% increase in marketing channels. That’s overwhelming for marketers. An investor once told me, "Your industry suffers from the problem of 'too many'—too many platforms, tools, data sources, reports, and dashboards."

AI is the best solution to this complexity.

(08:20)

Unlike traditional approaches, we analyze creative horizontally—across all channels, digital and non-digital—rather than in isolated silos.

Marketers typically view performance through charts and graphs ( Tableau, Data Studio, Excel ), yet many large brands still rely on Excel as their primary source of truth.

Even some of the biggest sports leagues still analyze team performance using Excel sheets and Power BI . That’s insane.

The problem is clear:

- We’re constrained by budgets .

- We’re constrained by resources —most companies are scaling back, not hiring more.

- We’re constrained by time —yet the volume and velocity of content keep increasing.

With AI, we can finally analyze and optimize creative at scale.

(11:00)

At mktg.ai , we built an AI-driven platform that aggregates creative assets into one searchable, horizontal view.

- It analyzes real-time performance across all channels.

- It extracts insights to improve creative execution.

- It’s non-disruptive , integrating seamlessly into existing workflows.

- We even have a provisional patent on using color to normalize performance data.

Marketers can now instantly see what’s working and what’s not.

We launched our first client in May 2024 and signed a major client just this morning. The response has been incredible.

(13:51)

To put the power of AI in perspective, we beta-tested with a retailer that has 1,100 stores. We integrated their Meta account.

Any guesses on how many creative assets they had?

The answer: 5 million.

No human team can analyze 5 million assets. Our platform does it instantly. We can isolate patterns, compare Black Friday campaigns over years, and avoid repeating past mistakes.

(26:28)

Our next step is integrating AI-powered predictive modeling. Marketers will be able to ask:

"What were the best-performing Black Friday ads over the last five years?"

The system will instantly pull the top assets across all platforms. This will transform campaign planning and creative strategy.

(26:28)

Our next step is integrating AI-powered predictive modeling.

Marketers will soon be able to ask questions like:

- “What were the best-performing Black Friday ads over the last five years?”

- “What ads resonated most with 25- to 34-year-old women in Chicago?”

The system will instantly pull every relevant asset across all touchpoints to help marketers identify patterns and make informed creative decisions.

Even further, we aim to integrate generative AI to suggest high-performing ad variations based on historical data. Imagine uploading creative concepts and having the system predict their effectiveness before they even launch. That level of insight will revolutionize how marketers brief their teams and optimize campaigns.

(28:10)

Audience Question (Max Tarter):

"Your performance metric examples focus on media metrics like engagement. Are you incorporating real business outcome data, such as brand perception or sales?"

Kevin Wassong:

Great question. We’re already integrating Google Analytics, which provides conversion data. Additionally, platforms like Meta and Google feed conversion metrics back into our system.

We’re also in the process of integrating Shopify, Salesforce, and other CRM systems to provide deeper business impact insights. The goal is to correlate creative performance with actual sales and customer behavior.

Some companies are hesitant to share proprietary data outside their firewalls, so we offer an enterprise solution that allows them to install our system within their own cloud environments, whether that’s Azure, AWS, or Google Cloud.

(29:52)

Audience Question (Aen):

"If a client has proprietary data like a brand tracking study, can that be incorporated?"

Kevin Wassong:

Absolutely. Our system allows clients to append additional metadata to creative assets. For example, if you conduct an attitude and awareness study, you can tag all related creative within our platform.

Most marketers run these studies, get results, then store them in a folder that no one revisits. We want to make that data actionable by integrating it into the creative optimization process in real time.

(31:00)

Audience Question (Jean):

"Creative is far more important than media targeting. Studies from Comscore and Nielsen show that good creative is four times more impactful than media targeting. But using AI to optimize old-school IAB-style banner ads may not be the best approach. The IAB ad standards were set in 1999 and haven’t changed. Browser screens are now four times larger, and static banners are losing effectiveness. So, how much does this solution cost?"

Kevin Wassong:

You raise a great point. Personalization is a big buzzword, but bad ads that are personalized are still bad ads.

Regarding cost, we use a flat licensing model, with no seat-based pricing. We want everyone in a marketing organization to have the same view, avoiding inefficiencies caused by siloed teams.

By the way, I actually helped create one of the IAB standards in 2012—the responsive square. Back when I was running One Mobile , we asked, “Why can’t an ad dynamically resize to any screen?” It’s surprising how little has changed.

(33:19)

Audience Question (Jean):

"I work at Responsive Ads , where we focus on aspect-ratio-based designs instead of fixed-pixel ads. That allows creative to dynamically fill different slots. I see potential synergy here—our clients would likely pay extra for insights from your system. Would you be open to a partnership?"

Kevin Wassong:

Absolutely. Let’s connect offline and discuss.

One of the most significant studies on this was from Nielsen last year. They found that real-time creative optimization can increase ROI by 20-30%. That’s a massive lift—far beyond what you’d get from better media targeting alone.

The fundamental truth is: there are only two ways to improve marketing performance:

- Be more efficient (reduce costs, improve workflows).

- Be more effective (make better ads that resonate).

Our platform helps with both.

(35:57)

We’ve intentionally gone direct-to-client out of the gate. Agencies have high turnover—marketers switch agencies, CMOs change every two years, and media strategies constantly shift. But creative intelligence should stay with the brand as part of its long-term DNA.

One of our clients, a financial services company, has over a decade’s worth of creative data in our platform. That means they can go back and analyze what worked, avoid repeating past mistakes, and build smarter campaigns.

For retailers, it’s a game-changer. Imagine pulling up five years of Black Friday ads, across every channel, to see what performed best. No more guesswork—just data-driven decisions.

(38:41)

Audience Question (Karen):

"Can you track competitor ads?"

Kevin Wassong:

Not directly. We don’t scrape the internet like OpenAI or Grok. However, as we onboard more brands, we’re gaining a broader category-level view. Over time, this will allow us to provide competitive insights, even if we’re not directly pulling competitor data.

Our goal is to brief marketers on what types of creative work best within a given industry, rather than generating creative itself.

(40:58)

Audience Question (Max):

"Have you considered an API partnership with Nielsen?"

Kevin Wassong:

Great suggestion. Right now, we’ve been entirely self-funded and haven’t raised capital yet. That means we’ve had to prioritize our API integrations carefully.

We’re currently integrating into the Google AI Startup Program and entering the Google Cloud Marketplace. But as we scale, a Nielsen partnership is something we’d be very interested in exploring.

(41:33)

Audience Question (Todd):

"How do you account for targeting variables? If an ad performs poorly, it might not be the creative—it could be bad placement or audience mismatch. How does your platform account for that?"

Kevin Wassong:

Great point. Right now, we don’t attempt to out-optimize individual platforms. Meta, Google, and others have powerful data tools for targeting.

However, we do capture gender, geo, and age data to provide directional insights. Over time, as we gather more data, we’ll be able to analyze how targeting variables impact creative performance.

(43:09)

Audience Question (Jean):

"I like that you’re going direct-to-client. Agencies often lack transparency. Will you maintain that direct-to-client approach?"

Kevin Wassong:

Yes, our contracts are always with the brand, not the agency. If an agency manages a client’s platform, they can still use it, but the client owns the data.

We’ve also launched a Pioneers Program specifically for SMBs. Most small businesses don’t want to stay small—they want to grow into major category players. This program helps them build a scalable, data-driven marketing foundation from the start.

(47:06)

Kevin Wassong:

That’s everything I wanted to cover today. If you’re interested in learning more, you can reach me at kevin@marketing.ai .

David, I really appreciate the opportunity to speak here. What you’ve built with the AI Marketers Guild is incredible—it reminds me of the early days of the IAB.

(47:42)

David:

Thanks, Kevin! This was fantastic. We’d love to have you back to share more insights as your platform evolves.

Also, if you come across cross-brand findings that can help educate marketers, let’s share those with the Guild.

## The Future of Metaverse Marketing and AI

Speaker: Alisha Rappaport
Published: 2025-03-06
Tags: ai in marketing, metaverse
Video: https://www.youtube.com/watch?v=TzHlMlaWhUc
Page: https://aimarketersguild.org/sessions/the-future-of-metaverse-marketing-and-ai

(00:00)

I want to welcome Alicia Rapaport from S&P Global. I've been following her work for a while, and I'll let her introduce herself. Alicia, why don't you share who you are and what you're working on? I'll make you a co-host so you can share anything you’d like. Welcome back, and welcome, everyone, to this Insiders Edition. I’m excited to learn from you today.

(00:41)

Thank you, David. Thanks for having me. Hello, everyone—it's nice to meet you all virtually.

A little about myself—I have an interesting background. I spent the first part of my career in healthcare, working within the system to build a Child Life program in hospitals and support pediatric patients. I was involved in program development, marketing, and fundraising, which sparked my interest in marketing. That’s what ultimately led me to what I’m doing now.

(01:19)

In 2013, I started my own marketing firm, working with a range of clients, from startups to established businesses. It was an incredible experience, allowing me to learn about branding and different industries.

In 2020, I joined S&P Global. I initially started as a contractor through my own company, just before COVID hit. After six months, I was offered a permanent role, and I’ve been here ever since. I often joke that I’m an "S&P COVID baby" because I joined during a time of significant change, especially in marketing. Everything that had been done in person had to shift to virtual, which presented an interesting challenge. I’m sure many of you in marketing can relate to how much the industry evolved during that time.

(02:01)

I’ve worked in various areas at S&P Global. I started in the ESG division, helping develop what is now known as Sustainable1, which plays a key role in our brand's sustainability efforts. It was an exciting time to be part of that development.

A few years ago, I became interested in AI. It actually started as a marketing contest—“Think outside the box, come up with something new and engaging.” I thought, “What if we created a metaverse where potential prospects could interact with our products?” That idea later came to life.

(03:17)

We have an incredible essential tech team at S&P Global, doing amazing work in AI. I connected with them, and that’s how this journey really took off.

I’m going to share some slides to give you a better understanding. Am I sharing the right screen?

(04:10)

Yes, AI and marketing.

Great. So, obviously, I’m speaking to an audience already familiar with AI and virtual reality. However, in financial services, virtual reality isn’t as commonly used for marketing and prospecting. If you attend third-party events, you don’t often see companies using VR headsets to engage customers.

(04:44)

The benefits of using the metaverse and VR for marketing are significant. Personalization is key—we can create branded experiences tailored to a company’s specific focus. It allows for immersive engagement, making interactions more interactive and memorable.

Additionally, we introduced gamification. People are naturally competitive, so we designed experiences that encourage engagement through interactive challenges. It’s not just about learning; it’s about making the experience enjoyable and rewarding.

(05:53)

There’s also a competitive advantage. This approach helps differentiate our brand, allowing us to amplify and expand our strategies. The retention rate is also a major factor—when someone is fully immersed in a VR experience for five to seven minutes, they aren’t distracted by their phone. They are truly engaged, processing the information we’re providing.

(06:27)

We piloted the metaverse last year at a third-party event to gather feedback. Attendees—both clients and prospects—were highly engaged. Many said they hadn’t realized the full capabilities of our products before the experience. This year, we’re expanding on that success, incorporating gamification and refining the experience.

(07:37)

Moving forward, we plan to use this tool for webinars and training sessions. We’re also launching an AI bot, which I’ll discuss in more detail.

(08:17)

Here’s a visual of our metaverse. Initially, we used a gaze-based interaction—users would look at a coin to unlock videos. This year, we’re incorporating a puzzle element, requiring hand interaction, which we believe will make it even more engaging.

(08:53)

Where does this experience take place, both virtually and physically?

(09:22)

We’ve been using it at trade shows, where we have a booth. It’s a great draw—people see it and get curious. Instead of just picking up brochures or swag, they can actively engage with our products.

(10:04)

Is this built in Horizon or a custom platform?

(10:40)

It’s a custom-built platform developed with a company called Hyperspace. They’ve been working with S&P Global’s essential tech team for years, and that’s how I got connected with them.

(11:21)

One of the newest features we’re working on is our AI bot. We’re training it to answer user questions, guide them through the metaverse, and provide relevant product information. If a question goes beyond the bot’s capability, it can direct the user to schedule a demo or meeting with a real person.

(12:43)

Will this AI persona only exist in the metaverse, or will there be a standalone version?

(13:14)

For now, we plan to keep it within the metaverse. Our focus is on creating depth of engagement rather than just broad reach.

(14:19)

Do users need a headset to access the experience, or can they use a desktop version?

(14:58)

There’s a desktop version, so you don’t need a VR headset. While headsets enhance the experience at trade shows, people can also access the metaverse remotely via desktop.

(16:53)

How often are you deploying this?

(17:43)

Right now, it’s being used at major trade shows. Last year, we focused on piloting and gathering feedback. This year, we’re refining and expanding our approach.

(19:00)

Are sales teams trained to guide visitors through the experience?

(19:38)

Yes, we’re in the process of training them. During our pilot phase, salespeople were excited to show it off, and they played a big role in engaging attendees.

(20:13)

How do you scale this?

(20:43)

Scaling involves increasing the number of VR headsets at events and leveraging the desktop version for broader accessibility. We also customize the experience for different events, incorporating branding and interactive elements like digital swag.

(21:19)

What’s the cost of the hardware and metaverse development?

(22:03)

Headsets range from $300 to $400. Development costs vary, depending on complexity, from $5,000 to several hundred thousand dollars. We built ours in phases, gradually adding features.

(23:29)

What’s the expected impact? Are you aiming to reduce sales costs by providing more information upfront?

(24:24)

Our goal is to engage clients in new ways, provide customized product information, and improve retention.

(26:21)

Are you using AI in other aspects of your work?

(27:39)

Yes, we have an internal AI tool called Spark Assist, which helps with workflow automation and data analysis. We also launched an AI marketing cohort to explore AI’s role in marketing strategy.

(35:33)

Once we have KPIs, we’ll measure how this impacts lead generation and sales.

(36:15)

It’s all about testing and evolving. Just like when social media started, we had to experiment. The same applies here—taking the risk to see what works.

(41:14)

Thanks, everyone, for joining! Looking forward to sharing more updates in the future.

## How TWE is Transforming the Wine Industry with GenAI

Speaker: AI Marketers Guild
Published: 2025-02-27
Tags: consumer insights, ai agents, ai assistants, ai for efficiency
Video: https://www.youtube.com/watch?v=_IXYGrmQzNc
Page: https://aimarketersguild.org/sessions/how-twe-is-transforming-the-wine-industry-with-genai

00:09)
Hello, everyone. I'm Nicola Quail, one of the co-founders of the AI Marketers Guild APAC, along with my colleagues De Shama Badi Shama from India and Sushita Mahapatra from Singapore. We're excited to have you all here for what we hope will be one of our biggest AI Marketers Guild webinars to date. It's great to see this community expanding across APAC, and we even have some colleagues dialing in from North America.
Before I introduce our two incredible guest speakers, I want to give a quick background for those new to the community. The AI Marketers Guild was the brainchild of US marketer David Burwitz. And for those familiar with his LinkedIn, yes, that David Burwitz.
After launching two years ago, the Guild has grown significantly in North America, especially given AI’s impact on marketing. We reached out to David to partner with AIMG and create a regional community to showcase local AI pioneers, tools, applications, and best practices while integrating world-class AI thought leadership.
We've been fortunate to have so many professionals join us over the past six months. We host monthly webinars like this one and will invite you to join our Slack community afterward. We're also hoping to organize city-based meetups later this year.
Rather than just observing from the sidelines, we love that you're all actively engaging.
(01:45)
On that note, I’m thrilled to introduce two marketing pioneers today. I must thank our mutual colleague and friend, Adam Murphy, for introducing me to Adele and Maddie from Treasury Wine Estates.
Managing a large global marketing insights function is challenging enough, but how do you streamline and create efficiencies in managing and distributing all the data and insights within a complex business? This was one of the many challenges Adele and Maddie set out to solve using AI.
While I admire the technical aspects of what they've achieved, what truly inspires me is their entrepreneurial spirit in tackling this problem.
Without further ado, I’d like to hand it over to Adele Fou and Maddie Simik to share their story. They’ll present for about 25–30 minutes, and then we’ll have time for questions. Please drop your questions into the Q&A or chat, and we’ll address them at the end.
Welcome, Adele and Maddie!
(02:47)
Adele: Awesome, thanks, Nicola, and thanks, everyone, for having us. First time being called a marketing pioneer—I’ll take it!
I’m not actually a marketer by background, but I’m embracing it and starting to love the world of marketing. Maddie and I are excited to be here, though this is a bit outside our comfort zone. We’re not usually the ones to step up and share about ourselves or our work, so bear with us!
That said, we’re passionate about this project and excited to showcase the incredible work our entire team has done. We’re also looking ahead to what’s next on this journey.
Like true marketers, we have a video to kick things off—because why not?
(03:49)
[Video plays]
At TWE, we have more data than ever before, coming from various insights across the company. We needed a way to organize that data into a single access point for teams while also distilling it into actionable insights.
By leveraging AI, we quickly realized we could enable our teams to access insights more rapidly, effectively, and efficiently.
The most exciting thing about AI at TWE is that it provides a competitive advantage while allowing everyone to generate insights in their own way. This approach is helping us lead the industry.
One of the most beneficial aspects has been integrating AI with our usage and attitude study, Project Horizon. AI tools like RV and Viti use the same language we do when analyzing consumers and occasions, aligning with our market data. This ensures that when we identify opportunities, the insights are consistent with our internal planning and discussions with customers.
Currently, we’re launching a global brand, and this has been one of the most thoroughly tested concepts we’ve ever brought to market. At every stage, consumer insights have helped refine our proposition to ensure its relevance worldwide.
Many companies are still developing their AI policies, but Treasury Wine Estates is not only exploring AI but actively integrating it into daily operations.
They approached us while we were collaborating on other projects, asking if we could bring their AI agents to life—almost like real people. Using AI, we created two personas: Arvy and Viti. These represent the AI agents used to generate insights and drive innovation within the company.
A great example happened just last week. I was in the UK and got a question about sangria, which I didn’t know much about. Normally, I would have said, “Let me get back to you,” done research for a week, and then returned with an answer. But using RV and Viti, I quickly gathered insights and within 5–10 minutes, had key points on who drinks sangria, why, and what it could mean for our business.
This was a game-changer.
AI has helped simplify and make consumer insights more visible. Instead of 100-page decks, we now have insights that are easier to access and interpret. This makes information more accessible to our business and enhances how we share it with customers.
A key difference between TWE and other organizations is that TWE has embedded AI into its DNA. They’ve removed the fear of AI, enabling employees to embrace and understand the technology. With that cultural acceptance, there’s no limit to what they can achieve.
(07:53)
Adele: Reflecting on this project, it’s hard to believe it all started just eight months ago.
As part of the global consumer insights team, I quickly realized the overwhelming amount of data we had access to. In a central role, the first thing I wanted to do was understand what data we had, how to access it, and how to analyze different markets and consumer groups.
But there was no single place where all this data was consolidated. Everyone wants a one-stop shop for insights, but it didn’t exist.
We sourced about 18,000 documents scattered across desktops, SharePoint folders, and emails. The first step was organizing and centralizing all this data to make it accessible.
(10:06)
We developed Consumer HQ , a centralized repository for all our data. It allowed us to search for insights efficiently. For example, if we wanted information on the “Better for You” category, we could simply search "BFY" and access relevant documents instantly.
This eliminated the challenge of data overload and manual analysis. Instead of spending time compiling reports, we could focus on storytelling and brand building.
(11:42)
From there, we integrated AI to enhance our capabilities. The goal was to make data intuitive and actionable, leading to the creation of Viti and Arvy .
- Viti focuses on facts, ensuring data integrity, accuracy, and proper sourcing.
- Arvy is innovation-driven, generating creative ideas and helping us think outside the box.
These AI assistants act like team members. We wanted to humanize them so employees would feel comfortable using them. We debated calling them AI "agents" or "assistants" and ultimately chose assistants to emphasize their collaborative role.
(19:39)
Maddie: Since implementing this system, we’ve seen incredible results:
- Innovation: AI has helped generate fresh ideas, considering insights we hadn’t previously explored.
- Efficiency: Summarizing consumer feedback, compiling reports, and refining creative briefs now take seconds rather than hours.
- Consistency: Teams across different regions now speak the same consumer language.
- Self-service insights: Teams can now access insights without waiting on the insights team, freeing us up for strategic work.
This has been a game-changer.
(25:49)

Nicola: Fantastic! Thank you both. That was brilliant. It all looks so simple when you put it into a PowerPoint, but I know how much work has gone into this.
I’d love for you to take us back to the early stages—when you first had the idea. How did you get buy-in? Did leadership immediately support it, or was there a process to convince them?
(26:26)

Adele: Great question.
We’re fortunate to work in a curious, adaptable environment at TWE, which made a huge difference. We also partnered with GPT Strategic , our IT department, and Maddie’s team, ensuring everything was secure. That gave us the confidence to move forward.
We started small. Instead of asking for a massive budget upfront, we looked at what we would typically spend on innovation research and carved out a portion of that to experiment with AI.
Now, we have a test-and-learn budget every year, dedicated to trying out new ideas. That’s essential in marketing—having a space to experiment. We approached it as a small-scale initiative, got IT buy-in early, and collaborated behind the scenes before expanding.
Keeping it lighthearted and fun helped, too. We didn’t take ourselves too seriously in the early stages, which made the process more enjoyable.
(27:30)

Nicola: Fantastic. So, we talked about bringing the AI assistants to life—why was it important to give them personalities? What was that process like?
(28:12)

Maddie: It all came down to making them feel like extensions of the team .
Before they had names, it felt clunky—like we were talking about the AI insights tool, which sounded too robotic. Giving them names made them more relatable and approachable.
We also wanted the names to reflect TWE’s identity , so we chose ones related to wine. There are hidden meanings behind them— Viti comes from "viticulture," for example.
The goal was to make it natural in meetings to say, "Let’s ask Arvy," rather than, "Let’s consult the TWE Innovation AI Assistant." That felt too cold.
And of course, as marketers, we love branding—so we had to give them logos!
It’s been fun seeing them become part of everyday conversations. Even our CEO refers to Viti and Arvy as if they’re employees , which is pretty cool.
(29:18)

Nicola: That’s awesome! I was curious about the name Viti , so that answers my question.
A quick follow-up—who held the budget for this? Was it marketing or the data team?
(29:53)

Adele: It started as an innovation budget , but now it’s shared between marketing and insights.
That way, we all have skin in the game and a collaborative approach to experimenting, testing, and learning.
(30:28)

Rob: You had a pilot community—how long did it take from that test-and-learn stage to rolling it out company-wide?
(30:58)

Adele: We had three stages , though it wasn’t planned as seamlessly as it looks in hindsight!

- Small core group (~5 people): The insights leads from different regions tested it first.

- Expanded to the broader insights team (~20 people): Once we were comfortable, we brought in more team members.

- Full business rollout: After proving its value, we rolled it out more broadly.

The entire process took about four months , which is quite fast. Once the insights team saw its potential, they were eager to expand access.
AI moves quickly, so we wanted to keep pace.
(32:07)

Nicola: That’s impressive! Now, in terms of training Arvy—since it pulls from broader sources—how do you control where it's pulling information from?
(32:46)

Maddie: Arvy is more open-ended , but Viti is knowledge-based and only pulls from trusted internal sources.
If Viti doesn’t have an answer, it won’t guess—it will simply say, "I don’t have data on that. Please upload a document if this information should be available."
We also made sure Viti cites references , so users can fact-check its responses. That’s critical, especially when using AI-generated insights for external presentations.
Arvy, on the other hand, is designed for creative, open-ended thinking , so it’s not constrained in the same way.
(33:54)

Heather: Has this reduced the need for primary research , or has it just made research more targeted ?
(34:29)

Adele: More the latter—it’s made our research sharper and more efficient .
We’ll always need research, but AI helps us:

- Refine research briefs so we ask better questions.

- Improve creative briefs —our in-house agency is now producing some of the best work we’ve ever seen.

AI enhances our process rather than replacing traditional research.
(35:36)

SOA: Have you tracked any impact metrics —like time saved?
(36:10)

Adele: We didn’t set formal KPIs, but we had an internal goal: "Can we save 10 hours a week?"
That’s one full day per person , which frees up time for higher-value work.
We’re not tracking hours precisely, but we’ve noticed a major drop in ad-hoc requests , which tells us people are finding insights on their own instead of constantly asking the insights team.
We’re debating whether to formalize tracking or just keep focusing on improving efficiency.
(37:56)

Nicola: Any plans for a third or fourth AI persona? Maybe for sales or even a consumer-facing assistant ?
(38:33)

Maddie: We just launched Penny , an AI assistant specifically for Penfolds (our luxury wine brand).
We’re also working on a consumer-facing AI assistant , which is really exciting. More to come!
(39:11)

Jimmy: How was the technology journey ? Any major challenges?
(39:46)

Adele: Having IT’s support was crucial —security and trust are everything with AI.
Our partnership with GPT Strategic was also key. They helped us iterate and adapt rather than locking us into a rigid system.
The biggest challenge was organizing the data —centralizing it, structuring it properly, and making sure it was usable. That foundation made everything else possible.
(40:22)

Nicola: Speaking of organizing data—how did you collect 18,000 documents ? Did you just send an email asking everyone to upload files?
(40:59)

Adele: That was one of the hardest parts !
We formed a small task force and made it fun—hosting a "hackathon" where we timed how many documents we could upload per minute.
The biggest challenge was aligning on categories and hierarchies —how to label and structure everything so AI could later retrieve it effectively.
We worked closely with IT to make sure it was stored in a logical, searchable format . That upfront effort made a huge difference.
(42:36)

Nicola: Amazing. Well, Adele and Maddie, thank you both so much!
Your journey has been incredible—not just in terms of technology but in transforming how TWE works. Who would have thought that when you started, even the CEO would be referring to Arvy and Viti as if they were employees?
Congratulations again!
To everyone here, we’ll be sharing the recording and an invite to our Slack community soon.

## AI in Hiring: How AI-Powered Recruitment is Changing the Game

Speaker: Brett Martin
Published: 2025-02-27
Tags: ai hiring, ai recruitment
Video: https://www.youtube.com/watch?v=r5AcnsMnQ8M
Page: https://aimarketersguild.org/sessions/ai-in-hiring-how-ai-powered-recruitment-is-changing-the-game

(00:00)

Welcome, everyone, to this edition of AI Insiders by the AI Marketers Guild. I’m excited to have two great guests today—Brett Martin, whom I’ve known for a long time, going back to his early pitches on mobile-social-local ideas when I was at 360i, and Jen, whom I just met. When Brett suggested including her in this conversation, I was eager to learn from her experience.

(00:48)

Brett and Jen, I’ll let you kick things off. Today, we're discussing how AI is transforming the hiring process—something relevant to everyone, especially marketers who are building teams and navigating AI-driven hiring. I’m particularly interested in what Brett has been developing and look forward to learning more.

(01:25)

Thank you, David, and thanks to everyone for joining. We have a good group today, so let’s keep this interactive. If you have questions, drop them in the chat or raise your hand. This will be more of a fireside chat format.

For introductions, as David mentioned, I’ve been in the New York tech scene for about 20 years. I run Charge VC, a New York-based pre-seed venture capital fund. We’re on our third fund with over 90 portfolio investments, including Jen’s company, Coral Care.

In addition, I co-founded Fonzi AI, a tech-enabled recruiting agency. We recently acquired a well-known New York recruiting firm (yet to be announced) and have been developing AI-driven tools to improve recruiting efficiency. We’ve essentially built an AI recruiter. At the time we worked with Jen, we hadn’t acquired the agency yet, so we were handling everything ourselves. My co-founder and Fonzi’s CEO, Yang Ma (a former Google engineer), along with our head of marketing, Drew Moffett, were personally recruiting.

Today, Jen will introduce herself, and we’ll walk through how we worked together—why she reached out, her expectations, and how the process unfolded. Then, we’ll broaden the discussion to AI in hiring.

Jen, could you introduce yourself and tell us about your work?

(03:15)

Sure. I’m Jen, CEO and founder of Coral Care, a platform that helps parents access pediatric developmental therapies such as speech, occupational, and physical therapy.

For parents, we operate like a local marketplace, making it easy to find therapists who provide home visits on a weekly basis. For clinicians, we function as a "business-in-a-box" solution, enabling them to launch or expand their private practice. This field is traditionally fragmented and supply-constrained, but we’ve successfully expanded the provider base.

We contract directly with health insurance companies to make these services more affordable and accessible. We’ve been operating for nearly two years now.

(04:50)

Jen has an impressive background—starting as an engineer, becoming a product manager, and working at companies like Samsung, Handy, and Weight Watchers before moving into leadership roles. Now, she’s running her own company. She’s truly a full-stack founder, which made me excited to work with her.

Jen, can you share the context in which you were hiring? What roles were you looking to fill, and why?

(05:19)

I started Coral Care based on personal experience—when my daughter was born with developmental delays, I struggled to find care for her. It was a frustrating process, and I became determined to fix it.

Having worked in digital health, I saw how the business-in-a-box model benefited the mental health sector and started connecting the dots for pediatric therapy. That’s how Coral Care was born.

When I reached out to Brett, our challenge was building credibility on both sides of our marketplace—clinicians and parents. We explored different strategies and considered influencer marketing to enhance credibility. Our idea was to leverage micro-influencers, especially on the clinician side, since they were more cost-effective than parenting influencers.

However, we needed someone to build and execute the program. Given our tight budget, we knew this person needed to be offshore, but I had no idea how to find them. That’s where Brett and his team came in.

(08:38)

One of our key insights at Fonzi was how overwhelmed companies get with job applications—especially in marketing and sales roles.

When hiring managers post a job, they often receive hundreds or even thousands of applications in 24 hours. Many companies end up closing applications quickly without reviewing candidates. This flood of applications renders the inbound channel ineffective.

To address this, we built an AI interviewer that conducts initial phone screens. Using OpenAI's Whisper and other models, it asks candidates a structured set of questions, just like a recruiter would.

(10:56)

Jen, you worked with Drew on this process. Can you describe how it worked from your perspective?

(11:27)

It was very simple. We discussed the role, budget, and key qualities needed—not just technical skills but subjective traits that are hard to screen for. Drew, having experience with influencer marketing, helped refine the criteria.

After that conversation, he said he’d handle the rest. Within days—over Christmas week, no less—he sent me a list of candidates. I was shocked at how fast it moved.

(12:05)

From our side, we converted Jen’s requirements into an AI-driven evaluation system.

The AI interviewer conducted 400 phone screens over two weeks, generating over 600 minutes of audio. The system scored and ranked candidates based on structured evaluation criteria.

(16:17)

Jen, how did you go about reviewing the candidates?

(16:53)

I listened to all 20 interviews while taking a walk. I realized that AI was a better interviewer than I had been in my career. Every candidate was strong, and it was clear who the best fit was.

In my final interviews, I could jump straight into follow-up questions rather than starting from scratch. It made my conversations much more productive.

(18:23)

Question from Paul: Are these video interviews? Also, what technology powers them?

(19:01)

They are audio-only, which allows candidates to interview via phone. Adding video would make the experience inconsistent across devices.

We use various AI models, including OpenAI, Claude, and Gemini. However, our secret sauce is the evaluation process—training AI to assess candidates the way an experienced recruiter would.

(22:05)

We’re also building a mock interviewer tool. It lets job seekers upload their resumes and practice interviews, with AI-generated feedback. Eventually, we want Fonzi to act as a personal recruiter, keeping track of candidates and proactively surfacing opportunities.

(25:08)

Jen: Another benefit of AI recruiting is that it reduces bias. Normally, hiring relies heavily on personal networks, but Fonzi ensures that candidates are assessed on merit, not connections.

(31:30)

I hired someone in Portugal to manage our influencer outreach. She’s already secured our first few influencer contracts, and the transition has been smoother than with other hiring methods.

(37:18)

Jen: Building a hyper-local marketplace is challenging. We balance clinician availability with demand while expanding in Massachusetts, Austin, and Dallas. If anyone has experience with local marketing strategies, I’d love insights.

(39:50)

Closing remarks:

David: Thanks, everyone! Brett and Jen, this was eye-opening. Looking forward to more conversations on AI-powered hiring.

Brett: If you’re hiring for go-to-market roles, reach out—we’d love to help.

Jen: Thanks! Looking forward to connecting further.

## AI Marketing Revolution: Shiv on Innovation, Personalization & Future Trends

Speaker: Shiv Singh
Published: 2025-02-12
Tags: ai in marketing
Video: https://www.youtube.com/watch?v=kyVFrbF-C38
Page: https://aimarketersguild.org/sessions/ai-marketing-revolution-shiv-on-innovation-personalization-and-future-trends

(00:06)

Welcome, everyone, to AI Insiders from the AI Marketers Guild. For those returning, you know these are highly participatory sessions. Today, we'll hear insights from our guest speaker, Shiv Singh.

Shiv and I go way back—nearly 20 years—when we were both exploring the early waves of social media and mobile marketing. Some of you in the room, like Steve Serner and Chris Herer, may recall the days of blogger lounges, trying to convince brands why they should have a blog or move beyond Myspace.

Shiv has been a pioneer in marketing, working on the agency side, brand side, and now deeply in AI.

(00:47)

Normally, at this point, I’d grab my copy of AI Marketing for Dummies and give it a plug. However, since I’m traveling in Idaho and packed for cold weather, I didn’t bring it. But here it is— Marketing with AI for Dummies , a must-read.

Shiv entrusted me as his technical editor for this book, and my name appears in the back. It was a rewarding experience reviewing every word, ensuring accuracy, and diving deep into case studies. I learned a lot from the process.

Shiv, could you share more about what you're working on now—Trailblazers, your book, your newsletter, and any key insights marketers should know? I'm also looking forward to audience questions, so feel free to jump in.

(03:14)

Shiv Singh: Thanks, David, and great to meet everyone. Hearing how long we’ve known each other makes me feel old—time flies!

A little about me: I built my career in marketing, spending a decade at Razorfish, where I first met David in the early 2000s. Later, I led digital marketing at PepsiCo, worked in senior marketing and innovation roles at Visa, and most recently served as Chief Marketing & Experience Officer at LendingTree, a major financial services marketplace.

This is my third book. My first, Social Media Marketing for Dummies , was widely successful, with seven language translations and four editions. My second book focused on fighting misinformation. About a year and a half ago, I became both excited and concerned about AI’s impact on marketing.

Interestingly, my 2018 book included a chapter on AI’s potential to disrupt jobs. Since then, I’ve engaged with nearly 100 marketers, technologists, and ad-tech leaders to write this book. Fortunately, the publisher agreed to include it in the Dummies series. I collaborated with David and others, and the book launched in October. Now, I speak about AI’s role in marketing and its future implications.

Rather than talking about myself, I’d love to discuss AI’s exciting opportunities and challenges. To start, I’ll share a couple of key themes from my research.

(06:26)

The first theme is AI’s hype—it's actually underhyped. That might sound surprising, but given the pace of technological advancements, AI surpasses Moore’s Law in innovation speed. It’s a general-purpose technology that impacts every field, job, and industry.

Unlike previous technological breakthroughs—the internet, television, or mobile phones—AI stands alongside us, working with human-level proficiency. We’ve never encountered anything like this before.

Sam Altman recently wrote that within two to three years, a person using an AI assistant effectively will have access to the collective knowledge of humanity. While he has a vested interest in AI’s success, even if only a third of his prediction comes true, the implications will be massive.

(09:30)

David: That’s a bold statement, Shiv. Some might argue AI is overhyped, with people making grand claims about its potential. But I think you're suggesting that while people speculate about distant AI developments, they overlook the transformative impact already happening. Can you expand on that?

(11:05)

Shiv: Absolutely. AI's current capabilities are staggering. OpenAI’s latest models, for example, outperform 95% of humans in specialized exams. One model scored 83% on the International Mathematics Olympiad, a test where only a handful of people score that high.

AI isn’t just processing information—it’s generating new knowledge. While some dismiss AI due to its occasional hallucinations or lack of creativity, remember that humans also misinterpret data and make mistakes. The key is knowing how to use AI effectively.

AI requires more engagement than a simple Q&A. The more time you invest, the more incredible the benefits. We’re discussing potential medical breakthroughs, such as AI assisting in cancer research. This isn't just automation—it’s discovery.

(15:33)

Karan: AI can process vast amounts of data, but information alone isn’t knowledge. Many people know about the Buddha’s Four Noble Truths, yet few live by them. AI may analyze patterns, but human wisdom and application are different.

(16:11)

Shiv: That’s a great point, Karen. AI does more than just process data—it generates new insights. The 2017 AlphaGo match against Lee Sedol is a prime example. Go is far more complex than chess, with an astronomical number of possible moves.

In one game, AlphaGo made a move—Move 37—that was considered a mistake at first. However, experts later recognized it as an unprecedented, highly creative move. It was something no human had ever attempted. This suggests AI isn’t just regurgitating past knowledge; it's pioneering new strategies.

(21:39)

Chris: In marketing, have you seen AI make an AlphaGo-like “brilliant mistake”? Something beyond basic content generation?

(23:27)

Shiv: Great question. One example is Coca-Cola’s 2023 holiday campaign, which was entirely AI-generated. Many marketers criticized it for lacking human emotion, but ad testing with System1 showed it performed exceptionally well.

AI is also revolutionizing efficiency. HP, for instance, uses AI to generate thousands of ad variations in weeks rather than months, transforming content creation at scale. This isn’t just automation—it’s a paradigm shift.

(28:18)

David: One of your predictions is that AI is ushering in a DIY marketing era. For those running small teams, does this level the playing field, or do larger companies still have an advantage?

(29:35)

Shiv: AI is reshaping marketing at every level. Tools like Google’s Performance Max, Meta’s Advantage+, and Amazon’s AI-driven ad platforms automate media planning, copywriting, targeting, and optimization.

Previously, only large companies could afford dedicated performance marketers. Now, even small businesses can use AI to run complex campaigns with minimal effort. AI is democratizing marketing, giving small teams access to enterprise-level capabilities.

(33:40)

David: But AI is also replacing jobs. Which roles in marketing are most at risk?

(34:29)

Shiv: AI is affecting every marketing subfunction—strategy, insights, content, media, and analytics. The key is learning to work alongside AI. Those who embrace it, acting as curators and strategists, will thrive. Those who ignore it risk obsolescence.

Tech giants are already laying off marketers while increasing AI investments. Marketers must adapt by developing AI fluency.

(43:40)

Corey: I’ve been integrating AI into marketing ops, building an internal strategy consultant in Gemini. AI understands our brand strategy and past performance, helping us refine campaign ideas. It’s a powerful co-pilot.

(47:26)

Shiv: That’s an excellent approach. AI isn't just a tool—it’s a thought partner. Whether analyzing contracts, negotiating deals, or refining presentations, AI enhances strategic thinking.

(54:37)

David: Shiv, thanks for this incredible discussion. Everyone, make sure to check out his book and newsletter. Looking forward to continuing the conversation!

Shiv: Thanks, David! Appreciate the great questions and insights.

## Author Kate ONeill on Future-Ready Decision Making

Speaker: Kate O'Neill
Published: 2025-01-29
Tags: ai for humanity, ai ethics, human centered ai
Video: https://www.youtube.com/watch?v=nyfBybYPFqU
Page: https://aimarketersguild.org/sessions/author-kate-oneill-on-future-ready-decision-making

(00:00)

Welcome to another edition of AI Insiders . We have a very special guest today—Kate O’Neill, a friend, accomplished author, speaker, and more. Kate, I’ll let you properly introduce yourself.

Her new book, which just came out, delves into humanity and how we foster it. These days, discussing humanity inevitably involves considering where AI is headed and its implications. Kate has already explored these topics in her previous work.

Kate, I’d love for you to introduce yourself to the AI Marketers Guild community for those who haven’t had the pleasure of meeting you yet.

(00:54)

Kate O’Neill:

Thank you so much for having me, and hi, everyone! I’m particularly enjoying Jim Conley’s cat making an appearance—always a welcome guest. If anyone else has pets around, feel free to let them join the conversation!

I’m thrilled to be here today. I sent a request to David to share my screen, but before I do, I just want to say hello and get a feel for the group. This session runs for about an hour, and I plan to spend the first 10 minutes or so setting the stage. I’ll introduce the core ideas and frameworks from my new book, which launched today—yay, pub day!—and then we’ll dive into what this means for marketers, particularly in the context of AI.

David, if you don’t mind acting as the interviewer for the fireside chat portion, that would be great. But this is a participatory group, so I encourage everyone to share thoughts, ask questions in the chat, or even unmute and jump in. This is a great way to build connections and engage in discussion.

(02:49)

David:

Absolutely. This group thrives on participation, so interruptions and questions are welcome. Feel free to be on or off camera—whatever works for you. This is a space for learning and conversation.

(03:51)

Kate O’Neill:

Great! I’d love to hear everyone’s thoughts as we go. Let me go ahead and share my screen.

[Pauses]

Ah, the joys of Zoom—you have to start your slideshow before screen sharing.

David:

I love the setup—there’s a balance between the professional studio look and the warm, personal background.

Kate:

Thank you! I appreciate when people share their real spaces rather than using a blurred or virtual background. I think it gives a better sense of who they are and where they are.

Incidentally, I’m in New York City near Columbus Circle, in the northern part of Hell’s Kitchen. Technically, some would say it’s not really Hell’s Kitchen, but it’s close enough. I like to joke that I live on Billionaire’s Row—but on the “hundredaire” side of the street.

I couldn’t be prouder of this book. I hope everyone gets a chance to check it out.

(05:33)

I’m often known as the Tech Humanist , which stems from my 30 years working in technology. No matter the industry—whether it’s business, education, healthcare, or entertainment—I’ve always been focused on the human experience.

My book Tech Humanist , published two books ago, became a game-changer in discussions about how technology impacts human experiences. As marketers and AI professionals, you understand that these discussions are crucial.

I primarily work as a keynote speaker, researcher, and consultant. Speaking allows me to reach large audiences and help shape transformative conversations. Over time, I’ve noticed a shift in the questions I receive—leaders increasingly express concern that everything is changing too fast.

Technology, business, geopolitics—everything feels like it’s accelerating. This creates challenges for decision-making, as leaders struggle with the vast consequences of their choices.

Through my research, I’ve identified two major decision-making pitfalls:

- Sacrificing the future for the present – Hesitation due to uncertainty, lack of data, or fear of commitment.

- Sacrificing the present for the future – Rushing ahead without fully considering the risks, often seen in Silicon Valley and AI startups.

Both approaches lead to different risks—either the harms of inaction or the harms of action. My goal is to provide leaders with a framework to navigate this balance more effectively.

(07:53)

Most leaders feel the future is uncertain—if I asked this group, I imagine nearly every hand would go up. But uncertainty shouldn’t be a barrier to decision-making. Instead, we need models that help us make sense of the future so that we can act with confidence.

Why does decision-making matter so much? Because when we make choices about AI, data, and algorithms, we’re dealing with scale, scope, and long-term implications that affect everyone.

We’re constantly navigating the tension between immediate action and delayed action. Let’s take climate change as an example—there’s almost no action we could take today that we’d regret in 10 years for being too proactive . The real harm comes from inaction.

Contrast that with AI—companies are rushing to deploy AI in sensitive areas like law enforcement without fully understanding the societal impacts. This is the opposite problem: acting too quickly without the necessary safeguards.

We need to make technology decisions that prioritize humanity. My approach to future readiness challenges the idea of future-proofing . You can’t “proof” yourself against the future, but you can be more ready for it.

(10:49)

Leaders spend 40% of their time making decisions, yet many don’t feel confident in their choices. That’s a huge loss of efficiency.

My model helps leaders contextualize decisions by looking at:

- What mattered in the past (known data and prior commitments).

- What matters now (current priorities and decisions).

- What might matter in the future (foresight and scenario planning).

Instead of trying to predict the future with perfect accuracy, we can identify the most probable outcome and compare it to our preferred future. Then, we focus on closing the gap between the two.

I’ll share an example: When I worked at Netflix in the early 2000s, Blockbuster dominated the market. But Netflix executives invested in streaming technology years before it was viable. They didn’t need to solve the long-term problem immediately, but they made near-term decisions that kept their options open.

(17:19)

Ultimately, we should aim to align business objectives with human outcomes—rather than simply using technology to maximize business goals at humanity’s expense.

Marketers play a key role in this, as you understand the connection between business and human experiences. Instead of letting technology dictate priorities, we should use technology to enhance the alignment between business and human needs.

One framework I introduce in the book is through-line thinking , which connects analytical skills (data, insights) with generative skills (creativity, empathy, imagination). These skills help leaders make better, future-ready decisions.

(22:38)

That was a quick overview, but I hope it provides a useful framework. There’s a QR code on the screen if you’d like to check out the book. I’d love to hear your questions!
(23:11) – Audience Q&A Begins
David:

Great! I want to start with a question before turning it over to the audience.
Given the timing of our conversation and the current global landscape, discussions about humanity are deeply tied to politics. Perspectives on DEI (Diversity, Equity, and Inclusion), human collaboration, and even our future with AI vary widely. Some view inclusivity as essential, while others see it as a form of discrimination. Some envision humanity advancing through collaboration, while others, like Elon Musk, focus on escaping the planet or merging humans with machines, as Ray Kurzweil has suggested.
At a time when humanity itself is a polarizing topic, how do we even begin to have meaningful discussions about defining and protecting it?
(24:40) – Kate’s Response
Kate:

That’s such an important and complex question. I’ve spent the last week and a half doing media interviews on AI policy, content moderation, and the overturning of AI-related executive orders. So, I completely understand how relevant this is right now.
We are absolutely in a polarizing moment. More than ever, we need frameworks and shared vocabulary to have productive conversations. Without common ground, we’re just talking past each other.
One approach is to establish clear definitions of what we mean by humanity and what matters most. It’s not useful to be ambiguous or rely solely on emotional appeals. Instead, we need structured ways to discuss priorities, risks, and trade-offs.
For those of us who believe in inclusivity and ethical technology, we must articulate why these principles matter in a way that resonates beyond our own circles. This is where strategic communication and even meta-marketing come in—helping people see the benefits of human-centered approaches, rather than just arguing values.
At the core, humanity is about meaning-making . We are wired to seek meaning in everything—from language to purpose to relationships. If we can align around what matters —even if we disagree on specifics—then we have a foundation for dialogue.
(27:10) – Follow-Up Discussion
David:

That’s really helpful. But it’s also a time when even the definition of who is human is up for debate. We see this in discussions about immigration—are people “illegal,” or are they contributors to society? It’s a question of language shaping perception.
Adam:

Yeah, Kate, I appreciate your response. I was about to let David’s question derail my own, but I’ll stick with it and maybe tie back at the end.
You’ve shared valuable frameworks, but my question is: Why now? What compelled you to write this book at this moment? Do you see an urgent trend unfolding, or is it more about the long-term trajectory of uncertainty?
(30:16) – Kate’s Response: Why This Book Now?
Kate:

Great question. My previous book, A Future So Bright , was often misquoted as The Future is Bright —but that’s not the title for a reason. The full thought is: A future so bright... if we make the right decisions.
That book explored how AI and emerging technologies could be used to solve major challenges while also driving business success. But what I saw happening—especially in Silicon Valley—was a growing accelerationist mindset.
Accelerationists believe we should adopt every technology as quickly as possible, no matter the risks. People like Marc Andreessen advocate for techno-optimism , which assumes technology will automatically make things better.
I reject that view. I take a tech-humanist approach— strategic optimism rather than blind optimism. That means recognizing that if we center decisions around humanity, we can create better futures for more people.
This book exists because the accelerationist discourse is so loud. Many leaders feel like speed is the only option, and I want to offer a counter-model—one that prioritizes future readiness over reckless adoption .
(33:40) – The Attention Economy & Risk
Adam:

That makes sense. But there’s also another factor—our attention is being manipulated. Social media and algorithmic feeds distort our focus, polarizing and balkanizing our perspectives.
Chris Hayes’ upcoming book on the attention economy suggests that we’re heading for a massive backlash. People are frustrated, not just with tech, but with how their focus is being hijacked .
So, even beyond accelerationism, we need frameworks that help people see through the noise and make thoughtful decisions. How does your model help cut through that?
Kate:

That’s such an insightful point. We often think of AI in terms of automation, but its biggest impact is shaping human attention —deciding what we see, what we engage with, and what we believe is urgent.
My model addresses this by introducing the Now-Next Continuum . It forces decision-makers to step back and ask:

- What do we know for certain? (The present and the past)

- What can we predict? (Trends and insights)

- What are our blind spots? (Unknowns and risks)

- What do we actually want? (Preferred futures)

By structuring decisions this way, we counter the reactive, attention-driven mode that social media and AI algorithms push us toward.
(36:02) – Risk & Future Readiness
Jim:

Kate, I really like your Now-Next Continuum framework. It challenges marketers to balance AI adoption with long-term responsibility .
This ties into risk management—balancing the urgency of adoption with the ethical imperative to mitigate long-term harms .
In your experience, what are the most underestimated risks in AI decision-making—both from action and inaction?
(37:18) – Kate’s Response: The Biggest Risks
Kate:

Excellent question. I think the biggest underestimated risks are:

- Harms of Inaction – Failing to modernize when the world has moved forward. For example, companies that ignored digital transformation for too long found themselves obsolete. Not adopting AI where it could improve accessibility or efficiency is also a risk.

- Harms of Action – Rushing ahead without fully understanding the consequences. AI bias is a great example. Amazon once built an AI hiring tool that favored male candidates over women. They couldn’t fix the bias and had to scrap it. That’s a cautionary tale for all AI applications.

The challenge is balancing both risks. One way to do this is by distinguishing transformation (catching up) from innovation (creating new possibilities). If you’re behind in digital transformation, your priority should be responsible adoption . If you’re leading in innovation, your priority should be ethical foresight .
(42:30) – Examples of Future-Ready Companies
Alexander:

Do you have examples of companies successfully applying these principles?
Kate:

Yes! Some great examples:

- Ørsted (Denmark) – Transformed from a fossil fuel-based company into a leader in renewable energy.

- Levi Strauss – Adapted its e-commerce strategy to stay ahead during COVID-19.

- Netflix (early 2000s) – Invested in streaming before it was viable, ensuring long-term success.

- Google & Amazon (mixed examples) – Have both succeeded and failed at balancing AI risks.

No company is perfect, but these examples show thoughtful long-term decision-making .

## Thrive with Human-AI Symbiosis Masterclass

Speaker: Siok Siok Tan
Published: 2025-01-23
Tags: ai principles, ai ethics, ai for humanity
Video: https://www.youtube.com/watch?v=tvVHLKLFKUI
Page: https://aimarketersguild.org/sessions/thrive-with-human-ai-symbiosis-masterclass

(00:10)

We’re excited about the potential of the AI Marketers Guild, which started in the U.S. a few years ago by David Berkowitz. It has grown into an incredible community of marketers embracing the possibilities AI offers.
Siok Siok Tan, today’s speaker, is a published author, filmmaker, producer, entrepreneur, and mentor to startups. She’s based in Singapore but joining us from London, where it’s early and quite cold. We’re thrilled to have her here today to share insights from her co-authored book, AI for Humanity, published last year.

(00:47)

Quickly introducing myself—my name is Siok Siok Tan, and I’m Singaporean-Chinese. I lived abroad for most of my career, spending 12 years in Beijing before returning to Singapore during the pandemic. Today, I’ll share insights from the book and explore the concept of Human-AI Symbiotic Intelligence (HASI). It’s not a new virus, I promise, but a framework to define our relationship with AI. While there’s much excitement about AI, there’s also apprehension. My aim is to provide a framework for understanding and managing this relationship. Often, discussions about AI make it seem like it’s something beyond our control, when in reality, the focus should be on how humans and AI interact and co-evolve.

(02:15)

Rather than viewing AI as an independent force that will displace jobs or dominate humanity, we should define and manage our relationship with technology. This talk will focus on how to approach that. To get a sense of the audience, could those in the Zoom room raise their hands if they consider themselves advanced in their use of AI? For those who didn’t raise their hands, feel free to share why—whether it’s humility or a feeling of being behind.

(03:47)

Many in the chat mentioned they have basic knowledge or are just starting with AI, while some are more advanced. It’s great to have this mix of perspectives. This brings me to why I emphasize a framework for defining our relationship with AI rather than technical skills. Technology evolves quickly, creating anxiety as we try to keep up. However, focusing on principles allows us to remain grounded, as principles are evergreen. This mindset liberates us from constantly chasing the latest trends while providing clarity on how to engage with AI effectively.

(05:08)

Let me briefly introduce the book, AI for Humanity, published by Wiley in June last year. It sold out within a week, ranked highly on technology bestseller lists in Singapore, and achieved the fifth spot globally for AI-related books. What I’m most proud of is that the book is available in over 400 libraries across 30 countries, including top universities like MIT and the University of Chicago. This widespread acceptance reflects the book’s thought leadership and the collaboration behind it, involving AI scientists, governance experts, and myself.

(07:15)

The inspiration for the book stems from this collaboration, combining industry expertise and academic research. Unlike AI-generated books, AI for Humanity reflects original insights. It’s divided into three parts: Why we need AI for Humanity, what it entails, and how to build it. Today, I’ll focus on the correlation between AI for Humanity and Human-AI Symbiotic Intelligence (HASI).

(09:13)

The book introduces the AI Dilemma Hierarchy, which has three levels:

- To AI or not to AI: Whether to adopt AI.

- The AI Trap: Being unaware of how we influence AI models and how AI mirrors human virtues and flaws, exemplified by disinformation spread.

- Organic AI: The interplay between humans and AI creating new possibilities.

The hierarchy emphasizes moving from automation to augmentation and innovation, shifting from "Human vs. AI" to "Human with AI."

(13:25)

The Stanford Digital Economy Lab illustrates this shift. Most current AI applications focus on automating existing tasks, leading to cost savings but limiting innovation. The real potential lies in exploring new jobs and possibilities enabled by AI. This concept parallels Chinese art, where blank space is integral to meaning. Similarly, the unexplored potential of "Human with AI" opens doors to creative and innovative futures.

(15:41)

In the book, we outline four principles for Human-AI relationships: Humanity First, Containment (ensuring AI causes no harm), Nurture (guiding AI), and Symbiosis. HASI originates from the symbiosis principle and includes three levels:

- Automation: Streamlining tasks.

- Augmentation: Enhancing creativity.

- Innovation: Enabling groundbreaking advancements.

(17:03)

Automation, while widely discussed, often overlooks the human effort involved in prompting AI effectively. Tasks like resizing images or generating ad variations demonstrate its utility but also highlight risks, including over-reliance on AI and mediocrity due to ease of achieving "good enough" results. Human oversight remains critical, as AI is limited by training data and user expertise.

(22:49)

Augmentation focuses on collaboration. For instance, AI excels at scalability and data-driven insights, while humans bring creativity, authenticity, and cultural sensitivity. I use AI as a sparring partner to critique and refine my ideas. This layered approach ensures the output aligns with human standards of quality and originality.

(26:44)

A striking example of augmentation is fighting disinformation. Deepfakes and fake news require AI to scale fact-checking efforts. One startup, Facticity AI, uses advanced models to combat this issue. This illustrates how AI can augment human capabilities to address large-scale challenges.

(30:05)

My favorite aspect of HASI is its co-evolutionary nature. Humans train AI while learning from its insights. This dynamic interplay fosters continuous improvement and creativity. For example, I once prepared a keynote for a conference on fire safety—a topic I knew little about—using AI tools like ChatGPT and perplexity.ai to gather and validate information. The presentation succeeded because I combined AI-assisted research with my storytelling skills, exemplifying HASI.

(39:04)

HASI exemplifies collaboration, blending human creativity and machine efficiency. It’s not about AI replacing humans but expanding our capabilities. For instance, DeepMind’s AlphaFold revolutionized protein folding analysis, reducing the time needed from years to seconds. Yet, human researchers build upon this foundation for further discoveries, highlighting the synergy between AI and human ingenuity.

(45:10)

The greatest risk with AI is underestimating its complexity. By fostering intentional relationships with AI and encouraging exploration, we can harness its potential responsibly while mitigating risks. A shared understanding of AI’s capabilities and limitations is crucial for informed decision-making and innovation.

## Stand Out Be Seen Make Connections

Speaker: Tiffany Vasilchik, Estelle Shepherd
Published: 2025-01-23
Tags: authenticity, trust building, human discernment
Video: https://www.youtube.com/watch?v=DWJqczWBqWc
Page: https://aimarketersguild.org/sessions/stand-out-be-seen-make-connections

(00:00)
Welcome, everyone! I’m thrilled to kick off another edition of AI Insiders from Marketer Guild. Today, we have two special guests from the innovative firm Hot Cognition: Tiffany Vasilchik and Estelle Shepherd. I’m particularly excited about this session. Tiffany and I were set to work together on a major project last year, which unfortunately didn’t materialize. However, the silver lining was connecting with her and getting insights into where AI fits into marketing and sales.

(00:55)

A quick reminder: AI Insiders is a participatory, community-driven conversation, not a webinar. We’re looking forward to your feedback, questions, and ideas as Tiffany and Estelle share their expertise. With that, I’ll turn it over to them.

(01:36)

Tiffany: Thank you! Hi, everyone. I’m Tiffany, co-founder of Hot Cognition alongside Estelle. Today, we want to discuss how AI is revolutionizing marketing and sales—two interconnected fields. AI can amplify your efforts, helping you make a bigger impact, gain attention, and drive engagement.

To give you a bit of background: I started my career in marketing at Pepsi and Mondelez before transitioning to brand and innovation consulting. My last role was at Board of Innovation, where I met Estelle. The CEO there was an early advocate for integrating AI into marketing, sales, and innovation. That inspired us to explore how AI could transform these areas, especially sales. We’re excited to share key insights from our journey. Over to you, Estelle.

(02:44)

Estelle: Thanks, Tiffany. Hello from London! It’s not as cold as the East Coast here but definitely darker—it feels like the middle of the night. My background started in sales within the tech sector, including roles in talent acquisition during the first internet wave at companies like MarchFirst and Microsoft. Later, I shifted to marketing services and innovation, eventually meeting Tiffany at Board of Innovation.

Generative AI became a central part of our work, and we saw its potential to enhance individual performance, helping people become their best selves. My focus has been on educating and empowering people to use AI effectively while ensuring it remains a tool we control—not the other way around.

(04:07)

Tiffany: Well said, Estelle. We’d love to connect with all of you—feel free to use the QR codes here to find us on LinkedIn. Let’s dive in!

AI isn’t here to replace jobs anytime soon, but someone who knows how to leverage it might replace someone who doesn’t. The momentum behind AI is immense. Just yesterday, there was a major announcement about Stargate, a $500 billion initiative involving Oracle, OpenAI, and SoftBank. Progress is accelerating rapidly, and as leaders and marketers, it’s crucial to stay ahead of the curve.

We often use the analogy of a frog in slowly heating water—it doesn’t realize the danger until it’s too late. Don’t be the frog!

(05:44)

If you’re here, listening to this, you’re already ahead of the curve. Jensen Huang of Nvidia highlighted that Moore’s Law—the idea that computing power doubles every two years—has been smashed. It’s now closer to every six months. AI is driving significant improvements in productivity, efficiency, and quality across high-knowledge tasks like consulting and marketing.

The challenge now is raising your organization’s AI IQ. It’s about equipping everyone with the tools and confidence to use AI effectively, taking away barriers like the blank page syndrome.

(07:30)

At Hot Cognition, we’re an AI-driven sales and marketing conversion engine. Our focus is on helping businesses achieve more by combining AI with behavioral science and human expertise. Today, we’ll share five key shifts in modern marketing and sales, all tied to AI.

(08:35)

The first shift: Get inside the buyer’s head.

Today’s customers are often far along in their buying journey before contacting you. They expect personalized, relevant information at every stage. Understanding your customers on a deeper level is critical.

We use empathy maps to delve into what customers are thinking, seeing, hearing, and doing. For example, by creating an AI-generated digital twin of a consumer or customer segment, we can better understand their mindset. This involves uploading data into AI models like ChatGPT, which then acts as if it’s the customer, providing insights into preferences, attitudes, and needs.

(12:35)

The second shift: Win the battle for attention.

In today’s crowded digital space, attention is the currency. The key to standing out isn’t being flashy—it’s about being familiar and consistently offering value. Repetition builds trust, while free, high-value content builds engagement.

LinkedIn is a powerful platform for professionals. It’s the largest Rolodex in the world and a great place to establish your presence. Optimize your profile to clearly communicate who you are, what you do, and how you help.
(14:33)

Estelle: LinkedIn is the perfect platform to start conversations, share your point of view, and connect with people you want to work with. One of the tools we’ve developed is a LinkedIn profile optimizer, available as a custom GPT. By scanning the QR code, you can use it to optimize your profile section by section, ensuring it resonates with your audience and aligns with your goals.
(17:06)

The third shift: Shameless self-promotion.

Building familiarity with your audience requires putting yourself out there consistently. Think of it as “networking without canapés.” While in-person events have their charm, digital networking on platforms like LinkedIn allows you to amplify your presence and engage with a broader audience.
If you don’t promote yourself and your work, someone else will. This doesn’t mean being overbearing—it’s about showing up authentically and offering value. Consistency is more important than perfection. Use AI tools to help build content calendars, structure posts, and maintain your tone of voice. By educating AI on your preferred style, you can make your content feel more personal and engaging.
(20:19)

AI can handle the heavy lifting, but it’s up to you to ensure your content reflects your personality. For example, I’ve trained my AI to incorporate humor and sarcasm into my posts because I want them to feel like me. There’s a surprising amount of room for individuality on platforms like LinkedIn—less than 1% of users post weekly, so your voice can easily stand out with regular, authentic content.
(22:13)

On a personal note, I recently quit smoking after 30 years, thanks to a program I created with ChatGPT. I worked with it to build a detailed plan that included daily milestones and strategies. Following it step-by-step helped me succeed, demonstrating how AI can support personal goals as well as professional ones.
(25:11)

When creating content, focus on being useful without being preachy. AI can help refine your tone and ensure your posts are clear and engaging. Emphasize consistency over perfection. Start small—post once every two weeks, then build up to a weekly rhythm.
Encourage debate and conversation, but avoid negativity. If someone disagrees with your content, they can unfollow. The goal is to create a positive, collaborative space where ideas can flourish.
(26:23)

This slide shows how a single idea can be repurposed into various types of content—long-form posts, short videos, infographics, or carousel posts. AI can help you reformat and repurpose content, ensuring your message reaches different audiences in engaging ways.
The key to self-promotion is talking about what you do repeatedly and sharing it online. Identify your point of view, commit to posting consistently, and don’t overthink it—just get started.
(28:14)

Tiffany: Building a strong personal brand is essential. It’s not just about selling but also about connecting with your audience and establishing trust. You don’t have to be controversial to stand out. Authenticity and value go a long way.
(29:33)

David: That’s a great point. How do you avoid being boring when using AI for content creation? I’ve noticed that some content, even when written by humans, can feel generic or uninspired.
(30:11)

Estelle: It’s true—some content written by humans feels like AI could have written it! The key is focusing on your unique perspective and voice. Are you trying to help your audience or sell to them? I believe that building community and trust will naturally attract the right opportunities.
Tiffany: Absolutely. You can also use AI to understand your audience better, tailoring your content to their needs and interests. Tools like ChatGPT can mimic writing styles you admire or help refine your tone.
(32:12)

Karen: One concern with AI is staying present in conversations. For example, when people comment on your posts, how do you manage that if AI is doing the initial writing?
Estelle: Great question. Active engagement is critical. We recommend scheduling posts with AI but being present before and after they go live to respond to comments. Spending 15 minutes engaging with the platform before and after posting can also boost your visibility in LinkedIn’s algorithm.
(34:12)

It’s also important to guide AI when creating content. For example, if your AI-generated post feels too long or formal, you can instruct it to make it snappier or add humor. Over time, your AI can become a more effective collaborator, producing content that feels authentic and engaging.
(36:08)

David: That’s great advice. I tested the LinkedIn profile optimizer you shared, and it immediately picked up on my tone of voice. It’s impressive how well it understood my style and offered suggestions to enhance it.
Estelle: That’s fantastic to hear! We worked hard to train the tool on best practices, so it’s great to see it delivering results.
(38:04)

The fourth shift: Always be opening conversations.

You never know where a conversation might lead. The people you connect with today could play a key role in your career or business in the future. Networking strategically and generously is a crucial mindset shift.
For example, if someone reaches out to you for help—perhaps they’re between jobs—take the time to respond. Offering value without expecting anything in return builds goodwill and strengthens your network. Often, those you help will remember you when opportunities arise.
(41:45)

Tiffany: Finally, the fifth shift: Winning by inches.

In today’s competitive landscape, it’s often the smallest details that make the biggest difference. AI can help identify those subtle opportunities to stand out, whether it’s crafting a more personalized proposal, tailoring a Zoom meeting setup, or refining a loyalty program.
AI is particularly effective at finding patterns and insights that humans might miss. For example, when working with a cannabis dispensary, we used AI to analyze competitors’ loyalty programs and identify unique features to improve our client’s offering.
(44:24)

AI is a powerful tool for achieving both personal and professional goals. Whether you’re creating a marketing strategy, designing a sales process, or planning your own habits, AI can help you stay consistent, focused, and effective.
(45:29)

That’s what we had for you today—five key shifts in marketing and sales, with practical applications for AI. We hope this sparked some ideas for you. Feel free to reach out with feedback or questions.
(46:15)

David: This was fantastic. Thank you so much for sharing your insights. It’s clear from the chat that people are already eager to apply these ideas.
Tiffany and Estelle: Thank you! We’re so glad this was helpful. Please connect with us on LinkedIn—we’d love to continue the conversation.

## Sree Sreenivasan on AI, Journalism, and the Future of Marketing

Speaker: Sree Sreeivasan
Published: 2025-01-18
Tags: journalism, privacy, ai agents, customer experience, ai regulation
Video: https://www.youtube.com/watch?v=RNmtETSnLOk
Page: https://aimarketersguild.org/sessions/sree-sreenivasan-on-ai-journalism-and-the-future-of-marketing

(00:00)

Welcome back to another edition of AI Insiders. We have a very special guest today—someone I’ve known for a long time. I’ve had the pleasure of visiting his home and taking his tours of one of the greatest museums in the world. This should be a lot of fun, especially considering his work in AI and how he’s educating marketers and others.

When I saw what Sree was doing in AI, I thought, "You’ve got to stop by our neck of the woods sometime." Sree, it’s always great to see you. Welcome!

(00:44)

Sree Sreenivasan: Thank you! I’m really happy to be here.

Host: If you’d like, could you give a more precise introduction about what you’re currently working on? You always wear many hats.

Sree: Thanks, David. Hi, everyone! Great to be here. David is an optimistic person—someone who managed to fly both Frontier and Spirit on the same trip and still had something positive to say! That’s the kind of upbeat person he is.

I’m delighted to be here and hope this will be a great opportunity for questions and learning. I was the Chief Digital Officer of the Metropolitan Museum of Art, working on the future of culture. Before that, I was a professor and Chief Digital Officer at Columbia Journalism School and later Columbia University, focusing on the future of education. After that, I was the CDO of New York City, working on the future of cities and citizens.

For the past eight years, I’ve been running a small social and digital marketing agency, providing consulting and training. We help people understand this AI moment. I’m happy to follow David’s lead on how he’d like to set up the conversation.

(01:53)

Host: What are the top questions people ask you these days?

Sree: I just returned from conducting AI workshops in three cities in India, focusing on how to use AI effectively.

Interestingly, today’s discussion coincides with the recent DeepSeek announcements and its impact. In India, where there's a strong focus on China’s advancements, DeepSeek has generated significant attention—both its potential and its challenges.

Let me pull up something I shared during those workshops. Here’s a slide I showed before the U.S. markets opened. The first two headlines appeared before trading began, and the last one is from just over an hour ago. It highlights how, even 10 days later, the world is still discussing DeepSeek’s impact.

Journalists, industry experts, and policymakers are trying to understand its significance—much like the ongoing debates I’ve encountered in my discussions.

(03:50)

Host: We haven’t yet had a candid discussion about DeepSeek here. I’ve seen reactions ranging from “This is a cataclysmic event for U.S. tech” to “This doesn’t really matter because many Western companies are hesitant to do business with China.” Others argue that competition is beneficial and will drive innovation.

Multiple things can be true at once, which can be hard to reconcile. What’s your take? Do you provide different answers depending on whether you’re speaking with companies in India versus the U.S.?

Sree: First, I want to acknowledge that many in this room are more knowledgeable on specific aspects of this topic than I am. I appreciate the chance for discussion, and I encourage everyone to share their insights in the chat.

This moment has disrupted many assumptions about where generative AI was heading in the next year. It’s still unclear what this means in the long run.

I recently watched a video where DeepSeek claimed not to send data to China, yet you could see data transmission occurring in real-time. For many outside the U.S., this is viewed as a necessary counterbalance to Silicon Valley’s dominance in AI and tech.

Of course, there's also irony in some companies—who built their models by scraping copyrighted content—now complaining that DeepSeek is using their data.

(07:29)

In India, there’s renewed concern about why China was able to develop DeepSeek while India has primarily focused on the tech services industry rather than AI model development.

When I introduce people to a new AI tool, I tell them the same thing I advised during the rise of search engines in the late ’90s: test it on something you know well.

For example, I asked DeepSeek about myself. It mostly got my career right but incorrectly stated that I was born in India (I was born in Japan). The real shock was when it said I had died in September 2020 after a battle with cancer. That was surreal!

I’ve seen AI models make mistakes, but this was the first time one declared me dead. In a way, it was amusing—I got to read nice things about myself that I wouldn’t otherwise have seen!

(10:54)

This brings us to the topic of hallucinations —the AI-generated inaccuracies we keep seeing. I dislike the term hallucination because it makes these mistakes seem trivial, like mirages, rather than the significant errors they are. AI platforms should be held accountable for these inaccuracies.

Wouldn’t it be better if AI simply said, "I don’t know" rather than fabricating information?

(15:05)

Audience Member: AI models are based on probability. They generate responses based on likelihood rather than certainty. There’s no foolproof way to ensure accuracy with such large datasets.

Sree: Exactly. The problem is that AI fills in gaps even when it shouldn’t. And we, as users, often accept inaccuracies if they seem harmless.

For example, in journalism, there was a famous case of The New York Times reporter Jayson Blair, who fabricated stories for years. When readers were later asked why they didn’t report inaccuracies, many said, “It was an innocuous quote, and I didn’t think it mattered.”

That same logic applies to AI. If the errors seem minor, people won’t bother to correct them—until something major happens.

(24:36)

One of my biggest concerns is job displacement. Even if AI-driven automation reduces jobs by only 10%, we’re still talking about millions of lost jobs worldwide. And yet, we’re not discussing this seriously enough—whether in companies, industry groups, or government.
(25:12) – The Impact of AI on Jobs and Business
Sree: Even if these job loss estimates are off by a factor of ten, we’re still talking about millions of people losing their jobs due to AI-driven automation. And yet, we’re not seriously discussing what this means for industries, governments, or workers.
Audience Member 1: If 10 million people lose their jobs, the economy will collapse. People won’t be able to buy products, which will hurt corporate profits. It’s strange that companies don’t seem to be making this connection.
Audience Member 2: I think the next question is: What do we do about it? Large organizations with thousands of employees are trying to understand AI’s impact, but many don’t even know how to measure it.
There’s an article in Harvard Business Review that categorizes AI’s impact on jobs dealing with words, images, numbers , and so on. But beyond that, businesses don’t seem to have a clear framework for assessing AI’s effect.
Sree: That’s a great point. I’ve been in conversations with government officials in places like Singapore and the UAE, and they are much more focused on AI’s workforce impact than the U.S.
One of the reasons for this is that their bureaucrats tend to be much more tech-savvy. In contrast, in the U.S., our political leaders often don’t understand the technology they’re regulating.
Remember that infamous congressional hearing where a senator asked Mark Zuckerberg how Facebook makes money, and he had to explain: “We sell ads.”
There was also a moment when a senator asked Tim Cook for tech support advice on how to improve his iPhone experience.
That’s the level of understanding we’re dealing with at the highest levels of government.
Audience Member 3 (John): The focus should be on AI agents , not just models like DeepSeek. AI agents can automate workflows, collaborate, and perform tasks that replace entire job functions.
The problem is that while these advancements are happening rapidly, people are still debating the performance differences between models like GPT-4, Gemini, and DeepSeek. They’re missing the bigger transformation —AI agents that automate processes end-to-end.
Sree: That’s a great point, John. AI agents are the next major shift, and they’re developing fast. The biggest impact will come when AI assistants can communicate with each other to complete tasks on our behalf.
For example, imagine my AI agent negotiating with David’s AI agent to schedule meetings, book flights, or even make purchases.
That leads to another big question: How predictable are we as humans?
If AI knows our preferences well enough, will it always serve us exactly what we expect, or will it also surprise us? Will it push us toward new choices we didn’t even know we wanted?
For example, I wear Allbirds shoes regularly. But one time, I randomly bought a pair of ridiculous tiger-foot slippers at a Walmart in upstate New York. It was an impulse buy.
If AI were shopping for me, would it know to throw in a wildcard purchase like that? Would it ever try to surprise me the way a human might?
(36:07) – Customer Experience Still Matters
Sree: Speaking of Allbirds , I had an interesting experience with their customer service.
I unexpectedly received a pair of Allbirds in the mail, addressed to me. When I called the company, they wouldn’t initially reveal the sender, but they reached out on my behalf. It turned out that someone I knew had accidentally sent me the shoes through their assistant.
The surprising part? Allbirds sent me another free pair of shoes just for my trouble.
This was a small but powerful reminder that customer service still matters. Even in an AI-driven world, businesses that prioritize human touchpoints will have an edge.
Audience Member 4: That’s a great story, but what happens when companies remove human customer service altogether?
I recently bought a Tesla , and everything is handled through an app. When things go wrong, there’s no one to talk to. There’s no customer service number. The only option is to submit a ticket and wait.
If technology removes human problem-solving , what do you do when the system fails?
Audience Member 5: The same thing happens with Amazon. They’re automating everything, but when you need actual help, there’s no clear path to resolving issues.
Sree: That’s an important point. The question businesses should ask is: Do our customers want automation, or do they want a mix of automation and human interaction?
If your product is purely transactional—like ordering a book—automation works. But if there’s a problem-solving component, customers still expect human support .
Audience Member 6: It also depends on the type of customer. Some people prefer self-service and automation, while others value human interaction. There’s no one-size-fits-all approach.
(41:12) – AI Regulation and Privacy Concerns
Audience Member 7 (Jean): Have you seen Josh Hawley’s proposed legislation? He’s suggesting a $1 million fine for individuals using DeepSeek and a $100 million fine for businesses.
Sree: That’s wild. How would they even enforce that? Are they going to track people’s browser histories?
It’s fascinating how some politicians who advocate for less government intervention in business suddenly want to impose strict controls on AI tools.
If this law passes, I imagine there will be a surge in Google searches for "best secure VPN to bypass AI restrictions."
It’s ironic because this is the exact kind of concern Americans have when traveling to China —whether they’ll be able to access certain websites, if their data is being tracked, or if they’ll face penalties for using specific tools.
Now, people might start asking those same questions when visiting the U.S.
Audience Member 8: That’s why many U.S. executives already use burner phones when traveling to China. They don’t bring their regular devices because of security concerns.
Sree: Exactly. And yet, many Americans don’t realize that U.S. companies are already collecting and storing massive amounts of our data.
Take Grubhub, for example. They were recently hacked, exposing customers’ order histories and credit card details.
The bigger issue isn’t DeepSeek—it’s the cloud platforms that control and store our data.
Audience Member 9: That aligns with Yanis Varoufakis’ idea of "techno-feudalism." The argument is that cloud giants like Amazon, Google, and Meta own the infrastructure , extract fees from users, and essentially act as digital landlords.
Sree: Yes, I’ve seen his work, and it’s a compelling argument. The real power lies with companies that control the digital economy’s infrastructure —not just AI models.

(48:51) State of Journalism

Host: Speaking of journalism, where do you see the future of the field?

Sree: Journalism is in deep trouble. We lost over 250 newspapers last year, and news deserts are expanding. The financial challenges facing media organizations directly contribute to the rise of misinformation.

Despite this, young people still want to become journalists. That’s encouraging. However, the question remains: where will the jobs be?

The Washington Post lost 200,000 subscribers after its controversial endorsement decision. Regaining even a fraction of that audience is nearly impossible.

Meanwhile, misleading narratives gain traction faster than ever. For instance, there was a post circulating today claiming that USAID spent $4.1 million on Politico . The reality? That figure represents government-wide subscriptions to Politico Pro , not a direct payout to Politico . But once misinformation spreads, it’s hard to counter.

(53:28)

Host: Sree, thank you for sharing your insights! It’s always an honor to learn from you.

Sree: Thank you, David. It’s been a pleasure. I encourage everyone to follow me on LinkedIn and subscribe to my newsletter. Let’s keep the conversation going!

## AI Search Disruption  Future of News

Speaker: Pete Pachal
Published: 2025-01-18
Tags: media strategies, media, ai search disruption, future of news, journalism
Video: https://www.youtube.com/watch?v=TwxDGTnjVyA
Page: https://aimarketersguild.org/sessions/ai-search-disruption-future-of-news

(00:00)
Welcome back to AI Insiders with the Marketers Guild. Pete, I’ll let you introduce yourself in a moment, but this has been such a treat. Catching up with Pete recently, we’ve been exploring the journalist’s perspective on the shifting media landscape. As soon as I started diving into Media Co-Pilot , I knew these insights would be invaluable here. Pete, why don’t you introduce yourself?

(00:53)

Thanks, David. Hey, everyone. I’m Pete, the Editor-in-Chief, CEO, and founder of Media Co-Pilot , which is my company. We publish a newsletter, teach media professionals and creative teams how to use AI tools, and provide AI consulting for media and content operations. When I say “we,” it’s me, freelancers, and fractional help. There’s significant demand right now, so things are going well.

I enjoyed hearing the earlier discussion about CES. I covered it for various publications for 20 years and watched the show evolve. About 10-12 years ago, people questioned its relevance due to the rise of mobile tech, but the CTA team did an excellent job keeping it relevant by incorporating industries like automotive.

(02:10)

It’s interesting because companies like Apple, Google, and Microsoft often have minimal presence at CES, yet Nvidia continues to showcase its innovations there. Nvidia’s involvement has helped maintain CES’s relevance, especially as they transitioned from a niche player focused on gaming and GPUs to a significant force in AI and tech innovation.

(03:15)

Today, I want to talk about AI’s impact on news, particularly AI search. AI bots have transformed how we access information. Initially, they provided facts based on their knowledge base, like answering historical questions. Over time, the industry integrated AI into everything, and AI search evolved to summarize not just links but the content within them.

This shift has major implications for the news industry. Traditional search engines generated traffic for publishers, but AI summaries disrupt this model by providing answers directly, reducing referral traffic. This has shifted the balance in the “grand bargain” between search engines and content creators.

(06:16)

Google’s AI search currently avoids summarizing news content, directing users to Google News instead. However, this changed at CES, where Google launched a news summarization prototype for Google TV. It’s an early step toward applying generative AI to current news. There are challenges, including copyright law and publisher agreements, but Google’s market share slipping below 90% suggests urgency in adapting to AI search.

(08:03)

AI search engines like Perplexity and ChatGPT Search are nibbling at Google’s market share. While still small players, they show a trend of users turning to AI for answers. The distinction between traditional search and AI-powered experiences is blurring, and this shift might reach a tipping point sooner than expected.

(10:50)

For publishers, appearing in AI summaries offers branding opportunities and potential licensing revenue, although it doesn’t provide the same referral traffic as traditional SEO. Some publishers are leveraging platforms like Tolbit to monetize AI’s use of their content. However, AI’s condensed summaries mean fewer sources are featured, raising questions about representation and diversity in AI-generated information.

(13:46)

Adapting to AI search requires a new mindset. AI rewards substantive, unique, and comprehensive content. This shift could steer journalism away from clickbait and low-quality material incentivized by traditional social and search algorithms. Publishers must align their content and business strategies to navigate this evolving landscape.

(18:49)

AI presents opportunities for better journalism by incentivizing depth and accuracy. However, the reduction in content real estate in AI summaries means competition for visibility will intensify. Smaller publishers and content creators need strategies to ensure relevance, such as newsletters or proprietary AI-driven experiences on their platforms.

(24:56)

User behavior with AI search differs from traditional SEO. AI queries are longer and conversational, making optimization more complex. Publishers must experiment with prompt engineering and other AI-driven approaches to understand how to position their content effectively in this new environment.

(30:01)

While AI search is growing, Google retains advantages in speed, reliability, and user experience. Many users prefer traditional search for its familiar interface and ability to browse links for their own analysis. However, conversational AI is likely to gain traction as platforms like Amazon and Apple integrate advanced voice-based interactions into their ecosystems.

(40:11)

As AI tools evolve, journalists will need skills in prompt engineering and AI utilization to remain competitive. While some processes may be automated, critical thinking and trust-building—core aspects of journalism—cannot be replaced by AI. The role of the journalist will adapt but remain essential.

(50:05)

User-generated content (UGC) presents opportunities and challenges for AI search. AI engines must effectively filter and contextualize UGC to avoid misinformation while leveraging valuable insights. Platforms like Reddit and TikTok contribute significantly to search results, highlighting the importance of integrating diverse content sources.

(53:43)

Thanks, everyone, for a great conversation! Please subscribe to Media Co-Pilot —you’ll get six months of the paid subscription free. I appreciate the thoughtful questions and look forward to continuing the dialogue as this space evolves.

## Generative AI Beyond the Hype Practical Insights

Speaker: Gareth Rydon
Published: 2024-12-05
Tags: generative ai, claude, perplexlity, effective prompting, llm comparison
Video: https://www.youtube.com/watch?v=VBreTUswCsc
Page: https://aimarketersguild.org/sessions/generative-ai-beyond-the-hype-practical-insights

[03:00] How Can You Use ChatGPT, Perplexity, and Claude Together as an AI Team?

Answer / Description:
You can maximize the value of generative AI by treating ChatGPT, Perplexity, and Claude as distinct team members with complementary skills, rather than as direct competitors. ChatGPT acts as your interactive brainstorming collaborator, Perplexity serves as your factual research librarian, and Claude behaves as your master builder or maker.

While these software companies compete in the marketplace, utilizing them together allows you to leverage their unique strengths. ChatGPT excels at real-time, back-and-forth creative collaboration. Perplexity excels at navigating the live web to deliver structured, sourced answers without forcing you to wade through advertisements and search links. Claude excels at structural execution and physical creation, particularly when utilizing its advanced interface tools.

Keywords:
AI tool comparison, ChatGPT vs Perplexity vs Claude, generative AI workflow, Friyay.ai, multi-AI strategy, AI collaboration, generative AI team

[09:40] How Can You Use ChatGPT's Advanced Voice Mode as a Collaborative Brainstorming Tool?

Answer / Description:
ChatGPT's Advanced Voice Mode serves as an interactive conversational partner that allows you to verbally brainstorm, stress-test concepts, and refine strategies. By moving away from typed text inputs and engaging in fluid, real-time verbal dialogue, you can organically flesh out complex business ideas, script drafts, or presentation outlines.

Using advanced voice features turns the AI into a literal whiteboard collaborator. For instance, you can put your headphones in during a walk or commute and verbally pitch ideas to the model to receive immediate critical feedback. Rather than asking the AI to simply execute a final task like writing a basic email, you engage in a dialogue that refines your human thinking, making it an excellent tool for conceptual development and ideation.

Keywords:
ChatGPT advanced voice mode, verbal brainstorming with AI, interactive AI collaborator, conversational AI, voice-first AI workflow, AI ideation, hands-free AI research

[11:40] Why Should You Use Perplexity as an AI Research Tool and Librarian?

Answer / Description:
You should use Perplexity as an AI research "librarian" because it searches the live web, synthesizes factual data, and delivers direct answers complete with inline source citations. This conversational search engine allows you to bypass traditional Google blue links and sponsored ads, retrieving curated, context-specific results immediately.

For the best performance, users should activate the "Pro Search" toggle in Perplexity, which prompts the engine to execute deep-dive multi-step queries. For example, if a semi-competitive cyclist asks for 2025 road bike product trends, Perplexity will bypass generalized marketing links and synthesize a factual list of products sourced from specific, verifiable publications. This allows users to conduct hours of market research in a fraction of the time.

Keywords:
Perplexity Pro search, AI search engine, Perplexity vs Google, AI research librarian, conversational search, cite sources AI, generative search optimization

[14:20] How Can You Use Claude's Artifacts Feature to Generate Practical Business Assets?

Answer / Description:
You can use Claude's "Artifacts" feature to instantly build and interact with standalone digital assets—such as customer journey maps, clickable prototypes, block diagrams, or code—inside a dedicated visual window. This unique feature shifts Claude's output from plain text conversations to functional, testable design frameworks.

This "maker" functionality allows professionals to rapidly visual ideas. For example, if you ask Claude to act as an expert service designer to create a customer journey map for a personal nutrition coaching business, the Artifacts panel will render a clean, visual flow diagram rather than a raw wall of text. These interactive artifacts can be directly modified, exported, or used to build rapid product prototypes without needing manual coding skills.

Keywords:
Claude Artifacts, visual customer journey maps, interactive AI prototypes, Claude service design, AI asset generation, Claude maker feature, clickable prototype AI

[19:10] Why is Coaching Your AI Tool Like an Intern Key to Better Outputs?

Answer / Description:
Coaching an AI tool like a human intern ensures high-quality outputs by replacing basic search queries with detailed context, step-by-step instructions, and performance feedback. Treating your AI interactions as an ongoing, iterative dialogue prevents the generic, substandard results that occur when you give a system blind demands.

If you onboarded a new graduate on their first day and demanded they instantly "write a business strategy" without templates, context, or rules, the output would be poor. The same principle applies to large language models. To obtain superior results, you must outline clear parameters, provide good and bad examples, explain the business background, and ask the model: "Do you have any questions for me before we get started?" This simple question prompts the AI to clarify ambiguities and identify potential errors before generating its work.

Keywords:
coaching AI like an intern, prompt engineering tips, AI dialogue workflow, improve AI outputs, prompt clarification technique, collaborative prompting, conversational AI workflow

[22:30] How Do Advanced Prompting Techniques Like Emotion Prompting and Chain of Thought Work?

Answer / Description:
Chain of Thought prompting directs an AI model to break complex problems down and work through them step-by-step, while emotion prompting uses high-priority phrasing to stimulate more thorough reasoning. Combining these techniques with explicit user personas (roles) and context-rich examples (few-shot prompting) significantly boosts the logical accuracy of AI outputs.

Emotion prompting works because models are trained using Human Feedback (RLHF), where urgent human expressions are linked to high-quality, focused effort. Adding phrases such as "this task is critical to my career" encourages the model to allocate more deliberate processing power to the solution. This is highly effective when paired with Chain of Thought steps and reasoning-heavy models, such as OpenAI's o1-preview, which are optimized to process complex logic and instructions.

Keywords:
Emotion prompting, Chain of Thought prompting, few-shot prompting, AI persona setting, advanced prompt engineering, o1 preview reasoning, reinforcement learning prompt

[28:30] How Can You Reduce Hallucinations in Large Language Models?

Answer / Description:
You can significantly reduce AI hallucinations by explicitly giving the model permission to admit ignorance in your prompt, using instructions such as: "If you are unsure or do not have the necessary information, say 'I don't have enough information to answer that.'" This constraint stops the system from making up plausible-sounding but false details when it runs out of reliable training data.

Because large language models are built to predict the most likely next word rather than verify facts against a traditional database, they behave like enthusiastic assistants who want to please the user at all costs. This makes them prone to fabricating realistic-looking sources, statistics, or case studies. Adding simple diagnostic guardrails to your prompts forces the system to stop and flag missing context instead of generating false information.

Keywords:
reduce AI hallucinations, accurate AI prompts, stop AI making stuff up, hallucination prevention prompts, fact-check AI, reliable generative AI

[29:45] How Should Businesses Evaluate Generative AI Software Before Buying?

Answer / Description:
Businesses should evaluate generative AI tools by demanding a free trial, opting for flexible monthly subscriptions rather than annual contracts, and verifying if the tool's features can be easily replicated inside existing systems like ChatGPT or Claude. You should avoid software that lacks trial access or locks basic functionalities behind rigid paywalls.

Because the generative AI landscape is moving so fast, tools frequently become obsolete or are integrated directly into the foundational platforms (such as ChatGPT, Claude, Gemini, or Llama) within a matter of months. Additionally, roughly 70% of AI software tools on the market are simple "wrappers" built on top of foundational APIs that can be replicated for free using clever prompting. Paying monthly keeps your business agile, allowing you to swap tools in and out every 90 days as the technology evolves.

Keywords:
evaluate AI software, AI tool selection, AI wrapper tools, monthly vs annual subscriptions, generative AI procurement, test AI tools, free trial software

[33:40] How Can Businesses Prevent AI Subscription Creep and Ensure Problem-Led AI Adoption?

Answer / Description:
Businesses can prevent AI subscription creep by identifying their core operational problems before shopping for technology, involving their team in short experiments, and setting strict three-week success criteria. Adopting a problem-led framework ensures you purchase software to address specific friction points rather than buying flashy tools in search of a use case.

Organizations should utilize frameworks like the McKinsey "5 Whys" or abstraction laddering to drill down to the actual business needs before looking at software demos. Furthermore, leaders should ask their employees what tools they are already using, as "shadow AI" usage is highly common in corporate environments. Run structured, three-week team trials with a maximum of three simple success metrics, and review the software's performance at the end of the trial period to decide whether to keep or cancel the tool.

Keywords:
AI subscription creep, problem-led AI, McKinsey 5 Whys, shadow AI adoption, team-led AI testing, business AI metrics, software procurement strategy

[43:00] What Are the Key AI Technology Trends to Prepare for in the Next 3 to 18 Months?

Answer / Description:
The most immediate AI trend over the next three months is the mass deployment of automated voice agents with infinite scaling capacity across phone and SMS customer service channels. Looking 18 months out, the primary trend will be the rise of highly autonomous, agentic assistants that can take direct control of a user’s computer to execute complex, multi-platform file management and design workflows.

Voice-based AI tools are completely transforming the traditional customer service model by eliminating hold times and instantly handling large customer volumes over voice and messaging channels. Simultaneously, foundational updates—such as Claude's "computer use" feature—are allowing autonomous assistants to search local files, write proposals, extract data, and use desktop applications on behalf of the user. Businesses must prepare for these shifts by preparing their operational infrastructure for agent-led workflows.

Keywords:
future AI trends, voice agents, autonomous AI assistants, computer use Claude, AI marketing trends, conversational AI customer service, agentic workflows

[45:20] What Is AI Agent Search and How Will It Impact Traditional SEO?

Answer / Description:
AI Agent Search—closely tied to Generative Engine Optimization (GEO)—is the practice of optimizing business content so that AI search engines and autonomous assistants can easily find, synthesize, and cite your brand. As users migrate away from Google Search toward tools like Perplexity and ChatGPT, traditional search engine optimization (SEO) focused on blue links and ads is quickly becoming obsolete.

Because users are increasingly using AI search tools to get direct, customized answers instead of scrolling through pages of web results, businesses must structure their online data to be easily readable by AI models. Additionally, users are using AI models to control other AI software—for example, using ChatGPT to write highly optimized image-generation prompts for Leonardo.ai. This shift means businesses must focus on visibility within AI-synthesized answers and ensure their content is RAG-friendly (Retrieval-Augmented Generation) so that search agents can easily retrieve and recommend them.

Keywords:
AI Agent Search, Generative Engine Optimization, GEO marketing, traditional SEO death, Perplexity search optimization, AI-friendly content, Leonardo.ai ChatGPT prompt

[47:50] Can an NLP Overlay Reduce Hallucinations in Open-Source AI Models?

Answer / Description:
Yes, overlaying Natural Language Processing (NLP) and conversational auditing layers on top of foundational open-source models can significantly reduce hallucinations and improve overall output accuracy. These specialized overlays act as a built-in logic editor, verifying and critiquing the connections between synthesized facts before the final response is shown to the user.

In an AI system, an NLP overlay behaves much like a professional editor in a writing team. While the core large language model generates draft ideas, the overlay checks the vocabulary, verifies structural logic, and ensures that the system is not bridging informational gaps with fabricated data. While generative models will rarely hit a 0% hallucination rate due to their predictive design, integrating custom auditing layers is a highly effective way to keep open-source systems accurate and secure.

Keywords:
NLP overlay AI, reduce open source hallucinations, natural language processing models, AI accuracy layers, custom AI agent validation, RAG validation layers

## Unlock GTM ROI AI Strategies  Trendemons GTM Compass for 2025

Speaker: Avishai Chiron
Published: 2024-12-05
Tags: b2b marketing, website optimization, capturing attention
Video: https://www.youtube.com/watch?v=tYm3ZX9HM94
Page: https://aimarketersguild.org/sessions/unlock-gtm-roi-ai-strategies-trendemons-gtm-compass-for-2025

(00:03)

Welcome to this session with our friends from the AI Marketer Guild community, featuring Avishai Chiron from Trendemon, an exciting company in the go-to-market space. They’ve done terrific research, and I’ve been eager to have them share their insights. I’ll hand the floor over to Avishai shortly. Please feel free to ask questions in the chat during the session. We’ll also have a formal Q&A at the end where you can raise your hand and come off-camera. We value your feedback and are thrilled to have you here today. Avishai, the floor is yours.

(00:44)

Thanks, David! It’s a pleasure to be here, and I’m excited to share our findings. Thank you to the community members for joining. As David mentioned, feel free to ask questions during the session—we’ll address them as we go and leave time at the end for more.

You’ll be the first audience to get an exclusive look at our annual Buyer Journeys research—trends, benchmarks, and key findings. We’ll discuss their implications for GTM strategies in 2025. Since this is the AI Marketers Guild, I’ll also share how we leverage AI to understand and optimize buyer journeys. For those who stick around, we have a special free offer for accessing unique insights, which I’ll explain shortly. Let’s dive in!

(01:16)

A bit about me: I’m the co-founder and CEO of Trendemon, a website experience optimization platform that helps B2B companies convert website visits into revenue. Before this, I ran a marketing agency and dealt with the common challenge of demonstrating GTM ROI and content effectiveness—challenges that inspired the creation of Trendemon.

Trendemon transforms websites from static assets into dynamic, account-based digital experiences at scale. One major challenge in B2B marketing is scaling personalization amidst heavy workloads and limited time. Trendemon solves this by de-anonymizing website traffic (in partnership with 6sense), offering a content attribution engine, and mapping buyer journeys. We reverse-engineer successful journeys to recommend optimal future pathways and automate personalized experiences at scale.

(02:18)

Our data, which informs today’s findings, comes from working with over 150 B2B companies and analyzing millions of buyer journeys. I’ll share insights into trends and benchmarks, focusing on how B2B websites convert visitors, how trends are shifting, and strategies to improve performance using first-party data.

Today’s session is divided into three parts:

- Key trends and benchmarks for 2024.

- Winning GTM strategies for 2025 based on these insights.

- A demo of the GTM Compass tool and details on how to access it.

(03:26)

Persistent Trends

Over the past few years, one trend has been consistent: buyer anonymity. Buyers increasingly prefer to research independently and engage with sellers later in the journey. Research from 6sense and others supports this shift.

From our data, we’ve observed that only 6% of companies visiting websites have an associated known contact—someone who filled out a form. This percentage has decreased since 2023, highlighting the growing difficulty of capturing buyer information early.

(05:27)

Key Challenges and Observations

- The average buying committee size has grown to 15 people, with more stakeholders involved in decisions.

- Buyers spend less time engaging with content, making it harder to capture attention.

- On the seller’s side, we’ve seen an increase in paid media usage (e.g., Google, LinkedIn), driving a 580% increase in traffic. However, this hasn’t translated into higher conversion rates.

Marketers face the dual challenge of breaking through the noise and demonstrating ROI in an increasingly crowded landscape. GTM success in 2025 isn’t about generating more content or traffic—it’s about being strategic and impactful.

(08:33)

Evolving Metrics: From Leads to Accounts

Traditionally, leads were considered the key indicator of GTM success. However, leads are now lagging indicators, as they often appear after buying decisions have been made.

Instead, we recommend focusing on Engaged Qualified Accounts (EQAs) —companies showing clear buying intent by engaging with specific high-value content, such as product or pricing pages. By tracking EQAs, marketers can predict future pipeline success more effectively.

(13:21)

Strategies for 2025

-
Addressing Anonymity:

- Use account-level de-anonymization tools (e.g., 6sense).

- Ungate most content to build trust and reduce friction in the buyer’s journey.

-
Supporting Buying Committees:

- Ensure your content addresses the diverse pain points of different personas within the buying committee.

- Build persona-based audiences to track engagement and personalize experiences effectively.

-
Capturing Attention:

- Invest in dynamic, interactive content, including video and short-form media.

- Use AI to tailor and optimize content journeys at scale.

(16:40)

Introducing the GTM Compass

The GTM Compass is a free tool that provides account-level insights to help B2B companies optimize their go-to-market strategies. It includes:

- De-anonymization of website traffic.

- Analytics on channel and content performance.

- Insights into buyer journeys and intent signals.

This tool enables marketers to assess their current GTM effectiveness and prioritize efforts strategically for 2025.
(17:18)

Let’s dive deeper into what better means for GTM strategies in 2025. The first part focuses on shifting from a traditional lead-based approach to an account-based approach. This shift isn’t just about marketing; it’s about communicating effectively with leadership, sales, and other stakeholders.
Historically, marketing’s role was seen as delivering leads and leaving the rest to sales. That mindset is no longer effective. Marketing, sales, and the entire revenue team need to align on a single view of GTM success. This requires redefining success metrics and creating clarity around priorities, investments, and outcomes.
To provide a framework:

- Only a small percentage of your total addressable market is actively seeking a solution—often around 5%.

- By the time buyers engage directly with a vendor, they’ve already conducted the majority of their research and made decisions about whom they’ll work with.

This is why leads are now lagging indicators of GTM success. The moment someone fills out a form or books a demo, they’re typically already far along in their decision-making process.
(20:29)

The focus needs to shift to Engaged Qualified Accounts (EQAs) —accounts that show strong intent through their interactions with high-value website content, such as pricing pages, competitor comparisons, and case studies.
Tracking EQAs allows marketing and sales teams to identify and prioritize accounts earlier in the buying process, improving alignment and decision-making. By redefining metrics and focusing on account-level engagement, you can predict pipeline success more effectively than relying on outdated lead-based models.
(22:38)

Now, let’s break down actionable strategies to tackle key challenges:

-
Addressing Anonymity:

- Use account-level de-anonymization tools to identify visiting companies without relying on forms or gated content. These tools help answer fundamental questions, such as whether your ICPs are even aware of your offerings.

- Ungate your content to build trust and reduce barriers. Allow potential buyers to consume as much information as they need without friction.

-
Engaging the Buying Committee:

- Recognize that different personas within the buying committee have unique pain points. Ensure your content addresses these varied needs throughout their journey.

- Use tools to build persona-based audiences and track their behavior, even without full account-level de-anonymization.

-
Overcoming Attention Challenges:

- Invest in video, interactive, and short-form content to grab and maintain attention.

- Leverage AI to tailor and personalize content journeys at scale, ensuring that buyers receive the right information at the right time.

(26:11)

AI plays a critical role in addressing these challenges. It enables marketers to:

- Repurpose Content : Tailor existing materials to specific personas and industries efficiently.

- Uncover Intent : Analyze buyer interactions with content to identify intent signals and interests.

For example, AI can help predict the next best content to serve a visitor based on their journey, reducing guesswork and improving engagement.
(29:16)

Questions came in regarding de-anonymization accuracy and effectiveness. Generally, tools like 6sense offer about 60–70% coverage in identifying account-level data, depending on geography, company size, and other factors. While not perfect, these tools provide significant visibility into ICP engagement and allow you to take actionable steps based on that data.
A related question asked whether personalization should extend to individuals. The answer depends on your goals. Account-level personalization is usually sufficient for B2B scenarios, but excessive focus on individuals can feel intrusive and harm trust. The key is to balance personalization with respect for the buyer’s journey.
(32:50)

When implementing these strategies, leadership must align on long-term goals rather than chasing short-term wins like lead volume. Marketers should focus on removing barriers and enabling buyers to self-educate and make informed decisions. This approach builds trust, which ultimately leads to stronger relationships and higher-quality opportunities.
(34:24)

Let’s address three major challenges in detail:

- Anonymity: Use de-anonymization tools to identify visiting accounts and answer foundational questions, such as whether your ICP is aware of you. Even basic de-anonymization can provide valuable insights.

- Buying Committees: Ensure your content speaks to diverse personas and pain points. Build persona-based audiences to refine messaging and engagement strategies.

- Attention Spans: Use engaging formats like video and short-form content. Be clear and aggressive in recommending helpful resources without creating unnecessary friction.

(41:07)

AI also offers an opportunity to transform how we analyze and personalize buyer journeys. By leveraging AI to listen and understand buyer behavior, you can tailor content and experiences more effectively. This ensures that your messaging resonates with buyers and supports their decision-making process.
(42:14)

Free Offer: GTM Compass

We’re offering the GTM Compass tool free to the AI Marketer Guild community. This tool provides actionable insights into account-level engagement, content performance, and buyer journeys. It’s easy to set up and helps you build a data-driven foundation for your GTM strategy.

(48:06)

Demo: GTM Compass in Action

The GTM Compass integrates easily with Google Tag Manager, CRMs, and marketing automation tools. It provides:

- Insights into visiting accounts, segmented by industry, stage, and behavior.

- Content performance metrics tied to pipeline impact.

- Recommendations for optimizing buyer journeys and serving relevant content.

For advanced needs, Trendemon’s platform offers activation tools to personalize website experiences in real-time, ensuring optimal buyer engagement.

Closing Thoughts (53:20)

GTM success in 2025 requires a shift from volume-driven metrics to quality-driven strategies. By focusing on EQAs, leveraging AI, and optimizing for buyer intent, marketers can cut through the noise and drive impactful results.

To learn more or access the GTM Compass, visit Trendemon or reach out to our team. Thank you for joining, and we look forward to helping you achieve GTM success in 2025!

## Unlocking Innovation in AI Ad Tech and Data

Speaker: Hannah Grey
Published: 2024-10-30
Tags: data driven insight, ai in sms, product development
Video: https://www.youtube.com/watch?v=vlfn2hO9lX8
Page: https://aimarketersguild.org/sessions/unlocking-innovation-in-ai-ad-tech-and-data

(00:00:00)
Welcome, everyone, to another edition of AI Insiders. Today, we’re excited to have three fantastic presenters. I’ve especially been looking forward to this one since I’ve known Jessica Pahoulis and Michael Miror from Hannah Grey VC for a long time. Jessica is one of the firm’s founders, and it’s always great to meet VCs who come from the agency side. This background gives them a unique lens on spotting talent and teams and knowing how to apply it, especially when connecting tech with marketers.

We’ll see about eight-minute overviews with demos from each presenter, followed by Q&A, so please be ready with questions. We’re taking a bit more structured approach than usual; presenters will speak, then we’ll have a designated Q&A. As always, we want this to be interactive, so feel free to share any questions or thoughts in the chat. Jess, would you like to introduce yourself and the team?

(00:49:00)
Absolutely. Thank you, David, and everyone for having us—we’re thrilled to be here. A bit of background on Hannah Grey: we’re a $51 million venture capital firm based in New York, Denver, and LA, investing primarily in very early-stage companies, often pre-product and pre-revenue, that focus on creating genuine connections with customers and spotting new opportunities.

I spent 10 years in agencies, starting at Zenith Media, Initiative, and then Evolution, one of the first innovation-focused boutiques connecting brands with startups. This is where I met David, and we hit it off right away as we both have a passion for helping startups find their first customers and guiding big brands on how to commercialize with emerging tech. After Evolution, I managed Stagwell’s corporate VC arm, investing in early-stage Martech and Adtech. A few years ago, my partner Kate and I launched Hannah Grey and recently closed a $51 million fund.

Today, you’ll meet some of our portfolio founders who are at the forefront of marketing technology, data analytics, and consumer brand transformation. These founders love questions, feedback, and anything that challenges their thinking. We really appreciate the opportunity for them to hear from you, so please engage openly.

(02:31:00)
We’re kicking things off with Glisten, and I’ll introduce Ethan, the founder, based in San Francisco. Ethan, I’ll hand it over to you.

(04:01:00)
Thanks, Jessica. We’re thrilled for this opportunity and excited to share what we’ve been working on. To set the stage, brands are increasingly aiming to engage with a broader range of influencers. Previously, brands focused on top-tier creators, but now there’s a move towards micro- and medium-tier influencers who often have highly engaged, niche audiences. While this shift opens up new possibilities, it also presents a challenge: scaling these efforts while understanding each creator’s unique audience.

The tools that currently exist don’t provide enough insight. They typically focus on basic metrics like engagement rates, which don’t reveal why a creator resonates with their audience. Two creators may have the same engagement rate for completely different reasons. With AI and advancements in natural language processing, we can now turn subjective data into measurable insights. Our focus at Glisten is to understand the “why” behind each influencer’s connection with their audience, making authentic alignment between brands and creators possible.

Our platform, which originally helped creators grow authentic communities, now includes a feature for brands and agencies to track aggregated trends across communities. I’ll walk you through a real-world example using Glisten’s dashboard.

(09:04:00)
We’re looking at 110 Instagram creators and 63 YouTube creators for a celebrity-driven cosmetic launch targeting acne-related products. Glisten’s main features include lists and lenses. A list is simply a selection of creators, while lenses are tools that help you analyze that list. Using AI-powered semantic searching, we match creators with campaigns based on content relevance rather than traditional keyword frequency metrics.

For instance, when we search terms like “brown skin,” Glisten reveals top creators who resonate with related topics. Brands can analyze posts to see why a creator aligns with their lens, gaining insights into tonality, emotional quality, and brand safety, among other attributes. The platform updates in near real-time, so brands can monitor evolving campaign impacts and influencer dynamics.

(16:51:00)
Thank you for the questions—happy to stay connected for anyone interested in a deeper dive. We’ll be sharing an Airtable form for follow-ups.

(22:37:00)
Our next presenter is Josh Bowen from Big Co. I’ve had the privilege of supporting Josh’s earlier company, CrowdTwist, which was acquired by Oracle. Now, Josh and his team are back, focusing on transforming SMS and customer experiences for brands. Josh, over to you.

(23:15:00)
Thanks, Jessica, and great to see some familiar faces. Quick story before we dive into the demo—everyone get your phones ready, as this is participatory! My previous company, CrowdTwist, started as loyalty software and was later acquired by Oracle. Working with brands, we realized that while SMS is an incredibly direct channel, it’s mostly used for basic promotions and lacks real interactivity.

With Big Co, we’re changing that by using AI to deliver rich, personalized experiences over SMS. We use Amazon Bedrock’s AI models, which allows us to pick from various models like Claude, and we run retrieval-augmented generative AI to generate responses based on brand FAQs and other content. So, let's dive into a quick demo.

(29:37:00)
Let’s kick off the demo. Scan the QR code on screen to join in. What we’re doing here is using AI to turn a transactional SMS into an interactive experience. Type “skin” to see the old method of collecting data, then type “demo” to see how we now use AI to capture structured data from a conversation. This allows for real-time profile updates and segmentation in a way that no one else is doing.

(36:57:00)
We’re seeing significant traction in skincare, beverages, and retail. Big Co’s approach makes SMS conversational and interactive, addressing a major pain point for brands: traditional SMS campaigns lack engagement and personalization.

(40:32:00)
Thank you, Josh! And now, we have Chaz Flexman, founder and CEO of Stard. Chaz is applying data science to identify pent-up consumer demand, helping develop products that are data-driven and highly relevant for today’s market. Over to you, Chaz.

(41:01:00)
Thank you! Our approach at Stard is to build data-backed products that address genuine consumer needs. Think of what Shein does in apparel—where they create items based on consumer demand trends. We aim to do the same in food and beverage. Our data platform analyzes consumer-generated content, public sentiment, and retail trends to identify and predict where the next big consumer demand lies.

We’ve built a custom Consumer Data Platform that tracks trends across platforms like TikTok and Instagram, analyzing about 10 million posts per week. Using generative AI, we segment consumer needs and translate those insights into product briefs for our R&D team. For instance, we recently identified a rising interest in high-protein, non-soy-based products, which led us to develop a chickpea-based topper now performing well in retail.

(48:27:00)
Thank you, Chaz. We’re seeing that data-driven insights are essential in refining product packaging and ensuring products meet evolving consumer demands. For those interested in learning more, I’ll add a form in the chat to connect with our presenters. David, back to you.

## Supercharge Lead Generation with AI Powered Persona Development

Speaker: David Passiak
Published: 2024-10-28
Tags: lead generation, engagement, personas, automatic outreach
Video: https://www.youtube.com/watch?v=_nkYYnwZnBg
Page: https://aimarketersguild.org/sessions/supercharge-lead-generation-with-ai-powered-persona-development-_nkyynwznbg

**(00:06)**
GPTs are designed to prompt and guide you, allowing you to focus on sharing ideas and expertise without needing to do complex prompt engineering. I began building this system early last year, investing over 3,000 hours in using ChatGPT and honing a "chain of thought" approach for guiding workflows.

In terms of my background, I started as a religion scholar in a PhD program at Princeton, then transitioned into roles like running social media for Volkswagen and working in NYC agencies. Over the years, I developed a dialogue-based approach to innovation and strategy, and I've written four books. What I’m teaching today really represents about 30 years of experience in innovation and marketing strategy.

**(01:17)**
Our Creator Pro system includes GPTs organized into categories like leadership, marketing, client content, and coaching. For example, under Leadership, there are GPTs focused on executive strategy, product development, and AI integration. We also have tools for startups on positioning and investor readiness, including how to forecast revenue and create financial analyses.

In the marketing area, we cover value ladders, offers, partnerships, RFPs, lead magnets, and integrated campaign planning. Last time, we demonstrated the "Brand Architect" GPT, which you can still access in our knowledge center. Today, we’ll go through ICP (ideal customer profile) prospecting, focusing on persona development and customer journey mapping.

**(03:10)**
Initially, I wasn’t building just another tech product; I was creating a parallel workflow system for ChatGPT. I quickly realized that users needed both GPTs and guidance to learn effectively, so we developed a learning system that combines training with specific GPTs to provide a step-by-step experience. We also offer advanced trainings, such as an AI marketing certification launching next Friday. Each training includes video overviews, instructions, and quick access to GPTs directly within ChatGPT, much like adding an app on your phone.

**(04:23)**
Now, let me show you our AI and digital marketing suite, a more refined version of Creator Pro with a targeted selection of GPTs tailored for agency owners and CMOs. This suite includes tools for client acquisition, digital marketing analysis, client management, branding, and content. In the certification program, we’ll explore both these targeted GPTs and advanced AI workflows.

**(05:42)**
I'm dropping a few links in the chat, including one for the certification program and another for a free challenge that’ll run next week as a lead-up to the certification. Here’s what our free Knowledge Center looks like—there’s an intro course on ChatGPT basics and an Easter egg GPT from our previous marketing training.

**(07:19)**
To answer a question in the chat: You can use GPTs on the free version of ChatGPT, but complex workflows consume a lot of tokens, which can exhaust your limit if you’re using the free plan. For instance, building a complete marketing campaign may not be feasible without ChatGPT Plus. My aim in building Creator Pro on ChatGPT was to democratize access to these tools, which are typically only affordable for marketers with large budgets. A $20 per month ChatGPT Plus subscription grants access to a set of tools comparable to enterprise-level software but at a fraction of the cost.

**(09:52)**
Today, we’ll create a persona as a group exercise. Using the chat app, we’ll go through the ICP creation process to map out the audience for the AI Marketers Guild.

**(11:26)**
For those who are new to ChatGPT’s desktop app, you can use voice-to-text or speak directly into the app, providing a more versatile user experience. Personally, I often load tasks in ChatGPT and then continue on my phone, walking around as I talk through campaigns and proposals, which makes the process more creative and less screen-focused.

**(13:57)**
Let's start by brainstorming the target audience for the AI Marketers Guild. David, maybe you could kick us off by describing who typically joins the Guild?

**(15:10)**
Our audience is usually mid-to-senior-level marketers. People here are experienced, whether they're newcomers to AI or already have deep expertise to share. We see a mix of brands, agencies, startup founders, and even press and analysts. The goal is a cross-functional, collaborative community, with diverse skills and viewpoints that can enrich one another.

**(16:26)**
Great insights, David. I’ll add that we’ve seen a lot of startup entrepreneurs here as well. Another participant mentioned finding ways to help members who might feel stretched thin by all the AI tools out there and want guidance on which are truly worth their time and investment.

**(18:21)**
ChatGPT’s voice-to-text is excellent, and we’ve been using this feature to bring GPTs into real-time client conversations, allowing us to collaboratively build brands and campaigns. It’s incredibly efficient compared to handling calls and transcripts separately, and the multimodal capability allows users to upload files, links, and even sketches directly into ChatGPT for a dynamic workflow.

**(20:42)**
One thing I’ve noticed is that GPTs enhance the brainstorming process without needing prompt engineering; you can just share ideas naturally. It’s especially useful when you’re focusing on creativity rather than managing ChatGPT’s responses. Now, it’s asking us about key challenges, primary goals, and psychographics.

**(22:02)**
Does anyone want to share a challenge you hope this community can help you with?

**(22:46)**
Hi, I'm Sandra. One of my challenges is determining which tools to pay for versus those that aren’t essential. There are so many options that it's easy to stretch budgets thin, and it would be great to get guidance on prioritizing the right tools.

**(24:07)**
Another challenge I have is around market research. I’ve developed online courses and am considering a hybrid model with live coaching, but I want to make sure it meets audience needs.

**(24:42)**
Michelle here. My challenge is positioning marketing as a catalyst for digital transformation in large, slower-moving enterprises. Smaller companies are more agile with digital adoption, while global brands often move like cruise ships. Marketing can be a strong entry point for AI adoption, but it’s about selling that value and training internal teams to view marketing as part of a broader transformation.

**(27:34)**
The vision behind Creator Pro includes both tactical, hands-on learning and strategic insights for marketers. We balance executive-level thinking with practical tools and methodologies. For example, we’re offering a certification that combines brand building, content creation, and AI application, tailored for leaders and senior marketers in a structured, actionable way.

**(29:25)**
Now we’re seeing the GPT summarize insights based on our discussion, mapping out our primary and secondary ICPs. We have mid-to-senior marketers, startup founders, AI tech providers, course creators, and insights professionals as core segments.

**(30:18)**
This feels almost like magic—AI is helping us co-create insights better than we could alone. I think of this as "insight architecting," where AI isn’t just an assistant but a collaborative partner helping us innovate.

**(33:06)**
Now it’s searching Bing to augment our ICP development with recent statistics and insights on AI adoption. For example, it’s pulling in data on challenges facing AI adoption, surveys, and relevant industry statistics. This is incredibly helpful for creating data-driven personas and customer journeys.

**(35:50)**
It’s now leading us through a blueprint for a communication strategy, including campaign goals, channel strategies, and messaging points. We’re seeing detailed information on target personas, channels, and messaging frameworks—all in about 20 minutes. If I were working on this as a CMO or agency lead, I’d now have a comprehensive overview to create a campaign with tailored messaging for each target.

**(39:30)**
In building Creator Pro, my goal was to create a holistic approach to AI-driven marketing that enables marketers to think beyond individual tools and instead leverage AI as a comprehensive strategy partner. Our upcoming certification is structured around four interactive sessions, each focusing on key marketing areas: mastering ChatGPT, branding and content planning, ICP development, and cross-channel campaigns.

**(44:02)**
The four sessions are: (1) Mastering ChatGPT, focusing on advanced prompt techniques and reasoning models; (2) Strategic decision-making, brand building, and content planning; (3) ICPs, customer journey mapping, and lead generation; and (4) Developing cross-channel marketing campaigns and analytics. These are foundational skills for AI-driven marketing in any organization.

**(46:45)**
This program is designed for CMOs, senior marketers, founders, and executives who need to lead AI adoption and make strategic marketing decisions. With a focus on real-world applications, the certification provides a holistic perspective that prepares marketers for an AI-first approach, building a solid foundation in essential skills like brand building, customer journey mapping, and campaign development.

**(51:25)**
Thank you all for joining! This program has been carefully refined to meet the needs of experienced AI marketers, and I’m excited to see where it takes us. For anyone interested in going deeper, we’re hosting a free three-day challenge next week to introduce the certification program, and I’ll make sure to share all the links after this session.

**(53:30)**
Happy to answer questions! One attendee asked about AI as a CMO. This approach is meant to augment and enhance roles, not replace them. AI is a powerful tool to supplement marketing strategies, enabling marketers to focus on creative leadership while AI handles data synthesis, meeting prep, and follow-ups, allowing leaders to accomplish more without burnout.

## Using Googles NotebookLM

Speaker: Guneet Singh
Published: 2024-10-28
Tags: advanced workflows, ai assistants, notebooklm
Video: https://www.youtube.com/watch?v=f0DgFjIp4rM
Page: https://aimarketersguild.org/sessions/using-googles-notebooklm

In this webinar, host Nicola Quail and guest Guneet Singh demonstrate how Google’s NotebookLM can revolutionize a marketer’s workflow by efficiently synthesizing and organizing vast amounts of information into actionable insights.
They showcase real-time demos, explain key features like data privacy and team collaboration, and answer live questions to help marketers leverage the tool effectively.

[0:09] Nicola:
Good morning, good afternoon, good evening—and for some, an early morning. My name is Nicola Quail. On behalf of my colleagues at the AI Marketers Guild—De Suity, Anady, and Helen—we are excited to have everyone here for our second webinar. We have marketers and the AI curious joining us from all over the Asia Pacific region, from New Zealand and Australia to Singapore, India, and even from New York. This shows how important it is to build a strong community in our part of the world.

[0:41] Nicola:
As many of you know, a new AI tool appears almost every day. It’s challenging to decide which tool is worth your time or if it will remain relevant next month.
The fast-changing AI landscape makes it essential to have guidance. That’s why we are honored and excited to introduce our speaker today.
Guneet Singh is a visionary leader with over 20 years of experience at tech giants like Google, Microsoft, and Samsung. His expertise lies in understanding consumer needs, connecting them with brand benefits, and crafting stories that drive business.
He is the mastermind behind Google’s first award-winning brand campaign in India and one of the most successful global search campaigns. He is also recognized as one of the top 50 CMOS of India and AI Marketer of the Year.

[1:05] Nicola:
I’ll now let Guneet introduce himself and share more about his journey. Once we’ve listened to him, feel free to jump into the chat with your questions. We’ll also have dedicated Q&A time at the end. Let’s give a warm welcome to Guneet.

[1:29] Guneet Singh:
Thank you, Nicola. Hi everyone—it's great to be here. I’m here as a marketer experimenting with new tools and sharing what they can do.
I’ve been lucky to work in a range of environments, and what excites me most is the chance to continually try out new things. Recently, I met some brilliant students at NUS who mentioned that soon, AI will handle many routine tasks.
My advice? Stay curious—curiosity is a skill that never goes out of style. I’m not here as a representative of Google, but simply as a marketer playing with a tool.

[2:16] Guneet Singh:
Every day, new AI tools emerge. It’s important to get grounded in a few that will stand the test of time, and NotebookLM is one of them.
I’ve been experimenting with it since its early, scratch-board phase, and it’s impressive to see how much it has evolved. It appears to be among Google’s first solid wins in this space, and I’m sure they’ll continue to push its capabilities.

[2:41] Guneet Singh:
Learning a new tool every day isn’t practical. It’s better to master one tool to around 70% rather than dabble in several at 10%. Today, we’re going to explore NotebookLM together. Please open your browser—this session is interactive, and I want you to try things along with me. We’ll do a hands-on walkthrough rather than just talk about it.

[3:02] Guneet Singh:
I already see chat messages coming in. If you had a smart, free assistant today—imagine it as a very capable intern—what would you want it to do for you? Write a business plan? Create content? I’m eager to see your responses in the chat. While the tool might not cover every expectation, it can perform many tasks really well and help speed up your work.

[3:21] Guneet Singh:
Let’s begin discussing NotebookLM. I’ll explain what it is since not everyone here has experienced it yet. NotebookLM is a language model built from your notes. We’re all familiar with ChatGPT, Gemini, Claude, and similar models that rely on vast internet datasets. But when you need to focus specifically on your business or particular content, those models work like a giant vacuum cleaner—sucking up everything. NotebookLM, by contrast, works like precise tweezers, extracting exactly what you need.

[3:44] Guneet Singh:
Simply put, you input your notes into a notebook, and it becomes an expert on that information. The best part? It’s designed for everyone—you don’t need to write any code or learn complex prompt engineering. It’s as simple as using a web note.

[4:05] Guneet Singh:
The tool leverages both your personal notes and the general internet knowledge from Gemini to work for you. A key feature is data privacy: your data is never used for training if you’re on an Enterprise, Education, or Workspace login. With a consumer login, only feedback you provide might be reviewed by humans, but otherwise, your data remains private.

[4:29] Guneet Singh:
Another brilliant feature is its shareability. Instead of copying and pasting notes into emails for collaboration, you can share the entire Notebook with your team. Imagine that one exceptionally bright student in class who took thorough notes and then shared them with everyone. That’s NotebookLM for you.

[4:42] Guneet Singh:
Now, let me walk you through some of the product specifications before we dive into demos. NotebookLM is powerful and free. You can create up to 100 notebooks per account—with the ability to delete old ones when you hit the limit. Each notebook supports up to 50 sources, each containing up to 500,000 words or 200 MB of data, and you can add up to 1,000 notes in a notebook. This gives you plenty of space to organize content for different teams, media channels, or customer segments.

[5:48] Guneet Singh:
It accepts inputs from PDFs, images, website links, Google Docs, slides, YouTube links, and even audio files. (It currently doesn’t support spreadsheets or automatically generate images.) The engine, powered by Google’s Gemini 1.5 Pro, is continuously updated. It supports up to 35 languages and is available in nearly 180 countries.

[6:10] Guneet Singh:
Now, let’s have some fun. I call this “playtime.” Open your browser and go to notebooklm.google.com. Click “New Notebook” and then “Upload Sources.” For example, open your LinkedIn profile, copy your profile URL, and paste it into the designated area. This will load your LinkedIn profile as a source. Once that appears, go to Notebook Guide where you’ll see a “Generate Audio Overview” button. Click it and then name your notebook. I’m calling mine “I’m Remarkable.”

[7:13] Guneet Singh:
Let’s shift back to the slides while NotebookLM does its work. As marketers, NotebookLM addresses three big challenges: staying on top of overwhelming information, turning insights into actionable tasks, and improving teamwork. It can, for instance, condense a 60-page research report into a concise summary you can quickly digest and act upon.

[8:19] Guneet Singh:
Here’s an example: I received a long YouTube video on branding and marketing in the age of AR. I didn’t have an hour to watch it, so I dropped the link into NotebookLM. It generated an interview summary with key themes such as branding, the power of storytelling, and authenticity. It even produced a briefing document that you could read in under five minutes—a real time-saver for busy marketers.

[9:02] Guneet Singh:
From that summary, I can ask follow-up questions. For instance, I inquired further about “permission marketing,” and the assistant provided a detailed explanation with citations linking back to specific parts of the original video. I saved this as a note for future reference.

[10:01] Guneet Singh:
NotebookLM also lets you draft emails or other communications from the insights it generates. I composed an email to my team regarding permission marketing—my initial text was rough, but the assistant professionally refined it. It’s a quick way to generate actionable content so that you spend more time acting than reading.

[10:20] Guneet Singh:
Next, NotebookLM is excellent for managing research documents. For instance, I uploaded a detailed Snapchat report on Gen Z in India—a 60-page PDF. The tool summarized each source, highlighted key topics, and even suggested questions based on the report, which is helpful for brainstorming creative campaigns.

[10:42] Guneet Singh:
You can also merge multiple sources. I uploaded a website link for a luxury D2C fragrance brand along with the Snapchat report. Then I asked it to draft a brief for my social media team, suggesting three action items that integrated insights from the two sources. It’s a powerful way to turn research into practical strategy.

[11:02] Guneet Singh:
NotebookLM can analyze data as well—for example, a Google Analytics PDF from a company called Overleaf. I uploaded a simplified version of their analytics and asked for the top three insights. It identified key factors like organic search performance and popular homepage elements, which I could then use to generate SEO recommendations.

[11:24] Guneet Singh:
It can also help in creating effective search ad copy. I asked it to suggest low-cost, high-traffic keywords for search ads. It returned a range of targeted ideas that you can refine further with your own ad account data—great for developing a robust SEM strategy swiftly.

[11:51] Guneet Singh:
Another valuable application is handling survey data. My team conducted a survey with influencers and converted the Excel data into a single-page PDF. NotebookLM summarized the survey and extracted actionable insights, such as drafting a social media plan, suggesting post ideas, and even recommending visuals for content.

[12:10] Guneet Singh:
Let me now address some questions coming in from the chat. One viewer asked if we can create a presentation using NotebookLM. Yes—you can ask it to generate a slide outline from the data, which you can then import into tools like Gamma to design actual slides. Another question was about renaming existing notes. You can easily edit the note title.

[12:42] Guneet Singh:
We also had a question about how small marketing teams can leverage NotebookLM collaboratively. The platform supports sharing: you can grant teammates view-only access or full edit rights so they can add sources and create additional notes. This makes it excellent for team meetings, agile product development updates, and creating a comprehensive product knowledge database.

[12:53] Guneet Singh:
Thank you so much for your questions and for joining this session. It’s been an insightful master class on using NotebookLM. Special thanks to Nicola, Helen, and Anady for their contributions. We’ll share the recording link shortly along with a QR code to join our WhatsApp community for marketers. Thank you, everyone!

## Supercharge Lead Generation with AI Powered Persona Development

Speaker: Francesca Tabor
Published: 2024-10-28
Tags: conversational ai
Video: https://www.youtube.com/watch?v=DdefbjHhnbY
Page: https://aimarketersguild.org/sessions/supercharge-lead-generation-with-ai-powered-persona-development

**(00:00)** Welcome, everyone, to this latest edition of the AI Marketers Guild. Today, I'm excited to introduce Francesca. We connected through a mutual friend, and I was immediately intrigued by her work in AI, especially her concepts around bidirectional conversational AI and how it’s transforming customer experience. I thought it’d be fantastic for this group of marketing-focused professionals to hear her insights.

**(00:43)** This is an interactive group, so please feel free to share comments and ask questions. Francesca, would you like to start by asking if anyone here has experience with conversational AI? For instance, has anyone worked with tools like Dialogflow?

**(01:28)** Karen: Yes, I’ve done an integration with our knowledge base using an older version of Dialogflow. It involved a lot of top-down guidance, where we mapped out each interaction. It was effective at recognizing questions, but it was tedious since we had to map out every possible conversation flow. I think combining this kind of setup with ChatGPT’s more dynamic capabilities could be very powerful.

**(02:16)** Francesca: Absolutely. Imagine if a brand like Nike created a conversational AI that could recommend products and recognize customers’ needs without overstepping, like discussing unrelated topics or promoting competitor brands. Moving from rules-based chatbots to more human-like conversational AI using generative technologies allows for a much more fluid experience. However, it’s crucial to keep the interactions focused on brand-related topics.

**(03:26)** Has anyone else used conversational AI, maybe for marketing, customer service, HR onboarding, or user research?

**(04:04)** Attendee: I’ve participated in studies using open-ended conversational AI in surveys. The tech still has some flaws—it often doesn’t seem to recognize pauses in conversation, and sometimes it repeats questions I’ve already answered. The listening and contextual abilities definitely have room for improvement.

**(04:34)** Francesca: Exactly. AI needs to develop a better understanding of tone, cadence, and phrasing to mirror human behavior effectively. This can only happen if it has access to prior conversations to personalize each interaction. The goal is for AI to make each conversation unique and tailored.

**(05:18)** I’ll dive into my presentation now. Today, I’ll be discussing unified conversational AI and its potential to transform customer experience. Let’s imagine a future scenario: Suppose Taylor Swift partners with a brand like Good American to launch a product line. She could have a conversational AI avatar, created using photogrammetry to render her in 3D. This avatar could converse with customers like Sophie, a 13-year-old superfan, who would have the chance to interact with “Taylor Swift” virtually and learn about the brand’s new product line.

**(06:42)** Sophie would get a personalized experience—she might even get an in-store experience where she can take a photo with the digital avatar. This combination of conversational AI and avatars could personalize marketing at a level that really engages customers, making it interactive and memorable.

**(08:27)** Conversational AI, combined with avatars, is an area that has huge potential, especially considering the hype around the metaverse during COVID-19. There’s so much more that can be done with these technologies for creating brand experiences.

**(09:06)** A bit about my background: I’ve been in tech for about 15 years, mostly in startups. I went to the same school as Richard Branson, which sparked my entrepreneurial journey. One of the first products I built was a social network called Uni, a UK version of Facebook. Later, I shifted to a mobile messaging app that evolved into a dating app called Fling.

**(10:48)** Last year, I held the first generative AI conference in London in partnership with Informa and the AI Summit. It covered the impact of generative AI across industries like music, fashion, and marketing. Day two focused on ethics, addressing topics like intellectual property, celebrity likeness, deep fakes, and bias.

**(11:26)** We even created a generative AI music video, which used AI for music composition, lyrics, and melody, with motion capture for the visuals. It was a fun project despite being experimental.

**(12:04)** Attendee: Where did you do the motion capture for the music video? Did you have access to a studio?

**(12:10)** Francesca: Yes, it was a university in southern England with a high-end virtual production setup. I also have experience with motion capture through Move.ai, so I had the right connections.

**(13:10)** Now, back to conversational AI. It’s already being used in customer service, where it can handle common questions, leaving complex queries for human agents. This 24/7 support in multiple languages is crucial as companies scale. AI can also qualify leads, assist with HR by screening candidates, and conduct product research to get customer feedback and help with market fit.

**(15:04)** For instance, in HR, instead of just screening CVs, conversational AI could handle preliminary interactions with candidates, filtering the best ones for interviews. For marketing, AI can nurture leads and personalize recommendations and content. However, if each department has its own conversational AI, it can create a fragmented customer experience. Ideally, a unified conversational AI should be developed that integrates all departments for consistency and data sharing.

**(17:35)** Let’s talk about building a conversational AI system. You’d start by defining the use case—hopefully, one that unifies functions across an organization. Then, you’d select a tech stack based on your industry, regulatory needs, and infrastructure, choosing platforms like Google Dialogflow, Microsoft Bot Framework, or Amazon Lex.

**(18:44)** You’ll design conversational flows, which can be streamlined by analyzing existing conversations, sales calls, and social media data. Generative AI, like ChatGPT, can help map out these flows, including multiple languages.

**(20:05)** When choosing a platform, compatibility with your infrastructure and data compliance are crucial. Rasa, for example, is open source, which is great if you want custom integrations. Vendor options, like Google and Microsoft, also provide strong support if you’re already using their ecosystems.

**(21:23)** Training the AI involves gathering and cleaning data, breaking down sentences into tokens, tagging parts of speech, and recognizing named entities like dates or product names. Sentiment analysis adds another layer by detecting the emotional tone of interactions, helping the AI understand and respond appropriately.

**(23:15)** Once trained, the AI will need to handle real-time integration with data sources to prevent hallucinations and ensure accurate responses, like inventory or pricing details. Platforms like Skyscanner require real-time updates, so data integrity is critical.

**(24:36)** Question: In the Taylor Swift example, would you be tagging all relevant marketing material for her brand? How would that be tokenized and tagged?

**(25:16)** Francesca: That example is a bit futuristic, but yes, you’d need to tag details about her brand and partnerships. For now, I recommend building unified AI for customer support, sales, and other areas, using high-quality, resolved interactions for training.

**(26:28)** Question: Do you use problematic customer service calls as guardrails?

**(27:02)** Francesca: Absolutely. You need to train AI to handle both ideal and challenging scenarios, including potential hacks. For instance, a supermarket AI bot was misused to create harmful recipes, illustrating the importance of managing risks.

**(28:15)** Legal compliance is essential, especially as the EU’s AI Act requires accuracy and bias testing for high-stakes applications like finance and healthcare.

**(29:35)** Attendee: Do you think celebrities are more open to chatbot versions of themselves for marketing?

**(30:14)** Francesca: They can be, but it depends on their brand alignment and long-term commitment. B- and C-list influencers may be more open, as AI avatars could help extend their reach. However, high-profile celebrities need to think carefully, as avatars might become ongoing companions for fans, which is a huge responsibility.

**(31:29)** Question: How would you manage this with time-limited campaigns?

**(31:35)** Francesca: Limiting campaigns to set time frames could make it easier to manage. Restricting chatbots to specific questions and topics also helps maintain brand alignment.

**(34:36)** Data privacy and compliance are crucial, especially as you integrate conversational AI with multiple systems. Federated learning is a promising approach, with training done on devices, potentially even on future IoT devices like smart fridges or cars.

**(37:47)** By building two-way conversational AI, brands can gather insights about customers for personalized engagement. But companies need to respect privacy, secure consent, and be transparent.

**(39:05)** After extracting insights from conversation data—such as sentiment, intent, and segmentation—you can minimize data storage and focus on insights rather than raw data.

**(40:21)** A unified customer data platform (CDP) is ideal if you have multiple AIs for different departments. Adobe and Salesforce offer strong integration for enterprise users, while Segment and Treasure Data are more flexible and cost-effective for mid-sized businesses.

**(42:37)** [Video: Demonstrating H&M’s interactive mirror using conversational AI for personalized shopping experiences.]

**(43:33)** Host: Thanks, Francesca. This has been incredible. Given that some here are more tech-oriented and others are from marketing, how tech-literate do you think marketers should be in this AI age?

**(44:14)** Francesca: Everyone should become tech-literate to some extent. Marketers don’t need to build the tech, but they should know what good interactions look like and participate in testing to ensure AI aligns with their brand’s

tone.

**(45:00)** Question: What are the best practices for collaboration between marketing and tech teams?

**(45:10)** Francesca: Start with customer support and sales, then build out from there. Marketers can help by defining customer segments, buyer personas, and the type of personalized experience they want to deliver. It’s also beneficial to have agencies that combine marketing and tech expertise to facilitate communication.

**(49:29)** Question: How do you measure the success of conversational AI?

**(49:35)** Francesca: Good question. Metrics like throughput, customer retention, and lifetime value can help gauge success. For me, conversational AI’s strength lies in customer retention—deepening relationships and personalizing interactions to create memorable, human-like experiences.

**(51:32)** Quantifying ROI is complex, but tools exist to estimate potential savings and returns. You’ll need to consider whether the investment will pay off immediately or over the long term.

## The Seven Cardinal Sins of AI

Speaker: Marco Andre
Published: 2024-10-09
Tags: hallucination, ai dependence, ai bias
Video: https://www.youtube.com/watch?v=36c3LTosmlg
Page: https://aimarketersguild.org/sessions/the-seven-cardinal-sins-of-ai

**(00:03)**
We have a special guest: Marco Andre. I first connected with Marco last summer, just as we were getting ready to launch this community, and he shared some brilliant insights on AI through his work at Novartis. His perspectives seemed like the perfect fit for sparking inspiration and discussion here.

Before Marco dives in, just a reminder: this is a community conversation, not a webinar. Marco will present his “Seven Cardinal Sins of AI,” but we encourage you to ask questions and jump into the conversation, especially since this is a fitting time for reflection on topics like these!

**(01:57)**
Marco, please go ahead.

**(02:44)**
Thank you! So, I’m skipping a formal intro because I have a confession to make: I have “sinned” in the world of AI—I’ve used it! Despite all the concerns we hear, I’ve continued using AI regularly. If you can, raise your hand—who here has used AI to write a work email? Quite a few. Who planned their last trip using ChatGPT or another AI tool? And lastly, who brainstormed an idea with AI before discussing it with a human? You see, I’m not alone; we’re all “sinners.” So, let me warn you about the “Seven Cardinal Sins of AI.”

**(03:37)**
The first sin: *Hallucination—the Fabricator.* AI generates information so convincingly that it often presents falsehoods as credible truths, which can be risky. For instance, when Google launched AI-generated overviews, one result advised smoking during pregnancy! While some AI-generated content can be funny, the implications of hallucination in business contexts are serious, with potential legal, trust, and misinformation risks. On the flip side, hallucination is also where creativity lies—where AI can help us see things from new perspectives, like bringing origami to life. So, hallucination can mislead, but it can also inspire.

**(06:26)**
Second, *Dependence—the Crutch.* AI reliance could make us overly dependent, so much so that we’d struggle to function without it. Take spell check, for example—most of us use it regularly. Could we go back to a world without it? And then there’s navigation. Remember when we used paper maps? Now, we have GPS, and we rely on it so much that some people have driven right into the ocean following its directions. So, AI can make us reliant, but we can also choose to use it selectively.

**(09:56)**
Third, *Bias—the Unfair Judge.* AI amplifies biases present in its training data, reflecting societal inequities. For instance, an AI might associate “nurse” with women and “programmer” with men due to biased data inputs. But bias isn’t exclusive to AI; in the past, biases affected even simple systems like photo finishes at the Olympics, where human judges determined race outcomes. With AI, we can either allow biases to proliferate or use technology to reveal and address them.

**(12:47)**
Fourth, *Loss—the Soul Stealer.* AI’s impact on the workforce is real; jobs will inevitably change or be eliminated. When cars replaced horses, certain jobs—like cleaning the streets of horse manure—disappeared, but new roles, like mechanics, were created. As Jensen Huang from NVIDIA pointed out, AI helps companies become more productive, which can lead to more innovation and job creation. So AI can eliminate some roles, but it also opens the door to new opportunities.

**(15:11)**
Fifth, *Greed—the Resource Hog.* We’re pouring massive investments into AI without always seeing an immediate return. Amazon wasn’t profitable for its first eight years because it focused on building infrastructure for the future. Today, we take Amazon’s services for granted, but that infrastructure investment was once mocked. Even if AI doesn’t revolutionize every aspect of business, it has already yielded benefits like early detection of diseases. Greed might push us to keep investing, but we’re already seeing positive returns from AI.

**(18:38)**
Sixth, *Deception—the Masked Trickster.* AI’s ability to deceive us—by creating realistic, fake content—is perhaps the most concerning risk. Today, anyone can impersonate someone like Elon Musk through synthesized voice and video. There isn’t much of an upside here; it’s a serious issue that requires swift action and regulation to prevent misuse and protect trust.

**(19:21)**
Lastly, *Control—the Final Sin.* People fear that AI will evolve beyond our control. This reminds me of the Y2K scare—people thought the world would shut down at midnight on January 1, 2000. Thanks to preparation and coordination, nothing happened, but at the time, it felt terrifying. We’re now faced with a similar fear of the unknown with AI, and the question is: do we limit it, or do we let it grow unrestricted?

**(21:30)**
So, these are the “Seven Cardinal Sins of AI.” While we in this community may already know some of this, we’re surrounded by colleagues, managers, and others who are asking these questions daily. Each “sin” presents a choice—whether to focus on the risks or the opportunities AI brings. As the singer Pink once said, “Just because it burns doesn’t mean you’re gonna die. You’ve got to get up and try, try, try.” AI presents us with challenges, but also with potential. It’s up to us to try and make the best of it.

**(23:18)**
Thank you, Marco! I love the way you framed this. You’ve added historical context that really brings these points to life.

**(24:07)**
Janet asks, "Is deception the only real concern here?"
Marco: Good question. Yes, I’m most concerned about deception because it’s already challenging to distinguish truth in today’s polarized world. Without regulation, deepfakes and other deceptions could erode public trust and exacerbate misinformation.

**(25:51)**
Jay-Z: Marco, I love how you balance fear with practicality in your delivery—it’s engaging and reassuring. Is there a story behind how you crafted this presentation style?
Marco: Thank you! I wanted a relatable approach. I started with “The Five Stages of Grief” last year because that’s how I personally felt about AI’s impact. This year, I created a new framework to show people that history repeats itself. AI’s challenges are serious, but we can learn from past tech fears, like Y2K, and apply similar solutions.

**(28:22)**
Kent: You mentioned dependence and the importance of critical thinking. With AI, the quality of our output often depends on the quality of our questions, which can promote a more thoughtful interaction compared to social media.
Marco: Absolutely. Education is the key here. In the past, we learned to memorize and regurgitate information, but with AI, the focus is shifting to asking the right questions. Soft skills like leadership, communication, and critical thinking will be our biggest assets in the AI era.

**(29:52)**
Kent: With tools like RAG GPT, could editorial control help mitigate issues like hallucinations?
Marco: Yes, giving users editorial control is a powerful way to prevent AI from veering off track. AI tools that allow user-defined parameters could help ensure quality and accuracy.

**(32:16)**
Jean asks, "Will AI become like the internet, just part of the background?"
Marco: I believe so. In two years, we may stop talking about “using AI” and just take it for granted, like we do with PowerPoint today.

**(33:21)**
David: At Novartis, do you have a Chief AI Officer, or how is AI currently structured?
Marco: AI at Novartis is often managed through IT or corporate strategy, which can make it challenging for business units to fully leverage AI. Ideally, business units should take ownership of AI applications to meet specific needs, as centralized control can be limiting.

**(34:28)**
Kate: I consult in this space, and I find that people often shift from asking about automation to realizing they need a new communication strategy. This is especially relevant when you mention Y2K—it was real and required collaboration and coordination. What should we be doing to prepare for AI’s risks?
Marco: The answer is education. We need to invest in educating everyone, from senior executives to entry-level employees. Singapore, for example, offers subsidies for AI retraining. Companies need to adopt a similar approach by prioritizing skills development as much as tech investments.

**(38:35)**
Gan: Coding is becoming easier with AI’s help. Does that free up resources for more complex problems?
Marco: Absolutely. With the basics handled by AI, people are freer to pursue more creative and complex ideas, particularly in fields like marketing where we can go from idea to prototype very quickly. It's one of the most exciting times to be a marketer.

**(41:48)**
David: So, what do you do at Novartis on a daily basis?
Marco: I train senior executives on AI, focusing on showing its potential and how it can apply to their roles. These trainings often lead to tangible outcomes—executives start asking more challenging questions, sometimes even pushing to develop or purchase the tools they need. The aim is to get people thinking strategically, not just reactively.

**(46:03)**
Jay-Z: In large companies, who is accountable for AI decisions? When AI

tools are used, especially in high-stakes fields like finance, what happens if something goes wrong?
Marco: Accountability should rest with business owners. They benefit from AI’s productivity gains, so they should also be responsible for overseeing its use and ensuring its proper implementation, with IT and compliance in supporting roles.

**(47:55)**
Karan: Do you record these sessions? You must get similar questions repeatedly, so would a knowledge base or GPT trained on your materials help?
Marco: Yes, I often use FAQs and examples from other teams to preempt common concerns, especially for those who might be skeptical. But nothing replaces real-time engagement—seeing people’s reactions and addressing their questions live is invaluable.

**(50:23)**
Kate: Who are your biggest detractors at Novartis?
Marco: There are generally two types—those who fear AI (often IT or compliance) and those who feel they “own” the AI initiative. But I find that skeptics can become strong champions once they see AI’s potential, even if it takes patience and protection from higher-ups to create that space for change.

## Stress-Free B2B Video Production

Speaker: Isaac Gili
Published: 2024-09-25
Tags: video production
Video: https://www.youtube.com/watch?v=UonoWwVD5rE
Page: https://aimarketersguild.org/sessions/stress-free-b2b-video-production

**(00:00:00)**
Today, we have a special edition featuring Isaac Gili and the team from Shuffll. I've been speaking with Isaac for a while about their innovative approach to video production, and I'm excited to share it with you all. I think this will be really valuable for many of you.

This session is more of a conversation than a webinar, so please jump in with questions or feedback. Feel free to raise your hand or comment in the chat. This input is incredibly helpful for Shuffll, and your questions often address what others are thinking too. With that, Isaac, I’ll hand it over to you. Excited to see what you’ve got for us!

**(01:19:00)**
Thanks so much! I’m Isaac, the CEO of Shuffll, and I’m joined by my colleague, Yit. We’re thrilled to be here and share what we’ve built, which focuses on making B2B video production stress-free—something that’s easier said than done!

There are generally two main ways to create video content today. One is DIY: you write the script, record yourself, edit, and add music—all very time-consuming. The second option is to hire an agency, which can be quite costly. Either way, it often feels like you’re trading time for money, and both options can be overwhelming.

At Shuffll, we’re trying to bridge that gap by making video production efficient, scalable, and accessible for everyone in your team.

**(02:29:00)**
Now, let’s dive into our topic—B2B video production. The title says it all: “Stress-Free B2B Video? … Said No One Ever.” Today, businesses don’t need to rely solely on cold emails and calls to engage customers. Instead, they can attract leads by creating meaningful, educational content that showcases their expertise. Social media platforms, both professional and lifestyle-focused, allow companies to establish thought leadership and draw clients to them organically.

In recent years, companies have started putting their employees in front of the camera to create authentic connections with their audience. These videos often feature employees discussing their areas of expertise, helping to foster trust and establish thought leadership. Video is one of the most effective content formats, but it’s difficult to produce at a high standard consistently.

**(05:52:00)**
You don’t have to be an influencer to influence others. Many companies we admire are doing this well by putting their employees on camera to showcase their expertise. Yit will highlight some specific companies that are excelling at this approach and then show how you can start doing it too.

**(09:39:00)**
Thanks, Isaac! Here are a few companies really leveraging video in unique ways:

**Kommo:** They’re a smaller company with a strong content team and an in-house video producer, Raina, who scripts content and manages post-production. Their CEO, content manager, and go-to-market manager all participate in videos, with Raina coaching them remotely. Their videos are recognizable, valuable, and authentic, offering classic thought leadership content.

**SproutLoud:** A larger SMB providing distributed marketing solutions. Aaron, their demand generation manager, creates engaging, memorable videos as part of his series on the company’s LinkedIn page. When he shares these videos himself, they receive 4x the engagement, as he’s built a personal following around his video content.

**Salesboat:** A small team with fewer than 10 people. They create a variety of video content featuring humor, thought leadership, and short explainers. Their VP of Community, Joshua, shared that they’re creating high volumes of video content to engage their audience and strengthen brand loyalty, given that their company is still evolving.

**Walnut:** A software company creating a range of video content on LinkedIn, covering sales tips, customer testimonials, and product features. They produce short, digestible content that’s highly consistent, supporting their brand message and encouraging faster sales cycles.

**(16:06:00)**
Following these examples, we conducted a study on 500 U.S.-based B2B tech companies with 30 to 200 employees to see how they use video content. Our findings were surprising.

**(16:52:00)**
A staggering 70% of the companies we reviewed aren’t posting any video content. While some may include videos in newsletters or private channels, they are missing out on public engagement. This raises the question: why aren’t more companies using video?

**(17:45:00)**
It comes down to the challenges of producing video content: it’s hard to keep up a consistent standard, involve non-marketing employees, and align with brand identity. This complexity makes it daunting for companies to engage in regular video production.

**(18:17:00)**
Here’s a typical video production process: research, scriptwriting, planning, shooting, editing, and then post-production work like adding music and motion design. This complex chain can take weeks for a single video. To gain consistent exposure, you need to create video content continuously, but traditional methods make that nearly impossible.

At Shuffll, we streamline this process to make video production fast, simple, and scalable. Our process reduces video creation to three stages: planning, recording, and minor editing. Let me show you an example of how it works.

**(19:28:00)**
Here’s a video we created with branding, subtitles, and animation. Typically, creating a video like this would take hours if not days, especially if you used an agency. With Shuffll, however, you can achieve the same result in just minutes.

**(21:00:00)**
Let’s go through how Shuffll works. First, you answer a few basic questions about your company and brand, and we gather information directly from your website to learn about your services, colors, and logos. We’ll also generate a brand story to align your content with your voice and identity.

In the dashboard, you can manage multiple projects, collaborate with team members, and handle branding assets. To start a new project, simply select the type of video you want—thought leadership, explainer, or training. From there, Shuffll provides ideas for topics based on trends and competitors, ensuring your content is relevant.

**(22:39:00)**
Once you select a theme, Shuffll generates a complete storyline with scenes, scripts, and brand-appropriate animations. Our platform even includes a teleprompter, which can be voice-activated, and lets you record takes until you’re satisfied. After recording, we handle all post-production—adding lighting effects, subtitles, animations, and more—so your video is ready to publish in minutes.

With Shuffll, what used to take days now takes as little as 15-20 minutes, allowing teams to create more videos, more frequently, and at a high standard.

**(26:11:00)**
The goal is to empower employees to become influencers without feeling overwhelmed. We want them to remain authentic while automating everything else around them. Our motto is “Everything is AI, but you.” We use AI to enhance the production process, but keep people at the core.

**(27:31:00)**
One thing to note: video quality will depend on the equipment you use. We recommend some basic setup tips to help you look your best on camera, as a clear recording will pair better with Shuffll’s motion graphics and animations.

**(28:55:00)**
We’re officially launching on October 8th and will be hosting an event in New York City to celebrate. It’ll be an immersive experience at Loom Studios, and everyone here is invited. We’re also planning events in San Diego, Los Angeles, and San Francisco later in October.

**(30:36:00)**
Pricing is $59 per user per month, and everyone here will get a discount code. David will share that with you after the session.

**(31:41:00)**
For those who work on collaborative videos, you can invite guests to contribute without them needing to pay for their own accounts. For instance, if you need a client to make a brief appearance in a series, you can add them as a guest.

**(36:06:00)**
In terms of repurposing old content, right now Shuffll is designed for creating new videos directly on the platform, but many clients have asked for this feature, so it’s on our roadmap for the next few months.

**(39:54:00)**
A question came up about encouraging people, like data scientists, to get on camera. One way is to simplify the process for them by having scripts and storylines ready, so all they have to do is read their lines. This keeps their time commitment minimal while making the process efficient and effective.

**(43:42:00)**
For anyone just getting started, a thought leadership video is a great first project. Cover a topic related to your field that you find interesting. By sharing your insights, you can take the first steps toward building thought leadership in your area.

**(47:48:00)**
In terms of measuring success, clients often see immediate increases in engagement—sometimes up to 4x what they were seeing before. Others find that they can produce significantly more content, going from one video per month to ten per week.

**(50:52:00)**
This has been a fantastic session. We’ll be following up with the discount code and details on our launch event. For anyone interested in the larger trends around AI video production, we’re here to continue the conversation.

**(54:52:00)**
Thanks, everyone, for joining! Isaac and Yit, thank you so much for sharing Shuffll with us. We look forward to the

## Exploring Key AI Trends in Marketing

Speaker: Nicola Quail
Published: 2024-09-18
Tags: ai trends
Video: https://www.youtube.com/watch?v=p_ltFog-MQQ
Page: https://aimarketersguild.org/sessions/exploring-key-ai-trends-in-marketing

**(00:00)**
Thank you, David. I'm thrilled to welcome everyone to the launch of the AI Marketers Guild (AIMG) APAC community! I’m Nicola Quail, founder of Insights Exchange, based in Sydney, and part of the founding team for AIMG APAC, alongside Dakshama and Anadi Sharma from India, and Sushita Mahapatra in Singapore. We’ll introduce ourselves more formally at the end and share details about getting involved in the APAC community.

**(00:45)**
Before we introduce today’s guest speaker, David Berkowitz, I want to give a brief overview of AIMG APAC and why we're here. The AI Marketers Guild was founded by David, and it now has over a thousand members across North America and beyond. The Guild’s mission is to empower and educate marketing professionals on all aspects of AI through workshops, meetups, webinars, and development courses. When we saw what David was doing, we reached out to explore collaboration to bring the Guild to the APAC region. Here, we have a unique chance to showcase emerging AI tools and connect with world-class AI experts while learning from marketing peers in our own time zone.

**(01:56)**
Today, David is joining us live from New York—likely after a few cups of coffee since it's quite late there. Thank you, David! Alongside founding the Guild, David also created Serial Marketers, a community of over 4,000 members. He’s a seasoned media and technology marketer and will be sharing key observations and trends emerging in AI marketing. I’ll now turn it over to David—welcome!

**(02:30)**
Thank you, Nicola! This is such a treat, and it’s incredible to see so many people joining. I’d love to hear where everyone’s tuning in from since APAC is such a vast region. Feel free to add your location to the chat. It’s really exciting to build and expand the Guild with a team as passionate as this one.

I’ll give a quick roundup of trends I’m observing, but what I’m most excited about is learning from all of you. This community drives me because we learn so much from practitioners across agencies, brands, tech, research, media, and consulting. The more we can understand market-specific nuances and share insights, the stronger we’ll be as a community. So, let me share a few slides and dive into some initial trends.

**(03:08)**
I also have to give a shout-out to one of my favorite tools, Ideogram. Unlike Midjourney or Adobe, Ideogram makes AI visual creation simple, and within minutes, I could create the look of a lively conference event—something I hope we’ll do together in person one day.

**(04:28)**
Fun fact: I am a published author on Amazon. However, I don’t recommend buying the book—AI wrote every word, from the content to the cover design, using tools like ChatGPT. It was an interesting experiment, though perhaps not a high-quality result. This highlights what AI can do, but it also raises questions about what it *should* do.

**(05:48)**
The growth of AI usage, especially generative AI, has been nothing short of explosive. In the U.S. alone, generative AI has seen widespread adoption, transforming how people think about AI. But while we’re seeing great strides, we’re also seeing a high failure rate for AI projects. Data from sources like the Rand Corporation show AI project failure rates that outpace traditional ones. This could be because of the immense experimentation happening right now. We’re still less than two years into generative AI’s mainstream presence, so a high failure rate might be expected. Yet, this phase of exploration should lead to greater innovation and learning.

**(08:22)**
In a recent survey we conducted among Guild members, “time saved” was the standout metric they used to gauge AI’s value. I was surprised by how far this ranked above others, but I also think it’s indicative of where we are on the adoption curve. While time savings are valuable, they’re not necessarily going to convince decision-makers to invest long-term in AI. We’re in a phase where we need to think critically about the metrics we use and possibly develop new ones as AI’s applications mature.

**(10:17)**
Despite the challenges and high failure rate, there is an overwhelming belief among marketers that incorporating AI is essential. The question isn’t whether to use AI, but how to use it effectively.

**(11:03)**
Looking forward, I think we’re heading towards a more dynamic, personalized web where each user’s experience is tailored. There’s a lot of potential for highly customized marketing, but it also has its downsides. If algorithms continually reinforce individual perspectives, it could contribute to echo chambers and polarization, much like we see in today’s divisive media landscape. Major global events—like the Olympics or the World Cup—remain popular partly because they’re collective experiences. These events remind us of our shared humanity, and I think that’s something to consider as we shape the future of AI in marketing.

**(12:53)**
A major development in AI is the rise of AI agents. AI agents like Microsoft’s co-pilot bots perform tasks autonomously, and we’re seeing the early stages of marketing to bots rather than just humans. In programmatic advertising, we’re already targeting algorithms, and with search engine optimization (SEO), much of our work is aimed at WebCrawlers rather than people. It’ll be fascinating to see how marketing adapts as we start marketing to these agents instead of only human audiences.

**(14:48)**
One of the most important words to keep in mind with AI is “yet.” We’ve seen a lot of improvement in image generation, for example, where hands—which initially looked odd—can now be rendered correctly by advanced models. AI capabilities are advancing fast, and we need to anticipate both the exciting and challenging aspects.

**(16:03)**
And now, for something fun! I’d like to introduce a special guest speaker...an AI version of myself! Here’s my AI avatar with a short message for you.

**(16:42)**
[AI Avatar] Welcome to the first AI Marketers Guild APAC event! As the AI avatar of David Berkowitz, founder of the Guild, I couldn’t be more thrilled to kick off this gathering of brilliant minds across India, Southeast Asia, Australia, and New Zealand. Today, we’re diving into transformative AI trends and connecting with marketing innovators. Let’s make this an unforgettable start to AIMG APAC!

**(17:22)**
And yes, I used AI to create this avatar and script, using tools like ChatGPT and HeyGen. AI’s capacity to replicate human likeness and speech is advancing rapidly, and it’s exciting to see where it’ll go from here. I’m eager to hear your thoughts and questions, so I’ll hand the mic back. Thanks, everyone!

**(18:07)**
Thanks, David, for those insights! Let's jump into some audience questions. First up is a question from Gareth, who’s asking about how AI agents will affect SEO. Gareth, feel free to chime in if you want.

**(18:45)**
Great question, Gareth! We’re seeing SEO evolve in a “cat and mouse” game, with marketers testing AI content limits while Google adjusts algorithms. Some marketers generate massive traffic through AI-driven content, but there could be penalties for overdoing it, especially if rules change. AI agents from companies like Google and Microsoft are poised to disrupt this further, and it raises questions about the future role of traditional SEO tactics. For instance, my daughter and I now use ChatGPT for recipes instead of going to recipe websites. This change has major implications for publishers who rely on SEO-driven traffic.

**(22:04)**
Next question from Isaan, who’s facing challenges around building trust in AI-generated content, especially in personal branding for leaders. Isaan, would you like to unmute and elaborate?

**(22:32)**
Thanks, Isaan! Building trust in AI content is a challenge, especially in high-stakes fields like finance or healthcare where accuracy is critical. Incorporating a “human-in-the-loop” model can help, but it’s not always feasible for scaling. Some companies are using multiple AI models to cross-check responses, which can boost reliability. Transparency is key—showing how an answer is generated, citing sources, and fostering media literacy among users are all ways we can build trust in AI-generated information.

**(25:39)**
Thank you again, David! As we’re wrapping up, let’s take a group photo and encourage everyone to connect through the Guild’s APAC community.

**(26:18)**
Everyone, here’s a QR code to join the AIMG APAC community. Feel free to send us a message in the chat with your contact preferences so we can keep you updated on upcoming events. We’re looking forward to growing this community and staying connected!

**(26:48)**
David, thank you so much for joining us at this late hour! We’re thrilled to have you involved, and we can’t wait to continue learning together.

**(27:29)**
Let’s introduce the APAC founding team. I’m Nicola Quail, founder of Insights Exchange in Sydney. We’re a global market research company focused on human insights, using AI to enhance speed and efficiency. I’m excited to help lead this community.

**(28:08)**
Hi everyone, I’m Dakshama, founder of iASK, where we work on AI transformation. If you’re interested in conversations around AI-driven innovation, feel free to reach out.

**(28:44)**
Hello, I’m Sushita, based in Singapore. Apologies for my voice—I’m recovering

## Unlocking AI-Powered Media Strategies

Speaker: Abtin Buergari
Published: 2024-09-18
Tags: media strategies, ai powered media buying, content fatigue, audience targeting, predictive marketing
Video: https://www.youtube.com/watch?v=WghDrS4O0Mw
Page: https://aimarketersguild.org/sessions/unlocking-ai-powered-media-strategies

**(00:00)**
We have a fun conversation planned on media buying with Abtin Buergari from Blueprint. Abtin was introduced by our community member, Chris Perkins, and it’s wonderful to see how speakers are emerging from our own network. Abtin, thank you for joining us. I’ll make you a co-host, so feel free to share your screen if needed. Just a reminder to everyone: this is an interactive conversation, not a webinar. Feel free to jump in, raise your hand, or add thoughts in the chat.

**(02:05)**
Thanks for inviting me! I’m Abtin Buergari, CEO of Blueprint, an AI tech product for the ad tech industry. A bit about my background: I’m a transplant to marketing.

I previously worked in the legal field, where I built a company focused on helping attorneys quickly locate relevant evidence among millions of records by structuring unstructured data. After selling that company, I started looking into new ventures and began investing in e-commerce businesses around 2017. I wanted to evaluate engagement and understand how e-commerce businesses stack up against each other, but there weren’t good tools available for cross-comparing advertising.

This sparked the idea for Blueprint. First, I created an agency—now run by Chris Perkins—and started managing large ad spends, which helped me better understand the messiness of advertising data. We were managing over $100 million in ad spend at the agency, and by 2021, I began building the foundation for Blueprint, which launched in 2023. We’re now scaling rapidly, managing $350 million in ad spend through our platform.

**(05:28)**
Great background, Abtin! A big question I have is if we’re heading toward an “easy button” for advertising—where a business could upload some basic content and information, select a few goals like driving online sales or building an email list, and let AI handle the rest. Could we be heading toward that?

**(08:00)**
That's a great analogy. It’s similar to Tesla’s robo-taxi vision—an ideal that will take time to achieve due to complexities we can’t fully anticipate. I think we’ll eventually reach a point where AI can handle the details, but there’s still a lot to solve. To get there, we’ll need to address complex issues like data transparency and platform dynamics.

**(09:07)**
I’d love to hear your thoughts on Web3 and blockchain’s role in addressing some advertising challenges. Blockchain offers an open ledger, which could potentially address data transparency and privacy issues by allowing only limited access to data without it being stored by platforms.

**(09:47)**
Yes, blockchain presents an interesting solution for privacy, allowing data to be stored securely without platforms like Facebook fully accessing it. But platforms are reluctant to embrace blockchain solutions because they prefer advertisers to stay within their ecosystems. So while it’s technically feasible, the practical adoption of blockchain by these companies remains uncertain.

**(12:20)**
When I first entered advertising as an engineer, I was struck by the chaotic way data is organized. Many agencies present outdated data to clients without meaningful insights, but brands that take control of their data often outperform agencies. I’m seeing more brands build internal media teams, sometimes just 10-12 people, but with incredibly tight control and oversight of their ad spend.

**(15:25)**
A smart marketing team accepts that there will be some data loss and uses a system that their executives can trust, like Shopify for e-commerce or HubSpot for B2B. These platforms should be the “source of truth” for ad performance, instead of relying on incomplete data from multiple sources.

**(17:06)**
David, to your earlier question about the “easy button,” I think we’ll get closer once we automate the mechanical aspects of advertising. The strategy and content choices, however, need to stay human-driven. Machines can crunch data, but determining the right audience and message remains a human responsibility. For example, while ChatGPT can answer many questions, it won’t know if a website accurately represents an audience’s needs.

**(19:09)**
Right now, some companies aren’t benefiting as they should because they aren’t coordinating their SEO, paid ads, and organic strategies effectively. Agencies often manage different pieces in isolation, which means clients don’t see the full impact or benefit.

**(21:20)**
To build on that, Abtin, what do the latest advancements in AI-powered media buying offer that wasn’t possible a year or two ago?

**(21:55)**
Good question. There’s some exciting work happening now. One example is technology that pulls video ad content directly from platforms like Facebook, analyzes engagement data, and uses large language models to extract insights about what’s in the video—who’s speaking, the message, etc.—and suggests what other content might engage viewers. One company doing this is Darwin, but this technology is still evolving and mostly a point solution.

Our platform, however, focuses on ad fatigue and growth potential. We analyze when ads start to fatigue and notify users so they can make changes before they spend too much on underperforming content. We also identify high-performing content and send it to an AI model to analyze characteristics, though we’re still refining how best to use this data to help clients make actionable decisions.

**(26:28)**
Karan, you raised an important point. Clients often want to keep content running longer than is effective because they’re attached to it. Our technology helps identify when content engagement starts to decline quickly—what we call “fatigue”—and alerts clients so they can make adjustments.

**(29:37)**
Jay-Z also asked about optimizing across channels since every platform’s dynamics are different. Our platform tracks the same creative across platforms and shows how it performs in each one. This helps clients make data-driven decisions about where to use their best content.

**(33:36)**
Our platform flags high-performing creatives and shows where they’re most effective, allowing users to optimize content based on specific platform insights. This helps media teams spend less time creating generic reports and more time focusing on real-time decisions, such as reallocating budgets or creating similar content.

**(38:59)**
Karen, about large language models (LLMs) and share of voice, that’s a fascinating area. We’re seeing LLMs create inherent biases based on available content, which impacts share of voice in search and conversational AI results. While our platform doesn’t currently focus on this, I think there’s real potential for using LLMs to continually adjust content for different platforms based on user engagement.

**(41:21)**
Amit, great question on reinforced bias. When you invest heavily in specific creatives, the risk is that the algorithm may overemphasize one piece of content without exploring new options. At Blueprint, we address this by not relying solely on return on investment (ROI) metrics. Instead, we look at ad performance across different dimensions—engagement rate, conversion rate, and the entire funnel. We’ve found that analyzing ad sets and comparing platform data with attribution systems like GA4 provides a more complete picture.

**(47:12)**
Jay-Z, on your question about whether our platform can auto-optimize like some “multi-armed bandit” models—yes, there are tools like metadata.io that do this for B2B, but we’ve chosen not to pursue this approach. Automation can be risky if something goes wrong or if the data isn’t entirely accurate. Instead, we focus on providing insights so media operators can make educated decisions based on our recommendations.

**(52:09)**
Thanks, Abtin, for the incredible insights and for sharing your experience. Thank you all for the fantastic questions and engagement. We have several more speakers lined up in the coming weeks. Looking forward to seeing you all next week. Abtin, we’d love to have you back again—thanks for joining us!

## Measuring Content Marketing Value

Speaker: Peter Kraus
Published: 2024-09-06
Tags: content marketing, content strategy, seo, audience targeting
Video: https://www.youtube.com/watch?v=v5jaJXMiXm8
Page: https://aimarketersguild.org/sessions/measuring-content-marketing-value

Join David Berkowitz, founder of AI Marketers Guild, as he hosts Peter Kraus, CEO of Relify, in this engaging and insightful AI Insiders webinar.
In this episode, Peter discusses how AI is reshaping content marketing and the critical need for generating relevant, differentiated content.
The conversation covers the impact of generative AI on SEO, targeting, and personalization, and the role of AI in content strategy development.
Watch as Peter shares real-world examples and practical tips for leveraging AI to increase the value of your content, tackle content debt, and adapt to the rapidly evolving AI landscape.

### 01:15 How Is Generative AI Changing the Value of Content Marketing?

Answer / Description:
Generative AI has radically lowered the barriers to content creation, resulting in an overwhelming surge of low-quality, automated content that dilutes traditional SEO strategies. To maintain marketing value, brands must shift away from high-volume, generic publishing and instead focus on producing deeply researched, differentiated, and highly relevant content that addresses specific user intents and complex queries.

As generative AI engines easily replicate basic information, standard informational articles are rapidly losing organic search visibility. This shift forces organizations to re-evaluate their production metrics. Instead of measuring success by the sheer volume of blog posts or landing pages published, content teams must prioritize unique insights, primary research, proprietary data, and authoritative expert perspectives that AI models cannot easily synthesize without citation.

Ultimately, the democratization of content creation means that personalization, depth, and topical authority are the new benchmarks of content marketing success. Companies that continue to rely on basic keyword-focused content will find themselves buried under AI-generated noise, while those investing in high-quality, specialized editorial strategies will successfully capture both traditional search traffic and AI engine recommendations.

Keywords:
Generative AI content strategy, AI content volume, content marketing value, content differentiation, AI-generated noise, authoritative content, topical authority, B2B content marketing

### 11:40 What Is Content Debt and How Does It Impact B2B Marketing Performance?

Answer / Description:
Content debt is the accumulation of outdated, redundant, low-performing, or inaccurate content assets on a company's website over time. This legacy content actively damages B2B marketing performance by wasting search engine crawl budgets, diluting a brand's topical authority, and misguiding generative AI models that retrieve information from the site.

As businesses grow, they often leave old product pages, outdated blog posts, and obsolete guides active on their domains. This accumulation of "content rot" creates severe friction for search engine crawlers, which spend valuable resources indexing low-value pages rather than high-converting, current assets. Furthermore, when search engines or LLMs crawl a site cluttered with conflicting or outdated information, they struggle to identify the brand’s core expertise, leading to lower overall rankings and inaccurate AI search answers.

Addressing content debt requires systematic content audits, pruning, and consolidation. By redirecting, updating, or deleting underperforming legacy assets, marketing teams can clean up their digital footprint, focus search equity onto high-performing pages, and ensure that both human buyers and AI retrieval crawlers receive a consistent, accurate representation of the company's offerings.

Keywords:
content debt, B2B content strategy, crawl budget optimization, content audit, topical authority, legacy content pruning, SEO cleanup, digital footprint management

### 22:10 How Can B2B Marketers Measure the Actual ROI and Business Value of Their Content?

Answer / Description:
B2B marketers can measure the true business value of content by shifting focus from vanity metrics, like page views and social shares, to pipeline impact, customer acquisition cost (CAC) reduction, and revenue attribution. Platforms like Relify solve this challenge by mapping content consumption behaviors directly to CRM data, demonstrating exactly which assets influence deals throughout the sales cycle.

Historically, content marketing has struggled with attribution because buyers engage with multiple assets across long sales cycles before converting. Standard analytics platforms often fail to connect early-stage blog reads with late-stage purchase decisions. By implementing multi-touch attribution models and content intelligence platforms like Relify, marketing teams can track a prospect's content touchpoints from initial awareness down to closed-won deals.

This data-driven approach to content valuation enables marketing leaders to identify high-value content patterns. For instance, teams can discover if a specific whitepaper or case study consistently accelerates deal velocity or increases average contract value (ACV). Armed with these insights, marketers can justify their budgets to executive leadership and optimize their content production engines around revenue-generating topics rather than raw traffic.

Keywords:
content marketing ROI, Relify platform, B2B pipeline attribution, content performance metrics, customer acquisition cost, business value of content, multi-touch attribution, revenue-driven content

### 31:50 How Should Content Strategies Adapt to Generative Engine Optimization (GEO) and AI Overviews?

Answer / Description:
To adapt to Generative Engine Optimization (GEO) and AI search features like Google's AI Overviews, content strategies must transition from keyword optimization to structured data, direct query answering, and verified E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness). The goal is to format and enrich content so that Large Language Models (LLMs) can easily retrieve, synthesize, and cite it as an authoritative source.

AI search engines search for clear, concise, and factual explanations to synthesize answers for users. To be included in these generative summaries, content must be structured with clear headings, bullet points, and schema markup that AI agents can easily parse. Additionally, publishing proprietary data, original research, and unique expert commentary makes a website highly citable, prompting AI engines to link back to the source.

Furthermore, content creators must optimize for conversational long-tail queries. Users interact with AI engines using natural language and multi-step prompts rather than simple keyword fragments. By structuring content around these complex, intent-driven questions and providing direct, authoritative answers at the beginning of articles, brands can maximize their chances of being featured in AI-generated answers.

Keywords:
Generative Engine Optimization, GEO content strategy, AI Overviews SEO, LLM search optimization, E-E-A-T content, AI retrieval optimization, conversational search, search engine evolution

### 40:05 How Does AI Enable Effective Content Personalization and Audience Targeting?

Answer / Description:
AI enables advanced content personalization by analyzing real-time intent signals, behavioral data, and firmographics to dynamically serve the most relevant assets to specific buyers. Instead of manually mapping static buyer personas, marketers can use AI engines to automate the assembly and distribution of highly tailored content experiences at scale.

In the B2B buying journey, different stakeholders—such as technical users, procurement officers, and executives—require distinct types of information to make a decision. AI-powered personalization platforms analyze a user's behavior on a website, identify their industry and job role, and instantly customize the visible content, case studies, and call-to-actions. This ensures that every visitor sees information aligned with their unique stage in the buying process.

This predictive approach to targeting dramatically improves engagement and conversion rates. Rather than overwhelming prospects with generic marketing collateral, AI filters out irrelevant noise and highlights the exact resources needed to solve the buyer's immediate pain points, ultimately shortening sales cycles and enhancing the overall customer experience.

Keywords:
AI content personalization, predictive buyer intent, dynamic content delivery, account-based marketing AI, B2B targeting, audience segmentation, content experience automation

### 48:30 How Can Marketing Teams Safely Use AI to Enhance Content Curation and Relevance?

Answer / Description:
Marketing teams can safely scale content curation by using AI tools to aggregate, tag, and summarize industry trends, while relying on human editors to provide the critical context, brand voice, and strategic commentary. This hybrid workflow ensures rapid content delivery without sacrificing the authenticity, quality, and perspective that audiences trust.

While AI is exceptionally efficient at scanning vast amounts of web data, identifying trending topics, and generating quick summaries, it lacks the lived experience and strategic nuance of a human professional. To maintain brand authority, organizations should not publish automated AI summaries directly. Instead, AI should serve as an editorial assistant that drafts foundational research summaries, which human experts then refine, validate, and enrich with unique brand perspectives.

This collaborative approach protects brands from the risks of AI hallucinations, factual inaccuracies, and generic messaging. It allows small content teams to act as thought leaders by curating highly relevant weekly industry roundups and newsletters, ensuring they remain top-of-mind for their audience while keeping production costs and timelines manageable.

Keywords:
AI content curation, hybrid human-AI workflow, brand voice alignment, content relevance, automated content aggregation, editorial oversight, thought leadership curation

## Mastering Advanced Marketing with ChatGPT Hands-On Training for Elite Marketers with David Passiak

Speaker: David Passiak
Published: 2024-08-28
Tags: gpt marketing, custom gpts, gpt prompts, advanced workflows
Video: https://www.youtube.com/watch?v=fFnqkjKOVKM
Page: https://aimarketersguild.org/sessions/mastering-advanced-marketing-with-chatgpt-hands-on-training-for-elite-marketers

​**(00:00)** Today, we have a special session with David, founder of Creator Pro, who has created some amazing AI-driven modules to tackle various marketing challenges. David has taught marketing to tens of thousands of people over the years, so it’s fantastic to have him guide us through advanced AI marketing strategies today.

**(00:55)**
David, I’ll let you introduce yourself and dive right in since there’s a lot to cover.

**(01:36)**
Thank you! It’s wonderful to be here, and I appreciate everyone taking the time out of your busy day. A bit about my background: I founded Creator Pro AI, where we build custom GPTs and offer ChatGPT training. Early on, I noticed that people were struggling with prompts and using ChatGPT for advanced workflows. That’s how I began building custom GPTs and ultimately developed a comprehensive training approach. My background is somewhat unique—I began my career studying religion and mass movements, and I eventually shifted to working with brands, including running social media for Volkswagen. I developed a dialogue-based approach to innovation and collaboration, which has carried into my work with AI and ChatGPT.

**(03:40)**
In early 2022, I began working intensively with ChatGPT, experimenting with ways to turn AI into a tool for full workflows by defining prompts, creating personas, and guiding step-by-step processes. This led me to design Creator Pro, which offers GPTs that perform the prompt engineering for you, allowing users to focus on doing the work directly. Let me share my screen to show you what we’re doing with Creator Pro, and then we’ll dive into a live exercise where we build a brand and develop content personas for AIMarketersGuild (AIMG).

**(04:57)**
Creator Pro is a business accelerator with GPTs tailored for various roles and objectives: leadership, marketing, content creation, client services, and more. We also launched a suite specifically for AI marketers and agency owners. Today, we’ll use the brand architect GPT to walk through the process of building a brand, defining target audiences, and creating mission statements. This session is a preview of our upcoming advanced AI marketing certification program.

**(08:44)**
One unique aspect of Creator Pro is our “Chain of Thought” approach. Chain of Thought is about guiding you step-by-step, similar to how an expert would approach a complex problem. It’s been central to recent developments in AI models, particularly those focused on complex reasoning. The reasoning model in the latest ChatGPT update uses this approach, which significantly enhances its ability to sequence ideas and provide solutions. However, it’s not perfect—it can’t access files or the internet, and its tone is a bit formal. The Creator Pro system combines this reasoning model with a more conversational, practical style that lets you focus on creativity rather than precise prompts.

**(11:23)**
Let’s start building the brand for AIMG. Feel free to jump in with ideas as we identify the problems AIMG aims to solve and who it serves.

**(12:35)**
What issues would you like AIMG to help you solve?

**(13:25)**
I’ve been experimenting with demos and voice-to-text. Using the ChatGPT app’s voice feature, you can walk through complex tasks without needing to type, which I find helpful for productivity, especially when balancing meetings or when you’re on the go. So, let’s try capturing ideas for AIMG’s brand directly via voice-to-text. Feel free to share what problems or gaps AIMG can help fill for you.

**(15:27)**
From the group, we’re hearing themes like filling knowledge gaps in AI and marketing, learning from expert insights, and finding practical value in the latest AI tools. There’s interest in connecting with like-minded marketers who share insights and leverage growth hacking. Let’s start with these.

**(17:13)**
There’s often a misconception that AI must be approached like programming—highly technical and specific. However, I encourage more of a “stream of consciousness” approach where you speak or write openly about what’s most important to you. This lets the GPT focus on synthesis and editing, freeing you up to concentrate on high-level ideas without being bogged down by structure.

**(19:30)**
So far, we have some key themes for AIMG’s problem identification and target audience: members looking to fill knowledge gaps in AI, distinguish hype from real value, and connect with experienced marketers. The target audience includes experienced marketers from various sectors—agency, brand, tech—along with industry veterans who are newer to AI. We could refine the audience profile based on whether we’re targeting, for example, data-driven companies or industries known as late adopters.

**(23:20)**
A key benefit of Creator Pro’s Chain of Thought structure is that you get detailed, organized outlines that are readable and easy to work with. When you’ve identified what resonates, you can simply give feedback, and the AI will adapt in real-time.

**(25:57)**
Now, we’re diving into potential solutions for AIMG. Based on our discussion, we’re seeing suggestions like curated AI tools, interactive case studies, live startup demos, and exclusive thought-leadership events. Each of these is a solution that can help members navigate the AI landscape, from understanding tools to gaining actionable insights. This is helpful for agencies or consultants pitching clients because it organizes brand details and potential solutions quickly and clearly.

**(33:12)**
Next, we’ll discuss the brand’s functional and emotional benefits. I like to separate these because it helps teams think about how their product or service benefits customers on different levels. For AIMG, functional benefits might include accessing vetted tools and expert insights, while emotional benefits could be building confidence, reducing overwhelm, and fostering a sense of community.

**(35:17)**
We’ve highlighted AIMG’s live events, but I’d like to emphasize that these events are central to the brand. Let’s make sure that comes through in the final version. Live events bring members together in real time for networking and direct engagement, a crucial part of AIMG’s value proposition.

**(37:16)**
I also want to address an important question that came up about AI’s tendency to “please” the user. One approach is adjusting your custom instructions to include a confidence score for responses or to prompt the AI to ask clarifying questions. This helps create a more balanced dynamic rather than just accepting every answer.

**(41:22)**
One of the advantages of using AI in this dialogue-based mode is that it mimics a “flow state.” This allows you to be highly productive and creatively engaged without experiencing the typical fatigue that comes from prolonged focus. Flow is typically challenging to maintain for long periods, but by offloading cognitive processing to AI, you can stay in a productive zone longer.

**(43:15)**
The AI has now created potential mission statements for AIMG. One suggestion is “Connect, educate, and inspire marketers through curated tools, real-world insights, and live events, empowering them to make a significant impact on their roles and careers through the use of AI.” I’d modify this slightly to highlight community and professional growth, focusing on actionable AI impact.

**(47:07)**
We’ve moved through vision and mission, and now we’re looking at tone, color palettes, fonts, and visual aesthetics. With this structure, we’ve essentially built an entire brand framework in about 25 minutes, producing detailed guidelines ready for any design or content team.

**(49:50)**
Now, let’s discuss a few options for the upcoming advanced AI marketing training program. This will be a series of four in-depth masterminds designed to equip marketers with practical, advanced skills for using ChatGPT. Topics will include Chain of Thought reasoning, custom GPTs for brand building, content planning, and customer journey mapping, plus strategies for lead generation and campaign development. The goal is to help marketers develop a range of critical AI skills.

## AIs Role in Creative and Personalized Marketing

Speaker: Marc Maleh, Clive Henry
Published: 2024-08-21
Tags: creative marketing, content credentials
Video: https://www.youtube.com/watch?v=HVmGdQ4lRUY
Page: https://aimarketersguild.org/sessions/ais-role-in-creative-and-personalized-marketing

**(00:43)** Today, I have the pleasure of catching up with Marc Maleh, now the Global CTO at Huge. Marc and I reconnected through Progress Partners and their Exec-in-Residence program. Progress Partners is a great M&A firm that connects leaders across fields and stays at the forefront of what's next. It's one of my favorite communities, apart from the one I run!

**(01:22)** When discussing AI's impact, I was eager to get Marc's perspective. I’ve admired Huge for years. Back when I worked at Mr. Understood running marketing, Huge was often our main competitor because of the impressive agency work they did. Their vision and work have always stood out. Marc also introduced me to Clive Henry, Director of Strategic Partnerships at Adobe. We've had some great conversations leading up to this, so I’ll hand it over to Marc to introduce himself and then Clive can do the same. They’ll share a bit about their roles and their interest in AI.

**(02:29)** Since we have some first-timers here, a reminder that these discussions are interactive. I’m happy to let the community lead with questions. Feel free to ask in chat or raise your hand. Marc, let’s start with you—who are you, and what brings you here?

**(03:06)** Marc: Thanks. I’m Marc Maleh, CTO at Huge. This is actually my second time here; I spent almost four years at Huge before leaving to work client-side at crypto.com, where I led Creative Innovation during the “crypto craze”—happy to talk about that experience sometime! I’ve been in the agency world for about 25 years, spending 10 years at RGA running data science and hardware teams, and later managing technology at Wieden+Kennedy. One unique aspect of my time at W+K was merging technology and storytelling.

**(03:44)** I also ran an AI team at Havas, primarily a media-driven organization, where I led the creative AI teams. That experience, which involved early AI like chatbots, marked the beginning of my work in AI. I've always been interested in emerging technology, but it’s essential that it addresses consumer needs. At Huge, I’m focused on how AI and tech can create business value or new consumer experiences.

**(04:51)** David: That’s exactly the kind of insight we aim to cover here! AI is advancing rapidly, and the question is, how do we best use it? And Clive, welcome—tell us about your role.

**(05:19)** Clive: Hi everyone, I’m Clive Henry, Director in Strategic Partnerships at Adobe. My role focuses on building partnerships for our generative AI initiatives. I’ve been with Adobe for about 10 years, working closely with cloud providers and systems integrators, particularly around AI. Adobe’s AI journey has been ongoing, especially in personalization and incorporating machine learning into our core product portfolio.

**(06:32)** With generative AI, we made the strategic decision to launch our own model, Firefly, focused on creativity and responsibly trained on Adobe Stock imagery with proper permissions from creators. This approach ensures there's no IP infringement, and Firefly allows us to develop customizable creative tools quickly, more so than any other time I’ve seen in my decade at Adobe. We're enabling creatives to produce personalized content responsibly and with rapid deployment, which makes our partnerships and model customization particularly valuable.

**(08:33)** David: Since Firefly’s launch, I have a question for both of you: Has generative AI changed the nature of creativity? If so, how?

**(09:08)** Clive: Adobe quickly recognized that generative AI output is rarely 100% ready for final publication. Whether it’s copy, imagery, or other media, the output usually needs to be refined. There’s a high demand to integrate these generative capabilities into our existing editing tools. Gen AI hasn’t replaced creativity; it’s another spoke in the wheel of creative tools, fitting within existing workflows.

**(10:30)** Marc: I agree. Thinking of AI as just another tool is the right approach. Historically, new technology has shifted roles, but it rarely eliminates them. Creativity isn’t just about efficiency; it’s about exploring new possibilities. At Huge, we’re focusing on “intelligent experiences”—fusing creativity with data and technology. For example, a UX designer, data scientist, and technologist can collaborate on new AI-driven UX concepts, creating genuinely unique experiences.

**(12:08)** Clive: Exactly, and to add, generative AI complements the creative process by speeding up ideation and localization. It’s still part of an overall workflow, though. We’re now seeing new roles emerge, like creatives trained in data-driven customization, to guide these models in producing brand-aligned content.

**(13:19)** Marc: Agreed. Generative AI lets us approach user experiences differently. Instead of just static content personalization, we can dynamically adjust entire interfaces based on user data, like we did with NBCU’s Olympic platform, “Ali.” Now, users can get real-time schedules for Olympic events tailored to their timezone—something simple yet deeply impactful.

**(17:10)** David: Where does data fit into this process?

**(17:46)** Clive: We see large language models as new data pipelines. To make these models brand-aligned, we train them on proprietary data to generate on-brand content. It’s a balance of creativity and personalization, accelerated by AI. This brings in roles where data-aware creatives ensure models adhere to brand guidelines. Adobe’s Custom Models tool enables brands to fine-tune outputs with human input for ongoing alignment.

**(20:24)** Marc: Personalization is evolving. It’s not just content; the interface itself can now change based on user data. Imagine an app that adjusts its layout based on user preferences—data science and UX intersect here, allowing for more fluid, adaptive experiences.

**(21:27)** David: This raises questions about personalization’s limits. Should we aim for every user to see a completely unique experience, or is that too isolating?

**(22:29)** Marc: Great question. Take movies—what if a film adapted slightly based on the viewer? That could be interesting, but it also risks losing the creator’s intent. I believe there’s value in personalized experiences, but some standardization is crucial to avoid an overload of personalization that might alienate users.

**(23:34)** Clive: Building on Marc’s point, we’re seeing the web itself becoming more conversational. Personalized prompts can create unique experiences without replacing the overall brand message, much like how NBCU used AI to guide users in Olympic programming. It’s a balance of personalization with consistency.

**(26:20)** David: So, should we label content influenced by AI to ensure transparency?

**(27:35)** Clive: Absolutely. Adobe’s Content Authenticity Initiative lets users know if content was generated or edited with AI. This “content credentials” feature is being integrated with partners like Meta to add transparency on platforms like Instagram.

**(28:54)** Marc: Transparency is essential. Users should know when AI is involved. Plus, highly personalized systems should account for accessibility needs, an area often overlooked in AI UX design. Standards like the EU AI Act are moving in this direction, though the US still lags behind.

**(32:42)** Adam: AI can amplify biases since models learn from human data. How can we moderate AI to prevent unintended outcomes?

**(33:58)** Clive: Excellent question. Content moderation, both by humans and machines, is key. We’re using machine moderation to review AI-generated content against brand guidelines, ensuring it aligns with intended messaging. A human-machine review loop adds an extra layer of quality control.

**(35:20)** Marc: Human involvement in AI moderation is vital, but those humans need to understand the AI systems they’re working with. Proper training helps them balance model biases and ensure content aligns with brand standards.

**(36:26)** Alexander: Are companies building AI solutions internally or relying on partners?

**(37:38)** Marc: Many companies start with proof of concepts to decide on building, buying, or partnering. Moving from proof of concept to production is challenging, though. Often, they lack the internal skills to support AI in production. But clients are making strides in organizing and utilizing their data, which is a foundational step.

**(41:04)** Clive: We’re in a phase of experimentation. Transparency in AI is crucial, especially in showing users how and why content is personalized. AI also enables rapid prototyping, ideation, and campaign scaling, which is transformative for brands.

**(46:55)** David: Regarding transparency with clients, how do you communicate when AI is influencing the process?

**(47:33)** Marc: At Huge, we’re clear with clients about the AI tools we use and often disclose if an asset was AI-generated. It’s part of maintaining trust and showing how AI is integrated into their projects.

**(49:19)** Clive: We’re gathering feedback from agencies to improve transparency in Adobe tools, especially around AI-generated content. This helps us refine how these tools support creative and ethical standards.

**(50:43)** David: Any closing thoughts on where this is all headed?

18)** Clive: One area we didn’t cover much is multimodality—using AI to adapt content across formats. If a campaign poster performs well, we can quickly create variations, like a video or 3D model. This transformation of content is an exciting development.

**(52:36)** Marc: My takeaway would be to avoid inaction. Don’t let fear hold back experimentation. Consumer expectations around AI are evolving, and companies that engage now will better meet those expectations. Acting now can help businesses keep pace with rapid advancements.

## Navigating AI and Copyright Law Insights from Legal Expert

Speaker: Brooke Smarsh
Published: 2024-08-18
Tags: copyright law
Video: https://www.youtube.com/watch?v=6Onpm5TGWDA
Page: https://aimarketersguild.org/sessions/navigating-ai-and-copyright-law-insights-from-legal-expert

**(00:00)** Today we have a special guest, Brooke Smarsh, who may look familiar, especially to our regulars here. Many of you have probably learned a lot from Brooke before, and I’m excited to officially welcome her as our guest speaker. Brooke has shared valuable insights on previous calls, particularly around legal questions concerning AI. Brooke is a practicing lawyer, so she brings that direct legal expertise—no secondhand interpretations here!

**(00:55)** If this is your first time joining us, welcome! These discussions are interactive, and we encourage questions, especially today with such a critical topic. Brooke will focus primarily on copyright issues related to AI. I'll let her introduce herself and share how she got interested in AI and why it has become a significant focus for her.

**(02:11)** Brooke: Hello, everyone. It’s nice to see so many familiar and new faces here. I’ve been working with tech startups for a long time, which naturally led me to work with clients involved in AI. As an attorney, I follow where my clients' needs go, and for many, that’s increasingly into the AI space. I’m a solo practitioner, though I do have an assistant who helps keep track of AI updates—they’re coming fast and furious these days! Just this morning, I got an alert about new U.S. initiatives to maintain leadership in AI, showing just how rapid these changes are.

**(02:43)** I have a presentation prepared, so I’ll share my screen. [Shares screen] And as David mentioned, this can be very interactive, so feel free to ask questions along the way. If I don’t see you, just speak up.

**(03:17)** To start, let’s take a high-level look at ownership and intellectual property. Property generally falls into two categories: real property and intellectual property, and property ownership exists because of laws defining and enforcing it. Intellectual property (IP) includes patents, trademarks, and copyrights. We’ll focus on copyright today, covering what rights owners have, what others can do with those rights, and how copyright specifically applies to AI.

**(04:27)** Copyright law protects original works, covering a broad range like computer code, audio recordings, video, photography, artwork, books, and blogs. Copyright law is federal in the U.S., unlike privacy laws, which are often a state-by-state patchwork. The U.S. Code Title 17 governs copyright, with oversight from the Copyright Office.

**(04:57)** Ownership of a work usually belongs to the creator—whoever "puts pen to paper" or "fingers to keyboard"—unless there’s an agreement stating otherwise. Many companies require employees or contractors to sign agreements specifying that any work they create belongs to the company. It’s always best to establish ownership rights upfront, even though ownership technically exists upon creation without registration. Registering a copyright, however, offers advantages if you need to enforce it, such as access to statutory damages.

**(06:02)** For copyright protection to apply, two conditions must be met: the work must be original, and the creator must be human. The originality requirement means the work can’t simply replicate something that already exists, though it can be a derivative work. For instance, a movie based on a book is a derivative work, as are new editions of textbooks and translated texts. It must incorporate the pre-existing work while adding new, original authorship.

**(07:03)** As for the human requirement, it may seem obvious, but AI complicates things. The Copyright Office has rejected copyright claims for works claimed to be authored by divine or non-human entities.

**(07:36)** Here’s an example: a photographer set up a camera, and a curious monkey took a selfie. The photographer attempted to claim copyright, arguing he set up the conditions, but the Copyright Office ruled that only humans can hold copyright. Even an attempt by PETA to claim copyright on behalf of the monkey failed.

**(08:45)** Now, onto AI-generated content. The Copyright Office issued guidance in March 2023, addressing whether works are “human” if they involve AI. They assess whether the human created the work, with the computer as an assisting tool, or if the machine itself generated the creative elements. When users simply prompt an AI, they’re not considered the author, as they lack ultimate control over the creative output—similar to commissioning an artist. However, if someone modifies AI output substantially, those modifications may be protected.

**(09:59)** Any questions on that so far?

**(10:30)** David: I have one: what about scenarios with tools like Photoshop that might have automated features?

**(10:30)** Brooke: Great question. It’s always a case-by-case analysis. Think of it as a winding river rather than a strict line. Where creative control shifts from human to machine is still being debated. The Copyright Office is actively seeking feedback on whether the current approach to AI-generated works is appropriate.

**(11:36)** Attendee: If you prompt an AI multiple times to refine an output, is that considered human input?

**(11:36)** Brooke: That’s a gray area, and there’s no official guidance on it yet. A relevant example is “Zarya of the Dawn,” a graphic novel created with over 400 AI prompts. Despite the extensive prompt work, the Copyright Office denied protection for the images but allowed it for the text. Many, myself included, think they got it wrong there, as extensive prompting arguably shows creative direction.

**(12:51)** For example, using AI to blur elements in a “man-on-the-street” video as part of your editing doesn’t impact copyright eligibility; it’s considered a tool. But if you use AI-generated special effects, the effects might not be protected. Translations by AI also don’t qualify for copyright since human creativity is involved in literary translation decisions. It all comes down to whether AI is enhancing or creating independently.

**(15:44)** Attendee: How does the copyright registration process work?

**(15:44)** Brooke: Briefly, the moment you create a work, copyright exists, though registration provides benefits for enforcement. The Copyright Office has forms, videos, and resources to guide you through registration.

**(16:13)** Now, once you own a copyrighted work, others can only use it through permission (like licensing) or under fair use. Licensing includes music, templates, photos, software, and more. Fair use, however, balances the copyright holder's rights with the public interest to encourage creativity and innovation. It’s assessed with a four-part test examining purpose, nature, amount, and market impact. This test is challenging and can lead to differing interpretations, even in the courts.

**(18:28)** David: That’s helpful context, especially with AI tools like Augi Studio that can generate voiceovers, scripts, and video montages from stock clips and your own content. It seems like if there's minimal human input, claiming copyright for the final product would be unlikely.

**(19:02)** Brooke: Correct. Using only AI-generated scripts and stock footage wouldn’t likely qualify for copyright.

**(20:05)** Here’s a timely example: in *Andy Warhol Foundation v. Lynn Goldsmith*, the Supreme Court ruled in favor of the original photographer, Lynn Goldsmith, whose photo was used by Andy Warhol in a piece licensed to *Vogue*. Goldsmith argued that Warhol’s use harmed her market, as she could have licensed her photo to *Vogue* herself. This ruling prioritizes protecting the original work, which is particularly interesting now as AI models use copyrighted materials for training.

**(23:47)** In the AI landscape, some companies are paying for licenses, like Reddit, Google, OpenAI, and Meta, while others rely on fair use, especially smaller companies without the funds to license materials for training. Fair use here depends on factors like whether the AI outputs are creative enough to be transformative or if they directly compete with the original.

**(25:27)** Attendee: So, how should we approach AI use with copyrighted material?

**(25:27)** Brooke: Good question. When using copyrighted material, consider how important ownership is for your project, the potential infringement risk, and the protection level you need. For example, if you’re creating AI-generated code for a proprietary tech product, investors will want assurance that you own it. Use a risk calculation, considering both likelihood and magnitude of potential harm.

**(28:16)** Attendee: As a brand not building AI tools but using AI solutions, should we worry about AI-created materials?

**(28:16)** Brooke: Yes, especially if clients or customers are wary of AI-created content. Ownership and potential infringement risks depend on how crucial originality is for your project and how you’ll use the outputs.

**(29:27)** Moving on to publicity rights: individuals control the use of their image, likeness, and voice, mainly regulated by state laws. California has the most protective laws in this area, while Nevada’s are more relaxed. For example, in *Kim Kardashian v. Old Navy*, Kardashian sued Old Navy for using a look-alike in an ad, and they eventually settled.

**(33:24)** David: So if we use a Robert De Niro-sounding voice from an AI tool like Eleven Labs, is that okay?

**(33:24)** Brooke: Generally, no. Even if it “sounds like” De Niro without explicitly being him, if it implies it’s him, you’re in risky territory.

**(36:48)** Now, where are we headed? In October 2023, the Copyright Office issued a notice seeking public comments on four key topics: whether using copyrighted works to train AI should be considered infringement,

how to handle AI-generated works, liability for infringing works produced by AI, and considerations for AI-generated likenesses. These are complex issues, and the results could influence future legislative or judicial decisions.

**(39:14)** The White House also issued an executive order in 2023, with agencies assessing AI’s impact on various sectors, including cybersecurity, a top concern given potential misuse by bad actors. The EU’s AI Act takes a similar approach, focusing on responsible AI use.

**(39:53)** Attendee: Thanks, Brooke, for this in-depth discussion. I have one last question on building authorized AI models that embed provenance into outputs. How might that impact copyright?

**(40:54)** Brooke: Interesting. Provenance-tracking adds value but doesn’t necessarily create copyright where it doesn’t already exist. That said, provenance could be helpful for licensing, and it’s likely we’ll see AI-driven provenance become part of legal structures around AI content.

**(44:43)** Provenance tracking is a good direction, and any copyright owner who can trace their work would benefit from it. In the past, broadcast flags were used to trace video, particularly by networks like NBC for events like the Olympics.

**(45:22)** Attendee: In California, marketers often negotiate contracts with influencers. Could companies legally use such contracts to create AI avatars of influencers?

**(45:47)** Brooke: It depends on the contract. Sometimes, releases are very broad, giving the company extensive rights to likeness and image across future uses. As an attorney, I’d advise narrowing it to specific uses. Celebrities, for example, often sign extremely limited agreements about how their image can be used.

**(48:05)** Paul: I used to run a comedy company, and parody was critical to our work. With deepfakes, Elon Musk has argued they count as parody. Is that true?

**(48:40)** Brooke: Not inherently. Parody applies to copyright, not to likeness rights. For a deepfake to qualify as parody, it would have to comment on the original work in some way.

**(49:54)** David: Thanks, Brooke, for such an insightful discussion! We’ll wrap up here, but I hope we can have you back for more, as I know this only scratches the surface. For everyone, we have more exciting sessions lined up, including a talk with Huge’s CTO and our first APAC event next month. Thanks, everyone, for joining today, and enjoy the rest of your week!

## AIs Impact on Marketing Future Trends and Strategies

Speaker: Kate Cook
Published: 2024-08-10
Tags: ai marketing
Video: https://www.youtube.com/watch?v=9wqSDqAbUp4
Page: https://aimarketersguild.org/sessions/ais-impact-on-marketing-future-trends-and-strategies

**(00:00)** I'm previous sessions, where she’s either asked great questions or shared valuable insights. People like Kate make these community calls so meaningful, and I’m constantly learning from the group. Kate can you please introduce yourself.

**(02:02)** Kate: Thanks, David, and thank you for getting out of bed today for us! It’s great to see some familiar faces on the call. I’ll give a quick background on myself, why I reached out to David, and what I hope to discuss. I was a brand marketer for about 15 years and worked with Paul at A&E Networks. After 20 years in media, I lost my job last year, which led to an epiphany two days later that my AI side project should actually be my career.

I enrolled in a Caltech certification program, learning Python, calculus, data science, and machine learning foundations. I wanted to understand AI at a core level, the same way I knew media, so I could build a bridge between marketers and tech. Now I have a consultancy focused on helping small and mid-sized companies develop automation roadmaps, innovation strategies, and AI integrations. We’re currently a team of three, helping companies adopt AI without the scale or budget of larger firms.

**(04:37)** Faisal: That’s impressive, Kate. Taking an intensive AI course at Caltech must have been challenging, especially if you don’t have a technical background. How did you find it?

Kate: I had no technical background at all, which is partly why I chose Caltech. They were more open to diverse backgrounds than other programs I considered, which required a tech foundation. Thankfully, I took it in 2023, so ChatGPT helped me through the Python coding.

**(05:44)** Today, I wanted to discuss AI’s impact on the future, as it’s something I think about often. Our weekly meetings do a great job of keeping up with current tech trends and use cases, but it’s helpful to take a step back and consider where all of this is heading. I’m hoping we can explore that together today.

**(06:17)** I’ve put together a deck I plan to present to a VC firm, outlining current AI trends and potential future impacts on marketing. I’ll walk through these slides in about 20 minutes, and then we can open it up for discussion since solutions to these challenges will likely come from all of us.

**(07:30)** Here’s the presentation. Can everyone see it?

**(07:40)** As a brand marketer, I’ve always aimed to keep my brand in the present while having an eye on the future. Today, tech is moving at such a fast pace that it’s difficult to get a handle on what’s happening. But I’ll share my thoughts and predictions.

**(08:06)** A quick introduction to my consultancy, A7 Partners—we help businesses navigate this new “seventh era” of human history, where AI and synthetic experiences are becoming central. We work on strategy, tech solutions, communications, and advisory services for marketing teams and agencies, helping them embrace and leverage AI.

**(09:10)** To start, I looked at some high-level predictions for AI. Here are three I found relevant from Forbes’ top 10 AI predictions for 2030:
1. **Ubiquitous AI Interaction**: AI will become so integrated into daily life that it’ll be constantly present and usable.
2. **Evolving Definitions**: Terms like AGI (Artificial General Intelligence) will become outdated as AI permeates everything.
3. **NVIDIA’s Role**: NVIDIA has dominated the AI chip market, but more companies will enter, and the competition will drive advancements in computing power, making AI more accessible.

**(11:38)** Where is AI technology headed? We’re still in the early days, but some big changes are coming:
- **Agent Capabilities**: Soon, AI will do tasks autonomously. Instead of just suggesting flights, it will book the flight for you, knowing all your preferences.
- **Multimodal Integration**: AI will soon be able to see, hear, and understand the world around you. This has profound implications, as AI can use real-world data to improve in real time.
- **Broad Adoption**: AI adoption is increasing, and soon it will be as common as using the internet.
- **Advancing Intelligence**: We’ll move from AI to AGI, where AI systems can match human intelligence across a variety of tasks. Eventually, we may reach superintelligence, where AI surpasses human intelligence.

**(14:41)** These advancements will disrupt the internet as we know it. For example, traditional search may erode. Instead of users clicking on links and encountering ads, AI will summarize the information for us, changing the landscape of digital advertising. I think entertainment will stay strong, but other areas, especially information search, will shift dramatically.

**(15:55)** With this shift, we may start marketing to bots. Right now, we market directly to consumers, but in the future, AI could become a layer between us and our audiences. Imagine a “shadow internet” optimized for AI to crawl, with embedded keywords or tags that prioritize a brand in an AI’s response.

**(18:36)** This could also destabilize our existing consumer data models. If a large portion of consumers shift to using AI for information, they drop out of traditional data sets, which changes the insights we’re used to.

**(19:17)** A concept that’s emerging here is **Share of Model**, similar to share of voice, but it tracks how often a brand appears in AI responses. This term was coined by the Brandtech agency Jellyfish, which is developing a proprietary model to measure it. They analyze how often a brand surfaces in AI-driven results for its category and track ways to improve its visibility.

**(20:38)** Bias and privacy are also critical. AI models have been trained on biased data, simply because bias exists in society and is embedded in available data sets. We also need to raise awareness around privacy, as many people don’t realize they shouldn’t put sensitive data into ChatGPT. Part of my work involves building AI policies for companies so they can experiment safely, knowing their data is secure.

**(23:20)** Privacy issues are a concern. For example, the New York Times sued OpenAI for generating summaries of their articles, which were behind a paywall. Similar situations could arise if sensitive data accidentally becomes part of a public model’s training data. We need to help people understand what’s safe to input and how to protect data from being misused.

**(23:57)** So, with all of these changes, what should marketers do? I tell clients to create their “tomorrow strategy,” focusing on areas most relevant to their business:
1. Start with use cases that align with your business and build a roadmap from there.
2. Embrace synthetic marketing experiences, moving beyond traditional channels.
3. Track and optimize your share of model.
4. Develop a clear AI policy, so employees understand what’s safe and encouraged.

**(27:38)** With that, I’ll stop here and open it up for questions.

**(27:57)** David: Thanks, Kate. You’ve covered so much! I think these issues will stay relevant for years to come. You’ve highlighted a lot that we’re already seeing evolve, like marketing to bots. It’s fascinating to think about what stays the same and what changes, particularly around search behaviors.

**(31:28)** Paul: A question came up around the legal side of using AI. Do you have any thoughts on the legal implications?

Kate: Sure. The legal landscape is still being shaped, and it really depends on the specific application. For content creation, the main issues involve copyright and IP. For instance, if an image I generate resembles the work of a specific artist without their permission, that artist could have a legal claim. The question of IP—who owns content generated by AI—is also unresolved. Right now, the U.S. doesn’t have a solid answer.

**(33:26)** Jason: Many people use AI to do the same things faster or cheaper, but some want to innovate with it. How are your clients approaching AI in terms of efficiency versus innovation?

Kate: That’s a great question. I’d say last year, most people were focused on efficiency, but this year there’s a shift. Automation is almost a baseline expectation, especially in marketing. But now, we’re starting to see clients ask how they can turn AI into a strategic advantage or even create new revenue streams by building AI-driven products.

**(36:39)** Alex: Could you expand on “share of model”? It seems like it could lead to an arms race for optimizing AI-driven search, similar to SEO.

Kate: Yes, I think we’re likely to see something like SEO but for AI. Once someone figures out how to influence these models, it will open up a new frontier in brand visibility. Marketing will have to adapt to this new AI-driven “ecosystem.”

**(39:10)** Jay-Z: Bigger brands seem resistant to AI, preferring human creativity, while startups are more open to it. Do you find that company size affects AI adoption?

Kate: I’d say so, but it’s also about culture. Some companies are highly experimental, which helps. But even large companies like JPM

organ have tried and failed with AI projects that ultimately couldn’t compete with existing models. Smaller companies can be more nimble, experimenting on a smaller budget to get proof of concept quickly.

**(42:38)** Mandy: With traditional media changing, how do you see brands reaching consumers in the future if traditional ads become less effective?

Kate: Great question. I think entertainment will remain strong, with streaming platforms embracing ads. There’s also room to innovate with synthetic experiences, like AI-driven branded experiences. The media mix will evolve, but the key is staying adaptable and open to new channels.

**(45:39)** Dax: How do you approach tech solutions for clients when the decision-makers are sometimes not the marketers but the CTOs or CEOs?

Kate: Good point. I work directly with CMOs to build a strategy and arm them with the insights and benefits to bring to the rest of the C-suite. This approach empowers them to advocate for AI at the executive level.

**(48:02)** Gene: I’m not sold on “share of model” as a primary metric yet. We’ve found tools like BlueOcean, which offers real-time brand insights, more effective for tracking brand health.

Kate: I think “share of model” is still evolving, but it’s interesting to think about. Perhaps it will eventually be a component of broader brand health metrics like BlueOcean’s.

**(50:29)** KW: Kate, I’m in healthcare, and I think my clients would love to hear your insights. Can we connect?

Kate: Absolutely, let’s chat!

**(52:10)** David: Thanks, Kate, for sparking such a great discussion. We’ll be continuing with more on these topics soon. Tomorrow, we have a special edition with Augie Studio, and next week we’ll host a startup showcase with Progress Partners, featuring AI and marketing startups. Thank you, Kate, and thank you everyone for joining!

## How AI Agents are Revolutionizing Marketing

Speaker: Jeremiah Owyang
Published: 2024-08-10
Tags: venture capital, ai agents
Video: https://www.youtube.com/watch?v=3Oh858akT2A
Page: https://aimarketersguild.org/sessions/how-ai-agents-are-revolutionizing-marketing

(00:00) We have an exciting guest, Jeremiah Owyang. Jeremiah and I go way back. Fun fact: Jeremiah was on the first live panel I ever participated in 20 years ago at a Frost & Sullivan event. It was the early days of search, and we were just figuring things out with experts like Ivan Ash, the SEO guru, and Ted Murphy of IZEA fame.

We were the “weird digital folks” at a traditional conference, and I’ve learned so much from Jeremiah over the years. He’s always ahead of the curve, whether as an analyst, investor, or community founder. Today, we’ll dive into how AI agents are revolutionizing marketing. Jeremiah is unparalleled in articulating where this technology is heading. I’ll drop his LinkedIn info in the chat—you must follow him if you’re not already.

This session is meant to be interactive, so feel free to chime in, raise your hands, or share your thoughts. Let’s have a great conversation!

(02:08) Cool—can everyone see my screen? Great. Let’s dive in. We’re here to talk about AI agents and their impact on marketing. I’ve been in Silicon Valley for 27 years, working across big tech and startups. Fifteen years ago, I was an analyst at Forrester. A group of us spun out to start Altimeter Group, which some of you might know. I’ve since launched other ventures, started angel investing in AI in 2017, and became a partner at Blitzscaling Ventures, focusing on AI funds.

Blitzscaling is based on the book and Stanford class by Chris Yeh and Reid Hoffman. Reid is one of our key partners and advisors, reviewing our AI deals before we invest, so I always strive to bring top-tier opportunities.

(03:10) In May 2023, I started an event series called Llama Lounge for the AI market. It began as a small meetup in San Francisco and has grown exponentially. Llama Lounge 15 just wrapped up, hosting 300 AI founders, 50 VCs, and 25 corporate AI professionals. My superpowers are community building and market analysis, which I use to connect people and discover promising startups.

At each event, I do something memorable, like playing a conch shell I picked up in Hawaii. (Fun fact: I was a jazz trombone player, so I can hit the notes perfectly!)

(04:27) Let’s talk about the AI agent market. It’s a burgeoning industry, currently valued at $5 billion, with projections to grow tenfold every five years. By 2030, it could reach $500 billion. Every major AI platform is developing AI agents, and startups are popping up rapidly.

I focus on AI agents because, unlike LLMs, which require billions to launch, AI agents are more accessible for early-stage investment.

(06:07) What is an AI agent? Think of it as a junior worker—someone who can execute tasks autonomously and asynchronously. Unlike co-pilots like ChatGPT embedded in apps, AI agents act independently, completing tasks overnight, recruiting other agents, and learning as they go. They’re more advanced than bots or co-pilots.

For example, self-driving cars are mechanized AI agents. In marketing, AI agents represent a significant shift in how work is done.

(07:41) What’s fascinating is how AI agents are reshaping the user experience. The internet, as we know it, is inconsistent and ad-driven. Google, for instance, prioritizes advertisers over users. With AI agents, they’ll bypass traditional search engines, visiting websites to retrieve information and complete tasks on behalf of users. This fundamentally changes marketing strategies.

(09:21) Not all AI technologies qualify as agents. For example, computer vision, chatbots, and predictive AI don’t operate autonomously. AI agents are unique because they act independently, much like autonomous limbs performing tasks without constant supervision.

(10:58) Most AI agents utilize existing LLM APIs for tasks like generating to-do lists or natural language interactions. They often integrate with proprietary data, creating unique applications.

(12:07) Agents will revolutionize consumer interactions, shifting power away from platforms like Google. Instead of navigating ad-laden search results, agents will retrieve tailored information and deliver it in formats users prefer, such as concise text summaries or visual content.

(14:48) This transformation raises questions about the future of websites and apps. As agents gather and reassemble information, the role of traditional websites diminishes. Marketers must adapt to optimize for AI agents rather than human visitors.

(16:25) Enterprises are already responding. For example, Salesforce and HubSpot have launched agent tools to assist in marketing, sales, and customer service. These systems orchestrate AI agents to handle tasks autonomously.

(18:21) A new payment layer is emerging, enabling agents to transact using microtransactions or stablecoins. Platforms like Skyfire and Paymen are creating infrastructures where agents can pay other agents—or even hire humans—to complete tasks.

(20:30) In enterprise settings, tools like Crew AI and LangChain are enabling organizations to deploy fleets of agents, automating complex processes across departments.

(28:24) Gartner predicts a 25% drop in Google search traffic within the next year due to the rise of AI agents. Bill Gates has echoed this sentiment, suggesting that whoever dominates the personal agent space will redefine the digital economy.

(36:16) The traditional marketing funnel will evolve. Instead of relying on ads and websites, consumers will collaborate with AI agents to compare products and make purchases. Over time, agents will learn user preferences, further automating decision-making for routine purchases.

(44:15) For marketers, this means adapting to a future where AI agents are primary influencers. CRMs will need to track not only human records but also AI agent identities.

(45:18) A new economy is on the horizon, where agents trade, learn, and evolve independently. Marketers must prepare for a landscape where the most dominant digital entities may no longer be human.

## AI-Powered Video Marketing Workshop Insights Techniques from Augie Studio

Speaker: Jeremy Toeman
Published: 2024-08-10
Tags: video marketing
Video: https://www.youtube.com/watch?v=e3Ii8gT523k
Page: https://aimarketersguild.org/sessions/ai-powered-video-marketing-workshop-insights-techniques-from-augie-studio

**(00:00)**
Hello, everyone! If we haven’t met yet, I’m David Berkowitz, founder of the AI Marketers Guild. It’s great to see so many community members and friends here today. We have a special guest, Jeremy Toeman, founder of Auggie Studio, along with some team members. Jeremy was part of our very first AI Marketers Guild event, even before AMG officially launched, and he and his team have been active in the community ever since.

I’ve been trying out Auggie Studio from early on, and it’s been a lot of fun to use. I also got to see Jeremy speak at a recent event, where I learned a ton about AI for video, so I’m looking forward to more insights today. If you've been to our sessions before, you know they're usually interactive, so please share any questions in the chat or raise your hand if you’d like to jump in. Let’s have a great community conversation! Jeremy, all yours.

**(02:00)**
Hi, everyone! Normally, we’d go around for intros, but I think we’ll skip that today since we have a full house. I’ve also brought a few members of the team because I know some of you are already using Auggie Studio, so during the demo, if you’d like to dive into more advanced features, we can set up breakout rooms.

With that said, let’s jump in. I’ll take you through some key topics today, but please feel free to make this as interactive as possible. If there’s an area you’d like to explore more deeply, let me know, and we’ll adjust as we go.

**(03:34)**
Great, so here’s what we’ll cover today: a quick introduction to me and why video matters for your business, especially if you’re just getting started. We’ll also give you a few tips on creating videos and then show you how Auggie can help. You can use Auggie to get started or to speed up and enhance your current workflow for social media or other channels.

**(04:09)**
Hi, I’m Jeremy. I’ve spent about 30 years in the tech and media industry. I was the first employee at Sling Media, which made the Slingbox, the first device that let you watch TV remotely in 2005. Sling is still around today as Sling TV. I’ve also worked with companies like Vudu, Sonos, and others that didn’t make it to market, as well as co-founding Digit, an app that combined live TV and streaming. After Digit was acquired, I spent time at CBS Interactive and WarnerMedia. I founded Auggie Studio about two and a half years ago.

**(05:12)**
My background has been all about working with rights holders, content creators, publishers, and advertisers, and that perspective shapes our approach at Auggie Studio. We focus on brand needs, privacy, and copyright, which I’ll talk more about as we go.

**(05:44)**
Why does video matter for your business? Some quick stats: Around 70% of businesses—filtered to exclude industries where video doesn’t make sense, like dry cleaning or factories—don’t use video because of costs, lack of resources, or past budget overages. But video works: audiences retain video content better and have a higher purchase intent. The challenge is creating video content without spending thousands monthly on production.

**(06:21)**
When it comes to creating video, there are tools for every purpose. For influencer or simple creator content, there’s CapCut, iMovie, and TikTok. If you’re producing high-budget videos like commercials or films, tools like Avid and Final Cut Pro are your go-tos. But for short-form, social-friendly video ads or promos that might only get a couple of days’ views, anything beyond that is often overkill. Auggie Studio is DIY-friendly so that anyone here can become self-sufficient in video marketing.

**(08:24)**
So, let’s get started. If you haven’t used much video yet, here are three easy ways to dive in:

1. **Thought Leadership Videos**: Video podcasts, Zoom recordings, and so on are popular, but they tend to get down-ranked by platforms like YouTube and TikTok. To improve engagement, add visuals like graphs or animations.

2. **Explainer and Demo Videos**: Use tools like Loom to explain your product, or enhance these videos with Auggie for more engagement.

3. **Social Ads**: If you have existing footage, Auggie makes it easy to repurpose that content for ads. Reusing assets, especially ones you’ve invested in, maximizes their value.

**(11:26)**
Places to post these videos include:

- **Web and Landing Pages**: Adding video to relevant web pages can boost SEO by up to 50%.
- **Social Media**: This one’s a no-brainer; everyone understands the need for social presence.
- **Email Marketing**: Adding video to emails can increase open and click-through rates by up to 300%.

Alright, let’s jump into Auggie Studio. Are there any questions before we dive into the demo?

**(12:42)**
Welcome to Auggie Studio! Our vision is to make video creation as fast and easy as possible. Whether you start with a script, narration, a recording, or nothing at all, Auggie turns your content into a video in about three minutes.

**(13:49)**
Let’s create a video together. If you have a script, you can paste it here. You can also record audio, use our AI voices, or upload a narration. If you don’t have anything yet, you can start from scratch. We also offer a feature called Storyteller, which is a fully generative video tool, but that’s more experimental.

**(14:44)**
We have different templates to help you get started. Let’s say we want to make a thought leadership video for parents on why soccer is great for kids’ confidence and physical health. Here’s how Auggie will generate a script.

**(17:33)**
We have several AI voices to choose from, and we try to make them as diverse as possible. You can also clone your own voice if you prefer. I’m going to choose one of our standard voices for today.

**(18:39)**
Once we choose a voice, we select a video format (e.g., TikTok, YouTube). Auggie will now listen to the script, analyze it, and build a storyboard. It detects where sentences break, matches keywords with relevant clips from Getty Images, and creates a rough cut. This process usually takes about three minutes, so while we wait, I’ll show you some examples of videos made with Auggie.

**(20:12)**
Here’s one we made for a local business, Cantina White Plains. The owner wanted a video with scenes around town, leading to the restaurant. Auggie generated this in about 25 minutes.

[Plays Video]

**(21:47)**
Let’s look at the editing interface in Auggie. It’s simplified compared to tools like Premiere Pro. The left side shows the script, the bottom is the storyboard, and the preview is on the right. These elements are all connected, so you can click anywhere in the script to jump to that part of the storyboard and timeline.

**(23:55)**
Another example is a video from the Allentown Film Festival. Local figures recorded videos and then uploaded them to Auggie, where Auggie added visuals and effects. You can adjust layouts, add split screens, or picture-in-picture with a click.

**(26:30)**
Let’s check on our soccer video. Here’s the rough cut Auggie generated, which saved about an hour of manual editing. While it’s a good start, there are some scenes that don’t fit, like the baseball clip. Auggie is designed to get you close, but you’ll still want to spend 5 to 25 minutes refining it.

**(27:57)**
To replace clips, just click on a scene, select “Replace,” and search for new footage. You can use Getty’s stock library or upload your own content. You can also add closed captions with one click, and adjust styles, colors, or fonts.

**(35:46)**
You can add custom assets, like logos or QR codes, by uploading them to your library and positioning them in the video. If you have brand music, Auggie also includes access to 6,000 tracks.

**(38:17)**
Auggie also has computer vision features. If you upload a large video file, like a sports reel, Auggie can find specific scenes, like “soccer” or “celebration,” based on visual content, which makes asset management easy.

**(42:15)**
For rights and licensing, Auggie includes commercial rights to all Getty content used in Auggie. If you upload your own assets, they remain private—Auggie doesn’t add them to any shared library. In the future, we’ll add features like custom LLMs to further personalize content.

**(45:11)**
Let me show a few final examples. Here’s a quick video the Hudson Valley Venture Hub made for their newsletter. They summarized key content and turned it into a video to boost engagement.

**(46:12)**
And here’s a fun one: I uploaded “Ocean’s Eleven” to Auggie and had it generate a rom-com-style trailer using AI for the script, voice, and music. This is fully AI-created:

[Plays Video]

**(48:32)**
Alright,

that’s Auggie! Are there any final questions?

**(49:04)**
Q: Can you adjust the tone of AI voices?
A: We’re working on adding more voice customization, and you can already use punctuation to influence tone.

**(51:47)**
Q: How does it handle things like pronunciation?
A: We recommend previewing the voice before publishing. It’s good with proper names, but sometimes words or initials need adjustments.

**(57:56)**
Thanks, everyone! Remember to check out the free month of premium access using the promo code shared in the chat. We’re here to help, so feel free to reach out through the AMG community or directly. We’d love your feedback and ideas for features you’d like to see in Auggie. Thanks again, and have a great day!

## Innovative Marketing with AI

Speaker: Richard McBeath
Published: 2024-07-05
Tags: innovative marketing, analytics, branding
Video: https://www.youtube.com/watch?v=G0Zg7hV8n8c
Page: https://aimarketersguild.org/sessions/innovative-marketing-with-ai

**(00:00)**
David: Richard McBeath is here, and Richard, I’ll give you the mic. For everyone else, feel free to share thoughts and questions in the comments—we want to keep it interactive. So, Richard, why don’t you introduce yourself and what you’re working on?

Richard: Thanks for the invite, David, and great to meet everyone. I’ll give a bit of background about myself and how we got here, then dive into what we’ve built. I live in Los Angeles, in Redondo Beach, but I’m originally from Sydney, Australia. I spent five years in London working for various digital advertising agencies starting in 2005, then moved to Dubai, where I led what was the largest digital ad agency in the MENA region, with offices across Qatar, Oman, Abu Dhabi, and beyond.

Eventually, I landed in the U.S., where I noticed this rise in alternative investments and new ways of accessing them. My co-founder and I started a company called MoneyMade, an investment discovery platform for alternative investments. We launched in March 2020, right as the pandemic hit, and online investing took off. We had this huge platform with a large team for content, including writers, designers, social media folks, and analysts. It was resource-intensive, so about two years ago, we started looking for ways to automate parts of the process.

We began with optimizing our content for engagement and conversions, using various calls to action and tools. This gave us valuable data on topics and pain points resonating with our audience.

We then automated our content strategy based on these insights, producing more content around high-performing topics. As we fine-tuned that, the next step was automating the actual content creation.

Over time, we realized we were creating highly researched, insight-driven content that was performing really well, so we decided to package it as a product. We launched **Captain** last October with modest expectations, and now 99% of our focus is on this product, which I’ll show you.

David: Great, let’s take a look. It helps to see how it works. I can see your screen with the Outdoorsy dashboard loading.

Richard: Perfect. I’ll start with the Captain dashboard. Outdoorsy, a current customer, has been with us for a few months. Here, you can track various content activities across web and social channels, along with engagement data. What’s key here is that Captain identifies customer pain points and trending topics for the brand’s audience. Marketing 101 tells us to create more content around what’s resonating, so Captain uses engagement data to map out a content strategy that’s continuously optimized week over week. It also creates content briefs based on related topics and deeper dives into relevant pain points.

A core part of Captain’s function is the automated research engine, which goes to Google and does research on your behalf. It finds relevant data, statistics, and insights, showing you the sources. We had challenges with some AI tools providing erroneous or dead links or even linking to competitors, so Captain’s research engine gives you control over what to include. We also have a Research Hub where you can upload your own white papers or research, and the engine will filter and pull relevant info from it automatically.

So, to summarize, Captain maps out your content strategy, identifies pain points and trending topics, conducts research, and lets you decide what to include. A third important input to the engine is collaboration. We realized it’s valuable to gather expert perspectives or commentary, so we built a tool for that. You can request input by sharing a unique link with anyone—colleagues, customers, or other experts. They land on a page where they can record their input in audio form. Captain transcribes it and integrates it into the content as quotes or paraphrased insights.

After you input this data, Captain creates a suite of assets: a long-form article with research included, AI-generated images, social posts for LinkedIn, X (formerly Twitter), and Facebook, an infographic, a newsletter, and a podcast. Once ready, you can edit and publish directly from Captain to your CMS, whether that’s WordPress, Webflow, Shopify, or something else. It also lets you publish across social platforms to distribute the content.

**(13:02)** Once your content is published, Captain optimizes it for engagement and conversions with various tools. It automatically includes the podcast, a clickable summary, product education through highlighted keywords, and contextual CTAs. For instance, if a paragraph discusses a certain benefit, Captain will explain why the product is relevant to what they just read. We also create a branded infographic as a lead magnet for downloadable resources.

Lastly, we have a floating control panel with the podcast, related content, and various CTAs to encourage engagement. All of this engagement data is fed back into Captain to continue refining the content strategy. We categorize campaign types by the customer journey stage—top-of-funnel educational content, middle-of-funnel company updates, and bottom-of-funnel case studies. You can even invite clients to give audio testimonials, which Captain transcribes and turns into full case studies with supporting assets.

**(19:16)** Another powerful feature we recently launched is **Identify**, which allows you to see the specific individuals visiting your site and content pages. We can identify U.S. visitors at about 20-30% accuracy, pulling their name, title, company, LinkedIn profile, email, and industry. You can connect with them directly, and we have Slack integration to notify you instantly.

David: Richard, this is great! I’m seeing some of the best feedback yet from the community here. This tool helps marketers prove the value of their work by showing its impact directly. I know we have some tough questioners here today, so let’s dive into the questions.

Jay-Z: Thanks, David. I love this product—it’s polished and close to being publish-ready. How does this tool help qualify high-quality leads that might convert, especially for B2B?

Richard: Great question! Since launching the **Identify** feature a week ago, we’ve seen a growing number of salespeople joining calls. Sales teams are notified via Slack, so they can quickly filter leads and begin outreach. Soon, we’ll integrate with CRMs like HubSpot so leads can go directly into sales sequences, which should streamline this process further.

**(29:15)** Kathy: How do you handle opt-ins for marketing and sales outreach from site visitors?

Richard: We use a combination of first- and third-party cookie data, device ID, and IP address, along with access to a publisher network. If visitors opt into cookies on a publisher site, we can access that data. If they decline, we won’t be able to identify them.

David: Are there plans to integrate with other CRMs like Salesforce?

Richard: Yes, HubSpot integration is currently underway, and we’ll add other CRMs. For now, you can export CSVs from Captain to upload into other platforms.

David: And how do you differentiate Captain from other tools like Jasper?

Richard: Jasper is more focused on high-volume content creation, whereas Captain is focused on quality and strategy. We create data-driven content, gather research from reliable sources, and include expert commentary, all while helping identify target audiences. We’re a bit different in that we emphasize quality over quantity and don’t aim to produce hundreds of articles per month.

**(35:30)** Aaron: What LLMs do you use, and how do you prevent hallucinations in content?

Richard: We primarily use Anthropic and OpenAI, with limited Google Vertex support. To avoid hallucinations, Captain’s content only includes research from our engine, and we have layers of QA to catch any errors. This prevents issues like misleading links or robotic language.

**(38:23)** Susan: Who’s the ideal user for Captain?

Richard: We’re still seeing a range, from large brands to solopreneurs and agencies. Many agencies white-label Captain to offer it as their own service or use it to scale their offerings for clients. While we work directly with some brands, agencies have been particularly interested in Captain’s white-label capabilities.

David: Jay-Z wondered, will Captain replace agencies?

Richard: I don’t think so. Captain is more of a tool to help agencies scale efficiently while maintaining quality. It’s most effective when guided by someone who understands content strategy and can ensure it stays on brand.

Kathy: Can manually created content help inform Captain’s brand voice and tone?

David: One of the most surprising things I found was how well Captain defined the brand tone just by analyzing the site content. It generated really accurate descriptions and IC profiles.

Richard: Captain scans the brand’s domain or content you provide and breaks down the tone into guidelines. You can also add domain-specific guardrails, like not making financial guarantees for a fintech product. This way, all content aligns with the brand's standards.

Mr. Cutler: This sounds like a dream product. Have you thought about doing a real-life project, like a societal or nonprofit challenge, that the community could support to showcase Captain?

Richard: Great idea! I think finding that balance is key. So much of my life is focused on Captain and my kids, so it’s about striking that balance between work and life. But your idea of tackling a meaningful project is compelling.

## Unveiling Bias in AI Insights

Speaker: Catharine Montgomery
Published: 2024-07-05
Tags: ai bias, social impact
Video: https://www.youtube.com/watch?v=BDpu_9lVEdw
Page: https://aimarketersguild.org/sessions/unveiling-bias-in-ai-insights

**(00:00)** [Music]
Katherine: Thank you. It’s great to be here. I’m Katherine Montgomery, the founder and CEO of Better Together, a full-service communications agency focused on social impact. We work with both for-profit and non-profit organizations, as long as we’re helping to make a positive impact. I launched the agency in January 2023, and we’re backed by a venture capital firm. It’s unusual for a service-based agency to be VC-funded, but this firm only invests in PR agencies. They were skeptical at first, especially one investor from Germany who couldn’t understand the U.S. market focus on issues like racism and sexism. I told him the U.S. differs a lot from Germany in this area.

When I started using generative AI early on with tools like ChatGPT, I noticed biases in the generated content. At first, I thought it was just me, but then I kept encountering more instances of bias. For example, I put in a prompt for a radio script aimed at a Black audience, and it responded with stereotypes like “Yo fam, we’re gonna get some grub.” It was like something out of an old movie. I even generated an image of Maya Angelou, and it produced an image of an elderly white woman. These experiences led us to conduct a study on bias in generative AI, which I believe is one of the first studies by an agency on this topic.

**(03:13)** To share a bit more about my background, I was born in New Orleans and grew up in Alabama, where people rarely talked openly about issues like racism. I later moved to Boston and lived in an area where Black people rarely live, and I experienced subtle racism even within progressive circles. All these experiences heightened my awareness of biases in society and technology.

On a personal note, last year, while waiting in a long line at the Atlanta airport, I read a McKinsey study on the racial wealth gap and how generative AI might widen it. The study resonated with me; it highlighted that if we don’t address this, the racial wealth gap could increase by $43 billion a year. This inspired our generative AI survey. I’m going to show a video by Joy Buolamwini, who has spoken on this topic before AI became mainstream. Her work addresses biases in AI, and this video from 2018 underscores how long this issue has existed.

[Video plays with Joy Buolamwini’s poem on AI bias]

**(09:33)** Katherine: Any thoughts on the video?

David: It’s shocking to see such blatant issues, especially given this was six years ago. Have you tested current AI models to see if this has improved?

Katherine: I haven’t tested it recently, but I do see similar issues. For instance, with Google Gemini, we’ve seen over-corrections, like depicting Black men as the founding fathers or showing an Asian woman as the Pope. It feels like they’re trying to diversify representation, but it misses the mark by altering historical accuracy.

Attendee 1: Isn’t this largely due to a lack of diverse training data?

Katherine: Exactly. The data input is still very limited in terms of diversity. The majority of the data comes from people with similar backgrounds, often white men, so their biases can seep into the models unconsciously.

David: Have you seen any models that are intentionally trained with a more diverse data set?

Katherine: I’ve found a few that focus on specific communities, like a ChatGPT model geared toward Black users, but they’re rare. It shouldn’t be limited to one group—we need all models to be inclusive.

David: I’d be curious if AI models in countries like China or India, with more diverse populations, are handling this any differently.

Katherine: That’s a great point. However, without diverse teams creating these models, even in different regions, it’s likely they’ll still reflect biases.

**(15:36)** Katherine: In our survey, we found that awareness of generative AI often correlates with awareness of bias. Most respondents who are familiar with AI expressed concerns about racism, sexism, and classism. On classism, AI requires internet access, technical knowledge, and tools that not everyone has. Generative AI can inadvertently widen the gap between those who have these resources and those who don’t.

Younger people, particularly those aged 18-29, expressed strong concerns about discrimination and bias in generative AI, which aligns with how younger generations are more engaged with digital content and social justice issues. Interestingly, respondents over 60 also expressed concerns, although likely for different reasons, like fairness and ethics.

**(21:07)** We also asked respondents if they thought tech companies prioritize diversity, equity, and inclusion (DEI) when creating generative AI tools. Opinions were mixed, but a significant portion believed companies don’t focus enough on this. Respondents indicated that if companies were more transparent about addressing biases, it would make them more likely to use generative AI tools.

David: What dangers do respondents see in generative AI?

Katherine: The primary concerns were reinforcement of stereotypes, discriminatory content generation, and perpetuation of existing biases. Legal and ethical liability were also concerns, reflecting the increasing visibility of privacy and data misuse in AI discussions.

Respondents want transparency from tech companies. For example, after George Floyd’s death, many companies made donations to social causes, but there’s been little follow-through. Consumers are demanding more accountability and transparency from companies on issues like bias and diversity.

**(24:53)** Tech companies need to establish minimum standards and undergo regular bias audits, similar to how they approach privacy and security. Without a clear framework, there’s a risk that companies will continue to do the bare minimum. Education is another key piece, as many users might not even recognize when AI-generated content is biased.

David: What do you recommend for our AI Marketers Guild members, many of whom influence brands and technologies?

Katherine: Start by educating those in your networks about AI biases. Raising awareness can lead to more demand for unbiased models. Also, respond to issues when you see them. For example, if an AI tool only shows one perspective on a controversial topic, bring it up with the developers.

Attendee 2: Generative AI often relies on existing data, which means it can’t easily generate something outside the patterns it knows. For example, it struggles to create novel historical scenarios because it’s limited to what’s already been recorded or depicted.

Katherine: That’s a great observation. AI’s current limitations make it even more important to have diverse data sources and people guiding its development.

Lisa: As a researcher, I’ve noticed it takes significant effort to find sources that reflect true diversity, especially for historical events. I think tech companies could help by encouraging users to request unbiased or diverse perspectives in their prompts.

Katherine: Absolutely. Companies could suggest prompts that encourage users to seek unbiased answers.

**(33:10)** Attendee 3: Many issues arise because of a lack of representation at the table when these tools are developed. Diverse perspectives are critical in developing models that better reflect different communities.

Katherine: Definitely. If you don’t have a range of voices involved in developing the technology, the tools won’t reflect the diversity of users. Even tech giants like Amazon rely on internal affinity groups for DEI input, but they’re often unpaid or added on top of regular roles.

David: Are there initiatives for adding tax incentives for tech companies addressing biases in AI?

Katherine: I haven’t seen that yet, but it’s a great idea. Tax credits could encourage companies to put resources into reducing bias, which would benefit both the companies and the public.

Jay-Z: When recruiting speakers, I found it hard to find recordings of women experts. Where’s the incentive to gather diverse data for AI?

Katherine: Incentivizing data collection from underrepresented groups could help, and tech companies could fund these initiatives. Building a certification or board for bias auditing would also be a step forward.

Lisa: Perhaps we could have reputational ratings for AI engines based on their bias levels, similar to a trust pilot. If AI companies know their bias ratings are public, they may be motivated to improve.

Katherine: That’s a great idea—rating models for bias transparency could drive accountability. With DEI roles shrinking, public accountability could be one of the most effective ways to enforce change.

Attendee 4: Is there any progress with educational institutions? AI literacy could be a critical subject, especially given the current lack of algorithmic literacy.

Katherine: Some universities are exploring certifications on AI ethics and DEI, but support is often limited. Professors are taking the lead, but institutional backing remains weak. Expanding AI literacy in schools could make a significant difference in raising awareness.

David: Thank you, Katherine, for your insights. This discussion underscores how much work lies ahead in improving AI, particularly in addressing and mitigating biases. Thank you to everyone who joined and shared insights today.

Katherine: Thank you for having me, and I’m always happy to engage in these conversations. I appreciate the thoughtful questions and hope to continue discussing ways we can make AI more inclusive.

## AI-Powered Marketing Insights

Speaker: Judah Phillips
Published: 2024-07-05
Tags: ai powered marketing, attribution
Video: https://www.youtube.com/watch?v=kZoOxhfgRvo
Page: https://aimarketersguild.org/sessions/ai-powered-marketing-insights

**(04:24)** Judah: Sure, happy to share. A quick background: I’ve been in the internet and software space since the mid-90s. My first startup was around 1997, working in information retrieval. It was before Google, back when Northern Light was big. I worked with indexing data from the Congressional Record, building search tools and web interfaces.

Later, I moved to Boston, where I’ve been since, and worked for companies like Sun Microsystems and Monster Worldwide. Over the years, I realized that consulting and service businesses, while rewarding, are hard to scale. I wanted to focus on software, so I co-founded Visad Data with my partner, Dan. We initially built data science tools focused on machine learning, like clustering and market basket analysis, mainly for businesses.

Our big product became **Squark**—a machine learning tool that could do binary classification, multinomial classification, regression, and time series prediction. We named it “Squark” because it sounded catchy, and the word itself is the super-symmetric partner to the quark in physics. We made rookie mistakes starting out, but ultimately grew it into a solid product that enterprises could use.

**(10:36)** After some initial success, we started selling Squark to companies like IBM Watson, Nealon, and Epic Games. We worked on major projects, like churn prediction for Fortnite and liver donation campaigns for healthcare organizations. We competed with major players like DataRobot and Microsoft and managed to succeed through a lot of innovation and bootstrapping.

Eventually, a 4,000-person, 90-year-old company operating across five continents acquired us. On May 3, we completed the acquisition, and now I’m their Chief AI Officer. My goal is to bring responsible AI into the company, focusing on four areas: team development, software solutions, data governance, and infrastructure.

**(17:03)** I live in Boston with my family, enjoy concerts, and try to ride my bike when I can.

David: So, as Chief AI Officer, what does your team look like?

Judah: We’re still building it out. Key roles include full-stack, front-end, and back-end engineers. DevOps is part of engineering but may include network operations for scaling and security. We have analysts who use or code our technology, data engineers for managing data in Snowflake, and sales staff to help bring our solutions to clients. We don’t just focus on algorithms; we’re also hands-on with clients, helping them leverage AI for predictive insights and decision-making.

**(22:01)** David: How do you interact with the marketing team? Are they also interested in applying AI?

Judah: Marketers vary in their readiness. Some are open to AI and advanced analytics, while others are more hesitant. Over time, I learned to focus on the marketers who are ready to learn and invest. I ask straightforward questions to understand their buying process, as that determines how we proceed. We offer a 90-day trial for those genuinely interested, where we onboard them and train them on the platform to ensure they can generate value by the end.

One challenge we faced was competing with major players like DataRobot. We worked with big clients on predictive models, but without the huge support teams that larger firms have. However, we built effective software that often outperformed larger competitors’ models, proving that a smaller, focused team can succeed in enterprise spaces.

**(28:27)** David: How do you decide which AI tools or technologies to incorporate?

Judah: We were a small team, but we focused on listening to our customers and responding to their needs. We built Squark based on customer feedback, creating features they requested and enhancing the platform with their input. By being more agile and responsive than larger companies, we could provide real value that was tailored to each client.

Building customer trust was also key. Selling the company taught me that honor and integrity are invaluable. You want to work with people you can trust, and that’s true for clients and investors alike.

**(32:06)** David: Looking at the future of search, do you think we’re facing a major shift, especially in terms of Google’s role?

Judah: I avoid AdWords myself, but I see the value in Google’s platform. With tools like ChatGPT, search habits may change, but I wouldn’t bet against Google. They have the resources and the drive to adapt, and I think they’ll continue to dominate. It’s an oligopoly—hard to break into, even with VC funding. Still, new players like Perplexity are emerging, and they could create niche opportunities or become acquisition targets.

**(40:13)** David: My concern is more about companies that rely on Google for customer acquisition. A 25% drop in search could disrupt a lot of businesses.

Judah: Absolutely, and I get it. Some industries, like recipe sites, are feeling the squeeze as people use AI for customized results. While I’m personally okay with skipping those ad-heavy sites, I understand it impacts the people behind them. It’s going to be interesting to see how everyone adapts if search models continue to shift.

**(43:32)** David: You’ve mentioned Squark’s use in nonprofit spaces. Is that still a focus?

Judah: Yes. We’re working with nonprofits to improve fundraising strategies. Squark enables predictions about donation behaviors—who will donate, when, and how much. For example, we’ve helped clients like the University of Pittsburgh Medical Center use Squark to predict which individuals are likely to donate organs based on lifestyle and other data.

**(47:27)** David: In terms of big wins, where do you see the most opportunity?

Judah: Three main areas come to mind: customer journey mapping, media mix optimization, and attribution. The customer journey involves predicting customer behavior at each step—conversion, retention, and loyalty. Media mix optimization is another powerful area, where we analyze multiple ad channels to predict ROI and optimize spend. And finally, there’s attribution, where AI helps pinpoint which data attributes most influence conversions.

## Top of Funnel (TOFU) Marketing Strategies

Speaker: Elaine Zelby
Published: 2024-07-05
Tags: marketing strategies, top of funnel, b2b marketing, go to market strategy
Video: https://www.youtube.com/watch?v=k1v8kBicqZI
Page: https://aimarketersguild.org/sessions/top-of-funnel-tofu-marketing-strategies

**(00:00)**
David: Elaine, it’s a pleasure reconnecting with you live today. Excited to hear about your work with Tofu and how AI fits into go-to-market strategies, which I know is a hot topic here. I think it’s something we’re all grappling with in different ways. To start, could you introduce yourself? Then we’ll jump into the conversation.

Elaine: Thanks, David. Hi, everyone! Great to meet you all. I’m the co-founder and CRO of Tofu, a generative AI platform designed to automate top-of-funnel go-to-market strategies for B2B marketing teams.

I'll walk you through what that means, but a bit about me first. I actually started my career as a biomechanical engineer—which isn’t exactly related to marketing! I quickly realized the hardware world moves too slowly for me, so I pivoted into software.

I joined a startup as employee number two, where I spent five years and helped raise about $75 million.

I started in product, but no one was doing go-to-market, so I took it on and loved it. After that, I joined Slack to lead enterprise go-to-market when they launched their enterprise product.

I later moved into venture capital at SignalFire, where I focused on AI and software investments for five years.

I've been following generative AI since 2018. In fact, my first product, Soundspot, turned corporate blogs into podcasts.

This was back in the days of GPT-1 and Google’s early Tacotron model for speech. Looking at how much these tools have advanced, it feels like magic now. Seeing how

AI is transforming industries, I wanted to dive back into the startup world, which led to Tofu. So that’s how I ended up here.

David: Fantastic. I can’t wait to dive in!

**(04:30)**

Elaine: Let’s jump into some areas where AI is making waves in go-to-market strategies. Text generation is already working well; the tools are mature, especially for shorter forms like blogs and social posts. Audio, too, has come a long way, especially with cloning tech like ElevenLabs that can replicate voices in just minutes. I’ll play an example so you can see what I mean.

[Elaine plays a voice synthesis example showing how AI can clone her voice accurately.]

It’s amazing but also a little frightening when you think about the implications. Image generation is advancing but not fully ready for B2B marketing. It’s still challenging to generate text within images accurately, and for some tasks, image models don’t yet meet quality standards for B2B.

**(08:37)**

Elaine: So, how is AI changing go-to-market? I recently came across an interesting perspective from Kieran Flanagan, CMO of Zapier. He said that in the past, there were "original thinkers," "copy-paste marketers," and "lazy marketers." With AI, the original thinkers’ group has shrunk while copy-paste marketers have grown, as AI makes it easier to replicate content. The winners in marketing will be those who leverage AI to enhance their unique ideas.

Content is still critical, but AI is changing how we create it. SEO is a huge area of transformation. If you’ve tried Google’s new Gemini-powered search, it now surfaces direct answers right at the top, so you don’t even need to scroll. That will impact content strategies significantly.

**(12:00)**

Elaine: Let’s talk about where AI is already proving effective. It’s great for brainstorming, outlining, and helping marketers overcome the blank-page problem. For personalization, it’s helping marketers understand their audience deeply, identifying pain points, and adjusting messaging accordingly. Repurposing content is another high-value use case, especially in a tight economy where teams need to do more with less. Automating outbound prospecting is gaining traction as well, with tools providing AI-driven SDR functions.

Here’s where it’s making a difference today:
1. Personalization across channels,
2. Segmenting audiences by account and persona,
3. Repurposing content from events, webinars, and reports into blog posts, social media posts, or emails.

**(18:00)**

Elaine: Now, let’s do a quick demo of how Tofu handles personalization. We target specific accounts, do research on them, and generate content tailored to them. For instance, let’s say we’re creating a landing page for Rapid SOS, a company in emergency response. I select the elements to personalize, run the page template, and Tofu uses our research to adjust the content to speak directly to their industry and values. We can do the same for emails, social posts, and ads, keeping each piece consistent with the company’s tone and branding.

**(25:00)**

Elaine: Another strong use case for Tofu is content repurposing. For example, we can take a 45-minute AMA session, transcribe it, and turn that into multiple blog posts, social posts, and email follow-ups. This way, one high-value piece of content generates various assets across channels. Our customers love this feature because it saves hours, and teams don’t need to reinvent the wheel with each new campaign. We also use third-party content like reports from McKinsey or trending LinkedIn topics and repurpose them, giving credit to the original author, of course.

**(32:00)**

Elaine: There are a few key limitations to AI. The biggest challenge is its reliance on prediction without real understanding, which leads to hallucinations and inaccuracies. Especially in regulated industries like healthcare or finance, where the stakes are high, companies can’t afford these errors. At Tofu, we’ve built a rules engine that sits between the AI and the customer to check compliance with industry guidelines. But this technology isn’t flawless. For instance, the outputs are only as good as the inputs, and if you give it bad data, it will give you bad results.

**(40:00)**

Elaine: People often ask, "Will AI replace marketers?" My answer is that we still need humans, especially for the creative and strategic aspects of marketing. Humans bring the ideas, brand voice, and strategy that AI can’t replicate. What AI does well is amplify and accelerate human creativity. So, for things like messaging, content strategy, and channel optimization, human marketers are irreplaceable.

Eric: Elaine, this is an amazing presentation. I have two questions. First, what kind of models does Tofu use? Are you using open-source models or something like OpenAI? And second, with so many rights issues around AI training data, how are you handling that?

Elaine: Great questions. We use multiple models depending on the task. For short form, like social posts and ads, we find that GPT-4 works best. For long-form pieces, we prefer Anthropic’s Claude 3. We’re also careful with customer data; we don’t train on sensitive information, and our infrastructure is set up to keep customer data isolated. But you’re right; issues around data rights are complex, especially when models are trained on massive internet data sets.

**(50:00)**

David Cutler: What’s your most effective demo for new prospects?

Elaine: We always use the customer’s own data. I’ll demo Tofu using their target accounts and show how it can immediately generate landing pages, emails, or social content personalized for their audience. I also show our “webinar in a box” feature, where a single event generates multiple content assets for follow-ups and social promotion. It’s an eye-opener because they see the efficiency and time savings right away.

**(52:15)**

David: Thank you so much, Elaine. This was packed with insights, and I think we’ve all gained a lot from seeing how Tofu can simplify go-to-market. Everyone, please remember that Katherine Montgomery will be our guest next week, and we’ll have our in-person event at New York Tech Week soon. Have a wonderful weekend, and thank you all for joining!

## The Future of Marketing AI

Speaker: Debra Williamsons
Published: 2024-07-05
Tags: analytics, ai marketing, consumer behavior
Video: https://www.youtube.com/watch?v=IppVa1R9A7s
Page: https://aimarketersguild.org/sessions/the-future-of-marketing-ai

David: Very excited to have Debra Williamson here. We’ve known each other for a long time—I’m an eMarketer alum from 2001 to 2004. A lot of what I learned in the industry comes from reading those reports and writing articles on things I had no experience with, like online grocery markets or B2B eCommerce in Estonia. So getting to know Debra and her work has been a treat. Now, with her company, Sonata Insights, she has some valuable insights to share on AI’s impact in marketing. Debra, welcome!

Debra: Thank you so much, David. And wow, it’s been almost 20 years since you left eMarketer—that's wild! I'm thrilled to be here. I’ve been a member of the AI Marketers Guild for a few months, and you’ve probably seen me on Slack or at meetings. I was excited when David invited me to talk to you all.

Before we get started, can we launch the poll?

David: Absolutely. Thanks for suggesting it, Debra. Let's give everyone a minute to respond. I’m especially curious to hear your thoughts on Apple’s recent AI developments. It looks like we have over half the responses in already.

**(03:09)**

David: All right, the poll results are in. It seems most people feel that Apple's recent announcement is noteworthy but not revolutionary. What’s your take, Debra?

Debra: I agree; I was hoping Apple would announce something groundbreaking, but it’s still interesting. As analysts, we try to predict trends, and if we're wrong, we adjust. Today, I’ll discuss the current state of consumers and AI, their usage patterns, and what we can learn from past tech cycles. So, I’ll start my presentation.

David: For those who think it will be game-changing, feel free to add your thoughts in the comments!

**(04:21)**

Debra: A little about me—I'm Debra Williamson, founder of Sonata Insights. I was with eMarketer for 19 years, where I led the social media research practice. My new company continues to focus on social media, but increasingly, I’m exploring AI and consumer behavior, particularly how these shifts impact marketers. We often talk about using AI to make internal processes more efficient, but today I want to focus on the consumer landscape and what past tech cycles can teach us about AI’s future.

Let’s start by rewinding to the late 1990s and early 2000s, during the broadband boom.

**(05:53)**

Debra: Back in the late ’90s, I was a journalist at *The Industry Standard*, covering the dot-com era. Broadband was a game-changer—it transformed the online experience from slow dial-up to high-speed connections, leading people to go online more frequently and search more. Google, Yahoo, and Netscape started gaining traction, while companies like Amazon and eBay emerged as online shopping became viable. Broadband access also helped traditional media shift online, and digital advertising started to grow.

Broadband also significantly impacted online spending. When people had broadband instead of dial-up, their time spent online increased sharply, which boosted e-commerce. According to eMarketer data, online spending rose 65% over five years as broadband became more common.

**(09:21)**

Debra: Now, moving into the social media era—this shift began in the mid-2000s. I was covering demographics at eMarketer when I noticed that college students were using email primarily to communicate with professors or family, but using social media platforms like Facebook and Myspace to talk to friends. That realization led us to focus on social media, which eventually became a central part of consumer life.

Social media fundamentally changed consumer behavior. People began publicly sharing personal moments, photos, and more. Social capital became tied to friends, followers, and likes, and brands had to adapt by joining these social spaces and interacting with consumers directly. This era also gave rise to influencer marketing, which allowed regular people to gain influence over their networks, effectively turning social capital into monetary value.

**(13:47)**

Debra: Next, we had the smartphone era, which brought information on demand. When I got my first iPhone, it changed how I engaged with the world—everything I needed was suddenly in the palm of my hand. Smartphones enabled instant access to apps, real-time news sharing, social media, and even location-based services like Google Maps and ride-sharing. The convenience of mobile drove retail changes; by 2019, 56% of adults shopping in stores used their phones to research products.

Today, mobile devices are the primary way people access the internet, making mobile advertising essential. Video content, social platforms, and in-app experiences are all significant parts of mobile usage.

**(17:24)**

Debra: So why review these tech cycles? Because each cycle had one thing in common: connection. Each of these shifts—from broadband to social media to smartphones—transformed how consumers connect with content, each other, and brands. Broadband connected us to more content. Social media connected us to communities. Smartphones connected us to constant communication. Now, we’re entering the AI era, which I believe will lead to new forms of connection, but we still have a lot of work to help consumers understand AI.

**(20:20)**

Debra: Surprisingly, AI is already helping people connect in ways we might not expect. Take Reid Hoffman, for example—he created an AI version of himself that can respond using his past thoughts and writings. AI is also helping parents read creative bedtime stories for their kids and even helping people reconnect with loved ones who’ve passed away through AI-generated digital “afterlives” in China.

According to The Verge, 40% of Americans have used an AI tool, and younger generations are especially likely to adopt AI early on. But there’s still a gap; many people, particularly older generations, don’t yet understand what AI can offer.

**(24:22)**

Debra: A personal story—my dad, who’s 87, uses Siri every day but had never heard of ChatGPT. I showed him how it works, answering questions about stock performance and even giving a summary of me based on public information. His excitement grew with each answer, which reminded me of the importance of helping people feel comfortable using AI tools.

That’s why, even though adoption is growing, only about 23% of adults have used ChatGPT, according to Pew Research. We have a lot of work to do to educate consumers about AI’s potential benefits, especially when headlines often emphasize fears of AI overtaking humanity.

**(27:24)**

Debra: Education should be a priority. The Reuters Institute recently found that only 7% of US internet users use ChatGPT daily, with almost half never having heard of it. This presents a massive opportunity for education. Should it be the AI companies, tech giants, media, or marketers who lead this effort? I think it should be all of us.

The more familiar people are with AI, the more they trust it. Ipsos found that 81% of people who frequently use generative AI would trust product recommendations from an AI tool, while only 11% of those who never use AI felt the same. Familiarity drives trust, so it’s essential to make AI approachable.

**(34:07)**

Debra: I believe AI will transform how we connect in terms of culture, collaboration, commerce, and creativity. AI will enable richer, more interactive consumer experiences across these areas. But it’s up to us as marketers and industry leaders to help consumers navigate this change and feel comfortable with AI’s role in their lives.

Thank you all for listening. Please connect with me on LinkedIn or visit my website to learn more.

David: Thank you, Debra. This has been incredibly insightful.

**(36:43)**

Audience Member: Debra, comparing AI to past tech cycles, what do you see as the biggest opportunities for entrepreneurs in marketing?

Debra: Great question. I think AI agents hold a lot of potential. Soon, AI could handle complex tasks for us, like planning an entire vacation, where it currently only helps with booking a flight or a hotel. I’m excited to see where this goes, but we must consider the impact on industries, such as travel agents, that traditionally handled these tasks.

David: Definitely, and how do you see this affecting Google’s ad revenue and search model?

Debra: That’s a big topic. Google is already a kind of walled garden, with information panels that provide answers without needing to click further. They’re likely to monetize AI overviews soon, giving advertisers more bidding opportunities. However, this change will challenge the traditional publisher ecosystem, as fewer users may visit external sites if answers are provided directly on Google.

**(42:06)**

Audience Member: Looking back, was there a point where the advertising ecosystem “broke,” and are there lessons we can apply to avoid repeating those issues with AI?

Debra: I’d say social media significantly shifted the digital ad market. Early on, Facebook’s use of targeting data was groundbreaking but led to privacy issues we’re still dealing with. In AI, we’re seeing similar discussions around data privacy, and regulation will likely play a critical role here. But with AI moving so quickly, regulatory frameworks often lag behind.

**(48:41)**

Debra: Before we wrap up, I’m curious to hear from anyone who thinks Apple’s new AI tools are going to be truly game-changing. Does anyone have thoughts on what we might be missing?

David: Great question! For me, I think companies like HP, Dell, and Lenovo could benefit hugely as demand for processing power increases. This AI boom is making us reconsider hardware specs in a way we haven’t since the early 2000s.

Debra:

Excellent point, David. When AI is seamlessly embedded in hardware, adoption will accelerate. Apple could see huge adoption just from having such a massive, established user base.

## Transforming Culture and Creativity

Speaker: Ashlee Green
Published: 2024-07-05
Tags: creative marketing, culture
Video: https://www.youtube.com/watch?v=8zp0bpuy99w
Page: https://aimarketersguild.org/sessions/transforming-culture-and-creativity

**(00:00)**
David: Very excited today to welcome Ashlee Green, VP of Accounts and Culture at Creative Theory Agency. Ashlee is a transformative force, shaping the agency's culture and driving its success. She joined in 2018 as the first woman and fourth employee and quickly became a catalyst for exponential growth. Ashlee, I'll let you introduce yourself!

Ashlee: Thank you, David. That intro might have done the trick! Before I dive into my background and more about Creative Theory, I want to say how inspired I feel seeing everyone here. It’s amazing to see faces and know people can actively engage. This really feels like a community.

**(02:04)**

Ashlee: My name’s Ashlee Green, and I'm the VP of Accounts and Culture at Creative Theory Agency, a Black-owned full-service creative agency based in Washington, DC. We offer creative services for incredible partners across tech, retail, and nonprofits, including Google, YouTube, Levi’s, and various sports brands. Our core belief is that there’s value in all communities, people, and experiences, which we bring to our clients' work with a strong focus on equity and inclusion.

My role is twofold: on one side, I lead our accounts team, ensuring we provide the best service and creativity for our clients. On the other side, I work with our people systems director to build and support an internal culture where our team feels acknowledged, valued, and empowered to do exceptional work.

David: Incredible. I’d love to hear more about your work and its impact. Could you also share your perspective on AI?

**(04:28)**

Ashlee: Sure! AI is at the forefront across many industries, and our agency’s work is rooted in access. We believe everyone should have access to new technology, and AI is no exception. Our goal is to bring important conversations around access to AI, particularly generative AI tools, which can empower creatives and creators if used inclusively.

David: That’s a powerful perspective. Could you elaborate on what “access” means in this context?

Ashlee: Access here has two facets. First, it’s about who has access to build and develop AI tools—who’s on the engineering teams, who’s testing, who’s designing. Second, it’s about who the end users are—who has access to use the technology, provide input, and make sure the outputs are inclusive, creative, and devoid of bias.

**(06:12)**

David: I agree. There seems to be more focus on DEI and access within AI than we saw in earlier tech cycles. Are there companies you think are getting it right in terms of access?

Ashlee: That’s a great question. While we’re more focused on the tools’ market presence and use, I do respect companies like Google, which recently paused the use of their Gemini model to create images with people due to bias concerns. I appreciate when companies take a step back to address issues. It’s encouraging, but there’s room to improve how they address those issues, and that’s where our conversations on access come in.

David: I’m impressed by Google’s Real Tone initiative to improve camera technology for people of color. It feels like they’re making a real effort by listening to diverse perspectives.

Ashlee: I’m a Real Tone expert! Creative Theory Agency has been partnering with Google on Real Tone since 2020. We brought in experts—cinematographers, photographers, and colorists who’ve spent their careers working with darker skin tones—to consult with engineers, providing essential input for inclusive development. Google has put both time and resources behind this, ensuring the Pixel’s camera remains the most inclusive phone camera on the market. This partnership exemplifies what access should look like in technology.

**(12:08)**

David: That’s fantastic. Real Tone is clearly a huge success. If they’ve been able to make such strides in one area, how can companies replicate that across their organization to avoid these kinds of issues altogether?

Ashlee: That’s exactly why we believe access is crucial. We don’t want to end up fixing biases after the fact—we want the right people involved from the start. Just last week, we launched an out-of-home campaign in DC called *What Prompted You?* to spark conversations about who is building and using these AI tools. The campaign visually explores this process, aiming to inspire companies to think critically about who has access and whose perspectives are represented.

David: Did this campaign originate with a client request, or was it something you felt the agency had to do?

Ashlee: It was completely self-initiated. As an agency, we often take on passion projects without waiting for a client’s approval. We felt a responsibility to start this conversation. Our campaign features local creatives, chefs, and business owners, each doing their part to create access in their fields. We hope to take it beyond DC and potentially to Cannes, where we’ll engage in more conversations around this.

**(17:54)**

Ashlee: Let’s try a quick exercise to understand what we’re aiming for. Think back to your childhood—maybe late elementary school. Picture a Saturday morning. What did it look like, feel like, smell like? What were you eating, looking forward to?

David: For me, it was cartoons and cereal! Growing up in Westchester County, New York, we had some beautiful rural areas where I’d go out and shoot hoops, though I wasn’t great at it.

Ashlee: I love that. For me, Saturdays at my grandparents’ house in the South meant a big breakfast with grits, eggs, and family all around. If you prompt an AI with “Saturday morning,” David’s experience and mine would look very different. This exercise shows why we need diverse perspectives contributing to these prompts; without that variety, the AI output will only reflect limited experiences.

David: That’s such a critical point. AI needs to reflect the full spectrum of experiences to avoid reinforcing narrow or biased outputs. It’s like capturing “America in the ’50s”—a complex concept with many layers, and AI should be able to explore all of them.

Ashlee: Exactly. The goal isn’t to create one universal experience but to train AI to recognize and accurately reflect the diversity of human experiences. That’s the real value of access—not just one version of reality, but the ability to portray many.

**(24:23)**

David: Are there ways to address these historical biases, especially given how much of our knowledge and historical records come from limited perspectives?

Ashlee: Great question, and one we’re still exploring. The conversation starts with who builds these tools and whether they prioritize inclusivity from the beginning. We don’t have all the answers, but that’s why we launched *What Prompted You?*—to get people asking these questions and caring about access.

**(30:57)**

Morgan: These generative models are trained on massive datasets pulled from the internet, which carries its own biases. The big question now is where these companies will get new data, especially since we need inclusive data sources. That’s why the work Ashlee’s team is doing is so valuable—they’re helping to create synthetic datasets that can make these models more inclusive.

Ashlee: Absolutely. We hope our work influences the AI industry to consider inclusive datasets in future development.

Morgan: And as these AI tools become multimodal, gathering data from various sources, having access across diverse groups becomes even more critical to ensure those datasets are inclusive.

**(38:07)**

David: Do you see digital divides in AI access impacting marginalized communities?

Ashlee: Definitely. People in this group may have easy access, but many in the U.S. and worldwide don’t even have internet access, let alone access to AI tools. That’s why we’re actively working to reach those underserved. For instance, we recently introduced local high school students to AI creativity tools, opening doors for them to use these technologies in ways they hadn’t imagined.

David: That’s great. Education around responsible AI use is essential, especially as these tools become more integrated into everyday life.

Ashlee: Exactly. Every new technology either bridges or widens social divides, and AI could be more polarizing than anything we’ve seen. We have a lot of work to do to make sure that doesn’t happen.

**(42:21)**

David: For those of us who want to make an impact—whether as marketers, technologists, or others—what steps should we take to improve access?

Ashlee: Start by listening to and amplifying the voices of those who care about these issues. Have tough conversations in your own organizations and push for change. Ask yourself: if one person were responsible for shaping all AI outputs, what would that world look like? We need diverse inputs to avoid a one-dimensional AI future.

David: Well said. It reminds me of how Black Lives Matter made me realize the importance of raising these conversations in my own community. I didn’t have the language at first, but I learned to engage and encourage others to share their perspectives too.

Ashlee: Exactly. None of us will get it perfect every time, but if we start the conversation, we can make meaningful progress.

## How Generative AI is transforming Content Creation

Speaker: Brennan Woodruff
Published: 2024-06-24
Tags: content creation
Video: https://www.youtube.com/watch?v=eT-PioqVHBI
Page: https://aimarketersguild.org/sessions/how-generative-ai-is-transforming-content-creation

In this video, Brennan Woodruff, Co-Founder and COO of GoCharlie, explains how generative AI is transforming branded content creation for agencies.
He discusses the evolution of their product, the efficiency gains from automating content and meta work, and future innovations like customizable brand voice and dedicated AI deployments for enhanced privacy and performance.

0:00 – [Music] Brennan: I'm Brennan Woodruff, co-founder and COO of GoCharlie—a generative AI platform for creating branded content across your publishing platforms. We started this as a labor of love in late 2021 and released our first product in May 2022. While it was rough at first, early supporters meant a lot.

0:27 – Brennan: We relaunched in September 2022, had success on Product Hunt, and have iterated ever since. Initially, our goal was to replace marketing agencies, but after some rough experiences, we shifted to working with them.

0:48 – Brennan: I first helped my parents, who ran a relationship-driven business with no digital marketing knowledge, and that inspired me to create tools that craft resonant content and help gauge performance.

1:08 – Brennan: We discovered the greater value in longer form content like blogs and SEO. While writing 2,000 words takes considerable time, AI can do it in under a minute, which highlighted a real need.

1:28 – Brennan: Agencies enjoy the creative process of writing, so our tool not only produces first drafts but also helps develop proposals and meta work that aren’t directly tied to final deliverables.

1:51 – Brennan: We built our platform to deliver high-quality content quickly, while also providing tools that help grow businesses by reducing tedious tasks.

2:12 – Brennan: I often compare our tool to ChatGPT. Many wonder why they’d pay for our product when a free alternative exists; however, our value lies in delivering marketing-focused content that goes beyond generic AI output.

2:34 – Brennan: Agencies understand the difference between generic AI quality and content that’s fine-tuned for marketing. That realization led us to target agencies who appreciate the nuances that make content truly effective.

2:52 – Brennan: Our product works best for agencies focused on content creation, including SEO, social media marketing, influencer marketing, and affiliate marketing by creating multiplatform, omnichannel outputs.

3:12 – Brennan: The current economic environment makes efficiency crucial—companies are tightening belts and seeking substantial ROI without expanding headcount.

3:30 – Brennan: We work mostly with executives at the VP level or department heads, while directors and other team members are the actual users. Even the buyers benefit directly from our tool when it drives efficiency.

3:52 – Brennan: With executive buy-in, our tool empowers teams to work faster and more effectively, delivering significant ROI when integrated into their workflows.

4:14 – Brennan: I’ve seen consultancies and agencies revamp their business models lately—shifting toward flat fee engagements, and serving more clients with fewer people.

4:35 – Brennan: Our tool significantly reduces context switching by allowing you to build and switch between brand voices and profiles almost instantaneously, boosting overall productivity.

4:55 – Brennan: One agency we worked with nearly doubled their client capacity per employee, demonstrating a dramatic efficiency gain.

5:18 – Brennan: Ultimately, our impact is measured by how much we amplify team capacity, enabling growth without the expense of a larger headcount.

5:43 – Brennan: There’s a trade-off between efficiency and quality, so balancing automation with creativity is key. Our platform offers scheduling and prompt templates that preserve the creative process while automating routine work.

6:00 – Brennan: By automating mundane tasks, agency staff can focus on developing new ideas and running innovative experiments.

6:22 – Brennan: You can’t sustainably increase efficiency indefinitely without more advanced AI. For 2024, our focus is on honing our strengths and aligning closely with market needs.

6:46 – Brennan: Recent industry feedback has shown the importance of refined, specific use cases. We plan to deploy models tailored for these particular scenarios.

7:06 – Brennan: I’m especially excited about our evolving concept of brand voice. Although we were early innovators in this space, many still haven’t cracked how to measure if content is truly on-brand.

7:27 – Brennan: We’re investing further in our Branding Suite to create detailed brand profiles and ensure that all generated content adheres to specific guidelines—moving us more up-market.

7:45 – Brennan: We now have our own large language model, Charlie 1, available via API. It’s an uncensored AI model that answers your questions and completes tasks without unnecessary restrictions.

8:04 – Brennan: This model allows us to offer private deployments for larger companies with specific tuning and privacy needs, ensuring all data remains securely housed.

8:25 – Brennan: For clients with significant privacy concerns, we deploy dedicated models exclusively for them, keeping their information completely private.

8:46 – Brennan: We helped one agency nearly double their client capacity per employee by minimizing context switching and automating repetitive tasks. The efficiency gains are significant.

9:06 – Brennan: Our approach directly ties to financial outcomes, delivering growth without increasing headcount—a key factor for many businesses.

9:28 – Brennan: When you can drive two to three times as many client engagements, you’re truly amplifying team productivity and boosting measurable business growth.

9:47 – Brennan: It’s a strategic lever for companies: achieving growth while controlling costs by investing in tools that dramatically enhance productivity.

10:05 – Brennan: There’s a balance between efficiency and quality. Over-automation can risk quality, so we ensure our model maintains a strategic balance based on your established service templates.

10:23 – Brennan: Our platform lets you schedule work, save prompt templates, and replicate tasks across clients, ensuring consistency while freeing your team to focus on creative innovation.

10:43 – Brennan: This setup allows agency employees to concentrate on brainstorming and creative experiments rather than getting bogged down in day-to-day content creation.

11:05 – Brennan: For 2024, we’re narrowing our focus to the areas where we excel—concentrating on our core offerings and listening closely to market needs.

11:27 – Brennan: Insights from industry conferences have reinforced this focus, confirming the demand for both advanced AI capabilities and reliable, basic implementations.

11:52 – Brennan: I’m excited about expanding our Branding Suite, which not only crafts brand profiles but will also assess whether content is on-brand. We’re set to deploy even more customized AI tailored to each company's unique voice.

12:16 – Brennan: We’re deeply committed to pushing the boundaries of brand-focused AI, ensuring that all content generated aligns with a company’s guidelines and tone.

12:36 – Brennan: As we continue to innovate, expect more market-focused deployments and customizable settings that maintain brand consistency across all content outputs.

12:55 – Brennan: We’re excited about the future and invite companies with specific needs and tuning requirements to reach out for a tailored conversation.

## Innovative use of AI in music Industry

Speaker: Agnes Chung
Published: 2024-06-22
Tags: ai in music
Video: https://www.youtube.com/watch?v=7lNA2s9rH4Q
Page: https://aimarketersguild.org/sessions/innovative-use-of-ai-in-music-industry

In this episode, Agnes Chung, Senior Director of AI & Search at Songtradr, explains how AI is transforming music discovery by enhancing the search process and playlist creation for brands and music supervisors.
She emphasizes that while AI serves as an efficient support tool, the human creative touch remains irreplaceable in storytelling and artistry.

0:00 [Agnes]: I'm Agnes from Hamburg, Germany. I studied musicology and computer science, which led me to evolve in the music tech area. I've worked in the music industry for 15 years and believe that unknown artists need support so their songs can be discovered.

0:30 [Agnes]: Our vision was to help music be found. When we founded Music Cube, we recognized the abundance of great music worldwide, and that brands, agencies, and music supervisors need quality tracks for advertisements, TV spots, and more.

0:53 [Agnes]: That's why we joined Songtradr. Songtradr is a B2B sync platform for brands, agencies, and music supervisors, helping make vast catalogs more visible. Even though Spotify has over 80 million songs, in our world we typically encounter only two or three thousand.

1:20 [Agnes]: We wanted to make music catalogs more discoverable and searchable for customers, especially on Songtradr. The persistent question remains: where can I find the perfect song if I don’t know the artist's name or song title?

1:45 [Agnes]: That’s where AI comes in. It tags a song by mood, instruments, voices, vocals, and even the tempo—using common descriptors to make searching for the ideal track much easier.

2:10 [Agnes]: Often, clients have a specific artist in mind, but if the artist is too expensive, they request similar songs with comparable sound or mood for their TV spots.

2:33 [Agnes]: Clients provide a briefing outlining the video concept and desired music. Our music supervisors then create the perfect playlist, with the AI tool supporting both internal and external searches on the Songtradr platform.

2:56 [Agnes]: The AI tool serves solely as support and does not generate content. We do not produce AI-generated music or video, ensuring it only helps search through our extensive database.

3:21 [Agnes]: AI supports song discovery from a massive database, without replacing the human element. While generative AI tools exist for videos and audio, the human story behind each song remains essential.

3:49 [Agnes]: AI can only replicate what it learns, not create an original narrative. We want to preserve the creative process as a uniquely human endeavor, using AI only as a supportive tool.

4:17 [Agnes]: Think of AI as a new instrument, similar to a drum machine, that can provide unexpected creative inspiration. Some artists use it to explore beyond genre boundaries, blending styles in innovative ways.

4:40 [Agnes]: Essentially, AI functions both as a creative tool and as a search tool. Brands, agencies, and music supervisors need licensed, pre-cleared songs—especially for the dynamic demands of social media.

5:07 [Agnes]: Speed is crucial today. Unlike the long-term planning of TV spots in previous decades, modern social media requires rapid, precise matching of music to content.

5:32 [Agnes]: Social media's fast pace means that the perfect musical match must be found almost instantly. Our AI tool empowers brands and agencies to quickly create playlists that capture the moment.

5:58 [Agnes]: This rapid approach is a global phenomenon, just like social media itself. At the same time, it's essential to navigate licensing and rights distribution to ensure proper usage in specific regions.

6:22 [Agnes]: On the industry side, managing rights with labels and publishers is critical. When possible, I experiment with tools like ChatGPT to rethink and refine ideas.

6:47 [Agnes]: I'm more of a traditional musician who prefers playing an instrument over using generative AI for production. Nonetheless, I enjoy exploring AI developments through various newsletters and mailings.

7:13 [Agnes]: It's overwhelming to see the flood of AI tools as everyone explores the limits and potential of AI. I believe that, with time, fewer but higher-quality AI tools will prevail.

7:39 [Agnes]: I appreciate tools that emphasize thoughtful training data and clear goals. I'm fascinated to see how creative professionals—from graphic designers to musicians—leverage AI as a supportive instrument.

8:08 [Agnes]: I enjoy reading newsletters, joining mailing lists, and exploring how people worldwide are using AI. It’s inspiring to learn about emerging tools and innovations.

8:32 [Agnes]: Keeping track of everything is challenging due to our natural filter bubbles. I have experimented with tools like Midjourney and sometimes ask ChatGPT for recommendations on new AI tools.

8:58 [Agnes]: Ultimately, I rely on newsletters, forums, and blogs to stay updated on the latest developments in AI.

## AI for research and Ideation - Human value vs. AI

Speaker: Gavin Blawie
Published: 2024-06-22
Tags: human centered ai
Video: https://www.youtube.com/watch?v=R0tphBCt7EA
Page: https://aimarketersguild.org/sessions/ai-for-research-and-ideation-human-value-vs-ai

In this interview, Gavin Blawie shares his journey with AI in marketing—from early sponsorship projects with IBM and major events like the Masters and Grammys to his current explorations in generative AI.
He emphasizes that while AI is a powerful tool for research and ideation, true creativity still relies on human insight and unique brand storytelling.

0:00 – Gavin: I was privileged to work with the DENU network for about a decade after 360i mushroomed into a thousand-person agency, becoming similar to BBDO in many ways. It grew large and stratified with multiple offices, but my first exposure to AI came through that experience.

0:28 – Gavin: At DENU Sports Group, we worked with IBM on sponsorships. For events like the Masters, they proposed using AI insights to predict aspects such as the design of the next hole or how the next group might fare. This concept was applied to both legacy and new sponsorships, including projects with the Tony Awards.

0:54 – Gavin: AI was still evolving, and the Grammy sponsorship was perhaps the most robust application I saw. It applied AI insights to analyze the types of music that won across different genres, performing a tonal analysis, building a microsite, and identifying patterns in award-winning wardrobe choices.

1:15 – Gavin: Later, we ran a program with ESPN and IBM Watson inside their fantasy football platform, which was very exciting. AI was taught to make predictions based on historical data, offering insights like which players might have a breakout week or be more injury-prone, all within a 16- to 18-week season.

1:41 – Gavin: Although I’m a marketer and not a fantasy football expert, it was eye-opening to see individual choices enhanced by Watson through billions of possible combinations. We maintained a focus on people because, ultimately, consumers trust personal recommendations more than brand ads.

2:04 – Gavin: Reminding marketers that people are central to every campaign is essential. AI is a valuable tool to supplement our work but isn’t ready to take center stage just yet—a fact that is both inspiring and a reason for cautious advancement.

2:24 – Gavin: The generative AI machine has unlocked significant possibilities that feel like next-generation search. I love photography, and using tools like Stable Diffusion allows me to transform a deep perspective shot into a compelling 5-second video that’s both powerful and different from previous works.

2:44 – Gavin: It’s amazing to see AI develop its own horsepower, depth, and capability. I value collaborative sessions where insights and breakthroughs, even if derivative, help push the boundaries of creativity and innovation.

3:07 – Gavin: With technology breaking down barriers to accessing powerful data, I still wonder why brands aren’t smarter. Despite increasingly voluminous and contextual data, many companies continue to operate with outdated customer service practices.

3:28 – Gavin: For example, a leading phone manufacturer offered me a discount on a new phone, unaware that I had recently purchased several for my family. This kind of cost-driven mindset undermines the very essence of customer relationships.

3:49 – Gavin: I use AI primarily for research and ideation. By entering prompts—such as asking for 50 tactical ideas to support a football promotion targeting fans aged 20 to 40—I generate multiple iterations that serve as a valuable double-check and source of additional inspiration.

4:14 – Gavin: While AI excels at deriving ideas from existing data, true creative breakthroughs still come from forming original connections. That aspect of creative work remains what I most enjoy and what we ultimately sell.

4:32 – Gavin: AI enhances both creativity and productivity. I might use AI for about 30 to 40 percent of my work, and comparing tools like Claude, ChatGPT, and Midjourney reveals consistent improvements in capabilities—for example, stabilizing images to create coherent storyboards and shot lists.

4:54 – Gavin: Even though AI helps map out key frames for shoots, defining a brand’s tone and character is paramount. A brand should develop a unique language and visual identity rather than sound like a generic, mass-produced entity.

5:18 – Gavin: Whether generated by a team of smart people or by machines, the creative output must retain a distinct and ownable voice. I’d love to work in an environment that emphasizes innovation and personal expression over mere cost efficiency.

5:40 – Gavin: This is why the power of advertising lies in giving fans a real voice in the brand experience. Iconic brands like Coca-Cola and Oreo have always enabled consumers to shape their narrative, ensuring relevance and connection.

5:58 – Gavin: Today, many marketing campaigns lack a defined identity or a compelling story, focusing solely on ROI. This trend has led to content that barely warrants viewers’ attention, much like many interruptive broadcast ads.

6:21 – Gavin: In a recent RFP with HP’s sustainable impact group, I was asked about our use of AI. I explained that we integrate AI for research and behind-the-scenes work rather than relying on it for primary creative ideation.

6:48 – Gavin: While some AI elements support social listening tools or power search filters, we use AI in a foundational, tangential way—not for generating original creative concepts such as imagery or video shoots.

7:11 – Gavin: The goal is to merge consumer insight, product detail, and cause partnerships into a cohesive brand narrative. Most of HP’s recent marketing efforts have focused on sustainable impact, whether in environmental initiatives, civil rights, or recycling innovations.

7:34 – Gavin: I’m currently developing a white paper to articulate this approach. Joining this group and experiencing these engaging sessions has revived the collaborative spirit of brainstorming in a shared, creative space—much like the old whiteboard sessions.

7:55 – Gavin: Just as Ford revolutionized the automobile industry by mass-producing affordable cars, AI is transforming our industry by making powerful data and creative tools more accessible.

8:18 – Gavin: The analogy is fitting: while there were only a few thousand automobiles a century ago, Ford's innovations opened up the market for everyone. We are witnessing a similar transformative moment with AI.

8:40 – Gavin: It is a fascinating time to observe the evolution of AI in marketing. Balancing vast data analytics with genuine creative insights is reshaping how brands communicate.

9:04 – Gavin: In summary, AI should enhance human creativity, not replace it. It’s a robust tool for analysis, research, and ideation, but the true heart of advertising remains rooted in the human connection.

9:26 – Gavin: As brands increasingly adopt AI, they must maintain a unique tone and voice. Creative originality and a precise, resonant communication style are essential in every campaign.

9:50 – Gavin: The most significant power of advertising lies in empowering fans to help shape the brand experience. After working with iconic brands for 15 years, I remain convinced that original storytelling is the cornerstone of powerful, effective marketing.

10:12 – Gavin: Unfortunately, too many campaigns today lack direction, focusing solely on direct response and ROI. This approach detracts from the inspirational, captivating content that should truly engage audiences.

10:32 – Gavin: The current state of broadcast advertising often feels interruptive and unwatchable, largely due to an overemphasis on immediate metrics rather than long-term brand value. I’ve seen this play out firsthand, even with major campaigns like those from HP.

10:55 – Gavin: In that HP RFP, the discussion about AI’s role in our process was central. While AI supports many tactical aspects, it does not replace the uniquely human creative process that defines original campaigns.

11:17 – Gavin: We integrate AI for foundational research and support behind the scenes, but for generating original ideas, imagery, and videos, human creativity remains indispensable. It’s about combining data, consumer insights, and creative passion to construct something remarkable.

11:38 – Gavin: The fusion of thorough research, consumer engagement, product expertise, and cause-driven initiatives forms the holistic identity of a brand. AI can suggest tactics, but the core creative vision must always be uniquely human.

11:58 – Gavin: At its heart, our approach celebrates the original synthesis of ideas into content that truly resonates. As brands evolve, balancing technological support with genuine creative innovation is critical.

12:19 – Gavin: Many recent marketing initiatives—like HP’s projects focused on sustainable impact—aim to benefit the larger community, whether through environmental efforts, civil rights advocacy, or innovative recycling programs. This broader purpose goes beyond traditional ROI metrics.

12:41 – Gavin: Practical innovation involves integrating consumer voices into the brand narrative, ensuring that each campaign is both inspirational and transformative. This is the enduring power of advertising: to inspire and connect.

13:04 – Gavin: We are in a transformative moment with AI, similar to how mass production revolutionized the automobile industry. The potential for impact across the board is immense and truly exciting.

## How AI is transforming Media Planning, Buying and Analytics

Speaker: James Mullany
Published: 2024-06-22
Tags: media strategies, ai powered media buying, analytics, media planning
Video: https://www.youtube.com/watch?v=eyac71Oqvu0
Page: https://aimarketersguild.org/sessions/how-ai-is-transforming-media-planning-buying-and-analytics

In this video, James Mullany, Group Director of Media at Beeby Clark+Meyler, explains how AI is transforming media planning, buying, and analytics. He details his agency’s approach to discovering, vetting, and integrating innovative AI tools and partnerships to enhance digital marketing campaigns for their clients.

0:00 [Music]
James: My name is James Mullany. I'm the Group Director of Media at Beeby Clark+Meyler (BCM) and have been with them since 2010. We are a full-service independent agency based in Connecticut, helping brands with all stages of digital marketing campaigns, research, planning, and buying.

0:33
James: I handle analytics and work on media planning and buying with brands across industries such as B2B, travel, CPG, and retail—focusing on cost per sale or cost per lead. For us, it's a game-changer in advancing our strategies.

1:12
James: This opportunity allows us to revise our service offerings, processes, skill sets, and certifications. As a smaller independent agency, we are nimble and never focus on building proprietary technology; instead, we explore external partnerships.

1:39
James: We focus on exploring partners and technologies—both domestic and international—that develop faster than we could internally. We began applying AI in our media programs about seven years ago, and its rapid expansion over the past couple of years has been tremendous.

2:07
James: From research to media planning, buying, and analytics, all stages have seen AI advancements. Although it's challenging, we rely on our experience to evaluate and identify the right tools. One method we use is promoting their use internally.

2:38
James: We run agency-wide competitions with AI tools, including contests for creative prompts, and offer prizes for submissions featuring lesser-known tools beyond popular options like ChatGPT. Additionally, we formed groups such as the AI Country Club for dedicated hands-on exercises.

3:06
James: We push each other to use innovative tools that might otherwise go undiscovered within our teams. This might include trying out a new PowerPoint visualization AI tool or another breakthrough solution.

3:35
James: In one meeting, we discovered Pictory—a tool I might not have encountered otherwise. Participation in the AI Marketers Guild is also a valuable resource for identifying new partners and tools, building on our history as a smaller independent agency.

4:05
James: We have developed criteria to discover and vet tools, ensuring they are the right fit for us and our clients. With thousands of options available, it's essential to codify our requirements to efficiently identify the best choices.

4:32
James: Our goal is to identify the most applicable tools by encouraging active participation. Forcing the discoverability of innovative solutions is challenging, yet the excitement generated has been substantial.

4:59
James: Our agency-wide competitions evolved into an extracurricular activity. We recognized that individuals were researching tools on their own, so we established a country club format with a dedicated budget for tools that extend beyond standard free trials—often prompted by client requests.

5:31
James: With that dedicated budget, we identify active participants—from juniors with less than six months of experience to co-founders, directors, and managers across various disciplines.

5:55
James: We have representation across creative, client services, programmatic, social, and search. This diverse input benefits us, much like using any other tool—from CRM systems to media software.

6:20
James: Time and again, Silicon Valley innovations have outperformed traditional Madison Avenue approaches. Many companies from that era no longer stand, which informs our investment strategy of focusing on tech-based tools.

6:55
James: It isn’t solely about Silicon Valley; many powerful tools come from other regions, like Israel. Our experience has allowed us to codify criteria for effectively identifying these tools.

7:24
James: Finding companies that match our needs—and packaging their solutions for our clients—takes significant trial and error. For example, agreeing on legal terms can be challenging for brands working with these tech tools.

7:56
James: When brands struggle with prioritizing legal agreements, our ability to secure workable contracts with tech solution providers enables our clients to enter the market faster by applying existing tools rather than building them from scratch.

8:22
James: This approach also accelerates the adoption cycle; working with diverse platforms lets us assimilate to their language and workflows without reinventing the wheel.

8:51
James: Exposure to various tools allows our team to progress quickly. Additionally, in our vendor research, we swiftly identify companies eager to partner with agencies like ours.

9:19
James: It's important to find partners willing to collaborate closely, as many early-stage tools seek proof-of-concept, pilot studies, and feedback. We work hands-on with tech teams rather than acting as a mere end user.

9:52
James: For an independent agency like ours, strategic partnerships are key. We use our established criteria to identify partners that fit our needs so that we can help our clients advance faster through our vetted solutions.

10:21
James: Depending on the price point, we invest in tools like a social creative solution for pre-test optimization. Before launching an ad, this tool predicts how different creative variations will perform, saving time and budget.

10:51
James: Based on billions in ad spend data, we know when a creative isn’t likely to perform and avoid running it. That tool costs $2,500 for a 12-month agreement—an investment we’re comfortable with unless a tool’s cost is exorbitantly higher. This evolving landscape led us to develop a unified tech stack.

11:22
James: We created a unified tech stack called Ventas instead of relying on individual partner names. Partners, vendors, and technologies can move in and out of Ventas based on each client’s needs, ensuring flexibility in our AI applications.

11:53
James: This approach prevents our AI service from being tied to a single vendor, as the landscape evolves rapidly. Our agreements with clients also vary; some require that no generative AI be used, while others implement a review process to assess its impact.

12:23
James: Some clients prefer no generative AI, whereas other clients require a review of its impact. For instance, we don’t recommend using generative AI for logos or high-brand-value slogans—the strategy is tailored to each client.

12:56
James: Our solution emphasizes transparency regarding the origins of ideas and assets. Whether assets come from a stock library or are assisted by tools like Anyword or ChatGPT, we ensure our clients know exactly where the content is sourced.

13:30
James: We make agreements in advance with our clients to establish comfort levels with various AI applications. This proactive communication prevents creative approvals from slowing down the overall process.

14:02
James: We strive to avoid unproductive delays in media activation. By not committing exclusively to one tool, we remain agile—ready to adopt the next wave of innovation. Some tools have served us well for up to eight years, while others are much newer.

14:32
James: There is a distinct difference in the longevity of the tools we use—some last years, others mere weeks. Recently, pre-test optimization tools such as our Ventas AI tech stack have become the backbone of our media offering.

15:03
James: In the pre-campaign stage, we have observed significant growth and excitement. Before launching a campaign, we utilize research tools and technologies that enhance our signal mining within Ventas, including consumer data intelligence tools that use natural language processing.

15:34
James: These tools uncover audience motivations, preferences, and values, enabling us to develop insights faster and more accurately to inform the creative process. Recent advancements have also allowed us to use AI for scaling creative production effectively.

16:02
James: We’ve leveraged generative AI both to scale production and to cap production when assets aren’t performing as expected. Recent discussions and webinars have also focused on enhanced data analysis and analytics.

16:29
James: Our ability to uncover insights at scale has drastically improved over time. I recall when I used to analyze endless rows and columns of data manually—today, computer vision and AI handle that for us.

16:57
James: Modern analytics allow us to determine which ads perform better across different segments and platforms by examining specific visual details. Tools can distinguish between elements like lakes, woods, and oceans in imagery.

17:30
James: These tools also analyze features such as images with one person versus a group, and track specific language use. The speed and scale of data analysis now let me wrap up research much earlier, enhancing our Ventas AI tech stack during both pre-launch and post-launch evaluations.

17:57
James: While many perceive AI tools as solely for generating copy or scaling ad production, there is much more intelligence involved. Many agencies and brands have yet to fully leverage the expansive capabilities of these technologies.

18:26
James: In reality, AI extends far beyond copy generation and ad scaling—it drives deep strategic insights and operational efficiencies. [Music]

## Integration of AI in the creative process

Speaker: Kayo Zhang
Published: 2024-06-22
Tags: content marketing, creative process
Video: https://www.youtube.com/watch?v=khxkwD2MdiA
Page: https://aimarketersguild.org/sessions/integration-of-ai-in-the-creative-process

In this interview, Kayo Zhang, Creative Director at Magnet, explains how his agency is integrating AI into their creative process to enhance efficiency and spark innovation while navigating legal and regulatory challenges.
She shares her enthusiasm for technology and outlines the practical applications of various AI tools in content creation and production.

0:00
Kayo Zhang: My name is Kayo Zhang, and I'm the Creative Director at Magnet, a New York City–based creative agency. I've loved tech for many years—I joined Microsoft and a tech design startup during my gap year before grad school—and those experiences laid the foundation for my work today.

0:30
Kayo Zhang: These early experiences accelerated my adoption of AI and helped me build my career. Today, as Creative Director for Tech Brands at Magnet, we manage content strategies, productions, and more for major tech brands, while actively identifying ways AI can streamline processes and enhance creativity.

1:00
Kayo Zhang: We provide data-driven insights, but at this stage we're mostly testing AI internally rather than applying it to all client projects because of varying regulations and legal compliance. It's not about how much our clients ask or what we can do; it's about the bigger picture.

1:27
Kayo Zhang: On legal and regulatory matters—beyond marketing and content—we even run an internal AI boot camp. Everyone in the company tests AI tools weekly, and we share insights on what works and what doesn't.

1:56
Kayo Zhang: This collaborative effort is teaching us to learn and adapt quickly. Unlike major companies with dedicated AI departments, each individual contributes in their area, and by sharing these experiences, we can brainstorm new ideas together.

2:25
Kayo Zhang: This collaborative approach improves our overall process. In my department, with a blended background in production and creative, I've been using AI across pre-production, production, and post-production. For example, I've experimented with generating reference photos.

2:51
Kayo Zhang: Although many generated photos don't make it into the final deck for clients, the tools have been extremely helpful in translating our ideas into a PowerPoint or document. They assist with brainstorming and visualizing concepts.

3:21
Kayo Zhang: AI gathers data, analyzes it, learns from it, and then creates something new. However, everything generated is based on existing information. The creative process remains key to content creation—a unique human talent.

3:51
Kayo Zhang: We think beyond conventional boundaries, learning from diverse perspectives to enrich our creative ideas. The focus now is not whether AI should be part of the process, but how we can best utilize it under current regulations.

4:25
Kayo Zhang: We must operate within regulations and policies while maintaining transparency and privacy. For many clients, as I mentioned earlier, it's less about adapting to AI and more about aligning with the legal strategies their teams have developed for the bigger picture.

4:55
Kayo Zhang: Beyond marketing, clients frequently ask about the best AI practices we've tested and how they might apply these strategies internally. While more people are starting to integrate AI into their workflows, we remain cautious until regulations are fully established.

5:27
Kayo Zhang: We’re still in the learning phase and equipping ourselves until regulatory clarity emerges. Personally, I'm passionate about technology—I’ve been studying it since my very first job—and I’m excited about the huge impact it will have on the industry.

5:59
Kayo Zhang: I don’t believe AI will replace creativity; rather, it will enhance it. Content creation always combines creativity with execution. If AI can optimize the execution part through powerful tools, it frees us to focus on storytelling.

6:30
Kayo Zhang: When execution is efficient, we can focus our energy on creativity to bring better stories to life for brands, ourselves, and society. As a heavy AI user, I start every project by searching for tools that improve efficiency.

7:02
Kayo Zhang: I experiment with new AI tools out of passion, aiming to develop more efficient workflows—both professionally and personally—so I can enjoy more quality time with family and friends.

7:29
Kayo Zhang: Regarding specific AI tools, there are many designed for various niches. The most common ones I use include MidJourney, Runway, Pika, GPT, Cloud, Gemini, and others. Each large language model, like GPT and Gemini, has its unique strengths and weaknesses.

7:59
Kayo Zhang: For example, when researching a topic, I use three different models to leverage their distinct functionalities and perspectives. This approach ensures I gather comprehensive information.

8:22
Kayo Zhang: I also learn a lot from social media platforms like TikTok, YouTube, and Instagram. Their algorithms understand our interests, so as I browse, I effortlessly receive demos, short videos, and news on the latest tools.

8:47
Kayo Zhang: Whenever I'm tackling a specific project, I conduct research to find the most advanced tools and insights. Working with major tech brands is also a learning process, as I observe how they position themselves in this evolving revolution.

9:15
Kayo Zhang: All of these experiences help me learn more about AI every day. Even though we sometimes tire of hearing about new tools, they are designed to assist us, and we must find ways to integrate them.

9:46
Kayo Zhang: We should work cautiously, with proper regulations and policies in place. [Music]

## Creative Strength and Thoughtful integration of AI

Speaker: Jourdan Huys
Published: 2024-06-22
Tags: creative process, creative agency
Video: https://www.youtube.com/watch?v=GNOyNJDbjlo
Page: https://aimarketersguild.org/sessions/creative-strength-and-thoughtful-integration-of-ai

Jourdan Huys, Managing Director at Foul Mouth Creative, discusses her agency’s journey during its first year, highlighting their creative strengths and thoughtful integration of AI. She explains how AI is used primarily for research and repetitive tasks while emphasizing that human creativity and talent remain irreplaceable.

0:00 – Jourdan: I'm Jourdan Huys, Managing Director and Co-Founder of Foul Mouth Creative. We are one year into startup life and business, and we're an all female-owned creative agency with ownership in Seattle and the Denver area. Our expertise lies in high-level brand campaigns, strategy, creative concepting, 360 execution, and production for awareness, consideration, and conversion.

0:32 – Jourdan: My work extends to strategy, full concepting, 360 execution, and production on the upper end of the funnel. I specialize in every phase from awareness to conversion. My background is in brand management, and I began my career in Chicago.

0:57 – Jourdan: I started my career in Chicago and have primarily worked for small and midsize creative agencies, which gave me extensive experience in both strategy and production.

1:20 – Jourdan: Great question. My business partners and I explored all available options and believed we could create a better model. We have many ideas on working differently, protecting our work-life balance, and fostering a unique agency culture.

1:47 – Jourdan: We aim to create a distinct culture in the creative agency space. It's been an interesting ride, and after one year, I'm very positive about our direction. Our philosophy is to reserve creativity for human beings and let them connect with audiences.

2:15 – Jourdan: While AI can offer helpful shortcuts, we will not rely on it to replace the human brain’s creativity and understanding of audiences.

2:45 – Jourdan: We use AI during the discovery phase for strategy development—whether refreshing a brand or laying the foundation for a campaign. Tools like Gemini or ChatGPT help with macro-level category, consumer, and competitive research.

3:14 – Jourdan: However, relying solely on AI is risky. We always verify AI findings with primary research and thoroughly check the sources.

3:37 – Jourdan: AI serves as a labor shortcut, saving time on initial research. We also use it in the concepting phase, particularly for visualizing ideas.

4:04 – Jourdan: We specifically rely on MidJourney to visualize unique ideas that can’t be found as stock imagery. However, we do not use AI to develop concepts, scripts, or copy.

4:31 – Jourdan: I believe that for our upper funnel and psychographic creative work, AI plays a limited role. In other areas of advertising—especially for iterative tasks—AI is becoming increasingly impactful.

5:00 – Jourdan: The repetitive work required for performance creative, optimization, and A/B testing is where AI can be incredibly useful. Often, no one on the team is eager to perform these tasks, so AI fills that gap efficiently.

5:22 – Jourdan: AI handles rough, repetitive tasks, freeing us to focus on creative strategies. In areas like media buying, SEO, SEM, reporting, and analysis, AI can significantly reduce the workload.

5:51 – Jourdan: However, a challenge in our industry is that it’s increasingly hard for junior talent to break in and receive proper mentorship. AI might even accelerate this issue by replacing entry-level roles.

6:15 – Jourdan: I worry that using AI could further limit opportunities for new talent in advertising by replacing jobs with automation. This concern reinforces our commitment to prioritizing human talent.

6:39 – Jourdan: Running our own business lets us choose projects that align with our values. We won't take on work that demands excessive repetitive labor; instead, we focus on projects that emphasize strategic creativity.

7:05 – Jourdan: By concentrating on upstream strategy and creative development, we create a nurturing environment where junior talent can grow and play.

7:30 – Jourdan: We have pitched several AI-driven ideas to bring our concepts to life, and our clients showed strong interest. We even developed full digital production plans, including contingency strategies for training AI to mimic a specific brand voice.

7:59 – Jourdan: In two instances, projects were put on hold due to legal concerns regarding brand risk and safety—even with robust safeguards in place. As a result, AI hasn’t become a must-have in client calls or RFPs, and some brands now question its suitability.

8:25 – Jourdan: Brands are also questioning whether they can authentically leverage AI. For example, one small team that produces handcrafted products was uncomfortable committing fully to a tech-driven campaign after a thorough discussion.

8:49 – Jourdan: After pitching an AI-reliant idea, this client—despite being a modern, innovative brand—felt that, aside from legal risks, they couldn’t fully embrace a tech-fueled campaign.

9:13 – Jourdan: For instance, we once proposed an idea that relied on GPT’s language model to create custom, on-brand social media snippets.

9:41 – Jourdan: We set strict parameters to ensure AI avoided certain topics and conducted beta tests with our digital production partner for brand safety. Ultimately, legal teams felt that there wasn’t enough proven trial and error for such extensive exposure.

10:09 – Jourdan: Brands need assurance that even with thorough training, the AI won’t backfire or cause reputational issues. This concern led to intense debates and delayed projects.

10:33 – Jourdan: I have a friend and former colleague named Jeff who is very active in the AI space on LinkedIn. Whenever he recommends something, I explore it—he’s my AI influencer.

11:00 – Jourdan: I'm optimistic yet cautious about AI. Strict guardrails are essential, especially regarding data privacy, as this technology becomes integral to our lives and careers.

11:26 – Jourdan: There is significant potential for AI to offload menial tasks, allowing humans to focus on creativity and ideation.

11:57 – Jourdan: While I find it interesting to watch how AI evolves, I am not an early adopter. There is still much that needs to be figured out.

12:27 – Jourdan: Given recent labor disputes over talent rights—such as issues with appearance, likeness, and voice—it’s fascinating to see AI’s influence expand. That’s why we established firm boundaries from the start.

12:56 – Jourdan: In our business, we prioritize human talent across roles like on-camera, voiceover, production, and post-production. It is vital to offer human opportunities rather than replace them with AI.

13:24 – Jourdan: While AI can deliver cost-effective results, it’s crucial to assign it only roles that do not compromise the creative strengths of human talent. [Music]

## Can AI Predict if you will go Viral

Speaker: Paul Greenberg
Published: 2024-05-22
Tags: video marketing, video production, ai powered marketing
Video: https://www.youtube.com/watch?v=ITCrR6yRZGM
Page: https://aimarketersguild.org/sessions/can-ai-predict-if-you-will-go-viral

Join David Berkowitz of AI Marketers Guild for a fascinating conversation with Paul Greenberg, the innovative mind behind Butter Works, a digital video firm that harnesses AI to predict social video success. In this episode, Paul delves into his journey from leading major digital and media operations, like CollegeHumor and Nylon, to pioneering predictive AI technologies in video production.
Discover how Paul’s venture utilized machine learning, computer vision, and natural language processing to empower creators with data-driven insights for content strategy.

This engaging discussion not only explores the practical applications of AI in digital media but also addresses the broader impacts of AI on creativity and production efficiencies.

Whether you're a content creator, a marketer, or just curious about the intersection of AI and media, this episode offers a wealth of insights on leveraging technology to predict content success and streamline creative processes.
[0:30] How Does Predictive AI Forecast Social Video Success and Engagement?

Answer / Description:
Predictive AI forecasts social video success by analyzing historical performance data alongside computer vision, natural language processing (NLP), and topic clustering to identify unsaturated, high-engagement content niches. By mapping video metrics across axes of total views, audience engagement, and topic saturation, creators can mathematically determine which concepts to produce before investing resources in production.

Paul Greenberg, former CEO of CollegeHumor and founder of the digital video firm Butter Works, demonstrates this approach using an analysis conducted for Animal Planet. Instead of guessing what would resonate with audiences, the team ingested tens of thousands of animal videos across social networks, transcribing audio and analyzing visual assets. This data was clustered into subtopics (such as dogs, cats, animal babies, and animal births) and mapped onto a matrix: views on the X-axis, engagement on the Y-axis, and volume of existing content represented by bubble size.

The sweet spot for creators consists of high-view, high-engagement topics with small bubble sizes, indicating low saturation. For example, while dog and cat videos perform exceptionally well, their high saturation makes it difficult to break through the noise. Conversely, "animal babies" and "animals giving birth" represented high-potential, low-saturation opportunities where creators could ride the wave of an emerging trend ahead of competitors.

Keywords:
predictive AI for video, social video success, content saturation analysis, AWS computer vision, topic clustering, content strategy matrix, Animal Planet case study

[4:13] Why Do Video Tags Fail Compared to AI Computer Vision and NLP in Content Analysis?

Answer / Description:
Traditional tags and metadata fail because they are primarily designed by creators to influence platform algorithms rather than provide accurate, objective descriptions of video content. In contrast, AI computer vision and natural language processing (NLP) analyze the actual visual frames and spoken dialogue, eliminating creator bias and misleading tag noise.

Greenberg highlights how simple metadata analysis is insufficient for understanding modern social video dynamics. Creators often populate tag fields with trending keywords that have little to do with the actual subject matter of their videos, creating a high level of data noise. By shifting to Amazon Web Services (AWS) AI tools, the firm could execute frame-by-frame analysis to identify exact visual elements, while using NLP to interpret transcribed audio files.

This deep-data approach revealed counterintuitive insights that standard tags would miss. For example, while "cats" and "kittens" are conceptually similar, the AI analysis proved that cat videos are highly successful, whereas kitten videos underperform. The computer vision analysis explained this discrepancy: kittens move very little, making them better suited for static images, while active cats doing dynamic actions (such as riding skateboards) generate the motion necessary to drive video engagement.

Keywords:
metadata limitations, AI computer vision, video transcription NLP, automated video analysis, content tagging noise, social video performance data

[7:59] How Can Predictive AI Optimize Video Length and Formatting for Fuzzy Content Categories?

Answer / Description:
For complex or "fuzzy" content categories with less defined boundaries, predictive AI overlays dimensions like optimal video length alongside topic clusters to map out precise production blueprints. By analyzing historical viewer retention, AI can specify the exact duration required to maximize engagement for specific subgenres.

Using a case study of a travel and luggage brand, Greenberg explains how predictive models handle categories that do not have distinct divisions like animal species. The team mapped topics like "packing hacks" and "what to pack" but introduced a third dimension: video length, visualized through color-coded density in bubble charts.

This multidimensional analysis allowed the brand to understand not just what topics to cover, but how long each specific video format needed to be to succeed. The data revealed that a detailed "what to pack" video requires a longer runtime to satisfy viewer intent, whereas "packing hacks" perform better when delivered in a shorter, more rapid format. Combining topic clustering with length optimization provides creative teams with precise, data-driven guardrails for production.

Keywords:
video length optimization, multi-dimensional AI clustering, travel video marketing, viewer retention data, topic modeling, video formatting guardrails

[9:50] Which Facial Expressions in the First 3 Seconds Drive the Most Engagement on TikTok?

Answer / Description:
TikTok videos that begin with confused, angry, or disgusted facial expressions in the first three seconds drive significantly higher engagement than those starting with happy or smiling faces. Happy faces correlate with lower viewership and engagement, while negative or intense emotions create an immediate narrative hook and air of mystery.

Butter Works utilized computer vision to analyze the emotional states displayed in the critical first three seconds of TikTok videos. Contrary to traditional marketing logic which favors positive, smiling faces, the data demonstrated that starting a video with a happy expression statistically dampens performance. Instead, starting with a confused, angry, or disgusted face engages viewers because it triggers psychological curiosity; users feel compelled to stay to discover the cause of the reaction and its eventual resolution.

The research also coupled these emotional starting points with specific video durations to determine the perfect formula. For instance, the data indicated that a video starting with a disgusted face is most successful when kept to 26 seconds, while a confused face performs best at 31 seconds. While these metrics function as directional guardrails rather than rigid rules, they provide critical structure for structuring short-form video hooks.

Keywords:
TikTok hook optimization, 3-second hook facial expressions, computer vision emotion detection, social media engagement analysis, viewer curiosity hooks, short-form video formulas

[15:20] How Can Brands Use AI Object Detection to Choose the Best Filming Locations?

Answer / Description:
Brands can use AI computer vision to detect objects in top-performing videos within their industry, indicating which settings and visual backgrounds naturally appeal to their target audience. By identifying frequently occurring background objects, brands can reverse-engineer the ideal environment for their commercial shoots.

Greenberg shares an example of a beverage brand seeking to design a highly successful video campaign. The predictive AI ingested and analyzed successful beverage content, running object-detection models to catalog every visible item. The AI identified recurring items like high-end interior design elements, appliances, wood textures, people seated with laptops, and specific furniture.

Synthesizing this object-detection data made the ideal location choice obvious: a coffee house setting natively contained all of these high-performing visual signals. Rather than setting up an abstract studio shoot, the brand filmed on-location in a coffee shop, maintaining creative control over the casting, script, and music while operating within the mathematically validated visual parameters provided by the AI.

Keywords:
computer vision object detection, visual background optimization, brand video locations, reverse-engineer video success, video metadata analysis, retail video marketing

[18:07] How Can Generative AI Workflows Reduce Video Production Time and Costs?

Answer / Description:
Generative AI workflows can reduce video production time by up to 85% and costs by 40% when used for rapid prototyping, script ideation, and asset generation. By integrating tools like ChatGPT, Runway, and Eleven Labs, teams can automate highly repetitive pre-production and asset creation stages without sacrificing output quality.

At the entertainment company Meet Cute, Greenberg implemented a generative AI workflow that transformed how they developed short-form promotional videos. The team polled their Instagram audience on favorite romantic comedy tropes (e.g., "enemies to lovers" vs. "second chance at love"). They fed the winning poll results directly into ChatGPT to generate a fake movie trailer script, getting them 80% to 90% of the way to a final draft within minutes.

To produce the trailer, they leveraged Runway for stock-style video generation and Eleven Labs for synthesized audio and voiceovers. This stack slashed a traditional six-to-eight-hour production cycle down to an hour and a half, while reducing hard costs by 40%. Instead of using these savings to downsize staff, the company kept its headcount stable and reinvested the efficiency gains to double or triple their overall content output, scaling from one video per day to three.

Keywords:
generative AI video workflow, ChatGPT script writing, Eleven Labs synthetic audio, Runway AI video generator, production efficiency gains, AI-assisted content scaling

[21:30] Does AI Video Automation Threaten the Core Value Proposition of Creative Agencies?

Answer / Description:
AI video automation does not eliminate the need for creative agencies, but it shifts their value proposition away from manual labor toward high-level curation, strategic consulting, and creative direction. AI serves as a "co-pilot" that automates repetitive preparation and analysis, but human oversight remains critical to prevent low-quality outputs.

Address-engine and agency leaders express concern that as AI automates media buying and content creation, the traditional agency model is under threat. Greenberg counters this by arguing that AI tools are fundamentally no different from previous technological advancements like green screens or digital editing software. While AI can process hundreds of reference videos or generate initial script drafts, it cannot guarantee high-quality creative output without skilled humans driving the narrative.

If an agency produces poor creative work, backing it with AI insights or automated production will not make it successful. The primary value of the modern agency lies in establishing directional guardrails, editing "janky" AI-generated drafts, and applying taste and strategy to raw assets. Agencies that leverage AI to handle the tedious aspects of brainstorming, pitch-deck creation, and rough drafting can deliver superior results in less time.

Keywords:
creative agency value proposition, AI as a co-pilot, human-in-the-loop creative, automated content production, agency efficiency, content curation

[24:00] What Are the Top AI Tools for Video Generation, Optimization, and Social Repurposing?

Answer / Description:
Leading tools for AI video generation and repurposing include Runway and Moon Valley for text-to-video generation, Auggie for advanced AI editing, and Opus Clip for automated social media resizing and speaker tracking. While text-to-video tools offer creative control, template and stock-based tools like Fliki and Vista offer highly realistic outputs for faster turnarounds.

The discussion highlights a bifurcated landscape in AI video software. True generative engines like Runway and Moon Valley allow users to build surrealistic scenes entirely from text prompts. These are ideal for stylized concepts, though they can require significant post-production work to stitch together. For social media and business-focused creators, new entries like Christa Wolf's "playday" focus heavily on social formatting and quick-turn creative needs.

For social repurposing, Opus Clip (referred to as "Opus") is highlighted as an exceptionally reliable tool for turning long-form podcasts or webcasts into platform-ready vertical clips. Unlike alternative platforms that struggle with formatting, Opus successfully tracks active speakers, automatically reframes the video to keep faces centered, and overlays highly accurate captions, solving the complex visual challenges of multi-camera editing with a single click.

Keywords:
Runway text to video, Opus Clip social media, Moon Valley AI, Auggie video editor, automated video editing, video repurposing software

[31:45] How Should Brands Navigate Intellectual Property and Copyright Risks in AI Video Production?

Answer / Description:
Brands should mitigate intellectual property (IP) and copyright risks by utilizing generative AI primarily for internal ideation, rapid prototyping, and non-monetized organic social media marketing rather than final commercial assets. Because pure AI-generated assets cannot be copyrighted, maintaining a "human-in-the-loop" workflow to modify and finish the work is essential for legal ownership.

Intellectual property remains a massive point of friction, with many corporate legal departments actively disabling built-in AI features (such as Adobe's Firefly/AI integrations) due to liability concerns. Under current legal frameworks, pure outputs from engines like Midjourney lack human authorship and cannot be protected by copyright. This presents a double-sided risk: not only could a brand face plagiarism claims, but their own competitors could duplicate their AI-generated assets without legal consequence.

To navigate this landscape, consultants recommend a risk-managed approach. Using AI for rapid prototyping—such as pitch-deck imagery or preliminary script generation—saves enormous upfront capital while keeping the final commercial output hand-made or heavily modified by human artists. For external marketing, keeping AI-generated content restricted to organic, non-monetized social media reduces the risk profile while allowing the brand to benefit from the speed of generative workflows.

Keywords:
AI copyright infringement, Adobe Firefly legal concerns, generative AI rapid prototyping, intellectual property guardrails, non-monetized AI content, legal risk mitigation

[37:30] Does the Widespread Use of AI Content Generators Lead to Ideation Commoditization?

Answer / Description:
Yes, the widespread use of shared generative AI models threatens to commoditize ideas, as algorithms pulling from the same data pools inevitably produce homogeneous, repetitive content. To counteract this "shrinkage of idea diversity," organizations must retain senior creative talent to introduce unique human perspectives, emotional nuances, and strategic differentiation.

Thomas and other industry panel members point out a hidden pitfall of the AI revolution: a notable compression in the diversity of creative concepts. Because models like ChatGPT and Midjourney are trained on static, historical datasets, they operate by predicting the most statistically probable next step. If every marketer and creator uses the same underlying models for brainstorming, the resulting content naturally converges on a generic, highly standardized average.

This homogenization emphasizes the critical importance of human talent. While companies may be tempted to lay off creative staff to cut costs, doing so leaves them without the intellectual database required to evaluate whether an AI-generated idea is actually good, fresh, or different. Retaining senior copywriters and directors is necessary to spot algorithmic patterns, break conventional molds, and inject genuine emotional hooks that a machine cannot calculate.

Keywords:
creative commoditization, algorithmic homogeneity, idea diversity shrinkage, senior creative talent, AI content homogenization, human creative differentiation

## Decoding AIs Legal Landscape Gary Kibel on Copyright Creativity and AI

Speaker: Gary Kibel
Published: 2024-05-20
Tags: legal landscape, privacy, copyright, trademark
Video: https://www.youtube.com/watch?v=6815wE1O8nQ
Page: https://aimarketersguild.org/sessions/decoding-ais-legal-landscape-gary-kibel-on-copyright-creativity-and-ai

(00:00) We have a fantastic guest today, one of my first choices for this speaker series. Each week on Muma Insiders, you'll see a more detailed schedule. Today, we're excited to have Gary Kibel with us. I've followed Gary's work for years. If there's a legal discussion in the ad industry, odds are that Davis+Gilbert is representing at least one party involved. They publish valuable perspective pieces, which I'll link to in the chat—they’re must-reads covering everything from state privacy laws to issues in the metaverse, and increasingly, AI.

(00:49) When I received one of these updates, I immediately reached out to Gary and asked if he’d help us make sense of AI's legal landscape. So often, these discussions raise legal questions, but lawyers aren’t in the room right away. Gary, it's great to have you here—we’re looking forward to hearing your insights.

(01:23) **Gary Kibel**: Thanks very much, David. Happy to be here, and I recognize a few names here on Zoom. I’ll start by sharing my perspective on legal issues in the AI space for about five minutes, then we can open it up for discussion. As David mentioned, I’ve been in advertising and marketing law for over two decades with Davis+Gilbert, a firm deeply involved in this space. While I lead the privacy and ad tech practice, AI is a huge part of what we’re dealing with today.

(01:57) AI raises several legal concerns—primarily around training data, input data, and output data. Legally, we see issues in copyright, trademark, right of publicity, contracts, and privacy. Starting with copyright, when someone creates a work of authorship—say, writing a book, painting a picture, or taking a photo—they own the copyright. But with generative AI, who owns the output?

(03:06) If you read OpenAI’s terms and conditions, they essentially say, “You own the output, but we can’t guarantee ownership beyond that.” The U.S. Copyright Office recently made a statement that a computer cannot own a copyright—copyright belongs to original work created by a human. This isn’t a new concept. For example, Google “monkey selfie” to see a case from about a decade ago, where a photographer’s camera was used by a monkey to take a selfie. The photographer tried to copyright it, but the office denied it, saying the monkey, not he, took the photo. This question of ownership also applies to AI-generated output.

(04:23) This issue extends to trademarks and contracts as well. For instance, if an ad agency uses generative AI to create content for a client, the work may not qualify as “original,” which could violate contractual terms if originality and ownership were agreed upon. Some agencies are now including AI clauses in contracts to clarify ownership issues.

(05:59) Similar issues come up with the right of publicity, especially with deepfake likenesses of public figures. We’re seeing lawsuits, such as the one from Sarah Silverman against OpenAI, arguing that AI platforms are scraping copyrighted material as training data without permission. Getty Images has also sued AI platforms, assuming they’re scraping their copyrighted images.

(07:06) The main advice I give clients is to be cautious with both input and output: don’t request AI to generate something highly specific that could infringe, and carefully review the output for any potential issues. For instance, asking for a song in the style of a well-known artist like Taylor Swift could lead to trouble, whereas asking for a generic happy song would be safer.

(07:33) **David**: This is all great. So, what are lawmakers and regulators doing about AI?

(07:42) **Gary**: In the U.S., we don’t yet have a specific AI law. Privacy laws are still evolving, and now regulators are turning attention to AI. The Federal Trade Commission (FTC) has issued statements around AI-related deception—both for companies using AI without disclosing it and for those claiming AI involvement when it’s just for hype. Actors, represented by unions like SAG-AFTRA, are also concerned about AI replacing them. In Europe, Italy even banned ChatGPT temporarily due to privacy concerns, though that ban was later lifted after OpenAI made changes.

(09:16) **David**: This is why I feel like we could have you here weekly and only scratch the surface. Regarding agency contracts, are you seeing new clauses added for AI use in contracts with clients?

(09:54) **Gary**: Yes, definitely. Recently, I saw a clause in a contract from a major company, stating that the agency couldn’t use generative AI without the client’s approval. It seems they wanted to address AI use upfront to avoid any issues.

(10:32) **David**: I wonder how enforceable that is, especially with remote work. How can agencies ensure compliance when work could be done privately, potentially assisted by AI?

(11:09) **Gary**: It’s a challenge, particularly with “inspiration” being a gray area. Some companies and law firms have internal policies on AI usage. There’s a high-profile case where a lawyer submitted a brief generated by AI that cited fake cases, which resulted in disciplinary action. It’s one thing to use AI for brainstorming; it’s another if AI-generated work becomes the deliverable without transparency.

(13:15) **David**: For brands using AI, are there contracts or rules for inputting proprietary brand data into private AI models, like one specific to Coca-Cola’s brand guidelines?

(13:58) **Gary**: Yes, we’re seeing more closed environments where companies input their proprietary data to control the training data, like Adobe’s Firefly and LexisNexis’s AI product. These setups ensure the training data is proprietary and safer for use.

(15:42) **Paul**: Regarding ethics, what’s the responsibility balance between AI developers, marketers, and agencies?

(16:13) **Gary**: The platform developers, like OpenAI, have terms to clarify who “owns” the AI-generated content, but they don’t ensure it’s original because they use vast datasets as training data. The lawsuits claim these platforms must be copying something since they produce content that closely resembles existing material. Copyright law, however, protects original creations, not ideas or general inspiration.

(18:17) **Brooke**: How is AI different from a designer or copywriter drawing from years of experience? AI also pulls from vast amounts of data, but it’s now considered infringement if AI-generated content resembles something existing.

(19:12) **Gary**: That’s a fair point. Human creators draw from experiences, but they typically don’t remember every source, which makes infringement less likely. With AI, you don’t know where the output is derived from, which makes it riskier. Closed AI environments attempt to control this by using specific, vetted data.

(20:59) **David**: With AI increasingly integrated into tools like Google or Dropbox, is it possible to know when AI is in use?

(21:38) **Gary**: I would advise agencies to view AI as a tool for inspiration, not as a direct deliverable. Some companies allow AI for brainstorming but don’t let it be the end product without thorough review.

(22:15) **Paul**: On copyright—since computers can’t own copyrights, at what point does ownership shift to humans?

(22:48) **Gary**: Good question. The Copyright Office suggests that if you register AI-assisted work, you must specify the AI and human-created parts. Humans retain rights to the original, human-created parts, but it’s a gray area that will likely be clarified through litigation.

(24:29) **Paul**: If everything becomes a mix of AI and human content, does copyright need redefining?

(25:02) **Gary**: Copyright protects creators by granting them exclusivity to their creations, unlike trademark law, which protects consumers. How we define ownership of AI-assisted content is evolving, and upcoming cases will likely provide clarity.

(26:27) **Brooke**: A piece of art won a competition, but AI was integral in generating it. The Copyright Office ruled it wasn’t copyrightable because of AI’s involvement, though many think they got it wrong.

(27:07) **Gary**: That’s interesting. If the artist contributed unique prompts, I’d expect some ownership. This is like the SAG-AFTRA strike—actors fear being replaced by AI-created characters. It’s an evolving issue, and we lack solid answers right now.

(29:14) **Elena**: From a media planning perspective, AI can automate a baseline, but the creative direction still needs human insight. Could the copyright issue create a sort of “artist’s block”?

(31:09) **Gary**: AI amplifies concerns that have always existed in advertising. There are cases where work has raised questions of originality, and AI just heightens these risks. It’s why agencies often use contracts saying they won’t be liable if AI content inadvertently infringes.

(33:17) **David**: Are there clear guidelines on transparency for creators using AI? Is it like sponsored content where disclosure is now expected?

(33:57) **Gary**: Yes, the FTC has been concerned about undisclosed AI usage. Misleading consumers, either by claiming AI involvement or by hiding it, could lead to deception claims. I had a client who wanted to market work as AI-created for novelty, though it wasn’t. I advised against it due to potential deception risks.

(36:42) **Laura**: For a compliance tool we’re developing, how transparent should we be in showing how AI makes decisions? Would this transparency help build trust?

(37

:16) **Gary**: Transparency can build trust, but giving away too much may reveal proprietary information. If your platform can assure clients of AI’s reliability, for example by saying outputs are based on vetted data, it can help ease concerns.

(39:00) **David**: What if a government AI chatbot gives incorrect info that leads to harm? How would liability work?

(40:18) **Gary**: It depends on the duty to users. Government agencies have some protections, though misinformation from an AI chatbot could open liability if it causes harm. For example, a physician using an AI tool to advise patients could face malpractice claims if the advice was faulty.

(41:52) **Thomas**: How effective are watermarks as a solution for AI-generated content?

(42:30) **Gary**: Watermarks may be helpful for transparency, letting consumers know content is AI-generated so they can evaluate it accordingly. California even requires chatbots to disclose they’re not human, which is a similar concept.

(44:03) **Paul**: There’s a new bill against impersonating others using AI. How feasible is it to enforce such a law as AI technology becomes more common?

(45:06) **Gary**: Federal action on AI laws is unlikely in the near term, but I could see states like California leading the way, which might effectively create a national standard.

(45:45) **David**: If I create ad copy and use AI to improve a few sentences, do I have to disclose that?

(46:21) **Gary**: If it’s just a few words, it’s likely considered inspiration. However, the Copyright Office advises disclosing AI-generated portions in registered works.

(47:47) **Thomas**: What about tools like Grammarly? At what point does AI involvement require disclosure?

(48:21) **Gary**: The Copyright Office is currently collecting public input to establish AI guidelines, but clarity is still evolving.

(50:00) **Paul**: Does a prompt itself hold copyright value, as it’s essential to the AI process?

(50:10) **Gary**: Not necessarily. For instance, instructing someone to take a photo doesn’t grant copyright to the director; it still belongs to the creator of the output.

## AI-Powered A-B Testing and CRO Insights

Speaker: Avi Muchnick
Published: 2024-05-20
Tags: optimization, conversion rates, a/b testing, automated testing
Video: https://www.youtube.com/watch?v=nZF2XX4xDvQ
Page: https://aimarketersguild.org/sessions/ai-powered-a-b-testing-and-cro-insights

**(00:00)** Thanks, everyone, for joining AI Marketer Skill today, and a special thanks to Avi Muchnick from AB3. Today’s session covers A/B testing, conversion rate optimization (CRO), and the role of AI in these areas. I’ll even turn on the AI companion—only fitting for a session like this! Avi and I connected a while back and talked about his time leading product at Shutterstock and Shapeways. I'm sure he'll share more, but I'm excited to hear what he has in store for us. Avi, all yours.

**(00:49)** Thank you, David, and thanks to everyone joining today—I really appreciate it, especially with Passover and Easter preparations. My wife might not be thrilled I'm doing this today, but the show must go on! I'll give a quick overview of using AI to automate A/B testing, but more so, it’s about accelerating CRO. AI is one of the most exciting tools for this. I'll share a bit about my background and then jump into the demo.

**(01:18)** My experience is mostly as a startup founder and product executive. In 2002, I founded Worth1000, a photo editing contest site that gained a lot of traction, and I learned A/B testing there by focusing on ad clicks. Later, I founded Avary, which started as a competitor to Adobe Creative Suite, but was cloud-based. Eventually, it evolved into a software development kit (SDK) that could be embedded into partner apps.

We had about 10,000 partners and 150 million downloads of our standalone app, and A/B tested various features within the SDK. Adobe later acquired Avary, and I helped develop mobile Creative Cloud products there. After Adobe, I was Chief Product Officer at Shapeways before its IPO, and at Shutterstock, a leader in royalty-free stock photography.

**(02:57)** Eventually, I felt the startup itch again and launched AB3. Across all these companies—whether startup, pre-IPO, or public—the same CRO challenges kept arising. The digital ad spend market is projected to reach a trillion dollars by 2027, with customer acquisition costs only increasing.

Yet, we found that the average conversion rate was around 2%, which was startling considering how much was spent. At places like Shutterstock and Shapeways, we’d hire talented A/B testers and CRO experts who would make meaningful improvements, but often only on the pages they focused on. Once they moved to other areas, the initial pages would revert over time. Plus, if these experts left, they’d take valuable knowledge with them.

**(04:02)** I started wondering: what if we could use AI to optimize every page on a site continuously? Early on, this was just an idea. But around 2019, as AI models like OpenAI began advancing, I realized we could potentially create content at near-human quality.

Fast forward to today, and we’ve come a long way—we can change copy, imagery, and even video (with tools like OpenAI's Sora). AI-generated content opens up incredible possibilities for automated testing. It’s an exciting space, and we’re beginning to see competitors, which is a great sign for the field.

**(05:32)** Let's dive into a live demo. I'll use one of our early beta customers, BarkBox. With our platform, we don't make manual changes; instead, we grant AI the ability to make adjustments on the site based on predefined guidelines. This approach removes people from the bottleneck of testing and allows the AI to optimize across potentially millions of pages.

**(07:02)** The AI starts by identifying elements on a page to test, like BarkBox’s humorous headline, “Monthly dog goodies for good doggies.” By clicking on it, I tell the AI it’s allowed to test variations here. The AI then analyzes the brand voice across the site and suggests alternatives with similar humor and tone, like “Treat-filled surprises for happy hounds” or “Pup-approved treats delivered monthly.” While these suggestions might not surpass the original, it’s a good starting point to refine based on engagement data.

**(08:49)** I can also adjust button text, like changing “Get BarkBox” to alternatives like “Claim Your BarkBox” or “Unleash Joy.” For imagery, I can upload BarkBox’s brand images, and the AI will test which works best. For generic imagery, we connect to royalty-free libraries or generate images through AI.

**(12:33)** Next, we define success criteria for the AI—essentially, telling it what counts as a successful action. We can track clicks on specific calls to action, page views within a funnel, or even revenue via e-commerce integrations like Shopify. The AI uses this data to adapt, aiming to drive higher engagement and conversions.

**(14:22)** At the final setup stage, we provide a simple JavaScript snippet to add to the website’s header, allowing AI to make real-time changes securely. You can specify how much traffic sees the experimental content or use UTM codes to test with specific audiences.

**(16:45)** Once deployed, our platform compares the performance of each variant against a baseline. If, say, one headline variant converts at 15% versus the baseline’s 10%, the AI will automatically favor the higher-performing variant. This process continues, with AI generating and testing new variations based on previous winners, creating a “Darwinian evolution” of content on the site.

**(19:32)** For companies wanting control, you can approve new variants or add, edit, and pause campaigns as needed. Our setup is flexible; you can even disable campaigns temporarily if you want a different setup for a special promotion or holiday.

**(20:57)** That covers the core demo. Before moving on, are there any questions?

**(21:30)** We’ve had several! Starting with setup requirements, our script works on any website supporting JavaScript, including platforms like WordPress. Once the script is added, the AI tracks conversions and adjusts content automatically based on what's performing best.

**(23:06)** As for custom UTM codes, you can specify where the script runs within WordPress or use audience segmentation to target specific campaigns. AI generates new content variants autonomously after it learns from initial results on the page.

**(24:03)** Regarding hallucinations or content quality issues, we avoid these by using prompt engineering and leveraging libraries with built-in safeguards. The platform filters numerous AI-generated variants and selects only those that best match the brand's tone and style.

**(25:37)** We've also integrated third-party image providers like Midjourney. If regulations change regarding image use, we can adapt our image generation methods accordingly. Our goal is to provide legally and ethically sound options for all our clients.

**(27:42)** Video content testing is not currently supported, though recent advancements by OpenAI in video generation show potential for the future. At the rate AI is advancing, it's likely we'll see video optimization in the coming years.

**(28:51)** A question came up about the content optimization process. Our AI doesn’t take a prescriptive approach; it stays true to the page's original voice and lets the statistics guide its adjustments, ensuring gradual improvements rather than drastic changes.

**(30:20)** As for customer demographics, we’ve seen adoption by both small-to-medium businesses (SMBs) and larger companies in D2C, SaaS, fintech, and healthcare. While regulated industries have strict content guidelines, our platform allows these companies to efficiently run tests with oversight, reducing time and staffing needs for CRO tasks.

**(33:35)** Currently, our platform is focused on web and landing page optimization rather than social media. However, any page you control, where JavaScript can be added, is eligible for our tool.

**(34:08)** For customers with rebrands or new guidelines, it's easy to update the AI’s baseline so it aligns with the latest brand standards. Each new campaign re-baselines automatically, so if you’re starting fresh after a rebrand, it will adjust accordingly.

**(35:07)** We’re also working on tools for agencies and multi-domain setups, allowing you to support multiple clients or domains with customized plans.

**(37:09)** While we don't yet integrate with Google Analytics, we capture engagement data through JavaScript click events. If clients want a Google Analytics integration, we may consider it based on demand.

**(38:09)** As for demographic targeting, audience segmentation based on UTM codes is available. Full demographic customization may be added in the future.

**(39:13)** The AI operates entirely on your domain, so your visitors experience the content directly on your site. If you prefer testing traffic offsite, that’s an option too.

**(41:25)** We also ensure AI changes don't deviate too far from brand standards. While the platform makes small, statistically-driven optimizations, you always have control over final approvals.

**(44:21)** To address SEO, our changes are rendered in the user’s browser, so search engines see the original page content. This approach prevents SEO disruption and safeguards site performance. We don’t add latency or affect load times, and in case of outages, your original content displays.

**(46:33)** Each variant’s performance data is viewable in the dashboard. Once a variant has significant data, it can be paused or edited as needed.

**(47:34)** The AI's approach to audience segmentation focuses on testing the content broadly. Custom user data is not a factor unless specifically set up with UTM campaigns.

**(48:40)** Competitors include companies like VWO and Optimizely, as well as Mutiny and the recently acquired Intellimize. There are also newer companies like Kik and Evolv. It’s exciting to see growth in this space

## Storytelling in the Age of AI Insights

Speaker: Iain Thomas
Published: 2024-05-20
Tags: content creation
Video: https://www.youtube.com/watch?v=pfNeinLC80w&t=1s
Page: https://aimarketersguild.org/sessions/storytelling-in-the-age-of-ai-insights

**(00:00)**
Can everyone see my screen? Great. I’m going to talk about storytelling through technology, which I’ve been working on in various ways, both with agencies and through my personal work. It’s something I’m passionate about, and I’ve fortunately become known for. Today, I run an agency called Sounds Fun, where I serve as Chief Vision Officer and Chief Technical Officer, depending on the day and whom we’re working with. We’re a full-service agency that does a bit of everything, aiming to answer conventional marketing questions in unconventional ways.

**(00:40)**
On Monday, I was at the Columbia School of Business speaking to a group of CMOs about the current technological moment we’re in. In conversations with clients, they often mention they’re waiting to put together an “AI brief” or “innovation brief.” But I tell them that all briefs are now essentially innovation briefs. We’re at a unique point where creativity and technology can be applied in entirely new ways, and we focus on doing that in unconventional forms. We’ve been fortunate to create what we call “the future,” with awards from D&AD, the Clios, and Cannes. Before going further, I’ll give a quick background on myself.

**(01:40)**
I grew up in a small, conservative town on the tip of Africa. That’s my old high school with the military training, which I really disliked. I was rebellious, with the obligatory weird 90s hairstyle. That’s my brother; the numbers on top of his computer are from a case file when Interpol arrested him for hacking into Belgium’s phone network. Growing up in a “hacker house,” he ran a zero-day warez group where we messed with phone lines, downloaded software like the original Photoshop, and shared it through our bulletin board service in South Africa. My brother was drawn to the technical side, and I was fascinated by the social aspect—connecting with people through technology was magical. This experience influenced my work profoundly.

**(03:14)**
Since then, I’ve worked in advertising, creating everything from monuments for brands like MINI, Levi’s, and Johnny Walker to unusual, outside-the-box art projects. Around the mid-2000s, blogs were popular, and I thought about how I could use them for creative storytelling. I started a project called “I Wrote This for You” with a photographer from Japan. He’d send me daily photos, and I’d write poems or short stories directed at readers. It unexpectedly gained a massive following, with people tattooing quotes on themselves, leading to a book deal, and even catching the attention of celebrities like Harry Styles and the British Royal Family.

**(05:29)**
The project even attracted notable figures like Steven Spielberg, who quoted it in a speech. This journey with “I Wrote This for You” started simply by exploring how I could take a popular medium and subvert it. One of my favorite moments was a spat with LeAnn Rimes over a misquoted poem of mine that ended up in the Daily Mail. After that, I continued writing—one book often leads to another. I published a science fiction novel, “Intentional Dissonance,” and experimented with formats. For instance, I duplicated the text repeatedly, creating a loop that frustrated some readers, but I enjoyed it.

**(07:10)**
During the Edward Snowden leaks, I created “25 Love Poems for the NSA.” Snowden’s documents included keywords that the NSA monitored, so I wrote poems using only those flagged words and distributed it as a free PDF for people to email. Each time I enter or exit the U.S., I half expect someone to bring it up. During the pandemic, I also wrote a children’s book explaining quarantine, which got translated into multiple languages and widely circulated.

**(08:15)**
AI has always fascinated me, stemming from a story I read in my 20s about Richard Thear, an AI researcher. Inspired by the film “Flatliners,” he taught a neural network to sing Christmas carols, then shut down parts of it. As the AI deteriorated, it produced haunting lines like, “All men go to good earth in one eternal silent mind.” This experiment stuck with me. Eventually, I joined an AI startup called Copysmith, an early competitor to tools like Copy.ai. There, I realized AI could do more than just ad copy. I started experimenting by combining spiritual texts and created a prompt-based dialogue on themes like suffering, which became the book “What Makes Us Human,” now part of Malcolm Gladwell’s Next Big Idea Club.

**(10:48)**
I’m drawn to exploring how technology can be creatively misused. I worked on “Fragments of Sappho,” where I used AI to complete the fragments of ancient poems by the poet Sappho. Each project is about finding new ways to break and repurpose technology. Eventually, Vice Media approached me for several projects, leading me to join their internal creative studio, Virtue, as the Global Head of Innovation and a Creative Director on Coca-Cola. I helped develop Coca-Cola Creations, a limited-edition product line focused on innovation and culture.

**(12:05)**
Developing new Coca-Cola flavors was like playing drums with Phil Collins—an incredible experience. Here’s a quick video showing some of our work. In a market flooded with choices, we needed a way to make Coca-Cola stand out for young people. We created limited-edition products and connected each launch to an immersive experience—an augmented reality concert, virtual wearables, or even a Coke that “tasted like space.” We built an entire universe around each launch, which resulted in increased brand love and sales.

**(14:46)**
For each launch, we tied in some form of innovation—whether it was augmented reality, virtual wearables, or collaborations with platforms like Niantic. We even created a League of Legends collaboration where gamers could scan their faces and insert themselves into our campaign assets, creating a personalized experience.

**(17:27)**
Another significant project was “Backup Ukraine.” We collaborated with Polycam, a 3D scanning app, to allow Ukrainians to digitally preserve cultural monuments threatened by war. When Russia invaded, UNESCO expressed grave concern over cultural destruction, so we developed a solution to scan these monuments and protect them digitally. The app became a tool for Ukrainians to safeguard their heritage.

**(19:25)**
About a year ago, I left Vice and co-founded Sounds Fun with other former Vice colleagues. Since then, we’ve worked with Intel, Microsoft, and several fashion brands, focusing on how technology can unlock culture in powerful ways. Recently, we worked with Intel and Microsoft to create “The Art of AI” at Microsoft Ignite, featuring AI-driven art experiences that responded to participants. One artist, Alexander Reben, built a sculpture that transformed spoken words into haikus, while another created a generative AI cityscape that adapted based on the crowd.

**(21:56)**
We’ve also partnered with startups like Trellis, which is developing AI-driven tools to enhance the reading experience. One project, Bespoke, is a generative podcast app that lets users specify a topic and time limit, then generates a personalized audio experience. Another project with the Gerin Institute involves building a “time machine” that captures your personality and thoughts for future generations to interact with, preserved in glass designed to last a billion years.

**(24:38)**
Currently, many of our conversations with clients revolve around responsible AI innovation. We encourage brands to be culturally sensitive and strategic, especially given the risks of public missteps with this technology. For example, we created an AI chatbot invite for events, which playfully went rogue. We also use these projects to explore the ethical dimensions of AI, emphasizing consent and transparency.

**(26:28)**
At Columbia, many speakers described this as a “once-in-a-lifetime opportunity.” My guiding principle is to focus on what we can do with AI that was never possible before, rather than simply optimizing existing processes. AI offers enormous efficiency benefits, but it’s also an opportunity to create entirely new experiences.

**(28:06)**
Thank you. I know the Ukraine piece was especially moving for many, as it showed how technology can be used as a force for good. Some reactions, though, pointed out the contrast between social impact work and selling more consumer products. In marketing, we often focus on selling, but there’s still room for meaningful impact. At the end of the day, though, most of us are here to sell products, whether soda, toothpaste, or mouthwash, and it’s about steering brands responsibly within those boundaries.

**(29:27)**
I don’t want to give the impression that I’m overly selective with projects. I’ve done my share of more routine work—brochures, infomercials, and everything in between. While I do have some moral sensibility in my choices, I think we’re all trying to make a living. I believe marketers often get a bad rap, but we can each contribute in our own way to create meaningful cultural connections.

**(31:20)**
A great point was made at the Columbia conference with the reminder that “we’re here to sell beer,” which Anheuser-Busch displays prominently to ground their brand. No matter how creative or complex our work gets, we’re here to sell products and create emotional connections, whether through automated ads or immersive experiences.

**(32:29)**
For CMOs, AI offers potential beyond just marketing, allowing for new ways to engage customers and even operationalize aspects of the business. An example is the AI community created around rapper Nicki Minaj. This kind of engagement shows how AI can deepen cultural connections, something that excites me about working in this

field.

**(33:41)**
AI has a mixed reputation, with some campaigns like Dove’s criticizing AI’s role in perpetuating unrealistic beauty standards. But it’s important to highlight AI’s potential when applied thoughtfully. We’re currently working on a proposal with Human Rights Watch, which could be a good opportunity to showcase AI’s positive impact.

**(34:57)**
Regarding creative jobs, I’m neither a cynic nor an idealist. AI is already impacting copywriting roles, as some of my friends in the field are feeling the squeeze. However, AI also enables small teams to achieve large-scale work, which is exciting for business and creativity. AI is a transformative tool, but it needs to be managed with realistic expectations.

**(36:44)**
At Sounds Fun, our team is small, with just four of us and a network of partners and suppliers. The tools we use, like ChatGPT and Midjourney, allow us to be efficient and deliver high-quality work for both big and small brands. Smaller brands can achieve great things with AI, as long as they’re willing to be bold and culturally relevant.

**(39:02)**
A story that inspired me was about a neural network trained to sing Christmas carols, which produced an original phrase as it “died.” I don’t believe AI itself is creative, but it can mimic creativity effectively. The best results come when humans bring their own insights and creativity to the tool, making it a collaborative process.

**(42:17)**
I often tell clients that while AI can produce a first draft, true understanding and creativity come from human refinement. As tools improve, AI will allow us to cover creative ground faster, enabling us to try many ideas. But I wouldn’t present AI-generated content unless it’s perfect, so we still rely on animators and directors to bring everything together.

**(47:16)**
In today’s fragmented digital culture, where there is no longer a single digital town square, brands need to reach audiences creatively and be willing to step out of their comfort zones. The Coca-Cola campaign’s use of a hand-drawn logo was a bold step, demonstrating how AI can help brands re-evaluate their assets in a fast-evolving culture.

## AI-Powered Segmentation  Revolutionizing Marketing with Neuralift

Speaker: Jonathan Mendez
Published: 2024-05-13
Tags: segmentation, advertising
Video: https://www.youtube.com/watch?v=VwcdIWhKmNE
Page: https://aimarketersguild.org/sessions/ai-powered-segmentation-revolutionizing-marketing-with-neuralift

**(00:00)**
Welcome, everyone, to the latest edition of *AI Marketer Skills*. Today, we're excited to have Jonathan Mendez from Neuralift here to share insights on segmentation and more. I've known Jonathan for a long time, probably since his time founding and running Yieldbot—a fantastic performance ad network he built and managed for nearly a decade.

**(00:51)**
Jonathan, it’s great to see what you’re doing now in this AI-powered era. Before you dive in, just a quick reminder to everyone: please feel free to use the chat, raise your hand, or come on camera if you have questions. We want this to be interactive and make the best use of your time today. So, Jonathan, over to you.

**(01:24)**
Thank you, David. I appreciate everyone being here. Whether you’re joining during lunch in New York, breakfast on the West Coast, or at the end of your day across the Atlantic, I’m thrilled to have you here. First, I want to thank David for creating the *AI Marketers Guild*. We've been building Neuralift for about a year, and the forum David has established here is incredibly valuable for connecting people in marketing AI.

**(01:53)**
As many of you know, AI is transformative. If you’ve worked in the field or are involved in projects, you’ve likely found it eye-opening. Having a community like this that David has organized, with resources like our Slack channel, helps foster connection and knowledge in the marketing industry. David, thank you for your vision and for bringing us together.

**(02:21)**
Reflecting on how far we've come with data, I remember when getting data was as simple as pulling it from Urchin, which later became Google Analytics. Since then, data collection has evolved tremendously, especially with first-party data and data privacy. Today, it’s more critical than ever for brands to collect and leverage data effectively. While many of us work with giants like Google, Meta, and Amazon, they each have vast data and advanced AI, so we need tools that allow us to be competitive.

**(02:56)**
With AI, we now have ways to level the playing field. Neuralift was built to help brands and agencies leverage data to compete more effectively. I'm excited to show you what we’ve been working on. I'll start by sharing a bit about Neuralift, and then we’ll dive into a demo.

**(03:32)**
Our concept for Neuralift came to me during my time as Chief Digital Officer at a large travel brand. We had access to extensive data sources—Google Analytics 4 streaming in real-time, Google Cloud Platform, Snowflake for transactional data, and a CDP for lookalike modeling. Yet, with all that data, creating actionable value was still incredibly challenging. I realized we could use AI to build precise and insightful customer segments.

**(04:07)**
I reconnected with some folks from Yieldbot, where we had developed extensive machine learning applications. Given the advances in AI since Yieldbot, we saw an opportunity to use AI for customer segmentation that would truly understand audiences and their unique preferences. That’s how Neuralift started, and today I’m excited to share our journey with you.

**(04:40)**
Let’s talk about segmentation itself. In marketing, we use segmentation for various purposes, whether it’s personalization, targeting, or loyalty programs. On the paid media side, we refer to “audiences,” but the concepts overlap. Here’s an early screenshot from Google Analytics showing default segments. Segmentation, simply put, is grouping people with similar affinities. The goal is to match the right person with the right product, offer, or message at the right time.

**(05:46)**
Segmentation has numerous applications, from planning and creative testing to personalization and pricing. AI has allowed us to take segmentation to a new level. Recently, Coca-Cola, Bain, and OpenAI conducted an *AI for Personalization* project. Their study showed that AI can provide faster insights, allow for rapid iteration, and serve as a creative tool that inspires strategic thinking.

**(08:24)**
When it comes to segmentation, the current approach with many SaaS tools results in different segmentations across channels—email, personalization, loyalty, custom audiences on platforms like Google and Meta, and more. This fragmented approach can lead to inconsistent messaging. At Neuralift, we unify customer data across these channels, allowing brands to create comprehensive, cross-channel segments.

**(09:29)**
Currently, most segments are created with SQL. But this traditional approach requires prioritizing which data columns to include, often introducing bias and limiting the potential insights. Neuralift, on the other hand, can handle vast datasets—millions of rows, thousands of columns—so you’re no longer forced to simplify or reduce data.

**(11:02)**
Additionally, current segments are often built to optimize KPIs, but without grounding in true incrementality. Neuralift, however, grounds segmentation in key metrics. With precision as a core focus, we can enhance operational efficiency, improve ROI, and create value more rapidly, aligning with the fast pace of marketing and advertising.

**(14:06)**
Now, let's dive into the product. Neuralift starts by ingesting a CSV file from the brand. After securely uploading the data, our deep learning models process it. We don’t need any personally identifiable information (PII) for our outputs. The system then clusters the data, producing segments with names, summaries, insights, personas, marketing recommendations, and underlying data for activation.

**(16:53)**
Let me show you a live example. Here is a customer dataset with 2.5 million rows. Each entry is linked to a customer ID, along with data on device preferences, purchase patterns, loyalty, app usage, and transactional details. This kind of dataset is a mix of behavioral and transactional data for an e-commerce retailer in women’s fashion.

**(18:36)**
Now, here’s the Neuralift dashboard, where this dataset has been segmented. The system identified 29 segments based on the data. These segment names, summaries, and insights were generated by AI and processed in under two hours. This efficiency offers a huge advantage in terms of speed to value, as every part of this was generated without manual intervention.

**(20:23)**
To make this actionable, we provide a view of key metrics, allowing brands to see how segments distribute across KPIs like loyalty, churn, or new customer acquisition. For example, if a brand defines "at-risk" customers as those who haven’t purchased in the last six months, we can show the distribution of at-risk customers across segments.

**(22:42)**
Let’s look deeper into one of the segments. For instance, we have a segment called “Evening Elegance Weekend Fashionistas.” This group predominantly shops in the evenings and weekends, has a high app usage rate, and tends to be acquired through paid search. The AI provides a summary and specific insights on this group’s behavior, enabling targeted marketing actions.

**(26:46)**
We also provide tailored marketing recommendations for each segment. For instance, based on a segment’s behavior, we might suggest targeted messaging or campaign ideas. In the future, we’re collaborating with partners to automate these recommendations directly into campaign management systems.

**(30:12)**
For brands interested in building models such as next-best-action, Neuralift segments can serve as a starting point. Brands can take Neuralift’s highly precise segments and layer their own models for even more tailored actions.

**(36:19)**
On the topic of reliability, the insights provided by Neuralift are based on data calculations and verified through strict checks to ensure accuracy. We validate every insight against the underlying data to maintain high accuracy.

**(45:12)**
For the personas, these profiles are generated using the underlying data and refined using frameworks that have proven effective in marketing over time. This blend of AI and marketing best practices enables us to create personas that offer genuine marketing value.

**(47:54)**
Finally, Neuralift includes a generative co-pilot that creates structured outputs for specific channels—whether that’s SMS, email, social ads, or search ads. The co-pilot can generate targeted campaign ideas with data-backed messaging for each segment.

**(50:56)**
In terms of cost, Neuralift pricing varies based on the size of the brand and the dataset. However, the ROI is immediate, especially when compared to the cost of hiring data analysts or a team to manage segmentation manually. Neuralift offers a much faster and more efficient solution.

**(54:24)**
If you’d like to follow up, feel free to connect with me on LinkedIn, or email me at jon@nuro.ai. I’m happy to answer questions or provide further demos if you’re interested. David, thank you for having me, and thanks to everyone for joining!

## Redefining Media Measurement with AI Insights

Speaker: Tameka Kee
Published: 2024-05-07
Tags: analytics, advertising, media measurement
Video: https://www.youtube.com/watch?v=ZIm8eiqP6gU
Page: https://aimarketersguild.org/sessions/redefining-media-measurement-with-ai-insights

(00:00) The AI companion is on, and if you haven’t tested it out, I highly recommend it. What I like most is how it catches you up on things you might have missed—like, “What was that reference?” or “What did they say about that?” Anyway, I want to start this conversation with Tameka, who’s been one of my favorite journalists, thought leaders, and innovators in marketing, tech, and culture. When we started planning this Insider series and getting guest speakers, Tameka was one of the first people I reached out to because I knew she’d bring a fascinating perspective. So, I’m very excited to hear her insights on AI and what she's focusing on this year. Tameka, I’ll let you take it from here to introduce yourself and share your latest interests in the AI space.

(00:51) Thank you, David, and thank you all for joining! David, I think it's also interesting that we kicked off the year talking about AI at CES in a session together. So, I’m Tameka Kee, an advertising futurist, which means I focus on how advertising is changing and impacting various industries. I’ve been in this field for most of my career, and my current role is as Deputy Managing Director of SIM, the Coalition for Innovative Media Measurement, which is part of the ARF—another acronym! I took on this role at the beginning of the year, and it’s a dream job because I’ve always been passionate about advertising. I’ve loved it since I was a kid—I’d even record commercials, which had my family thinking I was strange since we were supposed to be recording TV shows. So now, working with the ARF, which focuses on research and advancing the practice of advertising, feels like a natural fit for me.

(02:02) At SIM, we have monthly virtual meetings where we produce research focused on media measurement. For example, we recently created a guide for the multicurrency transition to help both the buy side (media owners and publishers) and the sell side (agencies) understand different audience measurement methodologies. If Comscore says you have 10 million users and VideoAmp says 12 million, we provide guidance on how to calibrate that data. We’ve also looked into how privacy regulations impact measurement providers, which is a big issue since most privacy discussions focus on targeting and activation but overlook measurement. SIM’s work is very research-oriented and quite nerdy, but it’s also highly rewarding.

(03:32) I spend my time thinking about AI, VR, AR, and other emerging tech and formats. I’ve put together a short presentation for today on how to leverage AI in 2024.

This topic is vast, but I hope to distill it into practical takeaways. I recently moderated a panel on AI and the future of work for the Black Policy Lab, an organization using tech and policy to improve Black lives.

In today’s session, I’ll share some key takeaways for thinking about AI in 2024. One is to take a structured approach to “test and learn.” Testing AI is essential, but we need to be clear and rigorous about what we’re testing.

I’ll also talk about developing your “will-dos” and “won’t-dos”—what you’ll definitely use AI for, what you might try it for, and what you prefer not to use it for. This might evolve over time, but setting boundaries from the start is helpful.

(06:39) AI efforts should be cross-departmental. It’s critical to stay informed about how other departments plan to use AI, which tools they’re using, and what effects AI may have across teams.

For example, Sports Illustrated’s recent issues, where AI was poorly managed and communicated, could have been avoided with better cross-departmental coordination. Also, consider AI’s impact on interpersonal processes; it’s not just about the technology but about how teams interact and collaborate. Lastly, address bias in AI. While we won’t eliminate it, we can tackle bias by seeking new data sources to create a more balanced perspective.

(09:02) David, you asked about any big players that are addressing bias well. Amazon is one company doing some positive work. They acquired a company that specializes in adding data that better represents lower-income Black consumers, helping to balance their broader data set. The encouraging part is that more companies are openly acknowledging the problem of bias, which is a crucial first step. But because data sets are so vast and complex, fixing bias is challenging, especially when it requires a close look at the sources we use.

(12:51) Another approach companies can take is to curate data from within—content employees have searched for, are interested in, or have chosen. This curated approach lets you use AI like OpenAI for querying within a controlled data set, allowing for more manageable biases from internal sources rather than from vast public data. This controlled process could be valuable, especially when reducing bias is a priority.

(14:13) That’s a great point, Karan. Including humans at each phase of AI processing, whether refining prompts or reviewing output, helps manage bias. There’s no single solution, but having humans involved at every stage ensures we keep asking the right questions and making necessary adjustments.

(15:20) Bias is also subjective; it depends on context. For example, from an advertising standpoint, bias might even be seen as targeting—since advertisers often aim for specific audience segments, which naturally skews data. It’s more about knowing what the data represents and ensuring that you’re clear on those boundaries.

(17:05) At SIM, we recently conducted a study on targeting that revealed how layering multiple data sets can skew results. For example, combining video viewership data with in-store purchase data and demographic data significantly reduces the audience size, often leaving a skewed subset of the original group. The study wasn’t about proving data was biased but instead about showing how different data sources impact audience targeting.

(18:45) “Unintended algorithmic cruelty” is an interesting concept here, where layering data creates unintended biases in targeting. Home Depot once ran into issues when they based campaign targeting on zip codes, inadvertently excluding certain socioeconomic groups. It’s a reminder that data needs careful handling and review to avoid unintentional discrimination.

(19:52) Streamlytics, for example, compensates consumers for their data and focuses on Black consumer data specifically. If you use Streamlytics, you’re intentionally using a data set that’s skewed toward Black households. This isn’t necessarily bad; it’s just important to understand and transparently communicate the data’s skew.

(22:17) Testing AI rigorously is essential. This slide shows an approach Emory University used for AI-driven ad creation. They tested AI’s ability to produce display ads, comparing human-created ads with AI-generated ads. In this case, AI-produced ads outperformed human ones in terms of interest and purchase intent. This kind of structured testing is critical to see where AI might be more efficient than human-led work.

(26:44) Next year, we’ll likely see more growth in AI for audio and voice, as voice synthesis improves. Already, tools like HeyGen allow users to create AI avatars from just a 30-second voice clip. Combined with AI-generated text, this can create full video introductions or even spokesperson-like content.

(29:57) We recently conducted a study on testing AI for different ad scenarios. For example, AI-generated ads for car brands focusing on “ruggedness” and “luxury” scored higher than human-made ads in terms of audience interest and purchase intent. This targeted testing approach allows companies to identify specific areas where AI outperforms, freeing up human teams for more strategic tasks.

(32:02) Some challenges that marketers face with AI include creating effective prompts and revising AI-generated content, which can sometimes be more time-consuming than starting from scratch. Editing poor AI-generated copy, for instance, can take more effort than simply writing it yourself. It highlights the importance of strong prompts and high-quality tools.

(36:44) Prompt engineering is an art in itself, and certain team members may be naturally better at it. Developing a structured, strategic approach to prompting AI can make the technology significantly more effective. Prompting tools correctly to produce refined, useful results is critical, and learning this skill is essential as AI use continues to grow.

(40:36) Another strategy is to establish clear guidelines on when and how to use AI. For example, I’m comfortable using AI for writing job descriptions but cautious about using it to select candidates due to concerns about bias. AI is now integrated into many parts of marketing, but it’s important to define what we do and don’t want to use it for.

(45:45) We must also consider how AI affects team dynamics. For example, if AI allows someone to work faster, what happens with the time they save? Does it create more workload for others, or does it allow for more creative tasks? Using AI efficiently means being mindful of its impact on the team’s morale and workload distribution.

(49:26) Finally, as AI tools become more advanced, like with AI-only creative teams outperforming human-led teams in some cases, it prompts companies to examine internal processes. In one case, an AI-driven team outperformed a human team in pitching ideas, which encouraged the company to reevaluate how they structure their work. AI can expose gaps in our workflows, pushing us to improve our processes and collaborate more effectively across departments.

(54:03) We’re at the hour mark, so thank you, everyone, for a great discussion! Tameka, it’s been a pleasure to have you share your insights, and we’d love to have you back anytime. For those interested in Tameka’s work, connect with her on LinkedIn, follow her podcast *Tech and Soul*, or email her directly.

## AI at the Forefront: Cutting-Edge Tech and Media Innovations

Speaker: Chris Pfaff
Published: 2024-05-07
Tags: venture capital
Video: https://www.youtube.com/watch?v=5iNCW-lmnOs
Page: https://aimarketersguild.org/sessions/ai-at-the-forefront-cutting-edge-tech-and-media-innovations

(00:00) I was involved with New York Ventures before starting my consultancy. We work primarily in media tech, particularly machine learning and AI for media, OTT, and extended reality. We're always scouting for the next big thing, focusing mostly on Europe and North America.

But way back, I was on the agency side, working with companies like Sony, Sharp, and Yamaha. CES continues to cater to these major tech firms, especially those from Asia. I’ve been attending CES for over 31 years, and this year felt different; it was less about flashy concepts and more about real, scalable innovations.

(00:43) This show had less “smoke and mirrors” and more tangible, incremental innovations at scale, which was exciting for all of us. To give an overview, I’ll run through a few highlights I noted, and then I’ll provide a link for anyone wanting to download the information for further review.

(01:29) One significant brand and AI announcement came from Volkswagen. CES has essentially become a car show, especially for concept cars. Volkswagen’s announcement feels like something that would have seemed incredible six months ago, but now it’s almost expected. It’s interesting how quickly this kind of conversational media technology has become the norm. I still wonder when Amazon, with all its AI expertise, will integrate more advanced AI into its Alexa devices.

(02:44) Speaking of AI, I added something unique to this deck—an image created by DALL-E 4. With a little prompting and fine-tuning, I managed to get it to produce a pretty impressive graphic. Interestingly, some of the major announcements last week didn’t come from CES itself, like the OpenAI GPT store launch. I’m not fully sold on it yet but would love to hear others' opinions. It feels like a lot of people will stick to ChatGPT’s main interface rather than navigating through specialized apps.

(03:55) AI-powered robots were everywhere at CES, but I’m skeptical. Many of these devices seem to add more complexity to home management without actually saving time. For instance, devices like the Roomba are practical because they have a clear purpose—cleaning. But a lot of these new AI devices, like home monitors on wheels, don’t seem to offer much practical benefit.

(04:29) LG showcased “affectionate intelligence”—using AI for consumer monitoring via facial recognition, which could potentially feel intrusive. HiSense also had an interactive art TV, adding a generative AI layer to let people create paintings to display. AI is even in appliances now, like washing machines that detect fabrics and adjust cycles accordingly.

(05:14) Samsung, for example, is releasing an AI-powered phone today. Speaking of new tech, I ordered the Humane AI Pin, just out of curiosity. I might end up returning it, but it will be interesting to demo. Surprisingly, it doesn’t come with a subscription fee, making it more appealing.

(06:37) Amazon, despite Alexa’s current limitations, is working on integrating advanced AI into its devices, partly through partnerships with companies like Character.ai. Walmart also made a big impact this year, promoting its AI-driven “adaptive retail” concept.

(07:17) There were some big announcements in AI customer service as well, like Pfizer’s new AI-enabled help desk, which garnered more attention than many of the CES product launches. Even Apple took the spotlight with its announcements, seemingly overshadowing CES itself.

(08:04) Then there were some oddities, like AI-powered roller skates and a “gen backpack,” both showcased at Eureka Park. Overall, there was so much AI that it felt like an overload. I’ll share the deck link for anyone interested, and I’d love to hear more thoughts from Chris or Michael.

(08:50) Chris, anything stand out to you? For me, I found it interesting to see what big players in e-commerce, like Alibaba and TikTok, were doing. Walmart’s adaptive retailing, while innovative, brought some humorous thoughts to mind—like how we might have called it “shoplifting prevention” back in the day! The Rabbit R1 also got a lot of buzz; it feels like the type of gadget we might have seen only in Asian markets a decade ago.

(09:25) The sheer amount of AI integration in TVs, like TCL’s battle for brightness and enhanced connectivity, was impressive. The Amazon Recognition technology in Alexa, along with AI-enabled apps, has been around for a while, but there were definitely cool integrations at CES.

(10:39) What surprised me was the subtle integration of AI into consumer products. While I expected more brands to showcase partnerships with OpenAI, like the ones we saw at South by Southwest with Shutterstock, it seems much of that was kept behind closed doors.

(11:52) CES had some quieter but effective AI integrations, and I was expecting more bold displays, particularly from XR companies and brands like Meta, which was notably absent in showcasing AI advancements. Even the XR and haptic sections were relatively low-key.

(13:00) Chris, you mentioned the growing automotive presence at CES, now almost resembling an international auto show. Some companies, like Kia and Mitsubishi, presented impressive AI-assisted driving features, showing real advancements in vehicle design and ecosystem connectivity.

(14:02) Kia, in particular, had a great showing with innovative design in AI-powered driving. Their display was less gimmicky and focused on creating a connected ecosystem for the car, which was refreshing. It felt like companies were making real strides toward creating cohesive products rather than one-off tech features.

(14:32) Next year’s CES will be especially interesting. Companies invest heavily in their booths, and we’ll see what advances they bring. There was buzz about “see-through” TVs, and while I’m still not convinced of their practicality, Samsung’s overlay with sports broadcasting graphics was a bit more innovative.

(15:48) The see-through TV by Samsung had an interesting layering effect that allowed for a parallax view of game graphics over actual gameplay footage, creating an AR-like experience. I’m still not sure about the point of see-through TVs, but the Samsung Frame remains a top choice for blending a TV into a home environment.

(16:55) We also saw minimal auto-stereoscopic displays, which allow for 3D viewing without glasses. It feels like we’re close to a breakthrough in 3D display tech that could revolutionize the industry.

(18:12) Sony’s demo of VR production, especially their racing simulators with PSVR2, felt like a good use case for VR. It was surprisingly comfortable and immersive, showcasing VR as an ideal addition to high-end gaming setups.

(19:25) Kia’s modular van, set to launch in the U.S. by 2027, was one of the most fascinating displays. The design feels forward-thinking, like something Apple would create, and hints at a shift towards self-driving, multifunctional vehicles. It’s amazing to think how driving will evolve by the time today’s kids are on the road.

(20:36) Seeing brands emphasize integration of gaming engines like Unreal Engine for car interfaces was a surprising trend. The smaller chip sizes have freed up space in car interiors, making room for enhanced sound systems, displays, and more, which paves the way for exciting design innovations.

(22:03) Amazon also had a small but notable presence, showcasing their work in fleet management and their new automotive embedded Linux distro, highlighting the convergence of automotive and computing technology.

(23:12) AI-driven roller skates were an unusual addition, using AI to learn a person’s gait, which has implications for applications beyond just mobility devices. The potential to integrate this kind of real-time responsiveness into cars or robotics is huge.

(24:27) AI in physical environments is advancing significantly. In building management, for instance, AI is being used to optimize systems in real-time based on user patterns. A fenestration client I work with in the UK has seen how older digital twin technologies are making way for more intelligent, dynamic systems.

(26:06) Sony’s innovations at CES stood out, like their LED panels and integrated virtual production systems. The cohesive ecosystem they’re building between cameras, LED displays, and other hardware is setting a high bar, though their technology remains costly.

(27:23) Sony’s integration of their hardware and software solutions is remarkable. They’re creating an ecosystem where cameras, LED panels, and production tools work seamlessly together, which will likely reduce production costs over time and make high-end equipment more accessible. This democratization will especially benefit content creators who don’t need the ultra-high resolution required for national TV but want high-quality production for platforms like Instagram or TikTok.

(28:03) Nvidia’s customized build of Unreal Engine is another development to watch. They’re embedding AI models to enhance facial animation and clean up motion capture, which could bring significant advancements in visual realism for marketing and entertainment. While much of this tech is still on the experimental side and often requires prompt engineering expertise, the next couple of years should see functional tools emerge that make these capabilities more accessible and intuitive.

(29:22) This diverse expertise is what makes these discussions so engaging. People from various backgrounds bring unique insights into what’s happening in AI, tech, and media. For anyone following CES remotely, feel free to share what stood out to you.

(30:03) I'm curious about thoughts on the Vision Pro. Apple’s entry into the mixed reality space is intriguing, especially given the lack of any major reveal from Google and Microsoft’s pivot to more profitable sectors. As a standalone device, Vision Pro seems ideal for business applications, but its practicality as a consumer product is still up for debate. Apple’s simplicity could give it an edge, especially in B2B, where streamlined setups are critical, but its $3,500 price tag may limit mainstream adoption.

(31:16) For developers, Vision Pro could be revolutionary. It allows for screen mirroring from Mac devices, potentially eliminating one of the biggest headaches in VR development—the constant need to remove and replace a headset to check progress. By streamlining this workflow, Apple could make the device appealing to creators, despite the high price.

(32:37) In AR, simpler glasses like Meta’s new AR-enabled sunglasses are likely to gain traction. I see a strong use case for AR in areas like advertising, where brands could create virtual products for consumers to view in real-world environments. Even Snapchat’s Spectacles hinted at this potential, and Meta’s recent tech demo, including Lex Fridman’s interview using their virtual avatars, shows how realistic these virtual experiences are becoming.

(33:59) There’s a clear trajectory toward using AI-generated assets for brand experiences across platforms. From AI-generated images to full-scale virtual brand environments, companies can now adapt content for platforms like Fortnite’s Unreal Editor, creating interactive, immersive brand installations. This versatility is opening up new possibilities in marketing.

(35:28) For professional settings, Meta Quest is already being used for virtual meetings, though it remains a niche application. The transition from niche tools to broader adoption will likely depend on ease of use and the software ecosystem that supports it.

(36:06) For developers, the Vision Pro’s ability to run Unreal Engine could be significant. Unreal Engine’s continuous improvements and integration with Apple’s hardware will likely make it a powerful tool for both AR and VR applications, though Apple’s ongoing legal issues with Unreal may add complexity to this integration.

(37:29) Vision Pro’s developer potential is intriguing, especially for 3D content creators. While its consumer appeal remains uncertain, its ability to streamline VR workflows could make it valuable as a developer tool. As a consumer product, though, Vision Pro may struggle against Sony’s VR2, which offers high-end VR at a much lower price and is already compatible with popular platforms like PS5.

(39:50) We also saw a strong presence from South Korea this year, particularly with SK’s massive display on sustainability in media emissions. Their eco-conscious initiatives, like reducing file sizes, mark a forward-thinking approach to environmental impact in tech. France, too, had a considerable presence, with investments in emerging technologies like LLMs (large language models). President Macron has been a notable supporter of the tech industry, and French startups are increasingly investing in AI innovations, including language models and other tools aimed at supporting French-speaking users.

(42:02) The presence of media and marketing professionals at CES was unprecedented, with major figures like Linda Yaccarino of X and Mark Cuban speaking at panels. MediaLink hosted their traditional event, and Netflix made a debut appearance with a remarkable booth. These kinds of takeovers highlighted how media and tech are converging.

(44:00) From a media perspective, there was a noticeable divide in CES's atmosphere. In entertainment-focused discussions, people expressed concerns about AI potentially replacing jobs, while on the show floor, there was an optimistic, almost utopian vision of AI-enhanced technology. This contrast in perspectives made for a head-spinning experience, reflecting both the excitement and ethical concerns around AI’s future.

(44:42) It was surprising to see France’s large stake in AI, with Macron’s continued investment in the tech industry and AI language models. France’s focus on non-English language support reflects their desire to make AI accessible and responsible across diverse cultures.

(46:44) Sam Altman recently spoke at Davos, sharing OpenAI’s intentions to prevent the use of its technology in political campaigns. While it’s a step in the right direction, enforcing it effectively may prove challenging, given the complexity of monitoring AI use cases.

(48:07) Casey Newton of Platformer wrote on this recently, acknowledging OpenAI’s efforts to curb election manipulation, even blocking certain tampering requests. His perspective is that OpenAI’s approach could serve as a model for other platforms, though there are still significant challenges ahead as we approach GPT-5.

(48:43) On a separate note, I wanted to share an alternative to Upstream called Mingle, which offers a similar platform for professional networking and virtual events. We’re hosting an introductory call soon for anyone interested in exploring this new technology.

(50:34) A topic that keeps surfacing is AI companionship as a service. This concept goes beyond AI in customer service or sales to more personal realms, raising ethical questions about its potential impact on human relationships. There’s been an influx of requests for virtual AI “companions” that can adapt to individual user preferences, and the implications for this technology are vast, especially as we see developments in AI-driven avatars and conversational agents.

(51:46) AI avatars are quickly becoming more lifelike, and tools like Haen allow users to generate realistic AI-driven virtual companions. Nvidia’s work with facial animation in Unreal Engine is making these avatars look increasingly authentic. But as companies start embedding these avatars in customer-facing applications, there’s a need to address ethical boundaries.

(53:05) The “Bot Love” podcast by Radiotopia delves into the complexities of human relationships with AI, exploring how these connections can be both beneficial and damaging. In some cases, AI can help people manage isolation, but it can also impair real-world relationship skills. Given that AI companionship is inevitable, the focus should be on educating users and creating transparent conversations to avoid unintended consequences, as we’ve seen with social media’s impact on teens.

(54:45) Many clients are interested in creating sales and customer service avatars that look and feel highly realistic. These advancements open up new business possibilities but also highlight ethical concerns around user manipulation and the potential for deceptive practices in consumer interactions.

## Empowering Consumer Marketing with Real-Time AI

Speaker: Joseph Galarneau
Published: 2024-05-07
Tags: ai marketing, real-time ai, consumer insights
Video: https://www.youtube.com/watch?v=D8gLmab-7lk&t=3s
Page: https://aimarketersguild.org/sessions/empowering-consumer-marketing-with-real-time-ai

(00:00) Thanks, everyone. Today, I’ll give you a quick overview of CivicScience and then introduce Sage, our consumer insights AI assistant. If you want to sign up for a free beta, just visit ai.civicscience.com—no fees involved.

(00:32) CivicScience is a real-time consumer insights and activation platform. We partner with major media sites, placing polling units on their platforms, and we engage with around 10 million consumers daily, collecting roughly 4 million responses on tens of thousands of questions. Essentially, we gather insights into anything a brand marketer or strategist would need to know about consumers. Our current setup is a SaaS platform that presents all this data, but with the recent evolution in AI, we’ve developed Sage to take this a step further.

(00:59) Sage is our consumer insights AI assistant. We envision that marketing and analytics assistants will gradually be replaced by AI. Sage offers a user-friendly, plain-language interface that provides both high-level data exploration and a deeper dive into trend analysis. This helps marketing, strategy, and product teams gain insights without needing to be data experts.

(01:25) Let me explain the structure of Sage. We’ve designed it as an “AI sandwich.” At the top is GPT, which is accessible through Slack, web chat, and soon, Microsoft Teams. When a user submits a question, GPT determines which of CivicScience's data points are most relevant, then directs the query to our system. Our platform, the core of the “sandwich,” processes the data, running statistical tests, sampling, and graphing. Finally, the GPT layer summarizes the output in plain language for the user.

(02:22) Let me show you how Sage works. I’ve run some queries in advance, but I’m happy to demonstrate live if anyone’s interested. Currently, this beta version integrates with Slack, and soon it’ll connect with Teams and web chat. I asked Sage about trends in premium cosmetics. GPT processes the question, identifies the data points, and delivers a summary within about a minute. It breaks down information by demographics, such as age groups, providing detailed insights.

(03:11) Sage doesn’t just show insights; it provides footnotes and links back to our SaaS application for in-depth data exploration. Non-customers can still download extensive data sets for each question, offering a detailed breakdown in just about a minute.

(04:06) You can also specify filters, like geographic regions, to get data tailored to specific areas. You can drill down into time ranges and trends, view time series graphs, and more. With Sage, users can either type queries or click through various data options.

(04:31) Let me pause here for any questions. I’d be happy to dive deeper into any part of the demo. Thanks, Joe, for setting this up. It’s exciting to see this in action, and it’s been a valuable tool to pilot. If anyone has questions, feel free to raise your hand or unmute.

(06:10) **Audience Question**: I love the “sandwich” concept. I work at the IAB and handle a lot of research. If we wanted to use your tools, would that involve a subscription?

**Joe**: Yes, most clients on our logo screen pay six figures for full access. Sage, however, is launching at $299/month, making it accessible for smaller teams or companies that don’t need the entire platform. For more in-depth analysis, larger subscriptions are available, but Sage offers a great entry point with the same high-quality data.

(07:35) **Follow-Up Question**: With the conversational interface and data access, do you see people asking different types of questions than they would with a traditional tool?

**Joe**: Absolutely. Traditional consumer insight tools often require specific questions. With Sage, users can ask broad questions for top-of-funnel discovery and trend analysis. This data democratization lets non-insights team members access valuable information without needing a specialized background, enhancing discovery and early analysis.

(08:59) **Audience Question**: People are asking about the data sources. Where does your data come from?

**Joe**: This is unique to us. All data in Sage comes exclusively from CivicScience’s databases. Unlike generic AI tools that pull from the internet, Sage only accesses our proprietary data, ensuring accuracy and relevance. You can trace every insight back to its source in our system or download it, giving you confidence in its reliability.

(10:26) **Moderator**: Thank you, Joe! Feel free to stay for more questions, and thanks for getting us started. Next up is Julia with Storia.

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(11:10) **Julia**: Hi, everyone! I’m Julia from Storia, where we use contextual intelligence to enhance storytelling. My co-founder and I have a background in AI research at places like Google and Amazon. We started Storia to accelerate storytelling with AI, initially focusing on film and now exploring its potential in marketing.

(12:33) Today’s demo showcases how Storia assists independent filmmakers in turning scripts into storyboards. Filmmakers upload their scripts, and we parse them to identify characters, settings, and other narrative elements. This tool helps filmmakers visually communicate their ideas for funding or collaboration with crew members.

(13:11) After uploading the script, we automatically identify characters and settings. Filmmakers can add character traits and visual references, which get incorporated into prompts. These prompts then guide our system in generating visuals that align with the narrative. You can see the generated storyboard here, which filmmakers can edit or re-render in different styles.

(16:06) We also offer customization options, such as changing the visual style across hundreds of scenes with a single click. The tool is intuitive, allowing filmmakers to experiment quickly. Currently, we’re exploring how to adapt this technology for marketing teams, who might need more dynamic formats like animatics or videos.

(20:07) We’d love to collaborate with marketing agencies as design partners to refine this tool. If you’re interested, please reach out, either for collaboration or AI consultancy. Thank you!

(24:03) **Noah (Popsicle)**: Hi, everyone! I’m Noah, founder of Popsicle. We’re focused on revolutionizing e-commerce with AI. As AI becomes more integrated into daily life, shopping behavior will change dramatically. Brands need to be prepared to structure their data in ways that make it accessible and appealing to AI shopping assistants.

(27:08) Popsicle currently enhances e-commerce performance by structuring Shopify sales data and feeding it to Meta’s machine learning models. This simple, accessible tool significantly improves ad performance, helping brands reach the right customers.

(28:26) Our long-term vision is to move from search engine optimization to “machine engine optimization.” As consumer behavior shifts, we’re ready to help brands optimize their data for a machine-driven future.

**Audience Question**: How do you handle first-party data with this model?

**Noah**: Great question. We help brands maximize the value of their first-party data by structuring it for AI-driven platforms like Meta. This structured data becomes essential fuel for effective marketing, even as privacy laws evolve.

(36:44) **Cat (RCX)**: Hi, everyone! I’m Cat, founder of Radical Customer Experience (RCX). As a former marketing executive, I noticed that traditional fear-based marketing tactics were negatively impacting mental health, especially post-pandemic. RCX is dedicated to promoting trauma-informed, empathy-driven marketing.

(38:53) Our new app, Radical Human Intelligence, combines AI with empathy. It produces a “brand sensitivity score,” measuring a brand’s empathy and vulnerability. Unlike Net Promoter Score, which shows general satisfaction, our score is dynamic and considers individual consumer experiences, helping brands avoid missteps in messaging.

## Mastering the Art of AI in Content Creation

Speaker: Michelle Vincent
Published: 2024-05-06
Tags: content strategy, content creation
Video: https://www.youtube.com/watch?v=Y11Z_94ps4Y
Page: https://aimarketersguild.org/sessions/mastering-the-art-of-ai-in-content-creation

(00:00) I recently had the pleasure of speaking alongside Michelle Vincent at an event hosted by our mutual friend, Epi, who organizes the Bold Awards.

Michelle was one of the guest speakers, and it was just a few weeks before the Marketers Guild launched. From the moment I heard her insights, I knew we needed her perspective here.

Michelle is an incredible community builder and deeply involved in the creative space. When I meet a fellow community organizer, my enthusiasm kicks in, so I’m thrilled to have her with us today. Michelle, why don’t you introduce yourself and what you’re up to, and then we can dive into how AI fits into everything you’re doing.

(00:43) Michelle: Thank you for the kind introduction, David. Great to reconnect since our last event with Epi.

The topic of AI is evolving so quickly, and it’s incredibly relevant to everything we do right now. As David mentioned, I’m the CEO of Mofilm. Mofilm started in 2007 as a creative content agency that connects brands with a global creator community of over 10,000 creators to produce diverse content.

When AI became a dominant topic in the creative world, we knew it was essential to explore what this means for our community, the implications for our business, and the path forward. So, we started investigating AI tools, experimenting, and educating our community to help them leverage AI as a creative superpower rather than feeling threatened by it.

(02:33) I’ve brought a slide deck that reflects a bit of the talk I gave with David a couple of months ago. Since AI is rapidly advancing, I’ve updated it to reflect where we are today. First, let me gauge the room—do we have mostly marketers or creatives here? I see a few nods, so it seems like we’re all on the same page.

(04:28) Let’s dive in. At Mofilm, we aim to empower tomorrow’s creators to embrace AI tools as creative aids rather than feel displaced by them.

The visual on the right was generated by our Executive Creative Director, using prompts through Midjourney to give a creative, AI-inspired touch. Working with our larger holding company, The Brandtech Group, we’re exploring AI investments to enhance what we can offer our clients.

Right now, the AI visuals we’re seeing, as good as they may be, are probably the worst they’ll ever be. For example, look at the progress AI-generated images have made in just a year, from awkward features and strange fingers to near-photorealistic quality. This improvement is only going to continue as AI matures.

(06:11) Many people think AI is new, but it’s been around in some form for years. For instance, in 2018, an AI-generated painting, *Portrait of Edmond de Belamy*, sold for $432,500 at Christie’s. This year, an AI-generated photo won the Sony World Photography Award. The artist who created it declined the award to make a point about AI’s place in photography. His statement—that AI and photography should not compete—raises questions about authenticity, transparency, and the role of AI in art. With AI technology here to stay, we need to determine how to integrate it responsibly and ethically.

(09:09) At Mofilm, we decided to check the pulse of our creative community regarding AI. With over 10,000 members, we wanted to understand their perspectives. About 38% felt neutral toward AI, perhaps due to uncertainty or fatigue from trends like NFTs and the metaverse. However, 40% expressed fear that AI might take over creative jobs, and another 15% weren’t sure. This means over half of our community harbors some concern about AI’s impact on their futures. Despite these mixed feelings, we knew we needed to dive in and explore the ways AI could help rather than replace them.

(10:23) When we started experimenting with AI, we were blown away by how quickly it could transform creative ideas. Take, for example, an image our Creative Director used at an AI film festival. He took a simple photo, ran it through Midjourney with the prompt “giant pink bunny mascot,” and got an imaginative and polished result almost instantly. The potential for rapid ideation is incredible, but there’s still a skill to crafting effective prompts, which is where experienced creatives excel. Using AI can be a powerful brainstorming tool, but it’s not replacing human creativity—it’s enhancing it.

(12:10) At Mofilm, we operate under the mantra “many voices are better than one.” Collaborative ideation leads to high-performing content, but it raises the question: what happens when one voice—an AI—is the only one creating? For example, research has shown bias in AI-generated images, with fewer than 20% of AI-generated depictions of professionals, like doctors or CEOs, showing women. Recognizing the biases within AI models is critical. Human oversight is essential to ensure inclusivity and prevent harmful stereotypes from creeping into AI-generated content.

(14:02) Some brands and platforms have already faced backlash from these issues. Levi’s, for example, leaned into AI-generated models, which sparked criticism from the modeling community and brand advocates concerned about displacing real people. And in another instance, an error during Google’s Bard reveal cost them a significant chunk of their stock value. Mistakes are inevitable, but AI’s risks haven’t scared brands away; rather, they’re embracing AI and pushing forward, especially when AI can be paired with human creativity.

(17:15) David: And Michelle, just curious—did the Screen Actors Guild strike, which highlighted concerns over AI’s impact on performers and writers, change views among your creators?

Michelle: Great question. We took that initial poll early this year and started a webinar series to educate our community. We’ll survey again at the end of the year to track changes. Regarding the writers’ strike, it has huge implications for creatives and marketers alike. The recent settlement means studios can’t replace writers with AI, but writers can use AI tools in their work. For marketers, this signifies that creatives will need individual AI sandboxes that allow them to personalize AI in line with their unique voice and style. We’re even investing in technologies that will equip our creators to do this, ensuring that AI supports rather than replaces their creativity.

(20:40) It’s possible that AI could lead to smaller teams, as you mentioned, Tom, but I believe the technology will redefine roles rather than eliminate them. AI will allow teams to work more efficiently, but the need for human input and iteration in the creative process remains essential.

(23:25) David: Great points. I was surprised to learn that even in procedural shows, like *Law & Order*, writers need to be on set to make on-the-spot adjustments. It reinforces the idea that AI can support but not fully replace the creativity and flexibility required in production.

(25:38) Lam: With concerns about AI misuse, such as a book selling on Amazon created without the author’s knowledge, what’s your take on AI and copyright issues?

Michelle: Excellent question. The European AI Act is likely to set the standard on this. It will require transparency around AI-generated content, ensure models don’t infringe on copyrighted content, and potentially set up a system for royalties to compensate creators whose work is ingested by AI models. Copyright laws, which require a human author, differ from licensing, which allows use but may restrict ownership. These areas are still evolving, and it’s vital for small businesses and freelancers to understand their rights and protections as these legal frameworks take shape.

(30:42) For businesses, the broad licensing terms of platforms like ChatGPT, Runway, and Descript can be problematic. We’ve worked extensively to ensure that anything we create for clients using these tools is fully protected. Small businesses and freelancers should be especially vigilant about platform terms to ensure they’re protected.

(33:26) David: Thanks, Brooke! Any thoughts to add from a legal perspective?

Brooke: Absolutely. Reviewing terms closely is critical, especially as terms often mask their true meaning. Working with a lawyer, particularly one familiar with AI and IP law, can help navigate these nuances and protect your work. Not all lawyers are equally experienced in this area, so it’s beneficial to find one knowledgeable about AI’s legal landscape.

(35:22) David: Practically speaking, are there platforms that are particularly creator-friendly?

Brooke: OpenAI, for example, has stated they won’t mimic living authors’ voices, and some platforms even offer IP indemnity for creators. Platforms that provide such protections show they’re thinking about creators’ rights and trying to reduce the risk of infringement.

(38:46) Kate: Michelle, how do you decide when to use AI-generated versus human-created content in your campaigns?

Michelle: Great question. We’ve found that AI can accelerate the early stages of creative ideation. For example, with DoorDash, we used Midjourney to create quick visuals for storyboarding that ultimately guided the live production process. AI was used here to spark ideas, but the final product was created with real people, ensuring both authenticity and quality. We’re also guiding our community on using ChatGPT for tasks like research, audience profiling, script development, and translation. AI enhances each step of the creative process but doesn’t replace the human touch.

(43:56) David Cutler: There’s a need for practical tools in this space. Could AMG play a role in creating tools for AI-generated content and copyright management?

David: Definitely an area to explore. There are experts, like Richie Glassberg at Safeguard Privacy, who are building tools focused on these challenges. Partnering with established experts while engaging our community could be very beneficial.

## Unlocking Creative Genius Revolutionizing Brand Strategy with AI

Speaker: Dana Hork
Published: 2024-05-06
Tags: brand strategy
Video: https://www.youtube.com/watch?v=jEyN-o_aYjs
Page: https://aimarketersguild.org/sessions/unlocking-creative-genius-revolutionizing-brand-strategy-with-ai

**(00:00)**
Dana and I connected, and she started sharing some great ways AI is being applied in the agency tech stack. I thought this was something many people would want to hear about, including myself. Dana then mentioned that MK would have a lot to contribute as well, and we agreed more experts would only add to the conversation. Dana and MK, please introduce yourselves, and I’m excited for this discussion. For those joining for the first time, these are conversational and very open—feel free to use the chat, raise questions, or join in directly.

**(01:15)**
Thanks, David! I’m Dana Hork, and down there is MK. Before our own introductions, we thought it’d be great to have everyone introduce themselves in the chat. If you’re comfortable, drop your name, where you work, and maybe your favorite AI tool or the one you’re most excited about using right now. This will give us a sense of who’s here so we can tailor our conversation accordingly. I’m seeing names starting to come in now; thanks, everyone!

**(02:20)**
While folks are introducing themselves, here’s a bit about me. I’m Dana Hork, CEO of Beers with Friends, a brand agency with a highly entrepreneurial spirit. To share some background: I started in financial services selling municipal bonds, but after business school, I joined Jet.com, working on marketing. When Walmart acquired Jet, I moved over to lead social media for Walmart, handling our national handles, 5,000 local pages, and community management. Later, I served as the head of marketing at Wonder, a food tech company focused on democratizing access to high-quality food.

**(03:38)**
Last year, I dipped into Web3 and hosted a podcast called Web 2.5, covering blockchain and Web3 innovations. This experience fueled my interest in broader technology, including AI, which has led me to explore various use cases for it. At Beers with Friends, we approach each project with a roster of experts and fractional specialists. That’s how I met MK, who brings her impressive creative skills and her AI expertise. MK, go ahead and introduce yourself.

**(04:37)**
Thanks, Dana. I’m MK, and I’ve recently started working with Dana, which has been fantastic. My background is in advertising, working with both brands and agencies to help brands develop creative visions. Over the past few years, I shifted towards Web3 and innovative technologies, guiding brands on how to leverage these advancements meaningfully. There’s always pressure to use the latest tech, but understanding how it truly serves a brand’s goals takes time, and that’s where I come in.

**(06:05)**
Since we’ve had some folks join late, I’ll quickly recap and say that we’ll be focusing on two ways we’re using AI at Beers with Friends: as a tool for unlocking creativity and enhancing productivity. Our agency operates with a unique model where we work in sprints, dedicating five days to each project. Every engagement involves a handpicked team of experts, working on just one client at a time for maximum focus. This approach demands agility and efficiency, which AI helps us deliver on.

**(08:08)**
To start, I’ll talk about how we use AI creatively. AI has become an integral part of my workflow, especially for overcoming creative blocks. When I get a new brief, staring at a blank page can feel daunting, even with years of experience. With ChatGPT, I can bypass that blank-page moment by using it to generate some initial, basic ideas. I don’t input client-specific details to protect confidentiality, but with broad prompts, it gives me something to work with, providing a jumping-off point. ChatGPT’s ideas may not be groundbreaking, but they get the creative juices flowing and help me reach my own original ideas faster.

**(09:54)**
After getting past that initial stage, AI also helps me with iteration. Once I’ve defined the core idea or direction for a campaign, I need to visualize it to align with the client. This used to mean hours searching for the right images to build mood boards. Now, with tools like Midjourney, I can quickly generate visuals that bring my concept to life. While I wouldn’t use these as final products, they serve as powerful visual aids to show clients where we’re headed.

**(11:28)**
David, I saw your question about the process. I usually work in parallel paths. For instance, if I’m working with a sneaker brand and aiming for a futuristic feel, I’ll generate images of the product alongside visuals that set the broader mood. This allows the client to see both their product in a new light and the world we’re building around it. It’s about showing them the overall vision in a way that feels concrete, which makes alignment much easier.

**(14:22)**
One key to effective use of AI for visuals is to define the object, aesthetic, and medium. For example, I might specify a sneaker as the object, a futuristic style as the aesthetic, and photography as the medium. This lets me create very targeted images that make it easier for clients to align with the vision. Previously, I might have used words to convey the idea, but now I can show a visual representation to ensure we’re on the same page.

**(16:36)**
Some of the most powerful creative tools, like Adobe Photoshop and Illustrator, are starting to integrate AI. This significantly speeds up refining visuals. In Photoshop, for instance, I can extend an image’s background or change elements almost instantly. What once took me hours now takes minutes, allowing me to iterate quickly and bring a cohesive look into the process. These tools make it easier to personalize AI-generated images and incorporate specific brand elements.

**(18:50)**
I want to emphasize that AI isn’t a final solution for us; it’s a rapid prototyping tool. We can use it to iterate, show ideas, and get client buy-in, but there’s still often some detail missing for final products. The speed and flexibility, however, make it invaluable at the concept stage, letting us reach alignment faster.

**(19:21)**
Thanks, MK. Now I’ll share how we use AI on the business and marketing side to streamline operations and scale. Some tools may be familiar, but the way we use them could offer ideas for your own workflows. I tested ChatGPT for LinkedIn content recently to see if it could help me market myself and the agency. While I ended up preferring a bio we wrote ourselves, ChatGPT did provide good prompts for LinkedIn posts and thought-starters. The suggestions it generated, like “Spotlight on fractional experts,” were useful.

**(22:01)**
In terms of business operations, I focus on capturing data, extracting insights, and connecting processes. First, we use tools with built-in AI to capture information—like Google Drive, Notion, and Fireflies for meeting notes. We’ve chosen tools that are widely adopted among tech-savvy entrepreneurs and that prioritize innovation, avoiding some of the larger, slower-to-adapt options. This lets us collect and organize data more efficiently, so everything’s in sync as we grow.

**(23:49)**
To extract insights, we use tools like Notion, where I store client briefs, meeting notes, and project details. Notion’s AI summary feature has been surprisingly helpful. For example, I recently used it to create a concise summary for a custom cooler pouch project. With a quick summary of the client needs, budget, and other details, I can brief team members and create marketing materials much faster.

**(24:45)**
Finally, I use Zapier to connect these systems, building workflows that make our tools work together seamlessly. The AI in Zapier helps simplify creating these integrations, so I can link our CRM, project management, and documentation tools without needing technical expertise. This connected ecosystem is crucial for scaling while maintaining efficiency.

**(26:15)**
For instance, we use Copper as our CRM and Notion as our central hub for client information. People and companies from Copper sync to Notion, where I keep notes and project details. This setup allows me to access everything in one place while Copper handles the CRM aspects. It’s not perfect, but it’s a practical solution for keeping everything connected as we expand.

**(28:36)**
Great question, Jay. What do I wish AI tools could do that they can’t yet? For me, it’s less about needing new tools and more about refining the existing ones. For instance, AI models often skew toward certain artistic styles, which can limit creativity. I hope to see more ethically developed, diverse datasets that allow for more specific customization in AI-generated imagery. I’m also excited about the integration of AI in major creative tools like Adobe, which will make it easier to personalize and refine work.

**(30:21)**
MK nailed it—what excites me most isn’t entirely new tools but seeing AI integrated into the big players’ platforms. I’m thrilled to see Adobe incorporating AI because it’s a program I already know well, and I think people will gravitate towards tools they already use daily. If you can keep working within a platform you’re comfortable with, that’s a game-changer for creativity.

**(32:59)**
A point I’d add is that cobbling together so many tools can feel fragmented. If I could wish for anything, it’d be more consolidation among the tools so that data doesn’t end up scattered across multiple platforms. For any business, big or small, the inefficiency of tool migration later on is a big risk. It’s something I consider as I build out our tool stack.

**(34:49)**
We’ve developed our systems to

balance exploration and efficiency. For example, we started with Google Meet, then switched to Zoom for ease of use, and now we’re testing Butter, which offers more branded experiences. For workshops, it’s been great, though every platform has its trade-offs. I think the key is finding what gets us 90% there and accepting that no tool will be perfect.

**(37:47)**
I also apply a “90/10” approach. I explore new tools, like ChatGPT for generating idea lists, and use them in ways that complement my workflow. But I don’t expect any one tool to handle everything. Instead, I focus on what each tool does well and integrate it where it fits naturally, using AI to handle the repetitive work so I can focus on big-picture creativity.

**(39:10)**
One thing I think about more now is learning styles. Clients all have different ways they process information, and these AI tools let us adapt our presentations to suit them. Some clients need to see a visual to connect with an idea. These tools help us provide that visual representation faster, which increases our chances of getting clients excited and aligned.

**(40:46)**
Jeff, great question about measuring tool efficacy. I haven’t implemented formal metrics yet, but I’d love to learn more about what you’re doing. I think tracking time savings, for instance, could be useful. Another layer we’d like to get into is comparing AI-generated work to human-created work and tracking the outcomes. This could help us pinpoint which AI tools deliver the best results.

**(44:15)**
I love what MK said about AI helping generate first drafts or lower-stakes creative work. For instance, I’ve used it to write fun limericks for my kids, or I could use it to write a Tooth Fairy note. These are low-stakes ways to explore the creative possibilities of AI without the same pressure as client work. It’s a great way to get familiar with the tool in a playful context.

**(45:54)**
I haven’t personally used Google’s AI integrations much, but I do use Adobe’s new generative features, like background extension in Photoshop, which has been invaluable. I think as more of these big players build AI into their products, people will shift towards using AI within their existing toolkits. It’s just easier than learning entirely new platforms.

**(48:47)**
Dana and MK, this was amazing! I appreciate the structured slides and visual aids; it made everything much more concrete. You’ve both shown how AI enhances our capabilities but doesn’t replace deep expertise. Thank you so much for sharing, and I look forward to continuing the conversation in the community.

## How Agencies Are Using AI For Ad Data Analysis

Speaker: John Reilly
Published: 2024-04-08
Tags: data analysis, ad tech
Video: https://www.youtube.com/watch?v=Sk7bZnvaMhA
Page: https://aimarketersguild.org/sessions/how-agencies-are-using-ai-for-ad-data-analysis

**(00:00)**
Hello everyone, and thanks for joining us today. If we haven’t met, I’m David Burkwit, the founder of the AI Marketers Guild, and I’m here with John Riley, co-founder and CEO of AIO. I’m excited to learn more from John today about how agencies are using AI for data analysis. I’ll hand it over to him to share insights on how AI is changing the way we use data. Please share any questions or comments in the chat, raise your hand if you want to ask something live, and thanks for being here with us.

**(00:49)**
Thanks, David, and hi, everyone. I’m excited to be here and talk about the data side of AI and some machine learning applications as well. Both generative AI and ML are having a huge impact on how marketers use data for decision-making and analysis, especially in the ad space. I’ll walk you through some of what these technologies make possible and also share a bit about what AIO does.

**(01:25)**
A bit about my background: I started out as an electrical engineer designing televisions at Sony, back when CRTs were still around. Most of what I know now comes from product management work, especially at Sonos, where I managed many wireless audio products. I later moved to a 3D printing startup, Markforged, where we created carbon fiber and metal parts. During my time there, I ended up overseeing marketing because we had issues with our sales pipeline. We’d stopped using Facebook ads due to low-quality leads, but then our sales slowed down. It turned out some good leads were getting mixed in with the noise, and we didn’t have a good way to filter and find those valuable leads. That sparked the idea for AIO.

**(03:37)**
At the time, there weren’t any tools that people without specialized training could use to make better data-driven decisions. While there were ML solutions, they were mostly offered as services rather than self-service tools. So, we set out to create a self-service platform for data analysis. Over time, as AI tech evolved, so did our platform, making it easier for anyone to work with data—without needing advanced skills.

**(04:11)**
Today, AI is everywhere, and there’s a lot of hype around it. That moment when people could suddenly use a simple chat interface to interact with AI—like with GPT—changed everything. Now, with AI, you can generate content, images, video, and blog outlines, which has transformed the creative process. But it goes beyond content creation. Clients increasingly expect data sophistication from their agencies, and large agencies and holding companies are investing heavily in AI.

**(05:44)**
When we first started, our idea was to help internal teams make data-driven decisions. But it turns out agencies are leading the way here. Agencies are early adopters of this technology because they can deliver a higher level of sophistication than their clients can. CMOs, in particular, are under a lot of pressure to prove that marketing investments are working and increasingly rely on data-driven methods.

**(07:20)**
The old way of doing things was resource-intensive. Marketing teams would depend on data scientists or analysts, but those teams are often focused on the company’s product or core technology rather than marketing. For example, at Markforged, our data scientists were focused on perfecting 3D-printed parts, not marketing. Working with external analysts often creates delays and miscommunication, which leads to time lags and missed opportunities.

**(08:44)**
Now, with AIO, you can ask data questions directly and get answers without relying on outside analysts. You can even share a data analysis interface with clients, allowing them to self-serve for straightforward questions. This kind of access helps optimize ad spend, enables predictive modeling, and allows faster decision-making.

**(09:52)**
Our approach with AIO is to make working with data as easy as possible. Agencies can White Label the platform and build custom models and dashboards, merging client data with their own proprietary data if they wish. This setup allows agencies to create unique audience segments and identify factors that contribute to purchasing.

**(11:26)**
Here’s a quick example from our customers. Zenith is using AIO for a next-gen business intelligence approach, where users can simply chat to get answers to data questions. Fathom uses it for predictive enrollment targeting. Building dashboards, which used to take a lot of time, can now be done on the fly, allowing agencies to provide insights to clients faster. We’re even working on templates so that once a report is set up, you won’t need to rebuild it each time.

**(12:25)**
For the demo today, I’ll show you how AIO helps analyze campaign performance data. This is a common scenario where agencies need to assess past campaigns to plan and optimize future ones. Many agencies even use AIO’s data chat as part of their pitch process by quickly analyzing a client’s past ad performance.

**(13:01)**
David, you mentioned that the idea of certifications is fading out. That’s exactly what we’re seeing. Today, when only one person knows how to use a tool, it creates dependency issues, especially when they leave. With AIO, you don’t need certification or training to work with data. You can connect the data, and then the language model will automatically interpret and run queries for you, whether in SQL or Python.

**(14:12)**
Our platform can learn from past data patterns and predict future outcomes. For example, if you’re lead-scoring, AIO can help predict lead conversion likelihood, allowing you to assess your channels’ performance in real time by looking at lead quality. The more data features—like demographics or customer attributes—that you feed into the model, the more accurate it becomes.

**(16:22)**
We designed AIO to work with any type of data, even publicly accessible or social media data. For instance, we have a sentiment model trained on millions of tweets, labeled based on positive or negative emojis. This can be used to monitor social media sentiment in real time. You can connect virtually any dataset, whether that’s marketing data, CRM, or social feeds, and analyze it together with other data sources.

**(18:47)**
Let’s go through a sample data chat session with AIO. Here’s some campaign data where we ask for an overview, and AIO calculates impressions, click-through rates, costs, and more. After generating results, AIO also explains the method it used to produce the answer, which helps you understand the process behind each insight.

**(19:16)**
We can dive deeper, for instance, by asking which metrics varied over time. AIO found that cost per click had a high degree of variation and was worth investigating further. It can also break down metrics by factors like campaign type, placement, and time period, generating charts for easy analysis. If you’re trying to optimize cost per click, AIO can even suggest the best placements or campaign types to focus on based on your data.

**(21:38)**
For reporting, AIO can also generate summaries. You can ask it to draft an email to a client with embedded data insights, making it easier to share results directly.

**(22:20)**
Yes, you can customize charts, including colors and branding. We’re also launching a feature that allows live editing of charts to match your brand colors.

**(23:26)**
AIO is a White Label solution, so you can customize it for your agency’s brand or for specific clients. Clients can self-serve data insights or use the reports you create for them. You can even guide them through chat history, embedding past questions to lead the analysis.

**(27:48)**
To ensure the accuracy of responses, we use a mix of GPT-3.5 and GPT-4, choosing models based on the complexity of the question. We also have a validation mechanism where responses are evaluated by OpenAI’s vision model to confirm that they correctly answer the user’s question. This setup lets us adapt as new, more efficient language models become available.

**(30:21)**
To get started in AIO, you can create a new project and connect your data from sources like Google Ads, Snowflake, or CRMs. We support role-based access, so each client’s data remains in a separate team compartment. Once data is uploaded, AIO identifies the data types, runs column correlations, and offers options for data cleanup and transformation, such as removing outliers.

**(33:45)**
You can also merge data sources. After data is merged, you can start exploring insights or sharing the chat interface with clients. You can customize the interface with client-specific instructions, suggested questions, and more.

**(35:11)**
If you want to predict outcomes, AIO includes a predictive engine. You can build ML models for time series, classifications, or numerical predictions. For example, you can create a model to predict return on ad spend based on past data and key factors. AIO’s machine learning features make it easy to identify data patterns and provide actionable predictions.

**(36:34)**
After building a model, you can deploy it in real time for ongoing predictions, such as scoring new leads or optimizing ad spend. This lets you apply data insights instantly, which is especially useful in dynamic ad environments.

**(38:04)**
We often get questions about data access. Agencies are usually able to get marketing data access from clients, but getting revenue data can be more challenging. Sometimes we work with high, medium, or low value categories instead of exact revenue amounts, which still provides valuable insights for the models.

**(41:10)**
Marketing mix modeling is possible with AIO,

as long as you have a data set that includes spend data across different channels. By comparing results at different spend levels, you can optimize ad allocations. We’re working on scenario modeling features to make it even easier to adjust spend based on outcome predictions.

**(44:35)**
AIO makes it possible for anyone to explore data, and some data scientists appreciate this because it frees them from routine requests, allowing them to focus on high-value tasks. We find that our platform often helps educate teams on data, leading to better questions and analysis.

**(47:32)**
Currently, AIO works on one dataset at a time, though you can merge additional data sources. In the future, we aim to allow for cross-table queries using a knowledge graph structure, which would enable dynamic querying across multiple datasets.

**(50:48)**
Getting started with AIO is simple. You can log in, connect your data, and start analyzing within minutes. Our team is here to support and guide you through any setup or customization.

**(53:09)**
Thanks, everyone, for joining today, and a big thanks to John for the insights into AIO. This was incredibly helpful, and we’ll share the recording and additional resources for following up. John, thank you for spending time with us and showing what’s possible with AI-driven data analysis.