AIs Role in Creative and Personalized Marketing

**(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.