This article was based on the interview with Adobe CMO Enterprise Rachel Thornton on agents as customers by Greg Kihlström, AI and Marketing Technology keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For those of us who have been navigating the MarTech landscape for a while, the rhythm of disruption is familiar. We mastered search engine optimization just in time for social media to rewrite the rules of discovery. We built mobile-first experiences as the desktop began to feel like a relic. Each wave demanded new strategies, new tools, and a new understanding of the customer journey. We, as marketing leaders, adapted. We built teams, invested in platforms, and learned to speak the language of clicks, conversions, and customer lifetime value. It has been a constant, if sometimes exhausting, evolution.
Now, a new shift is upon us, and this one feels different. The change isn’t just in the channel or the device; it’s in the very nature of the audience itself. The entity evaluating our brand, comparing our products, and shortlisting our services may not be a person at all, but an autonomous AI agent working on their behalf. This isn’t a subtle shift in consumer behavior; it’s a fundamental change in the chain of command. How do you market to something that doesn’t respond to an artfully crafted story, doesn’t get a “feeling” from your brand’s aesthetic, and processes information with the cold, hard logic of a machine? According to Rachel Thornton, CMO of Enterprise at Adobe, this isn’t a far-off hypothetical. It’s a present-day challenge that demands a new playbook, one that balances machine-readability with human resonance.
1. Brand Visibility is the New SEO
The first and most immediate challenge for any marketing leader is simply being found. For two decades, we’ve poured resources into understanding and mastering search engine algorithms. Now, the search bar is increasingly being replaced by a prompt. When a customer asks an LLM to find “the best durable luggage for international travel,” the brands that appear in the response are the only ones in the consideration set. If you’re not there, you’re invisible. Thornton frames this as the critical new battleground for brand visibility.
“I think for every brand, you now have to think about brand visibility. So how we define that is where are you showing up and how are you showing up? As consumers shift from looking for things in, you know, very traditional places like SEO to now looking at, uh, LLM, so whether it’s ChatGPT or it’s Claude or it’s Gemini, as a marketer, you have to think, I had an SEO strategy, maybe we even continue to have great SEO metrics, but if we’re not showing up in LLMs, then your brand isn’t visible at key moments when customers are making decisions.”
This isn’t just a matter of keyword stuffing or backlink strategy. It’s about ensuring your brand’s value proposition, product specifications, and customer reviews are structured in a way that is easily ingested and understood by these models. Thornton emphasizes that this requires making your content “agent-readable.” This means structured data, clear product attributes, and quantifiable claims become just as important, if not more so, than the narrative prose on your “About Us” page. For enterprise leaders, the directive is clear: your digital presence must now serve two masters. It needs to engage the human eye while simultaneously feeding the AI brain the clean, organized data it craves. The risk of ignoring this is not a lower search ranking, but outright exclusion from the conversation.
2. Codifying Your Brand to Combat Sameness
The logical next question is a troubling one for any brand steward. If we all start optimizing for the same machine logic—prioritizing quantifiable attributes like price, shipping speed, and feature lists—don’t we risk a race to the bottom? In a world evaluated by agents, does every brand flatten into a generic, commoditized version of itself? This is where the human element of branding becomes more critical than ever. The solution isn’t to abandon brand identity, but to codify it so that it can be scaled consistently, even when using AI tools for content generation.
“Every brand has to say, ‘We have a set of brand guidelines. We know what looks right for our brand.’ And you have to make sure that…every asset they build is on brand… what we’ve built with Adobe Brand Intelligence is brand ontology. We built computer visioning and then a reasoning engine that basically, as your creatives are building, Adobe Brand Intelligence helps ensure absolutely what you said, that everything is on brand, that it doesn’t morph over time into something that is basically slop.”
Thornton’s point is a powerful one for leaders grappling with how to scale content creation with generative AI without losing control. The answer lies in building a “brand ontology”—a structured, intelligent system that understands the core components of your brand. This isn’t just a PDF of brand guidelines with color hex codes and logo placement rules. It’s a dynamic model that can evaluate a generated image or piece of copy against the core tenets of the brand’s look, feel, and voice. The goal is to prevent the slow, insidious drift into what Thornton bluntly calls “slop.” By defining your brand in a way that machines can understand and enforce, you create guardrails that allow your teams to leverage AI for speed and scale without sacrificing the distinctiveness you’ve spent years building.
3. Move from “Bolted On” AI to Integrated Workflows
The arrival of powerful AI tools has created a significant capability gap within many marketing organizations. It’s one thing to give your team access to a generative AI platform; it’s another thing entirely to fundamentally change how they work. The most common mistake is treating AI as a point solution—a tool to be “bolted on” to an existing process. The real transformation, and the real gains in productivity and effectiveness, come from rethinking the entire workflow with AI at its core.
“Customers who’ve had the best success with AI, is when they think about it not as like, ‘I bolted it onto a thing,’ but, ‘I really looked at my teams and I looked at our work processes and our workflows and I thought, how do I create, how do I get those to be optimized using AI?’”
This is a challenge of change management as much as it is a technological one. For a CMO, it means moving beyond simply encouraging experimentation and starting to actively re-architect core marketing functions. How does campaign planning change when an agent can analyze market signals and propose initial audience segments in minutes? How does content creation evolve when a first draft can be generated instantly, shifting the team’s focus to refinement, strategy, and brand alignment? Thornton’s observation highlights that the most successful organizations aren’t just using AI; they are building new operating models around AI. This requires a commitment to upskilling, process redesign, and a leadership vision that sees AI not as a feature, but as a foundational element of the modern marketing engine.
4. The Human Anchor: Your Brand Promise
With all this talk of agents, automation, and machine-readability, it’s easy to get lost in the technology and forget the ultimate purpose. What, in this new world, remains unequivocally human? According to Thornton, the most critical function a CMO must retain is the definition and stewardship of the brand promise. AI can scale a message, but it cannot invent the soul of the brand. That remains the leader’s responsibility.
“I think every marketer, every CMO has to think, what does my brand stand for? How do I make sure that comes through in every interaction a customer has? How do I not leave that, not just to agents, but in a way, not just to chance, right?.. Every company has a brand. Every brand has to think about what is their brand promise and how do we scale that promise effectively using AI, but you have to do the work of understanding your brand and bringing that brand to life.”
This is perhaps the most grounding insight for any leader navigating this transition. The technology is a powerful amplifier, but it needs a clear signal to amplify. Before you can build a brand ontology or optimize your site for agents, you must have an unshakeable understanding of what your brand stands for. This moment of technological disruption is, ironically, forcing us all to return to the first principles of marketing. The pressure to make our brands “agent-readable” is also a mandate to clarify our brand promise with a level of precision we may have never needed before. If you can’t define your brand’s value in clear, unambiguous terms, how can you expect an algorithm to?
The emergence of AI agents as a primary audience is not a future threat to be monitored; it is a current reality to be addressed. It represents a fundamental rewiring of the paths of discovery and evaluation that we have spent our careers mapping. The work ahead is not to abandon the art of marketing for the science of data, but to fuse them more tightly than ever before. We must become architects of brand ontologies, stewards of machine-readable content, and leaders of teams that operate in entirely new ways.
This isn’t about creating soulless, robotic brands. On the contrary, it’s an opportunity to achieve a new level of clarity and consistency. The process of preparing your brand for an AI-driven world forces a discipline that will ultimately benefit your human customers as well. A brand that is clear, consistent, and delivers on its promise will resonate whether the initial evaluation is done by a person or a processor. The agents are here, and more are coming. The only question is whether we’ve done the foundational work to ensure they understand who we are when they arrive.
