This article was based on the interview with Forrester’s Chuck Gahun on AI agents as decision makers in the buyer’s journe by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For decades, we’ve built our marketing empires on a deep understanding of human psychology. We’ve mastered the art of emotional storytelling, crafting compelling narratives that resonate with our audience’s hopes, fears, and identities. We know how to build trust through relationships and guide consumers through carefully designed, deterministic experiences. But what happens when the entity evaluating our brand isn’t a person at all? What if your next—and increasingly influential—customer is an AI agent, operating on pure logic, devoid of emotion, and capable of processing more data in a second than a human could in a lifetime?
This isn’t a far-off hypothetical; it’s the new reality taking shape in the buyer’s journey. The rise of “agentic AI” represents a fundamental fork in the road for marketing strategy. It’s no longer enough to optimize for human perception alone. We are now tasked with a dual mandate: to continue captivating the human heart while simultaneously satisfying the cold, hard logic of the machine. In a recent conversation I had with Chuck Gahun, a Principal Analyst at Forrester who is researching this very shift, we explored this evolving landscape. Generative Engine Optimization (GEO) isn’t merely an extension of SEO; it’s the emergence of a new persona—the AI agent—that demands its own unique approach, its own content strategy, and its own definition of value.
The Duality of the Modern Customer: Human Emotion vs. Machine Logic
The first and most critical step for any marketing leader is to internalize the profound difference between these two audiences. Treating an AI agent as just a more efficient human searcher is a strategic misstep. They operate on entirely different principles, a duality that requires us to build parallel, not blended, experiences. The immersive, JavaScript-heavy brand site that wins design awards might be an unreadable mess to a data-parsing AI crawler. Conversely, the dense, semantically-rich product detail page that an AI agent devours would likely bore a human consumer to tears.
As Gahun articulated, the distinctions are stark and run across every dimension of interaction. Recognizing this is the foundation of any successful strategy in this new era.
“There’s a duality that exists between human consumers and AI agent consumers… We’re driven by emotion. They’re obviously machines, so they’re driven by logic. We’re shaped by identity, like who we are as individuals, and they are shaped and built by value… We are influenced by trust and relationships. They are influenced by trust and context… We browse in deterministic experiences… Well, AI agents, answer engines, these are all non-deterministic experiences.”
This quote lays out the new rules of engagement. Our brand’s “identity” is less important to an agent than the objective “value” it can deliver. Our carefully cultivated “relationships” are superseded by verifiable “context” from third-party sources. The guided, deterministic navigation path on our website is irrelevant to a non-deterministic agent that can instantly synthesize every piece of content on our domain and cross-reference it with the entire web. This means the old-school, spec-heavy B2B product pages, once considered a design liability, are now a goldmine of the structured, machine-readable data that agents crave. The challenge for us as leaders is to facilitate this dual optimization: continuing to invest in beautiful, emotional storytelling for our human audiences while simultaneously creating clean, structured, and semantically unambiguous pathways for our new machine personas.
Operationalizing a “Business-to-Agent” Strategy: Context, Fluency, and Trust
Once we accept this duality, the next question is a practical one: How do we actually do it? This isn’t about chasing every new protocol that gets released. It’s about building a sustainable content strategy based on three core pillars that AI agents are engineered to look for. Gahun’s research distills this down to a clear and actionable framework.
“And what we found through the research is that agents are looking for three things. They’re looking for context, they’re looking for fluency, and they’re looking for trust… Fluency is about, you know, I feel, I can’t help but feel like we got really creative in certain places with product names and descriptions… agents don’t, they need to be able to reconcile you within your sector in the same language that everybody else in your sector is using. So that creativity needs to be standardized.”
Let’s unpack these pillars. Context is about an agent’s ability to understand your brand and products in the broader market. This means paying close attention to third-party sentiment, reviews, and how you are discussed across the web. It requires an active strategy to engage with and even shape that external narrative. One of Gahun’s examples was a brand that successfully used on-site FAQs to directly address negative reviews found elsewhere, providing the AI crawler with the necessary context to understand that previous issues had been resolved.
Fluency is perhaps the most challenging for creative marketers. It demands that we standardize our language. Vague, emotionally evocative terms like “eco-friendly” are meaningless to an agent without quantifiable data to back them up (e.g., “made from 80% recycled materials,” “certified by X organization”). Creative product names need to be accompanied by clear, descriptive metadata that aligns with industry-standard terminology. This isn’t about abandoning creativity; it’s about ensuring that our creativity is layered on top of a machine-readable foundation of clarity and precision.
Finally, Trust is built through consistency and authority. An agent needs to see that you are a reliable, frequently updated source of information on your topic. This means a commitment to regularly publishing deep, authoritative content that establishes your topical expertise. If you publish once and disappear, an agent will learn to de-prioritize you as a source. These three pillars—Context, Fluency, and Trust—form the operational checklist for building a brand that is not just visible, but credible to AI agents.
The Business Case: A Content Program, Not a Tech Spend
As we move from experimentation to execution with AI, the inevitable question of ROI arises. The bill, as they say, has come due. How can marketing leaders justify investment in optimizing for a non-human audience? The key, Gahun suggests, is to frame this not as another line item on the MarTech budget, but as a strategic evolution of an existing, core competency: content.
“This is a traditional content strategy business case in really what would be more of a white space frontier for your brand… This is not about tech implementations. This is about taking the tech you have and building your content strategy… build a content program. That’s the business case. It’s not for tech investment. It’s more for a broad content program that drives measurable results for your business over time.”
This is a crucial distinction. We are not advocating for the purchase of a new, shiny “AEO platform.” We are advocating for a strategic reallocation of focus within our content, product, and SEO teams. It’s about using the tools we already have—our CMS, PIM, and analytics platforms—in a smarter, more targeted way. The business case is rooted in finding and dominating “white space.” Instead of competing in crowded, human-centric search terms like “good headphones for runners,” as one of Gahun’s examples from Harman International detailed, the opportunity lies in identifying and owning the nuanced, data-driven queries that an agent might use to solve a consumer’s problem.
This approach transforms the investment from a speculative tech buy into a measurable content initiative. The goal is to build a moat of authority around specific topics that positions your brand as the definitive answer for answer engines. By doing so, you intercept the customer journey at an earlier, more influential stage, directly connecting your brand to revenue and market share in a way that is both defensible and highly efficient.
The Agentic Future: Strategy Over Urgency
The landscape of agentic AI is evolving at a breathtaking pace, and it can be tempting to feel a sense of urgency, a fear of being left behind. However, the most prudent path forward is one of strategy over haste. This is a long-term shift, not a short-term trend. As Gahun noted, the ecosystem will mature to include not just discovery but full-fledged commerce, from autonomous replenishment of printer ink to agent-only product drops and loyalty programs. The brands that succeed will be those that build a solid foundation now, rather than those that chase every new development.
The work begins today, not with a massive budget request, but with a series of strategic conversations across your marketing, product, and IT teams. It’s about auditing your content for semantic clarity, mapping your information architecture for machine readability, and beginning the hard work of building a content program that speaks with equal fluency to both human and machine. The rise of the AI agent isn’t a threat to marketing; it’s an expansion of it. It’s a challenge that forces us to be more precise, more data-driven, and ultimately, more clear about the value we provide. The brands that win will be those who not only continue to speak to the heart, but also learn to speak, with clarity and authority, to the machine.
