TL;DR
The buyer’s first encounter with your brand increasingly happens inside an AI conversation, not on your website. ChatGPT, Perplexity, Gemini, and Google AI Overviews are forming opinions about your company before a prospect ever clicks a link. Most marketing teams aren’t even monitoring this layer, let alone optimizing for it. That’s a problem with a shrinking window to fix.
Something shifted in buyer behavior over the last year, and most marketing teams missed it.
Your prospects are still researching solutions. They’re still comparing vendors and reading reviews. But they’re doing it differently now. Instead of starting with a Google search and clicking through ten blue links, they’re opening ChatGPT or Perplexity and asking a direct question: “What’s the best platform for X?” or “How does Company A compare to Company B?”
And they’re getting a direct answer. One synthesized response. One impression formed. Often without ever visiting your website.
If your marketing strategy still assumes the buyer journey starts with a click to your homepage, you’re optimizing for a path that fewer buyers are taking.
The discovery layer you’re not managing
Here’s the uncomfortable reality: AI systems are already making recommendations about your brand, your competitors, and your category. They’re doing it every time someone asks a product question, requests a vendor comparison, or looks for the best solution to a problem you solve.
The data makes this hard to ignore. Google AI Overviews now appear in over 25 percent of all Google searches, roughly double where they were a year ago. Around 93 percent of AI search sessions end without a single website click. AI search traffic grew 527 percent between 2024 and 2025. And according to Forrester, 89 percent of B2B buyers now use generative AI as a primary source for self-directed research throughout their buying process.
This isn’t a niche behavior. It’s the new default.
And yet, when I ask marketing leaders how their brand shows up inside ChatGPT or Perplexity, most don’t know. They’ve never checked. They have no monitoring in place. They’re investing heavily in a website experience that an increasing share of their buyers may never see, while the AI layer that’s actually shaping perceptions goes entirely unmanaged. I wrote about this blind spot in AI Visibility 2.0: Why Marketing Strategy Must Account for AI-Driven Discovery, and the gap has only widened since.
What AI systems actually see when they evaluate your brand
Large language models don’t experience your brand the way a human does. They don’t see your homepage design, your brand video, or your carefully art-directed landing page. They see structured data, third-party mentions, content clarity, and consistency across sources.
When someone asks an AI tool to recommend a solution in your category, the model is evaluating several things at once: how frequently your brand appears in credible, third-party contexts. How clearly your content defines what you do and who you serve. Whether your claims are corroborated by independent sources. And how well your information is structured for extraction. As I explored in Brand Is a Signal System, Not a Message, what the AI “sees” is the aggregate pattern of signals your brand puts into the ecosystem, not the story you tell about yourself.
This is where the concepts of generative engine optimization (GEO) and answer engine optimization (AEO) become critical. GEO focuses on increasing the likelihood that AI platforms cite, recommend, or mention your brand when users ask relevant questions. AEO ensures your content is structured so AI systems can easily extract and present your information as direct answers. Search Engine Land’s overview of GEO is a solid primer if you’re new to the concept.
These aren’t incremental tweaks to your SEO strategy. They represent an entirely different optimization surface, one where the rules of visibility are fundamentally different from what most marketing teams have spent the last decade mastering.
Three things your competitors are already doing
The brands that show up consistently in AI-generated recommendations aren’t doing it by accident. They’ve made deliberate investments in three areas.
Building authority through third-party validation. AI systems weight independent mentions heavily. Brands that appear in analyst reports, industry publications, customer case studies on third-party sites, and expert roundups get cited more frequently. Your own website content matters, but it’s not enough on its own. The model is looking for corroboration, not just claims. This is a core reason why competitive intelligence has changed so fundamentally in the AI era.
Structuring content for extraction, not just consumption. The way you organize information on your site directly affects whether AI systems can find and use it. Clear definitions, structured data markup, direct answers to common questions, and well-organized comparison content all increase your chances of being surfaced. If your content requires a human to interpret context clues and navigate between pages to understand what you actually do, an AI system will struggle with it too. If you want a practical starting point, my guide on how to audit your AI discoverability in 30 minutes walks through the basics.
Monitoring and measuring AI visibility as a distinct channel. The most forward-thinking teams have started treating AI visibility as its own channel with its own metrics. They’re tracking how often their brand gets mentioned across ChatGPT, Gemini, Perplexity, and AI Overviews. They’re monitoring what the AI says about them versus competitors. And they’re using that data to inform content strategy in the same way they’ve used search rankings for years. According to recent data, 63 percent of enterprise marketers are now planning dedicated AI search budgets for 2026. I covered the measurement side of this in detail in How to Measure AI Visibility Without Vanity Metrics.
Why this matters more than you think
The downstream effects of AI-mediated discovery are significant, and they compound over time.
When a buyer’s first encounter with your brand happens inside an AI conversation, and the AI positions you favorably, that prospect arrives at your sales conversation with built-in credibility. The “why you” question is already answered. These buyers move faster through the pipeline because the AI did the pre-selling for you.
The reverse is equally true. If the AI doesn’t mention you at all, or worse, positions a competitor as the better option, you’ve lost ground before your team even knew there was a conversation happening. You can’t outrun a first impression you didn’t know was being formed.
This is why I view AI visibility as a leadership-level concern, not a tactical SEO task. The decisions about how your brand shows up in this layer affect pipeline, positioning, and competitive dynamics in ways that compound quarter over quarter. If you’re thinking about what it takes to lead marketing effectively right now, this is near the top of the list.
The window for first-mover advantage is closing
Right now, the bar is still relatively low. Most brands aren’t optimizing for AI discovery in any systematic way. The ones that are, are seeing outsized results: citation rates 10 to 20 times higher than brands relying solely on traditional SEO.
But that gap will close. As more organizations recognize what’s happening and invest accordingly, the cost of catching up increases. The brands that build their AI visibility infrastructure now will have a structural advantage that’s difficult to replicate later. This is the same dynamic I described in The AI Readiness Gap: the cost of waiting isn’t standing still, it’s falling behind.
I’ll be going deeper on this topic at a presentation next month, covering the practical frameworks for generative engine optimization and answer engine optimization that marketing teams can implement now. If you’re a marketing leader who hasn’t started thinking about this layer yet, the time to begin was six months ago. The second-best time is today.
Kevin Farley writes about AI visibility, AI readiness, and competitive intelligence for marketing leaders. Read more on the blog.