Most B2B marketers treating AI visibility as an SEO problem are solving the wrong thing.
That’s not a criticism. It’s a category error. SEO is about ranking in a list of links. What’s happening now is different: AI systems are synthesising answers, and either your company is part of that answer or it isn’t. There’s no page two.
The customer journey has been compressed
Something structurally important has changed in how buyers find information. What used to be a sequence of touchpoints – a search, a few clicks, a comparison page, a review site – has been collapsed into a single AI-mediated response.
The buyer asks a question. The AI answers it. If you’re not in that answer, you didn’t lose the click. You weren’t in the consideration set at all.
This is the part most marketers haven’t fully absorbed. They’re still thinking in terms of traffic and rankings. But AI Overviews alone have reduced organic click-through rates by 38-61% depending on query type, and 93% of searches in AI Mode now end without a single click to an external website. AI systems don’t send traffic in the same way. They render verdicts.
SEO, AEO, GEO – and why the distinction matters
It’s worth being precise about the different layers of AI visibility, because they require different things from you.
SEO remains the foundation. Structured, crawlable content that search engines can read still matters. Nothing here has become irrelevant.
AEO (Answer Engine Optimisation) is about being the source AI Overviews and featured snippets draw from. This is largely a content structure problem: are you answering questions clearly, with the kind of direct, citable prose that AI systems prefer to quote?
GEO (Generative Engine Optimisation) goes further. The term was formalised in a 2023 paper from Princeton, Georgia Tech, and the Allen Institute for AI, which found that the right optimisation strategies can improve visibility in generative engine responses by up to 40%. It’s about how large language models represent your company, your category, and your positioning when someone asks a question you should own. It’s not just about whether you appear. It’s about how you appear, what you’re associated with, and whether the model’s understanding of your brand matches the one you’ve actually built.
The mistake is treating these as the same thing, or assuming that doing SEO well automatically handles the rest. It doesn’t.
Brand and AI visibility are the same problem
This is where the thinking gets more interesting. AI visibility isn’t purely a technical content problem. It’s a brand problem.
LLMs are trained on everything that’s been written about you: your own content, yes, but also third-party coverage, analyst mentions, forum discussions, review sites, and the general texture of how your company is talked about on the internet. If your brand positioning is weak, inconsistent, or absent from the places that matter, that’s what gets reflected back. I’ve written more about this in Brand is how you impact GEO.
Which means the work of building AI visibility is, in large part, the work of building a coherent, well-documented, frequently cited presence in your category. Not just publishing content. Being a source worth citing.
That’s a harder, slower job than tweaking meta descriptions. But it’s the one that actually compounds.
The marketers who will be well-positioned in two years are the ones who understand this now, not as a new channel to optimise, but as a reason to be more rigorous about what they stand for and whether the internet actually reflects that.
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