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The digital strategy team at Stakewise (a pseudonym), a New Zealand-based investment platform, watched a new channel — referral traffic from large language models (LLMs) — grow steadily in their 2025 analytics data. By midyear, ChatGPT was generating roughly 700 referral sessions to the site each month, up from only a handful a year earlier. Visitors arriving through ChatGPT also had lower bounce rates and viewed more pages per visit than those arriving from other referral sources. The site was performing strongly by conventional SEO measures, including keyword rankings, impression volume, and organic click-through rates.
But the team could not answer an important question: What were AI systems actually telling customers about their business? Conventional analytics showed the traffic arriving, but not the conversations happening before the click. Finding out meant auditing the answers themselves.
When the Stakewise team began systematically querying ChatGPT with the questions real customers were asking — about fees, fund choices, and how the platform compared with competitors — they found responses that were confident, fluent, and frequently wrong. One widely repeated incorrect claim stated that the platform offered five (not the actual seven) funds in its KiwiSaver product. Fee structures were mischaracterized. A protective investment safeguard was described as restrictive. Features from the investment platform were conflated with a separate product. For a financial services provider, this meant prospective customers were receiving inaccurate information at exactly the moment they were researching retirement savings options.
Two Key Challenges of GEO: Visibility and Accuracy
As generative AI search proliferates, new LLM search algorithms are giving rise to what practitioners now call generative engine optimization (GEO), also referred to as answer engine optimization, or AEO. GenAI search introduces two strategic challenges for leaders. The first is visibility: ensuring that the brand appears when customers ask questions relevant to its offerings. The second is accuracy: ensuring that those systems represent the brand correctly, consistently, and with up-to-date information. A company can win on one count and still lose on the other — a costly outcome, and an increasingly common one.
The SEO-GEO distinction may sound subtle, but it is profound. Without SEO, there is nothing for AI to find. Without GEO, what AI finds may no longer be true. Traditional SEO asked, “Can customers find us? Where do we rank?” GEO adds a new question: “What is AI saying about us in answers we cannot see?”
Organizations with strong SEO foundations are generally better positioned for GEO than those without them, because the content that ranks well in traditional search is also, broadly, the content that AI systems draw on. Strong SEO makes it more likely that an LLM will use a company’s content. But for the comparison and discovery queries that increasingly drive purchase decisions — category comparisons, product alternatives, and “best X for Y” searches — the LLMs may examine many sources. But only a smaller, highly ranked subset — the sources chosen by the platform’s own constantly updated black-box algorithm — are treated as trustworthy and credible, which ultimately shapes the shortlist of results, the comparison set, and the user’s first impression. Many lower-ranked options are thus omitted, so a potential customer may never learn about a company or reach its site.
Visibility: Building On and Beyond SEO
Practitioners with experience in both SEO and GEO describe GEO as the most rigorous version of what modern organic strategy should be: content that is structured, semantically clear, contextually authoritative, and genuinely useful to the person asking the question. As such, GEO adds meaningful new demands.
The distinction between being found and being cited is more than semantic. Being found and ranking well enough to be retrieved by an LLM is necessary, but not sufficient, for visibility. While SEO helps AI find you, GEO shapes AI’s responses. One digital marketing practitioner told us, “We’re now writing for two audiences: the human reader who still values depth, narrative, and context, and the AI assistant. The real challenge is balancing those two so that you don’t lose authenticity while still making sure AI can ‘see’ and surface your expertise.” Content that cannot be cleanly extracted because the claim is buried, the structure is loose, or the evidence is vague doesn’t make it through this second filter, regardless of how well it ranks.
GEO heightens the importance of extractability. Beyond being persuasive or visually appealing, content must also be easy for AI systems to parse. Headings need to communicate substance, not just brand tone. Key answers must appear clearly rather than being buried in long prose. FAQ sections, structured data, and page architecture matter more when systems are synthesizing answers rather than simply listing links.
GEO also expands the arena of authority beyond owned web properties. As one interviewee put it, “AI doesn’t just crawl links. If your brand shows up in discussions on Reddit, Quora, LinkedIn, or niche forums, that builds semantic relevance.” Reputation management, community presence, and external validation now influence whether and how brands are surfaced in AI-generated responses. Appearances in well-ranked “best of” lists, strong review profiles, and credible customer examples can also shape citation outcomes.
Additionally, GEO requires organizations to treat technical access as an operational issue. Many enterprise sites still inadvertently block AI crawlers through legacy robots.txt settings. Structured markup may be missing or inconsistent. Content priorities may be unclear to LLMs processing the site. In these conditions, a brand’s site cannot be accurately read at all. While SEO remains the foundation — elevating the brand into the set of sources AI can draw on — GEO determines whether those sources are clearly understood, credibly cited, and accurately synthesized for customers. To be represented accurately in AI answers, companies need to manage both SEO and GEO.
For many companies, the economics of digital marketing long favored immediacy. Paid search delivered quickly attributable results. Organic search worked over longer horizons, with returns that were harder to trace and easier for skeptical leadership to dismiss. The result was a familiar asymmetry: large paid media budgets alongside organic search teams struggling to justify modest investments in structural content improvement.
The conseque
