Generative engine optimization — GEO — is what happens when the answer to a search query is no longer a list of links but a synthesized response generated by an AI. The mechanics of how you earn placement in that response are different from traditional SEO, and most teams have not caught up yet. This talk covers what has actually changed and what to do about it.

Why This Topic Matters Now

Google AI Overviews now appear in a significant portion of queries. ChatGPT, Perplexity, and other AI-native search tools are adding users fast. When someone asks one of these tools a question, the response cites or draws from sources the model has been trained on or can retrieve in real time. Whether your site appears in those responses — or is passed over — depends on factors that are meaningfully different from keyword rankings.

The core shift: traditional SEO optimizes for a crawler to index and rank a page. GEO optimizes for a language model to understand, trust, and surface content as a reliable answer. That requires a different strategy.

The scale of the shift is still playing out, but it is not small. Studies tracking AI Overview appearance rates across query types consistently show informational and definitional queries at high coverage rates. For sites that built significant organic traffic on top-of-funnel content, the exposure is real. The teams that address it methodically now will be in a better position than the teams that wait for the traffic drop to become undeniable.

What the Talk Covers

I structure this session around three questions the audience can take back to their teams:

  • How do generative AI engines decide what to cite and what to skip?
  • What content and technical signals increase the likelihood of appearing in AI-generated responses?
  • How do you measure GEO performance when the traditional ranking report does not capture AI traffic?

Within those three questions, I cover entity-based SEO, the role of schema markup and structured data in machine comprehension, content clarity signals, and the practical difference between optimizing for a search index and optimizing for a language model inference pass.

The GEO Signal Stack: What Actually Moves the Needle

Based on implementation work and testing, the signals that consistently increase AI citation likelihood cluster into three categories.

Entity clarity. A page that clearly identifies the entity it is about — a business, a person, a concept — and connects that entity to authoritative external references is more citable than a page that covers the same topic without those connections. This means clear author attribution, Organization markup with sameAs links to Wikidata, LinkedIn, or a Google Business Profile, and content that names and defines the central entity in the first paragraph rather than working up to it.

Structured content signals. Direct answers at the top of the content, FAQ sections that match natural language questions, HowTo markup for process content, and clear definition blocks for conceptual terms. A language model retrieving content for a synthesized answer is pattern-matching against question-answer structures. Content that is organized that way is easier to use as a citation source.

Source authority signals. Inbound links from sources the model has been trained to trust, mentions on credible external domains, and content that has been cited or referenced in contexts the model can identify. This overlaps with traditional E-E-A-T signals but goes further — being mentioned in industry publications, having a named author with an established presence, and having content that others link to for specific claims all contribute.

Measuring GEO When Ranking Reports Do Not Help

One of the most common frustrations I hear from marketing teams is that they cannot tell whether their GEO efforts are working. Traditional ranking reports do not track AI Overview appearances reliably, and the traffic signal is lagged and noisy. There are a few measurement approaches that give more useful signal.

Google Search Console AI Overview data, where it is available, is the most direct. For accounts where it is not yet surfaced, proxy metrics include CTR trends on queries where you hold strong positions, direct monitoring of specific queries in AI search engines to track whether your site is being cited, and brand mention tracking across AI-generated content using tools like Mention or Brand24 set up for AI search contexts.

None of these is a perfect measurement system, but together they give a reasonable picture of whether your content is being treated as citable by AI engines or not.

My Technical Foundation for This Talk

Schema markup is my core specialty. I have built custom structured data implementations for clients through Salterra Digital Services for years, and structured data is one of the clearest bridges between traditional technical SEO and GEO. When a model is deciding whether to trust and cite a piece of content, machine-readable signals — schema, entity clarity, source authority — matter in ways that are measurable and improvable.

I also teach this material in the live cohorts at SEO University. Running students through it in real time means I have tested which explanations land and which ones need reworking. The classroom is a useful proving ground for conference material because students at varied skill levels ask the questions that expose the gaps in an explanation.

Audience Fit

This session works best for:

  • Marketing teams at companies with an established content library that needs to be repositioned for AI search
  • SEO professionals who understand traditional optimization and want a clear framework for extending it into GEO
  • Agency teams working with clients who are asking questions about AI visibility
  • Conference audiences covering digital marketing trends

If your audience has no SEO background, this talk can be adjusted to start from basics. If they are deeply technical, I can go further into the implementation specifics without the introductory framing.

Where GEO Fits in a Broader Search Strategy

GEO is not a replacement for traditional SEO — it is an extension. The foundation still matters: crawlable site architecture, clean technical SEO, relevant content, inbound authority signals. What GEO adds is a layer of machine-comprehension optimization on top of that foundation. A site with poor technical SEO basics is not going to benefit much from GEO-specific tactics, because the AI engines that do real-time retrieval are still subject to crawling and indexing constraints.

I frame GEO in this session as the next layer, not a separate practice. Teams that have their traditional SEO in reasonable order and are now seeing AI search affect their traffic patterns are the right audience for this talk. Teams that have significant unresolved technical or content fundamentals issues should probably address those first — I will say so if that appears to be the situation.

Related Talk: AI Search More Broadly

GEO is one lens on the AI search shift. If you want a broader session covering how AI changes the whole search behavior landscape — not just optimization tactics — the AI search speaker page describes a different session that covers more ground at a higher level.

For the full overview of speaking formats and topics, the main SEO speaker page is the right starting point.