AI SEO: Brand Visibility in AI Search

AI SEO: Brand Visibility in AI Search

AI SEO is the work of getting a language model — Google AI Overviews, the AI mode in search, ChatGPT, Perplexity or Gemini — to name your company inside the answer it generates for a user. The reference point stops being a position on a list of ten links and becomes presence inside the one answer the user reads instead of that list.

This changes the mechanics of search in three places. First, the query no longer reaches the index in a single form: the engine breaks it into a dozen or several dozen sub-queries asked in parallel (query fan-out) and assembles the answer from their results. Second, what enters the answer is a fragment of a page, not a page — the model quotes the paragraph, list or table that answers a sub-query on its own. Third, the user often ends the session on the answer and clicks nowhere.

For a B2B company the practical conclusion is this: visibility in AI is a separate channel that needs its own work, but it settles against the same outcome as everything else — B2B lead generation.

What is AI SEO called now: GEO, AEO and LLMO

Four names are in circulation for one phenomenon, and it is worth knowing where each comes from, because in a conversation with a supplier the difference is sometimes purely a matter of marketing.

The practical distance between GEO and AEO is small. The real dividing line runs elsewhere — between optimizing to be cited by a model and using AI as a tool to produce content. The first is a visibility strategy; the second is a way of working. A company buying “AI SEO” should know which of the two it is getting, because the deliverables, the timeline and the way the work is measured have almost nothing in common.

One more consequence of the naming: search engines index the words people use. If your category writes GEO and your page says only AI SEO, the page competes for a narrower term cloud than it could. Covering the terminology is not a stylistic decision — it is coverage.

What AI SEO changes against traditional SEO, and what it leaves untouched

The foundation stays untouched: crawlability, a sensible site architecture, speed, correct headings, consistent entities. The answer model draws on the same index and the same quality signals as the classic results — a page a search engine cannot process has nothing to be cited from.

What changes is the unit of success and the unit of content.

The most consequential shift concerns links. In the classic model, links voted. A language model also reads plain mentions — sentences about a brand with no link attached, in other people's articles, roundups and community threads. A mention without a link, which until recently counted for nothing, now does work.

The second shift is temporal. A classic ranking moves slowly and holds. An answer is regenerated on every query, so presence in it is a distribution, not a position — which is exactly why it has to be sampled repeatedly rather than checked once.

Two games: a mention on informational queries, a click on commercial ones

This split decides where a B2B company should put its effort. Informational queries are played for a mention; commercial queries are still played for a click and a lead.

A published analysis of the query set that triggers AI Overviews (Ahrefs, 2026) points in one direction: almost all of those queries carry informational intent — “what is”, “how to”, “how long does it take”. There, the AI answer eats the click: the user gets the explanation and does not visit the site. Queries with buying intent — “services”, “for companies”, “implementation”, a category name with a B2B qualifier — trigger the AI panel far less often, and the user goes to the site anyway, because they want to see the supplier.

Hence two games, run in parallel and by different means:

• Informational content → the game for a mention. It stays in the plan, but it settles against presence in the answer, not against a click. The job of such a text is to be citable: to contain a fragment the model can take whole, and to carry something the other ten sources do not have.
• Commercial content → the game for a click and a lead. A service page, a segment page, a page with proof. This is won the way it has always been won: a clear offer, specifics, and a short path to contact.

In the drySEO laboratory this difference collapses into a single editorial rule: before a text is written, the query gets the label “mention” or “click”, and the structure, the length and the settlement method follow from the label. The same rule governs the whole commercial layer described on our SEO and SEM services.

Can ChatGPT do SEO for you?

Partly — and the limit is not the quality of the model, it is what SEO work is made of. A model executes the bounded tasks well and owns none of the decisions.

A language model performs well on tasks with a closed input and a checkable output: drafting an outline from a collected term set, rewriting a paragraph, generating title variants, clustering a keyword list, a first pass at intent classification, reviewing a text for repetition. That is a genuine saving of hours, and a company that refuses it is paying for typing.

What the model does not do for you:

• it has no access to your data — to your analytics, campaign costs, CRM, and to the record of which queries turned into orders; without that, every recommendation it gives is generic by construction;
• it does not know your competitive position beyond what it finds in public text, and the decision about which queries are worth contesting at all is a business decision;
• it contributes no unique data — and unique data is today the main fuel of mentions, because a model picking a source to cite picks what it cannot find elsewhere;
• it carries no consequence — accuracy of the choice, consistency of implementation and honest measurement stay with a person.

A sane division of labour looks like this: the model accelerates execution, the human decides direction and supplies what the internet does not yet contain. A site assembled entirely from generated text with no proprietary input is indistinguishable from dozens of others — and a model choosing whom to quote distinguishes.

There is a second-order effect worth naming. Because models are cheap to run, the volume of adequate content is rising fast, and the scarce good is no longer fluency. It is evidence: a measurement, a denominator, a date, a case with numbers attached. That is what raises a fragment out of the candidate pool.

How to build content an AI model can cite

Citation happens at the level of the fragment, so the text is designed out of fragments.

How do you measure the effectiveness of AI SEO?

In three layers at once: brand presence in answers to a fixed prompt set, referral traffic from AI engines, and the outcome on the lead. None of the three is sufficient alone, and the last one is binding — the first two can be produced without touching the sales system, which is exactly what makes them easy to report and hard to act on.

The layers, from the least to the most binding:

Two figures from the drySEO laboratory show why that discipline is not a formality:

• The cost of acquiring a lead from advertising, within one account and one period, is PLN 73, PLN 237 or PLN 452 — depending on whether the cost is divided by all leads from the foreign markets served, by leads carrying a campaign parameter, or by the conversions counted in the ad panel. The 6.2× spread is definitional, not a market effect: one phenomenon counted with three denominators (drySEO lab data).
• The ad panel under-reports conversions by 1.4–1.9× — leads carrying a campaign parameter and found in the CRM, divided by the conversions counted in the panel. The direction is one-sided: the panel always sees fewer (drySEO lab data).

If that is how measurement behaves in a channel that has its own panel and its own tracking parameters, then a measure of AI visibility — indirect by nature — needs its denominator stated all the more. A metric without a denominator is not a result, it is a number. The full method of setting denominators before the first report is described in the cost guide.

AI SEO in a B2B company: from visibility to a sales enquiry

Assembled into a sequence, work on AI visibility in a B2B company looks like this:

Steps 1–4 belong to the channel. Step 5 is where the channel meets the outcome it is run for: visibility turns into a lead on the landing page, not inside the model's answer. That is why AI SEO runs as one channel among several within B2B SEO, settled against the same outcome as the rest.