Generative engine optimization — also called AI search optimization — is the practice of making your site more likely to be cited inside AI-generated answers. It has acquired an industry’s worth of mystique in a short time, so here is the honest version: most of it is search engine optimisation applied with more attention to structure, and a small part of it is genuinely new.
Being clear about which is which saves a lot of money.
Why the term exists at all
Search used to end with a list of links. Increasingly it ends with an answer: Google’s AI Overviews, ChatGPT’s search mode, Perplexity, Copilot. Each of those builds a response by retrieving content and summarising it, then naming some of the sources it used.
That changes what visibility means. Ranking third is no longer the goal in itself; being one of the three sources the answer is built from is. The term GEO was coined to describe optimising for that, and the reason it spread so quickly is that it names a real shift. The reason to be sceptical of much of the advice attached to it is that the shift is smaller than the marketing suggests.
The eighty per cent that is just SEO
Generative systems cannot cite what they cannot retrieve, and retrieval leans on the same foundations as ranking. In practice that means the following still decide most of the outcome.
Crawlability and technical health. If a page is slow, blocked, or renders its content only after heavy client-side JavaScript, it is harder to retrieve and parse. Nothing about AI changed this; it raised the stakes.
Topical coverage. A site that comprehensively covers a subject is more likely to be treated as a source on it. This is the cluster argument, and it applies identically here.
Authority and reputation. Links, mentions, reviews and consistent business information all feed the trust judgement. Google’s helpful content guidance puts demonstrable experience at the centre, and a system choosing a handful of sources to stake an answer on has more reason to weigh that than a system returning ten links.
Structured data. Markup does not request inclusion in anything, but it removes ambiguity about what a page is, who wrote it and what it describes, which helps any system reasoning about your content.
If a site is weak on those four, no amount of AI-specific tactics will compensate, because the content never reaches the point of being considered.
The twenty per cent that is actually new
Three practices genuinely differ, and they are cheap.
Write passages that survive being lifted. A generative answer quotes a chunk. If your key point only makes sense after two paragraphs of build-up, there is nothing to quote. State the answer directly under a heading phrased the way people ask, then elaborate. This is the highest-return change available and it is pure editing.
Cover the adjacent questions. Answers synthesise across sub-questions, so a page addressing the follow-ups as well as the headline query gives the system more reason to return to you. The “people also ask” box is a published list of what those adjacent questions are.
Be described consistently everywhere. Models build a picture of an entity from many mentions. If your business is described three different ways across your site, your profiles and your listings, that picture is blurry. Consistent naming, a clear description of what you do and who for, and accurate details across the handful of platforms that matter all sharpen it.
What nobody can sell you
There is no submission form for AI answers. There is no markup that requests citation. There is no ranking dashboard, no guaranteed placement, and no verified relationship between paying anyone and appearing in an organic AI answer. Google states directly in its AI features guidance that no special optimisation is required.
Two claims should end a sales conversation: a guarantee of appearing in AI Overviews, and a precise report of your “AI visibility score” presented as measurement rather than estimate. Neither is currently possible.
What the citations actually look like
It is worth checking reality rather than assuming. For the query “what is generative engine optimization,” Google returned an Overview drawing on six sources: three YouTube videos, a course platform, a large software vendor and an SEO tool. The specialist publications you might expect to own that phrase were not among them.
That single observation carries two lessons. Format matters more than commentary suggests, because video was over-represented for an explanatory query. And the citation list is not simply the top of the rankings rearranged, so checking who actually gets cited for your priority queries beats reasoning about who should.
How to measure it honestly
No product reliably reports this yet, so build a routine instead of buying a number. List the twenty questions a prospect might ask before choosing someone in your category. Run them across the assistants your audience actually uses. Record three things: whether you are named, whether you are cited with a link, and who else appears. Repeat monthly.
That list will tell you more than any dashboard, partly because it shows you the competitors the systems consider authoritative, which is a competitive brief you would otherwise pay for.
Where this leaves a marketing budget
If you already run a serious search programme, GEO is not a new line item. It is a structural editing pass on the content you have, an emphasis on covering questions rather than keywords, and a monthly measurement habit. If you do not have a search programme, GEO is not a shortcut around building one: everything the generative layer depends on is downstream of the fundamentals.
For the mechanics of how one system chooses its sources, we went deeper in how AI Overviews choose the sources they cite, and for the underlying question of how models learn about you at all, see LLM SEO. And if you would rather this were someone’s job, it is ours.
