Answer engine optimisation: what it is, and how it differs from SEO

What is answer engine optimisation?

Answer engine optimisation is the practice of making an organisation easy for an answer-composing system to understand, trust and quote. The name is newer than the practice: most of what it consists of — stating facts plainly, declaring what an entity is, being described accurately by sources other than yourself — has been good practice for as long as machines have read the web. What changed is the consequence of getting it wrong. A page that a search engine misunderstands ranks poorly and can still be found; a company that an assistant misunderstands is simply left out of the answer, with no list for the reader to scroll.

How is it different from SEO?

The difference is what each is optimising toward. SEO works toward a position in a list of results, where the reader makes the final choice by clicking. AEO works toward being included in a composed answer, where much of the choosing has already happened before the reader sees anything. One competes for attention among visible options; the other competes to be one of the options mentioned at all.

The two goals lead to a practical difference in what you optimise. A page written for search can rely on context: a heading, a preceding paragraph, an image, the rest of the site around it. A passage quoted into an answer arrives alone, stripped of all of that. So the unit of work shifts from the page to the paragraph — each claim has to make sense without the sentence before it, because that is the form in which it will be reused, if it is reused.

The third difference is measurement, and it is the one that catches teams out. SEO has a stable thing to count. AEO does not: answers vary between runs and change as providers update their systems, so the only reliable read is a fixed question set, asked repeatedly, with the results retained. A one-off check of what an assistant says about you is an anecdote.

Is AEO replacing SEO?

No, and treating it as a replacement is the most expensive mistake available here. The systems composing answers largely retrieve ordinary web pages to do it, which means a page still has to be discoverable, crawlable and comprehensible in the conventional sense before it can be drawn from. Technical SEO is not a legacy concern in this work; it is the precondition for it.

Buyers have not stopped using search either. In practice the same person frequently does both — a search for options and a question to an assistant about the same decision — which is why treating them as one channel with two interfaces produces better decisions than picking a side.

What does the work actually consist of?

Four things, in roughly this order. Establishing what is currently said about you and which sources it comes from, because everything after this depends on it. Making the underlying presence unambiguous: the structure, the declared entities, the relationship between an organisation and the people in it. Building the material that is genuinely missing, which the measurement identifies rather than a content calendar. And measuring the same question set continuously afterwards, because the systems change underneath you whether or not you do anything.

What it does not consist of is tricking a model. There is no markup that compels a system to recommend a company, and structured data in particular is often oversold here — Google's own documentation is explicit that it does not guarantee a ranking, and its guidance for generative features says no dedicated markup is required for them. What structured data does is remove ambiguity about what a page is describing, which is worth doing on its own terms and is not a lever.

Where does the detail live?

Two pieces go further. On why an assistant cites a competitor rather than you, which usually comes down to sources rather than to the quality of your own pages. And on what structured data actually does for a model, including what its own documentation says it does not do.