AEO vs SEO: What's Actually Different?

What is the difference between AEO and SEO?

SEO works to rank a page for a query. AEO works to have a business named inside a generated answer. They share most of their technical and editorial foundations, and differ mainly in what counts as success, how stable the result is, and whether the focus is a page or the business as an entity.

The honest summary is that these are two ends of one discipline rather than two disciplines. Most of the work that improves one improves the other, and the businesses treating them as opposed tend to underperform at both.

It is worth saying what this article is not, because the genre is crowded with the alternative. It is not an argument that SEO is finished, and it is not an argument that AEO is a rebrand of it. Both of those positions are being sold, usually by people with something to sell. What actually changes when you move from ranking pages to being retrieved, cited or recommended is specific, measurable in places, and narrower than the marketing suggests. It is also more interesting than either extreme.

What SEO is

Search engine optimization is the practice of improving how well a site's pages rank in conventional search results for the queries its audience uses.

It is worth describing it properly rather than as a straw man, because the comparison only means something if both sides are stated at their best. Competent modern SEO covers:

• Crawling and indexing. Whether pages can be reached, rendered and stored, and whether the right ones are.
• Relevance. Matching a page to an intent, which for twenty years has meant topics and entities rather than keyword density.
• Rankings. Position for a query, across devices, locations and query variants.
• Result features. Snippets, panels, local packs, product results and everything else that occupies a results page alongside the ten links.
• Impressions and clicks. How often a page is shown, how often it is chosen, and the relationship between the two.
• Entity and local signals. Which good local SEO has taken seriously for a decade, well before anybody called it AEO.

Success is measurable and reasonably stable: a position for a query, impressions, clicks and what those visitors go on to do. The discipline is considerably more sophisticated than ranking ten blue links, and anyone describing AEO as the arrival of entities, structured data or user intent is describing SEO in about 2015.

What AEO is

Answer engine optimization is the practice of making a business easy for AI answer engines to find, understand, trust and name in a response.

It covers much of the same ground, plus the accuracy and consistency of how a business is described everywhere else on the web, and the evidence supporting its credibility. Success is a frequency rather than a position: the share of relevant questions in which the business comes up.

The part that genuinely differs is that AEO has four outcomes rather than one, and they need measuring separately because they move separately:

• Retrieval. Whether your material enters the set a system considers at all. Necessary for everything else and invisible from outside.
• Citation. Whether a source is attached to the answer, and whether it is yours. Frequently the citation is a third-party page describing you.
• Mention. Whether your name appears in the answer text. It may be positive, comparative or unflattering, and it may arrive with no citation of your site at all.
• Recommendation. Whether the answer puts you forward as the thing to choose. This is the commercial outcome, and it is the rarest of the four.

Collapsing these into visibility is the single most common analytical error in the category, and it produces reports that are internally inconsistent without anybody noticing. A rising citation count and a falling recommendation rate is a coherent and quite common situation. Described as visibility, it is unreadable.

What they share

More than the marketing around either suggests.

• Pages have to be crawlable and readable.
• Site structure has to make sense.
• Content has to genuinely answer what was asked.
• The business has to be identifiable and consistently described.
• External credibility carries weight in both.
• Both are measured over time rather than judged on one observation.

If an AEO recommendation would actively damage your SEO, it is worth a second look. In our experience that combination usually indicates bad advice rather than a genuine trade-off.

Ranking versus recommendation

This is the difference that changes strategy. Ranking is ordinal and gradual: moving from position eleven to position six is real progress with real traffic attached, and it can be reported honestly as progress even when the destination is still some way off.

Recommendation is closer to an inclusion threshold. You are named or you are not, and there is no second page to be on. In search, being the eleventh best candidate is worth something: some people scroll, and the traffic is small but real. Being the fourth best candidate when three get named produces the same number as being fortieth, so effort spent getting from tenth to fourth is not visible in the outcome at all until it crosses.

The consequence for reporting is the part most people miss. A plateau just below the threshold looks identical to total absence, which is why measuring only recommendation presence can hide genuine progress. This is the practical argument for tracking citations and mentions alongside it: they move continuously where recommendation moves in steps, and they are how you tell a business that is nearly there from one that is nowhere near.

Retrieval

Search retrieves from an index it maintains. Answer engines retrieve from a mixture of what the model absorbed in training and, in many products, live results fetched at the moment of asking.

The live retrieval path is the one that connects the two disciplines most directly, and it is why search visibility remains one of the more reliable inputs into AI visibility.

The training-data path behaves differently: it is fixed at a point in time and tends to favour entities that are extensively documented. A new business cannot influence it quickly, which is another argument for working on the retrievable web.

The mechanism that most complicates the relationship is query fan-out. A conversational question is a poor search query, so systems appear to reformulate: one prompt becomes several retrieval queries covering synonyms, narrower sub-questions and supporting facts. Nobody publishes how this works for any major assistant, so it should be treated as a reasonable inference from observed behaviour rather than documented architecture. What it explains, though, is a result that otherwise looks impossible: cited pages frequently do not rank for the words the user actually typed.

It is worth being precise about what that chart proves and what it does not, because it gets used for both.

What it establishes: AI retrieval is not the original Google results page rendered as prose. If it were, the top-ten share would be far higher than 12%. Something else is selecting the sources, and a strategy built purely on ranking for the literal prompt is aiming at the wrong target.

What it does not establish: that SEO no longer matters. The study measures overlap with the top ten for the original prompt wording, which is exactly the thing query fan-out would break. A page found through a reformulated query was still found through search. The finding narrows what ranking guarantees; it does not show that search visibility is irrelevant, and the same study found engines varying enormously in how closely they tracked Google.

That last point deserves its own numbers, because the variation between assistants is larger than the headline average suggests.

This is the useful version of the finding. Not that ranking is obsolete, and not that it is sufficient, but that its value is engine-dependent and currently much higher in the products built around live search than in the general assistants. Any strategy that treats AI visibility as one surface is averaging over a fourfold difference.

Citations

Search results are the destination. Citations are supporting evidence attached to an answer, and they behave differently.

A cited source is not necessarily the recommended business. Frequently it is a review platform, a directory or a comparison article that mentions several companies. That means being cited and being recommended are two separate things to measure, and improving one does not automatically improve the other.

Across the whole of the same dataset, rather than split by engine, the gap between being cited and being named is the headline result:

The first row is what gets called a ghost citation: your page is the evidence behind an answer that never says who you are. Whether that matters depends entirely on the outcome you are buying. If the objective is brand recognition, a ghost citation delivers almost none of it. If the objective is being the source a system relies on in your category, it is exactly the result you wanted, and it is invisible to any measurement that only counts brand mentions.

The third row is the mirror image and is easy to overlook: a quarter of appearances were mentions with no citation at all, meaning the answer named a business on the strength of something it did not link to. No amount of work on your own pages produces that outcome directly. It comes from the wider record.

Entity understanding

Search can rank a page without a confident view of the organisation behind it. An answer engine recommending a business is making a claim about that organisation, so it needs a clearer picture.

The two can therefore diverge, and understanding why is more useful than the observation. Page-level success asks whether this document is a good response to this query. Entity-level confidence asks whether we know what this organisation is, well enough to put its name forward. Those questions have different evidence requirements, and a business can satisfy the first completely while failing the second.

The practical shape of that failure is familiar: a well-optimised service page ranking respectably, behind which the organisation has two trading names in circulation, no stated category, credentials asserted but not verifiable, and an address that moved. Nothing is wrong with the page. There is simply not enough about the company to justify a recommendation, and a recommendation is a claim.

This is where AEO adds work that traditional SEO often skipped: making sure the name, location, category, services and credentials agree across the entire web rather than just reading well on the site. It is also why the two disciplines can produce contradictory reports about the same business, and why reading them together is more informative than either alone.

Authority

Both care about external credibility, but they weigh it differently, and the difference is one of emphasis rather than replacement.

Search has historically leaned heavily on links, and it is worth being clear that this has not stopped mattering. Link-based authority still does at least four jobs that bear directly on AI visibility: it improves the odds of being discovered at all, it drives conventional ranking which is a live retrieval path, it contributes to whatever domain-level strength a system perceives, and the linking pages themselves frequently become the third-party sources that get cited. A trade publication that links to you is not only a link. It is a page that may end up as the evidence behind an answer.

What AEO adds is explicit attention to things link-building was never aimed at:

• Mentions without links. A name in text, which does nothing for link equity and plenty for corroboration.
• Professional registers. Verifiable, categorical and usually unlinked.
• Directories that are consulted rather than counted. Chosen for whether they appear in answers, not for volume.
• Reviews. As a body of descriptive text, not as a star rating.
• Source diversity. Several independent kinds of source, rather than many of one kind.
• Consistent descriptions. All of the above saying the same thing about you.

For recommendation, what tends to matter is corroboration: several independent sources describing the business consistently and positively. A professional register entry, an accurate directory listing and a body of recent reviews can carry more weight than a link that would once have been prized. The relevant research also suggests that domain-level evidence outweighs individual page improvements by a wide margin, which is a fairly consequential claim and one we go through properly in what makes AI cite a page.

What changes in emphasis

Most of the shared foundation is genuinely shared, so the useful way to describe the rest of the difference is as a change of emphasis on four things rather than four separate workstreams.

Structure

Largely the same requirements, with a stronger emphasis on extractability: clear headings, one idea per section, explicit nouns instead of pronouns, and answers stated before elaboration. The test is whether a section can be quoted on its own without becoming misleading. If it cannot, it is harder to use than it needs to be, for readers as well as machines.

Content

SEO rewards content that satisfies a query better than the alternatives. AEO rewards content that can be extracted accurately and attributed confidently, which is a stricter version of the same requirement rather than a different one.

The change is which parts of a page carry the weight. Under SEO, a page competes as a whole document. Under AEO, a specific paragraph either survives into an answer or does not, so the facts a general model cannot supply for itself become disproportionately valuable: the price, the exclusion, the timescale, the eligibility rule. Vague marketing copy has always performed poorly. It has simply become more obvious which paragraphs were doing nothing.

Links

Links still matter for search, for the reasons set out above. What changes is that for recommendation the mention itself often does the work whether or not it is linked, which shifts the emphasis from acquiring links to being accurately present in the right places.

For a local or professional services business this changes the budget allocation more than the tactics. Fixing eight inconsistent listings and getting onto two professional registers is usually a better use of the same money than a link campaign, and it is measurable within weeks rather than quarters.

Reviews

Reviews have always influenced local search. For AI recommendation they take on an additional role as citable evidence supporting a claim about quality, which changes which reviews are useful.

Under the old model a rating was a signal and the text was for humans. Under the new one the text is the material: a review that names the service, the location and the outcome can support a specific claim, and a five-star rating with no words cannot support anything. Recency matters for the same reason, since evidence about how a business operated three years ago is weaker evidence about how it operates now.

Measurement

The clearest practical difference, and the one most often skipped.

SEO measurement is comparatively direct. A ranking can be checked, impressions and clicks are reported by the platform itself, and the numbers are stable enough to read week to week without aggregation. When something moves, you can usually see it move.

AEO measurement is indirect and statistical, because there is no position to check and any single answer is a sample. It requires a fixed prompt set, run repeatedly, aggregated, and compared against the previous run of exactly the same set. Five things are worth tracking, and they behave differently from their search equivalents:

• Recommendation presence. The share of prompts naming you. Moves in steps rather than smoothly.
• Citation presence. Whether your pages or supporting sources appear. Moves more continuously, and is the better early indicator.
• Mention presence. Whether your name appears in the text. Independent of the above, as the split earlier in this article showed.
• Share of voice. Your appearances relative to named competitors. Definition-sensitive, so the methodology has to travel with the number.
• Cross-engine variability. How much the picture differs by product. Not a vanity metric: it tells you how much any single engine's result can be trusted.

The consequence is that AEO reporting is only meaningful if the prompt set is frozen and the sample is large enough. Change the questions between runs and the comparison is worthless. Run ten prompts and the variance swamps the signal.

Anyone selling AEO without a repeatable measurement method is selling something unverifiable. That is the question worth asking first, and the answer should include how many prompts, how often, across which products, and what happens to the set when the business changes.

Why most businesses should treat them as one system

Because the inputs are shared, the retrieval paths are connected, and splitting them creates work that gets done twice or not at all.

A business that fixes its crawlability, states clearly what it does and where, answers real customer questions directly, keeps its listings accurate and accumulates genuine reviews has improved both at once. There is no separate AEO project hiding behind that list.

Where AEO genuinely adds something is in measurement and in entity work: running prompt sets, tracking share of voice and citations, and taking consistency across the wider web more seriously than a conventional SEO engagement usually would.

The operating model that follows from all of this is a single one with six components, not two competing programmes. Technical discovery, content, entity clarity, authority, reputation and measurement, feeding both search visibility and answer visibility, reporting into one commercial outcome.

Read that list and notice what is missing: nothing on it is exclusively an AEO activity. Five of the six are things a good search programme was already doing, done with more attention to the entity and to consistency across the web. The sixth, measurement, is the one that genuinely has to be built from scratch, because rank tracking has no equivalent here and prompt sets do not exist until somebody writes them.

That is the honest answer to what is actually different. Not the tactics, mostly. The unit of success, the stability of the result, the amount of attention paid to how the business is described in places you do not own, and the fact that you have to build the instrument before you can read it.

If you want the fuller definition of the discipline, our introduction to AEO covers it in more detail, and the evidence on what actually predicts citation is in what makes AI cite a page. If you want to see what it looks like applied, Work covers how we measure it and Research covers what we are still trying to establish.