AEO vs GEO vs SEO: The New Playbook for Getting Found in AI Search

For two decades, getting found online meant one thing: rank a page high enough on Google that someone clicks it. The acronyms changed, the tactics churned, but the target never moved — a position on a results page, measured by a click. That target is now splitting into three.

People increasingly get their answers without ever seeing a results page. They ask ChatGPT, read Google's AI Overview, or query Perplexity, and the response arrives pre-written — a paragraph or two that names a few brands and cites a handful of sources. The click, if it happens at all, is optional. So a new vocabulary has appeared to describe optimising for that world: AEO and GEO, sitting alongside the SEO everyone already knows.

The trouble is that these three letters get thrown around interchangeably, usually wrapped in language that promises you can "dominate AI search" the same way agencies once promised page-one rankings. Most of that is hype. But the underlying shift is real, and the three terms do mean genuinely different things. This piece pulls them apart, explains what actually changed, and lays out a practical playbook — including the parts where the honest answer is "you can't control that, you can only influence it." We sell some of this work, so we have a stake. We've tried to write it so it's useful even if you never hire us.

SEO, AEO, GEO — definitions without the hype

Let's define the three plainly, because the hype versions are useless.

SEO — Search Engine Optimisation. The discipline of getting your web pages to rank highly in a list of links. The deliverable is a position; the win is a click that lands a human on your site. Everything classic SEO does — keyword targeting, backlinks, technical performance, content depth — serves that one goal: be the link the person chooses.

AEO — Answer Engine Optimisation. The discipline of becoming the answer itself, not a link the user has to click. When someone asks a question and a system returns a single synthesised response, AEO is the work of making your brand or your facts the thing that response is built from. The deliverable isn't a ranking — it's being named, cited, or quoted inside the answer. This is older than it sounds: Google's featured snippets and "People also ask" boxes were primitive answer engines years before generative AI.

GEO — Generative Engine Optimisation. A newer and narrower term for AEO's hardest case: getting represented inside text that a large language model generates on the fly. There's no fixed snippet pulled verbatim from one page; the model writes a fresh paragraph blending several sources and its own training. GEO is about raising the odds that this generated text mentions you accurately. In practice GEO and AEO overlap heavily — many people use them as synonyms — but the useful distinction is that AEO covers any "be the answer" surface (including structured snippets), while GEO is specifically about the probabilistic, synthesised output of generative models.

Here's the comparison in one view:

The single most important row is the last one. SEO let you target a keyword and reasonably expect to move for it. AEO and GEO don't work that way: you cannot guarantee a model names you for a given question. The same prompt yields different brands on different days, across different engines, sometimes from one minute to the next. The realistic goal shifts from locking in a slot to raising the probability that you're surfaced, and surfaced accurately.

None of this means SEO is dead. It means SEO became one layer of a larger problem. The page still matters — but the page is no longer the whole game.

What changed: zero-click answers, AI Overviews, and chat as the homepage

Three things shifted under the industry's feet, and they compound.

Zero-click became the default, not the exception. "Zero-click search" — a query that gets resolved without anyone clicking through to a website — isn't new. Snippets and Knowledge Panels were eating clicks for years. But generative answers accelerate it sharply, because instead of lifting one snippet from one page, the engine assembles a complete, self-contained reply. There's simply less reason to click. For a brand, that means traffic-based metrics start understating your real visibility: you can be more present in answers while your click-through falls. If your only scoreboard is sessions in your analytics, you'll misread the entire shift.

AI Overviews put generated answers above the links. Google now frequently leads with an AI-written summary sitting on top of the traditional results — fused tightly with its existing search ranking. This matters because it blends the old world and the new: the sources an AI Overview pulls from are heavily influenced by what ranks well and by what reads as a trustworthy, citable answer. Classic SEO still feeds it, but being rankable is no longer sufficient; you also have to be the kind of source a summary wants to cite.

Chat became the new homepage. For a growing share of users, the first interaction with a category isn't a Google search or a brand's website — it's a question typed into an AI assistant. "What's the best tool for X?" "Is [company] reputable?" "Who are the main players in [industry]?" The assistant's answer is now the first impression, and your homepage — the page you spent years optimising — may never be seen. The implication is uncomfortable but clarifying: the most important "page" about your company is increasingly one you don't own and can't edit — it's the answer an engine generates about you.

Put together, these three shifts move the centre of gravity upstream. You're no longer optimising an output you control (your page). You're shaping the inputs an engine draws on to produce an output you don't control (its answer). That's a slower, more indirect lever — and it's the entire premise of AEO and GEO.

The metrics that replace rankings

If clicks and rankings no longer capture visibility, what does? Three metrics have emerged as the practical replacements. None is as crisp as "we rank #3 for this keyword," and anyone who tells you these can be measured to a decimal place is overselling. But they're directionally real and you can track them.

Citation share. Of the sources an engine cites when answering questions in your category, what fraction are yours — or describe you? If you ask ChatGPT or Perplexity ten category questions and tally the cited domains, citation share is your slice of that pie. It's the closest analogue to "ranking," because it measures whether the engine treats you as a source worth pointing to. Perplexity makes this easiest, since it shows its sources prominently; ChatGPT and Gemini surface them less consistently.

Mention rate. Across a set of relevant prompts, how often is your brand named at all in the answer — cited or not? A model frequently states "leading options include A, B, and C" without linking anywhere; being named in that sentence is valuable even without a citation. Mention rate is simply: of N prompts where you could plausibly appear, in how many did you actually get named? This is often the first metric to move, and the most motivating to track, because it's binary per prompt and easy to score by hand.

Share-of-voice in answers. The richer, comparative version: across your category's key questions, how much of the answer space do you occupy versus competitors? It blends mention rate and citation share and weights for prominence — being the first brand named in a recommendation carries more than being fourth in a list. This is the metric that maps most cleanly onto the old marketing concept of share-of-voice, just relocated from ad impressions to AI answers.

A blunt caveat on all three: they're noisy. Outputs vary run to run, engine to engine, country to country. The discipline is to sample enough prompts, repeat them on a schedule, and watch the trend rather than obsess over any single answer. Treat these like a polling average, not a stopwatch.

The 4-layer AI-visibility model, applied

Underneath AEO and GEO sits a structural model that makes the work concrete. Think of your AI visibility as four stacked layers, built bottom-up. Each layer makes the one above it more effective, and the most common mistake is building top-down — pouring effort into the top while the foundation is missing.

Layer 1 — Entity. The machine-readable identity of your organisation: a Wikidata item with a stable identifier, presence in the knowledge graph, a clean Google Business Profile, consistent listings in industry databases. This is bedrock. Before an engine can say anything correct about you, it has to be confident you exist as a distinct thing and not be confusing you with a similarly named company. This layer is unusually binary — either the grounding systems know you exist, or they don't — which makes it the highest-leverage place to start.

Layer 2 — Encyclopedic. The neutral, authoritative reference layer, chiefly Wikipedia where notability genuinely supports an article. This is the most heavily-weighted, high-trust source that engines (ChatGPT especially) lean on, and it does double duty: it feeds the training corpora models learn from and it reinforces the entity layer beneath it, since a Wikipedia article almost always strengthens the linked Wikidata item.

Layer 3 — Community. Reddit, Quora, YouTube, LinkedIn — the discussion and opinion layer. This is where recommendation-shaped answers come from ("we switched from X to Y because…"), and it's disproportionately important for Google's AI surfaces and Perplexity. It has to be earned genuinely; it cannot be faked without eventually backfiring.

Layer 4 — Owned. Your own website, blog, documentation, and structured data (schema markup). This is the layer you control most directly — and, counterintuitively, the least independently trusted, because a model knows your site is your own marketing. Owned content matters for live retrieval and for feeding clean facts into the layers below, but it can't carry the load alone.

Applied, the model reframes the whole AEO/GEO question. SEO instinct says "publish more, optimise the pages, build links" — which is all Layer 4 work. But a brilliant blog sitting on top of a non-existent entity is a blog the AI can't attribute to anyone. The leverage runs the other way: fix the entity, earn the encyclopedic and authoritative coverage, build genuine community presence, then let owned content amplify. Our AI visibility work is organised around exactly this stack, foundation first.

The channel map: authority, community, and sourcing

Translating the four layers into channels, three groups of platforms do most of the work — and each plays a distinct role.

Authority — Wikipedia and Wikidata. These anchor Layers 1 and 2 simultaneously, which is why they're the highest-value channels in the entire map. There are structural reasons engines over-rely on them: Wikipedia's neutral, attributed house style is exactly the tone a model wants to reproduce when sounding factual; its predictable article structure is easy for retrieval systems to parse; its open license means it gets included in training sets broadly and repeatedly. And Wikidata's stable entity IDs are the connective tissue grounding systems use to know precisely who you are. A Wikipedia article gives a model clean prose; the linked Wikidata item gives it structured, machine-readable truth. The mechanics of that structured half are worth understanding on their own — we wrote them up in Wikidata and the knowledge graph.

Community — Reddit and Quora. These power Layer 3 and drive recommendation-shaped answers. Reddit is the standout, cited heavily across ChatGPT, Google's AI surfaces, and Perplexity, because its threads contain candid, comparison-rich discussion that maps onto the questions people ask an assistant. Quora appears prominently in Google's AI surfaces in particular, because its question-and-answer structure mirrors how users phrase queries to an engine. Both reward genuine, useful presence and punish astroturfing — detection is good and the blowback is real. The legitimate play is to actually be present where your audience already talks, which we cover in Reddit AI visibility and Quora AI visibility.

Sourcing — PR and earned media. This is the substrate everything else is built from. Independent, reputable editorial coverage is what makes a Wikipedia article possible in the first place (no notability, no article), what models treat as high-trust when recalling facts about you, and what gives the community layer something real to discuss. Earned media isn't just for humans anymore — it's the high-trust material the engines learn from. A brand with substantive independent coverage is a brand an AI can cite confidently; a brand with only its own marketing pages is one the model has to hedge about.

The honest through-line: none of these channels is a dashboard you can buy your way into. They're a source base you build, and their credibility is the reason engines trust them. The moment a channel becomes gameable, it stops being trustworthy — and stops being cited.

A practical 90-day AEO roadmap

Here's a realistic sequence. It's deliberately foundation-first, because that's where the leverage is, and it assumes you're starting roughly from scratch on AI visibility.

Days 1–15: Baseline and entity audit. Before changing anything, measure where you stand. Ask ChatGPT, Gemini, and Perplexity the questions a customer would — "What is [company]?", "Who are the leading players in [category]?", "Is [company] a good choice for [use case]?" Record three things per engine: are you mentioned, are the facts right, which sources get cited. That's your baseline mention rate and citation share. In parallel, check the entity layer: is there a Wikidata item, is it accurate, does a Google Knowledge Panel appear for your brand? This fortnight tells you whether your problem is non-existence, inaccuracy, or invisibility in recommendations — three very different problems with different fixes.

Days 16–30: Fix consistency and the entity layer. Pull your core facts — founding year, HQ, leadership, one-line description — as they appear across your site, LinkedIn, Crunchbase, directories, and old press. Flag and reconcile every discrepancy; each one is a reason for a model to hedge or err. Create or correct your Wikidata item and complete your Google Business Profile. This is unglamorous and high-impact: consistency is one of the most common reasons AI answers about a company come out subtly wrong.

Days 31–60: Shore up authority and sourcing. Map your genuinely independent coverage from the last couple of years (be strict — your own blog and press-release syndication don't count). If the list is thin, the honest priority is earning real coverage, because no AEO tactic substitutes for it. Where notability is already supported, this is the window to begin the encyclopedic layer — a Wikipedia article that strengthens both Layers 1 and 2. Where it isn't yet, the work is media-building first.

Days 61–90: Community presence and owned amplification. Find the Reddit and Quora conversations already happening in your category and join them genuinely — answer real questions, correct inaccuracies, add specifics. Only now, on top of a solid foundation, does heavy investment in owned content pay off: publish clear, well-structured, factual pages and add schema markup, so retrieval systems have clean material to pull and the layers below have consistent facts to reinforce.

Then loop. Re-run the day-one baseline prompts and compare. AEO is compounding, not a launch — the curve bends over months, not weeks.

How to measure it: prompt testing across engines

Because the metrics are noisy, measurement needs a method rather than a single check. The practical approach is prompt testing: a repeatable panel of questions you run across the major engines on a schedule.

Build a fixed list of 15–30 prompts that mirror real customer intent — definitional ("what is [company]?"), comparative ("best [category] tools"), and evaluative ("is [company] good for [use case]?"). Run the identical set across ChatGPT, Gemini, and Perplexity, because they cite differently: ChatGPT leans encyclopedic and authoritative, Google's AI Overviews lean on community platforms alongside ranking pages, and Perplexity is retrieval-first and exposes its sources openly. Testing only one engine gives you a distorted read.

For each prompt and each engine, score three things by hand: were you mentioned (yes/no — that's mention rate), were you cited with a source (that's citation share), and how prominently did you appear versus competitors (that feeds share-of-voice). Repeat the whole panel monthly, because any single run is noisy — the same prompt varies day to day. Watch the trend line, not the individual answer.

A few honest limits. Outputs differ by account, location, and the model's mood that day; treat results as a polling average. Citation share is easiest to read on Perplexity and hardest on engines that hide their sources. And there's no public dashboard that gives you ground truth — this is sampling, not telemetry. Anyone selling you a precise, real-time "AI visibility score" is selling more certainty than the underlying systems actually offer.

Where Wikipedia fits as the foundation

Step back from the acronyms and one channel keeps surfacing at the base of all of it. Across the analyses published through 2026, Wikipedia is consistently the single most-cited domain in ChatGPT's answers — in several studies, roughly half of its top factual citations trace back to Wikipedia. The exact figure varies with methodology and shifts month to month, so treat any single number as an order of magnitude; what's durable is the pattern. Encyclopedic sources dominate, and community sources like Reddit are the strong second tier.

That's why, in the four-layer model, Wikipedia sits at the foundation rather than the top. It does three jobs at once that no other channel does together: it's a heavily-weighted training source the models genuinely learned from; it reinforces your machine-readable entity through the linked Wikidata item that grounding systems rely on; and its neutral, well-sourced prose is exactly the kind of material an engine reaches for when it's trying to sound factual. Fix the encyclopedic layer and you've usually fixed the entity layer beneath it at the same time — two of the four layers, from one piece of work.

But the honesty that runs through all our writing applies most sharply here. A Wikipedia article is not available on demand. It exists only where your organisation genuinely meets Wikipedia's notability bar — meaning substantive, independent coverage in reliable sources. There is no shortcut, no paid placement, no way to inject a page that survives review without that foundation. That constraint is not a flaw in the plan; it's the very reason the citations are trustworthy and the reason engines weight them so heavily. The same independent source base that makes a Wikipedia article possible is what makes every other layer of AI visibility work.

So if you take one thing from the SEO-versus-AEO-versus-GEO debate, make it this: the labels are new, the surfaces are new, the metrics are new — but the underlying work is the slow, compounding business of becoming a brand the internet describes accurately and consistently. Do that, and when an answer engine reaches for a source, yours is the reliable one it finds. That's not a hack you buy. It's a base you build.

WikiBusines builds the encyclopedic and structured-data foundation that AEO, GEO, and AI answer engines rely on. If you want an honest read on where your brand stands across ChatGPT, Gemini, and Perplexity, email team@wikibusines.com and we'll run a baseline.