AEO vs GEO vs SEO: Three Acronyms, One Strategy for AI Search Visibility

Three acronyms, three measurement surfaces #

The heading deliberately replaces "three goals" with "three measurement surfaces". The acronyms describe what you measure — rankings, snippet ownership, citation rate — not three separate things you build. Each lens points at the same substrate from a different angle. Treat them as three procurement categories and you will pay three vendors to fight over the same fixes.

SEO (Search Engine Optimisation): measure ranking in the ten blue links #

SEO is the discipline of earning visibility in the classic Google SERP — the ten blue links and the rich elements stacked around them. In Australia, Google still handles roughly 94% of all search queries according to StatCounter. That demand is not disappearing because AI Overviews appeared at the top of the page; it is being re-stacked.

The SEO measurement surface is well understood: keyword rankings, organic clicks, click-through rate, indexation depth, and the revenue attributable to non-paid Google referrals. The thing that wins SEO in 2026 is the same thing that wins AEO and GEO — clean technical foundations, structured content, and entity authority — observed through Google's ranking algorithm rather than through a snippet or an LLM response.

SEO Visibility is the discipline applied to this measurement surface specifically.

AEO (Answer Engine Optimisation): measure ownership of the answer in snippets and AI Overviews #

AEO is the discipline of owning the answer block rather than the result list. The answer block can be a featured snippet, a People Also Ask expansion, or — since Google AI Overviews rolled out globally at Google I/O in May 2024 and reached Australia later that year — the AI-generated overview at the top of the page.

The AEO measurement surface is binary on a per-query basis: either your content is the extracted answer or someone else's is. There is no second place inside an AI Overview. The mechanics that win AEO will be familiar to any SEO who has chased featured snippets — clear declarative answers, question-shaped headings, FAQPage and HowTo schema, concise paragraphs structured for direct extraction. What is new is the model in the middle. AI Overviews stitch their answer from multiple sources and link out to a small subset; AEO discipline is how you become one of those sources.

Schema Engineering for AI inside AI Search Visibility is the discipline applied to this surface.

GEO (Generative Engine Optimisation): measure citation rate inside conversational AI responses #

GEO is the discipline of being cited inside a conversational AI response — when a buyer asks ChatGPT, Claude, Perplexity, or Gemini a question and the model synthesises an answer with named sources. The category was named in the Aggarwal et al. KDD 2024 paper, "GEO: Generative Engine Optimization", which formalised both the term and the first measurement framework. Their experiments showed structured GEO interventions producing up to 40% visibility lift inside generative engine responses.

The GEO measurement surface is statistical, not binary. Across a defined set of buyer queries, what percentage of conversational AI responses cite you, in what position, and with what sentiment? Perplexity exposes its citations directly; ChatGPT and Claude cite when grounded in retrieval; Google's AI Overviews cite a curated subset. The lever that moves GEO is entity authority — clean schema, dense sameAs graph, structured FAQs, citation-friendly prose, and a pattern of being mentioned on sources the model already trusts.

GEO is delivered through AI Search Visibility and the deeper Generative Engine Optimisation whitepaper, with Share of Model as the measurement framework.

The single strategy that wins all three #

The lenses look different. The substrate is the same. Every visibility lens — SEO, AEO, GEO — pulls from the same foundation: structured content, entity authority, and technical health. Build the substrate and all three measurement surfaces light up in sequence. Skip it and no acronym-specific tactic will close the gap.

Structured content: schema markup as the substrate #

Schema markup — formally Schema.org vocabulary expressed as inline JSON-LD — is the single highest-leverage investment in this stack. It tells Google what your page is about in machine-readable form, qualifies the page for rich results and AI Overview inclusion, and gives RAG (retrieval-augmented generation) systems the structured context they need to cite you confidently.

A FAQPage block, for example, achieves three things from one source of truth: it qualifies the page for FAQ rich results in Google's SERP, it formats Q&A pairs for direct extraction into AI Overviews, and it presents the same content to LLMs in the structure they reliably parse. Article, Organization, Person, BreadcrumbList, HowTo, and Service schemas extend the same logic across page types.

The brittleness most enterprises have is not absence of schema — it is unmaintained schema, drifted from the visible content, contradicting itself across templates, or generated client-side where AI crawlers cannot see it. The fix is governed schema: server-rendered, version-controlled, and validated on every deploy.

Entity authority: E-E-A-T, sameAs, citation graph #

Google's E-E-A-T quality rater framework — Experience, Expertise, Authoritativeness, Trustworthiness — is the most public statement of what authority means to a ranking algorithm. The same signals turn out to drive what LLMs cite. A model deciding whether to surface your brand in a generative answer is making a near-identical judgement to a quality rater: does this source know what it is talking about, and is it the kind of source other authoritative sources reference?

Three concrete moves build entity authority. First, a complete sameAs graph linking the canonical Organization or Person entity to Wikipedia, Wikidata, LinkedIn, regulator registers, and any other authoritative directory listings. Second, named-author bylines with Person schema, credential disclosure, and links into the entity graph. Third, a citation pattern in which authoritative third parties — industry publications, academic sources, peer companies — already mention you. None of this is new SEO. What is new is that LLMs read the same signals as Google does, often more strictly.

Technical health: the table-stakes layer #

The third foundation is unglamorous and non-negotiable. If a page cannot be crawled, rendered, and cached cleanly, no schema or entity graph will save it. Three checkpoints matter most.

Server-side rendering for AI crawlers. GPTBot, ClaudeBot, PerplexityBot, and Google-Extended do not reliably execute JavaScript. A page that renders critical content client-side is invisible to most of the agents you are trying to influence; an SPA that ships to AI crawlers as a JavaScript shell returns a blank page to the model.

Core Web Vitals and indexation. Largest Contentful Paint, Interaction to Next Paint, Cumulative Layout Shift — Google has been explicit that these contribute to ranking decisions. Beyond ranking, slow or broken pages get crawled less frequently, which means substrate updates take longer to propagate.

Robots, headers, and crawler permissions. AI crawlers respect robots.txt, X-Robots-Tag, and content-policy headers. Many enterprise sites silently block GPTBot and ClaudeBot via WAF rules added during a security review and never reverted, then wonder why ChatGPT does not know they exist.

This is the layer where the Law-to-Code Methodology is decisive. Every visibility-affecting change to schema, headers, or rendering pipeline needs the same audit-trailed treatment a regulated change receives, because the downstream effect on AI citation is now as material as the downstream effect on revenue attribution.

Where the three lenses look at the same substrate differently #

The diagram, the substrate argument, the unified strategy — none of it means the three lenses are interchangeable in operation. They are not. Each lens optimises for a different observable, measures success on a different surface, and shifts first when the underlying field moves. A board paper that rolls all three into "AI search" without naming what gets measured will produce vendor briefs no one can fulfil and dashboards no one can defend.

The table below is the working version. Read it as "what each lens optimises for and measures", not "where the disciplines diverge". The substrate underneath is the same.

The first column changes per lens. The second column changes per lens. The third column changes per lens. The investment underneath does not. Schema work that lifts your AEO eligibility also lifts your GEO citation likelihood. Entity authority that earns you LLM citations also earns Google's trust for ranking. Technical health that satisfies AI crawlers also satisfies Googlebot. Same substrate. Same investment. Three measurement surfaces.

The practical implication: brief one team to build the substrate, then instrument three measurement surfaces against it. Three vendor contracts is a procurement problem you have invented.

What this means for AU listed and pre-IPO companies #

The category-defining content for AEO and GEO is overwhelmingly American, written for B2C marketers and direct-to-consumer brands. Listed and pre-IPO companies in Australia have a different problem to solve and a different audience to brief. Three points matter most.

Why investor relations content needs GEO discipline #

When a journalist asks ChatGPT "tell me about [your company]", the model assembles its answer from your prospectus, your annual report, your investor relations page, and whatever else has been said about you publicly. If the IR substrate is unstructured — PDFs without text extraction, JavaScript-rendered single-page apps that AI crawlers cannot see, named-entity ambiguity that confuses a retrieval model — the gap gets filled with competitor coverage, outdated press, or model hallucination.

This is not hypothetical. The GEO measurement protocol on AI Search Visibility regularly surfaces ASX-listed entities for whom the model's first-pass description is materially out of date, or sourced from a competitor's framing. The fix is the same fix that delivered the $208M prospectus attribution figure for MoneyMe — governed, structured, attribution-grade content infrastructure (MoneyMe case study).

Why the first-pass investor view is now AI-mediated #

AI-mediated answers are increasingly how analysts, journalists, and retail investors form their first-pass view of a listed entity. What ChatGPT, Claude, Perplexity, and Google AI Overviews say about you sits alongside the analyst notes and media coverage that have always shaped investor perception — only now the answer is assembled by a model from whatever your IR substrate makes legible to it.

The implication for boards: the IR substrate now needs to be engineered for machine consumption, because machines are increasingly the first reader.

Why your platform team needs a seat at this table #

Visibility to AI agents is not solely a marketing problem. The substrate AEO and GEO depend on — schema architecture, entity graph, server-side rendering, agent-readable content surfaces, and emerging Model Context Protocol (MCP) endpoints — sits inside the platform team's remit. Buying a "GEO tool" without involving engineering produces dashboards, not lift.

A unified visibility stack treats Visibility (Layer 1), Content Operations (Layer 2), and Platform & Agent Infrastructure (Layer 3) as one architectural problem under one methodology. That is what the three-acronym vocabulary is gesturing at without saying. Engagements like the NSW Department of Industry data platform program show the platform-side discipline that makes AI-readiness defensible at audit, not just marketable.