Key takeaways
- AEO is not separate from SEO. Google treats optimizing for generative AI search as part of the same search experience, so an audit should extend existing SEO work rather than replace it.
- Schema clarifies entities but does not buy citations. Structured data should mirror visible content, not invent facts or promise inclusion in AI answers.
- The audit must start with buying questions, not keywords. Comparison, alternative, pricing, and risk prompts reveal more about AI visibility than generic informational terms.
- A source gap, where AI answers cite competitors or category pages but never your business, is the single most actionable finding an audit can surface.
What is an AEO audit?
An AEO audit is a review of whether AI answer systems can understand, trust, cite, and recommend your business for the questions buyers ask. It is not a rebrand of SEO with new vocabulary, and it should not be sold as one.
An AEO audit examines the same signals that matter for search visibility, then asks a narrower question: when someone types a buying question into an AI answer engine, does your content show up as evidence. That requires checking content quality, technical health, entity clarity, and third-party corroboration together, not treating any single tactic as sufficient.
Google has said AEO and GEO are terms people use, but from its perspective optimizing for generative AI Search is still optimizing for the search experience, and remains SEO. An audit that treats AEO as a separate discipline with separate rules will mislead clients. AEO is best framed as SEO work focused on answer eligibility.
What questions should the audit start with?
Start with buying questions, not generic informational keywords: best, alternatives, comparison, local, price, risk, implementation, and problem-solving prompts. These are the query types AI answer engines most often turn into direct recommendations.
Before touching a single page, build a prompt map. List questions buyers ask at each stage: discovery questions, comparison questions like X versus Y, validation questions like is this safe, and decision questions like which vendor fits my budget. Run each through at least two AI answer engines and record whether your brand appears and is cited.
This matters because keyword tools built for traditional rankings do not capture how AI systems phrase or bundle questions. A prompt map built from real buyer language, support tickets, and sales calls surfaces gaps that a generic keyword list misses, giving the audit a defensible starting point instead of guesswork.
| Prompt Type | Example | What It Reveals |
|---|---|---|
| Best/top | best CRM for small agencies | Category inclusion |
| Alternatives | alternatives to [competitor] | Competitive visibility |
| Comparison | X vs Y for compliance teams | Positioning accuracy |
| Price/risk | is [category] worth the cost | Objection handling |
| Implementation | how to migrate to [category] | Practical authority |
How should the content be audited?
Audit every priority page for answer-first structure, specificity, proof, freshness, internal links, and whether it solves one intent cleanly. A page that hedges or tries to cover five intents at once is a poor citation candidate.
For each priority page, check whether the first two sentences answer the implied question, whether claims are backed by specifics rather than adjectives, and whether the page links to related proof pages. Google has said unique, valuable, non-commodity content matters more than recycling what others said, so pages that just restate competitor content should be flagged as low priority.
The anti-hack position matters here: do not create a new page for every micro-variation of a question. Splintering content into thin pages targeting slightly different phrasings dilutes authority and confuses both search engines and AI crawlers. Consolidate related questions into fewer, deeper pages that answer clusters of intent well.
What technical checks matter for answer engines?
Technical AEO checks are ordinary crawl and index checks: robots rules, noindex tags, canonical accuracy, rendered content, page speed, mobile usability, and snippet eligibility. No separate technical stack is required beyond a healthy, crawlable site.
Confirm priority pages are not blocked by robots.txt, do not carry accidental noindex tags, and have canonical tags pointing to themselves rather than a competing URL. Check rendered HTML to confirm answer-first content appears in the initial render, not injected late by client-side JavaScript a crawler might skip.
Page speed and mobile usability still matter because slow or broken pages get crawled less often, limiting how frequently AI systems refresh their understanding of your content. None of this requires exotic tooling. Standard crawl audits, log file checks, and index coverage reports do the job before any schema work begins.
What should the schema/entity audit include?
Schema should mirror visible content and clarify entities; it should not invent facts or promise AI inclusion. Check Organization, Article, FAQPage, BreadcrumbList, and Service markup only where content actually supports it.
Google states that structured data is not required for generative AI search and there is no special schema.org markup needed for Google AI features. An audit should never sell schema as a shortcut. Instead, check that existing FAQPage markup, which schema.org describes as a page presenting frequently asked questions, matches the visible Q&A content rather than a padded version.
Google's structured data guidelines describe JSON-LD, Microdata, and RDFa as valid formats, with JSON-LD recommended in general practice. Confirm consistent naming, sameAs links to verified profiles, and accurate Organization details across the site, since inconsistent entity signals make it harder for any system to confirm who you are.
How should source gaps be audited, and what is the fix plan?
A source gap exists when AI answers cite pages mentioning competitors or explaining the category while your business is absent. The fix plan should name the page, source, owner, evidence needed, expected AI signal, and retest prompt.
Build a source map with fields for the prompt tested, the engine used, the citations returned, whether your domain appeared, and the source type, such as a review site or forum thread. Repeat this monthly, since citations shift as source pages update. A single snapshot tells you where you stand; repeated mapping tells you if outreach is working.
Example tasks: rewrite the homepage intro to directly answer what the company does, add FAQPage markup matching existing visible questions, request a correction on a directory listing with outdated pricing, publish a comparison page addressing top alternatives, and pitch a mention on a review site that already ranks for your category. Retest each with the original prompt before marking complete.
Reader questions
Frequently asked questions
Is AEO different from SEO?
Not fundamentally. Google says optimizing for generative AI search remains SEO. AEO is best treated as a focused subset of SEO centered on answer eligibility and citation-worthiness, not a separate discipline.
Does FAQ schema help AEO?
It can clarify structure, but it is not required and does not guarantee AI inclusion. Google states structured data is not required for generative AI search, so FAQ schema should support accurate visible content, not replace it.
How long does an AEO audit take?
A focused audit on priority pages typically takes one to two weeks, covering prompt mapping, content review, technical checks, schema review, and source gaps. Larger sites need a phased audit over several weeks.
What tools do I need for an AEO audit?
Standard crawl tools, an index coverage report, a structured data tester, and access to at least two AI answer engines for prompt testing are enough. No proprietary tooling is required beyond a disciplined checklist.
Can AEO guarantee AI citations?
No. Nothing guarantees inclusion in AI-generated answers, since systems select sources dynamically and change over time. An audit only reduces obvious gaps in eligibility and evidence, improving odds without promising outcomes.