AEO audits

Set the audit boundary before opening tools

Start with a business question, not an undifferentiated spreadsheet. Choose a site section, audience, market, and decision to inspect for example, whether SaaS integration pages answer evaluation questions or clinic service details are current. Record the date, the pages included, and what would count as a meaningful defect. Separate a technical eligibility issue from an editorial opportunity and from a measurement limitation.

For copy level expectations, compare sampled pages with the guide to writing answer first content; the wider AEO implementation hub connects audit findings to practical publishing work.

Trace discovery, crawl access, and index eligibility

Start with URLs that matter to customers and revenue, then confirm each has a working response, is reachable through ordinary internal links, and is not unintentionally blocked by robots directives, a page level noindex, authentication, or a broken rendering path. Inspect redirects and canonical signals so that your audit is testing the URL search systems are expected to use, rather than a duplicate or retired version.

Google’s guidance says an AI Overview or AI Mode supporting link must meet Search technical requirements, be indexed, and be eligible to appear with a snippet; meeting those conditions still does not guarantee crawling or display. Fix widespread template, deployment, or navigation problems before polishing isolated copy.

Judge the answer against a real reader task

For each sampled page, write down the question it is supposed to resolve and the reader’s next decision. Then read the page as that person would: is the direct answer easy to find, is its scope clear, and are important conditions stated beside it? A pricing page, for example, should make the billing unit, exclusions, and relevant plan differences understandable; a professional services page should define the service and clarify the circumstances that change its scope.

Look for named entities, current dates, clear definitions, and plain language trade offs. Record the excerpt and reader question so revisions can be reviewed instead of labelled vaguely as “add more AI content.”

Check whether claims can be verified

Mark factual statements that could affect a decision: prices, compatibility, eligibility, service coverage, clinical or legal claims, product specifications, and performance assertions. Ask what supports each statement, whether the source is authoritative for that fact, and whether the page explains a material limitation. Choose a source suited to the claim: vendor documentation for product features, or government and standards sources for requirements. Attribute data near the claim and identify relevant expertise. Label hypothetical examples clearly; use client evidence only when approved and documented.

Remove or qualify claims that cannot be substantiated rather than dressing them up with more confident wording. For recurring customer questions, map missing or weak answers to the content gap analysis for AEO, which helps decide whether to improve a current URL or create a genuinely distinct resource.

Treat structure as a clarity tool, not a ranking trick

Descriptive headings, explicit units, meaningful lists, and links to detail help readers find useful passages. Not every page needs the same answer template. A comparison may need criteria before its conclusion; a troubleshooting guide may need ordered checks. Keep qualifications that make an excerpt accurate. Google’s documentation says there are no special schema.org requirements for AI Overviews or AI Mode.

Its structured data guidance describes markup as explicit clues about page meaning and says it must represent content visible to users. Use the structured data explainer for terminology, and the schema validation guide to check a particular implementation.

Validate markup and the page that produces it

When a page uses structured data, compare every important property with the rendered page: names, dates, prices, availability, authorship, ratings, and other claims must not contradict the visible experience. Use the official test, then inspect representative URLs after deployment; one valid example does not prove the CMS template works sitewide. Check for conflicting plugins, stale values, missing feature requirements, or markup for hidden content.

Structured data can help systems interpret a page and may enable eligible search appearances, but valid markup is not a certification of accuracy or a promise of a special result. Keep markup changes proportionate to actual content and documented feature requirements. If no applicable enhancement exists, prioritize a useful, accurate page over adding speculative properties.

Measure each platform on its own terms

Before changes, record the date, property, market, range, and report. Google’s Generative AI performance report documentation says the Search report includes impressions for AI Overviews and AI Mode, grouped by dimensions such as page, country, date, and device. It may not appear when a site has not received enough qualifying impressions; it does not provide a universal citation or answer quality score.

Microsoft describes its Bing Webmaster Tools AI Performance public preview as reporting citation activity, cited pages, and sampled grounding queries across supported experiences, while expressly cautioning that those data do not show ranking or placement within an answer (Microsoft’s report announcement). Treat each metric as evidence of the thing it measures. Use the AEO KPIs and tracking guide to keep visibility, site visits, conversions, and manual answer checks distinct.

For manual checks, keep the query, location or language, platform, date, answer, cited URL, and capture method consistent. A single result is an observation, not a trend. Annotate major changes and avoid attributing causation without a controlled comparison.

Prioritize repairs and close the loop

Sort findings by impact, confidence, and reach. First fix problems that block important groups of pages, such as an accidental crawl restriction, broken canonical pattern, or defective template. Next address high value pages with inaccurate, incomplete, or poorly evidenced answers. Then handle isolated markup issues and measurement gaps. For each action, name an owner, affected URLs, evidence, intended change, and review date. Recheck after deployment, allowing time for systems to revisit the page. Record improvements, uncertainty, and whether to expand the sample.

This creates a useful service deliverable for an agency: a traceable set of findings and decisions, not a speculative “AI readiness” score or a promise of citations.