Your content may rank on page one, yet AI answer engines still skip it. That gap is exactly what an AEO audit exposes: the difference between content that performs in traditional search and content that gets cited by ChatGPT, Perplexity, or Google AI Overviews.

This checklist walks through 48 concrete factors that affect whether AI systems can find, extract, and cite your content. It covers technical access, content structure, authority signals, schema, freshness, and measurement. The goal is practical: help you identify what blocks extractability today and what to fix first.

What is an AEO audit?

An AEO audit evaluates how well your content performs in AI answer engines like ChatGPT, Perplexity, and Google AI Overviews. Instead of measuring success by traffic alone, it asks a more direct question:

When someone asks an AI a question in your category, does your content get cited as a source?

A complete AEO audit looks at four areas:

• Technical access for AI crawlers
• Content structure for extractability
• E-E-A-T and brand authority signals
• Measurement and citation tracking

In practice, AEO audits align closely with emerging AI visibility metrics that track how often, where, and in what context brands appear inside generated answers.

Traditional SEO audits focus on keywords, backlinks, and rankings. An AEO audit focuses on whether AI systems can understand your content well enough to reuse it accurately in answers.

Why your brand needs an AEO audit

AI answer engines are changing how people consume information. Instead of scanning ten blue links, users increasingly get a single synthesized answer. Google AI Overviews now appear in a majority of informational searches, and tools like ChatGPT and Perplexity influence research, buying, and decision-making.

AI systems select sources based on extractability and trust. If your content lacks clear structure, direct answers, or authority signals, it may be skipped even when it performs well in traditional search.

Visibility inside AI answers is also volatile. Research from AirOps shows that only about 30% of brands remain visible from one answer to the next, and just 20% stay visible across five consecutive runs. That instability makes one-off checks unreliable and turns consistent tracking into a requirement rather than a nice-to-have.

An AEO audit helps teams see past snapshots and understand patterns. It shows:

• Where AI systems fail to extract your content
• Which pages lose citations to better-structured competitors
• What changes are most likely to increase citation frequency over time

AEO audit vs traditional SEO audit

Traditional SEO audits evaluate whether a page can rank in search results. They focus on keywords, backlinks, and page-level authority, with success measured through impressions, clicks, and click-through rate.

An AEO audit asks a different question: can AI systems extract a clear, accurate answer from your content and confidently attribute it to your brand? Success is measured through citation frequency, consistency of appearance, and how often content is reused inside generated answers.

This distinction matters because rankings no longer predict visibility in AI search. According to AirOps research, roughly 60% of AI Overview citations come from pages that do not rank in the top 20 organic results. Pages can perform well in traditional SEO and still be invisible to answer engines.

In practice, traditional SEO asks whether Google can rank your page. AEO asks whether AI systems can reuse it. That shift reflects how AI search evaluates relevance today, prioritizing clarity, structure, and trust signals over position alone.

Technical factors for AI crawlability

Seven technical issues determine whether AI systems can access and process your content. Problems here block everything downstream.

AI bot access and crawl permissions

1. Robots.txt access: Review your robots.txt file for rules that block AI crawlers such as GPTBot, Claude-Web, or PerplexityBot.
2. Confirmed AI bot activity: Check server logs to confirm AI bots actually visit your site. If they never crawl your pages, citations will not happen.

Aleyda Solis noted in an AirOps webinar that many AI visibility issues trace back to basic crawlability gaps, especially on sites that rely heavily on client-side JavaScript.

Page speed and mobile rendering

3. Page speed: Slow pages discourage both users and AI retrieval systems. Many AI-driven queries originate on mobile, where speed matters more.
4. Mobile content parity: Compare mobile and desktop versions of your pages. If mobile hides or alters key content, AI systems may miss important information.

URL structure and site architecture

5. Clean URLs: Short, descriptive URLs signal clarity and importance.
6. Internal linking depth: Pages buried several clicks deep or left orphaned often receive less attention from AI systems.

Security and rendering

7. HTTPS and clean rendering: AI systems favor secure sites and content that appears in raw HTML, not only after JavaScript rendering. Fix broken pages, redirect chains, and server errors.

Content factors for AI extraction

Fifteen content-level factors determine whether AI systems can extract usable answers. Structure matters as much as substance.

Question and answer formatting

8. Immediate answers: Place the answer first. Content that delays the answer with long context often gets skipped.
9. Explicit Q&A structure: Format important sections as direct question-and-answer pairs. Avoid rhetorical questions that lack clear answers.

Heading hierarchy and semantic structure

Pages that follow clean, sequential heading hierarchies show 2.8 times higher citation likelihood in AI answers, in part because clear section boundaries make it easier for models to identify and reuse specific passages over time.

10. Aligned headings: Every H2 should clearly match the content beneath it. Misaligned headings confuse AI systems and reduce extractability.

As Aleyda Solis explained during an AirOps webinar, AI relevance increasingly operates below the page level:

“With AI search this happens at a passage or chunk level of relevance.” — Aleyda Solis

This makes section-level alignment critical. Each heading signals a discrete concept that AI systems may extract independently.

Content depth and original insights

11. Summary statements: Include short, extractable takeaways AI can reuse independently.
12. Original data or perspective: First-party research, benchmarks, or unique analysis attract citations more than generic commentary.
13. Expert attribution: Attribute insights to named experts with relevant experience.

Comprehensive coverage matters more than ever. Content that treats topics shallowly or fragments related ideas across multiple pages is less likely to be cited.

“It is even more important than ever to have comprehensive coverage, ideally through topic clusters for each one of the relevant topics that our brands are about.” — Aleyda Solis

Definitions and readability

14. Clear definitions: Use direct “X is…” definitions early in sections.
15. Sentence simplicity: Shorter sentences improve AI comprehension.
16. Short paragraphs: Two to four sentences per paragraph works best.
17. Scannable formatting: Use bullet points and bolded terms for key ideas.

Structured formats and accuracy

18. Tables and lists: AI systems frequently extract content from tables and structured lists.
19. Verifiable claims: Support factual statements with sources. Unsupported claims reduce citation likelihood.

Brand entity and E-E-A-T factors

Eight factors establish your brand as a citable authority. AI systems look for E-E-A-T signals when selecting sources.

Brand entity clarity

20. Consistent NAP information: Your name, address, and phone number should match across platforms.
21. Clear About page: State who you serve and what you specialize in without ambiguity.
22. SameAs entity links: Connect your brand to LinkedIn, Crunchbase, or Wikipedia where appropriate.

Author credentials and expertise signals

23. Named authors: Avoid anonymous content on important pages.
24. Author bios: Include relevant experience and areas of expertise.
25. Credential links: Link to LinkedIn profiles or published work.

External validation

26. Authoritative backlinks: Citations from trusted publications strengthen AI trust.
27. Unlinked brand mentions: Even without links, mentions contribute to entity recognition.

Unlinked brand mentions often play a larger role in AI discovery than direct backlinks, since models rely on broader brand signals to establish authority and consistency across answers.

Schema and structured data factors

Six factors help AI systems parse your content accurately. Schema markup acts as a translation layer between your content and AI understanding.

Organization and author schema

28. Organization schema: Establishes your brand entity.
29. Author schema: Connects content to specific experts with credentials.

Content-specific schema

30. FAQPage schema: For Q&A content sections
31. Article schema: For blog posts and news content
32. Product schema: For product pages and comparisons
33. HowTo schema: For instructional content

Each schema type helps AI systems understand content purpose and extract relevant information.

Freshness and update factors

Five factors signal content currency. AI systems prefer content that appears maintained and current.

Publication and update dates

34. Visible publication dates
35. "Last updated" timestamps

Add both the original publication date and the last review date. Update these only when you make substantive changes to the content.

Content maintenance signals

36. Review schedule: Audit high-value pages quarterly
37. Version tracking: Document significant updates
38. Broken link checks: Fix outdated references promptly

Stale content with broken links and outdated statistics loses citation potential over time. According to AirOps research, more than 70% of pages cited by AI were updated within the last 12 months, which sets a minimum freshness bar for slower-moving categories.

Maintaining update cadence also supports long-term AI search visibility, particularly as models favor sources that reflect current market language, pricing, and positioning.

Measurement and monitoring factors

Four factors help you track AEO performance and identify improvement opportunities across models as AI search behavior continues to evolve.

AI citation tracking

45. AI citation tracking: Monitor how often AI systems cite your content across ChatGPT, Perplexity, Google AI Overviews, and Claude

Track citation frequency over time to spot trends and regressions.

Brand mention monitoring

46. Brand description accuracy: Check how AI systems describe your company and offerings

Misrepresentation often signals weak entity clarity or missing context.

Share of voice and referral traffic

47. Share of voice vs competitors: Compare your citation frequency against competitors for key queries.

AI referral traffic in GA4: Identify traffic originating from AI-driven sources

This data helps connect AI visibility to business impact.

How to prioritize AEO audit findings

A 48-factor audit generates a lot of findings. Prioritization determines whether you see results quickly or get stuck in analysis paralysis.

Start with pages that already rank well or historically drove organic traffic, since those are most likely to earn citations once structured correctly.

This approach is especially important for mid-funnel content, where buyers rely on AI tools to compare options, validate claims, and narrow decisions before visiting a site.

Score each factor by impact and effort

Not all factors carry equal weight. Technical access issues block everything else, so fix those first. Content structure improvements typically deliver faster results than authority-building efforts.

PriorityFactor CategoryTypical TimelineCriticalTechnical accessFix immediatelyHighContent structure0-30 daysMediumSchema implementation30-60 daysLowerAuthority building60-90+ days

Build a 30-60-90 day roadmap

• Days 0–30: Fix crawl blockers, add dates, implement core schema
• Days 30–60: Restructure high-value pages, tighten answers, improve headings
• Days 60–90: Build authority signals, expand schema, formalize monitoring

Track citation changes at each milestone.

Once those priorities are clear, the challenge becomes executing consistently and measuring whether citations actually improve.

Key takeaways

• AEO audits measure citation readiness, not just visibility in traditional rankings, which makes extractability and trust the primary success signals.
• Technical crawlability remains the foundation. If AI bots cannot reliably access your content, no amount of structure or authority will compensate.
• Content structure determines whether answers get reused. Sequential headings, clear sections, and scannable formats materially increase citation likelihood.
• Freshness functions as a trust signal. Pages that fall outside common update windows lose citations faster, especially in competitive or commercial categories.
• AI visibility fluctuates by design, which makes ongoing measurement across multiple models necessary to understand performance trends and prioritize fixes.

Turning AEO audits into lasting visibility

An AEO audit only matters if it leads to action. Identifying extractability gaps, unclear structure, or missing authority signals is the first step. The real work comes from fixing those issues consistently and tracking whether AI systems actually start citing your content more often.

That’s where a repeatable system matters. AirOps gives teams visibility into how their content shows up across AI search, highlights which pages lose citations, and supports repeatable updates with humans in the loop. Instead of guessing what answer engines prefer, you can see what’s working and improve it with confidence.

Book a demo to see how AirOps helps brands turn AEO audits into sustained citations and AI search visibility at scale.