Has anyone done an AI content audit? What did you actually learn?

I’ve conducted AI content audits for 15+ clients. Here’s what they actually involve:

Core components:

1. Brand Representation Assessment

• Ask AI platforms questions about your company
• Document what they say
• Identify inaccuracies or outdated information
• Compare to how competitors are represented

2. Content Discovery Audit

• Which of your content appears in AI answers?
• Which content types get cited most?
• What’s missing that should be there?

3. Accuracy & Currency Review

• Is cited content current?
• Are old articles training AI with outdated info?
• Does current content reflect current positioning?

4. Authority Signal Check

• Author credentials visible?
• Expert attribution present?
• Third-party validation signals?

5. Structural Optimization Audit

• Extraction-friendly formatting?
• Schema markup implemented?
• Clear, parseable headings?

The typical finding:

Most companies have significant legacy content that’s training AI with outdated or incorrect information about their brand.

We did a comprehensive AI audit 6 months ago. Here’s what we actually learned:

Surprising finding #1:

A 2019 blog post was our most-cited content in ChatGPT. The problem? It described a product feature we deprecated in 2021. AI was actively misinforming prospects.

Surprising finding #2:

Our “About Us” page had weak entity signals. When people asked ChatGPT about our company, the description was vague and partly wrong.

Surprising finding #3:

Competitors with newer content were getting cited where we should have been, despite us having more authority in the space.

What we did:

1. Updated or removed 45 outdated articles
2. Restructured About page with clear entity definitions
3. Created new content targeting queries where competitors dominated
4. Added schema markup across key pages

Results after 6 months:

• 60% increase in accurate AI brand mentions
• 3 competitors no longer outrank us in AI
• Legacy misinformation largely corrected

Process perspective on AI content audits:

How we structure the audit:

Week 1: Discovery

• Set up AI monitoring tools
• Create key question list (50-100 questions)
• Run baseline tests across AI platforms
• Document current state

Week 2: Content Inventory

• Identify all content cited by AI
• Flag outdated content (>2 years old)
• Assess accuracy of cited content
• Check brand representation

Week 3: Analysis

• Compare to competitors
• Identify gaps and opportunities
• Prioritize issues by impact
• Develop recommendations

Week 4: Action Plan

• Content to update
• Content to remove/redirect
• New content to create
• Technical fixes needed

Time investment:

For a site with 500+ pages: 40-60 hours total For a smaller site: 15-25 hours

Worth it? Absolutely. The insights are actionable and the risks of not doing it are real.

Technical SEO perspective on AI audits:

Technical elements to assess:

Schema markup:

• Article schema with author, dates
• FAQPage schema for Q&A content
• Organization schema for entity clarity
• HowTo schema for instructional content

Crawlability:

• AI bots can access all key content
• robots.txt not blocking AI crawlers
• Pages load quickly
• Content in HTML (not just JavaScript)

Structural optimization:

• Clear heading hierarchy
• Short paragraphs (40-60 words)
• Bullet points and lists
• Tables for comparisons

What we commonly find:

60-70% of sites have schema issues 40% have content AI bots can’t properly access Most content isn’t structured for AI extraction

The technical audit alone often reveals quick wins.

Competitive dimension of AI audits:

What to analyze:

1. Share of voice: What percentage of AI answers in your category mention you vs. competitors?
2. Positioning: When mentioned, how are you positioned relative to competitors?
3. Missing queries: Where do competitors appear but you don’t?
4. Sentiment: Are mentions positive, neutral, or negative?

What we discovered:

Our main competitor was appearing in 3x more AI answers for our core product category. Analysis revealed:

• They had fresher content on key topics
• Their content was more structured for AI extraction
• They had stronger entity signals

The action:

Created targeted content for 20 high-value queries where competitor dominated. 6 months later we’ve closed the gap significantly.

Competitive analysis should be a core part of any AI audit.

Really helpful frameworks. Here’s our audit plan:

Phase 1: Baseline Assessment (Week 1)

• Set up Am I Cited monitoring
• Create 50 key questions about our industry/company
• Test across ChatGPT, Perplexity, Google AI
• Document current state

Phase 2: Content Inventory (Week 2)

• Identify content being cited
• Flag outdated/inaccurate content
• Assess legacy content risks
• Check brand representation

Phase 3: Technical Review (Week 3)

• Schema markup audit
• AI crawlability check
• Content structure assessment
• Extraction-friendliness evaluation

Phase 4: Competitive Analysis (Week 3)

• Share of voice assessment
• Gap analysis
• Positioning comparison

Phase 5: Action Plan (Week 4)

• Prioritized list of updates
• Content to remove/redirect
• New content opportunities
• Technical fixes

Expected outcomes:

• Clear picture of AI visibility
• Actionable improvement list
• Competitive positioning data
• Baseline for measuring progress

Thanks everyone for the detailed guidance.

ROI perspective on AI content audits:

Cost of audit:

• Internal: 40-60 hours of team time
• External (consultant): $5,000-$15,000 typically

Value of findings:

Our calculation:

We found that outdated content was actively hurting us with prospects who used AI for research. Sales team reported multiple cases of prospects confused by AI-sourced outdated information.

The audit paid for itself in avoided sales friction within 2 months.

The question isn’t whether you can afford to do an AI audit. It’s whether you can afford not to.

Ongoing audit perspective:

Initial audit is just the start.

The AI landscape changes rapidly. What’s true today may change in 3 months.

Recommended cadence:

• Comprehensive audit: Quarterly
• Monitoring: Continuous (automated)
• Quick checks: Weekly (15 min)

What to track between audits:

• Citation patterns (Am I Cited)
• Competitor movements
• Brand representation changes
• New content performance

The evolution:

Your first audit establishes baseline. Subsequent audits track:

• Did fixes work?
• What’s changed?
• Where are new opportunities?
• What new issues emerged?

Build the audit into your regular content operations. It’s not a one-time project.