We Analyzed 82 Real AI Visibility Audits. Here Is What the Data Shows.

What Does Our Own Audit Data Show?

Across 82 domains audited with Radar (counts as of 8 May 2026), the average AI readiness score was 45 out of 100, and more than half of the audited sites scored D or F.

Most AI visibility advice is theoretical. "Optimize your content for AI search." "Make sure LLMs can find you." Generic guidance based on assumptions, not measurements.

Updated 2026-05-08: refreshed counts to reflect 82 unique domains audited (up from 62 at first publish, 77 at the 2026-05-05 update). The A-grade count fluctuates as domains re-audit and the latest score per domain wins; 1 domain currently holds an A. The live dashboard shows current numbers as more audits land. Industry and dimension breakdowns below reflect the original 62-domain dataset of 12 April 2026 and have not been re-mapped.

We have something different: actual benchmark data from 82 real audits run on the Radar AI Visibility Platform across 6 core industries. Not synthetic tests. Not hypothetical scores. Real domains, real scores, real patterns.

The headline finding: the average AI readiness score is 45 out of 100, a number from Radar audits that pair technical checks with live LLM queries in full audits. More than half of the audited sites scored D or F. We found a similar pattern when we scored 50 named brands.

This is the benchmark data backing Step 4 (E-E-A-T signals) of the GEO Playbook. The playbook tells you what to do; this post tells you where the average B2B site actually scores.

Score Distribution: Where Businesses Actually Stand

The score distribution tells a clear story. The majority of domains cluster in the middle to lower ranges, with a heavy tail toward failure.

Here is how 82 domains distributed across score ranges:

The A column has fluctuated between zero and two as domains re-audit; it currently sits at 1 of 82. That is still a striking finding: 99% of domains have not crossed the 85/100 A threshold, and only 11% have reached a B grade or higher. Even the best-performing domains have significant gaps in their AI visibility infrastructure.

Correction: an earlier version of this post labelled the A band 90 to 100, described D and F sites as invisible to AI, and did not date its figures. Radar's A grade starts at 85, which does not change any count above; the grade counts are from the 8 May 2026 snapshot of 82 domains and the industry averages from the 12 April 2026 benchmark of 62 domains; and these checks do not measure whether AI engines cite a site. As of 24 September 2026, this version replaces it.

“54% of domains scored D or F: their latest audit averaged below 50 across Radar checks.”

Pixelmojo Labs, State of AI Visibility 2026

This is not a problem of awareness. Many of these domains have invested in traditional SEO. They rank in Google. They have content strategies. But their technical infrastructure was built for a search paradigm that is being displaced. AI search engines use different signals, different crawlers, and different content evaluation patterns.

Industry Breakdown: Who Is Leading and Who Is Falling Behind

AI readiness varies significantly by industry. Healthcare and SaaS lead, while services and travel lag behind. The industry averages below are from the original 62-domain benchmark of 12 April 2026.

Healthcare's lead is not accidental. Years of medical SEO compliance (structured data for health content, schema markup for practitioners and procedures) translate into better AI readiness. The infrastructure was built for Google's health content requirements, but it serves AI systems equally well.

SaaS companies score second because they tend to have technical teams who understand crawl accessibility and are more likely to experiment with newer conventions like llms.txt.

The services and travel sectors trail because their sites are often built on template platforms with limited control over robots.txt, structured data, and server-side rendering. Heavy JavaScript rendering is particularly problematic: AI crawlers frequently cannot execute client-side JavaScript, so content that loads dynamically is invisible to them.

The 6 Dimensions: Where Domains Score Best and Worst

Each domain was evaluated across 6 AI readiness dimensions using the Radar platform. The gaps between dimensions reveal where the industry is investing and where it is neglecting infrastructure.

The pattern is telling. Domains score highest on basic crawlability (65/100) because most sites are at least accessible to web browsers and standard bots. But the gap drops sharply once you move into AI-specific infrastructure.

Robots.txt (43/100) is problematic because many sites use blanket Disallow rules that were designed for aggressive SEO crawlers but inadvertently block GPTBot, ClaudeBot, and PerplexityBot. The fix is often a 3-line addition to robots.txt, but most site owners do not know these AI-specific user agents exist.

llms.txt (37/100) scores low because adoption is still in its infancy. This standard is less than a year old, and most CMS platforms do not generate it automatically. Sites that implement it score higher on this dimension because Radar's current score counts the file; no major AI engine documents reading it.

Answer engine optimization (25/100) is the worst-performing dimension because it requires content-level changes, not just technical fixes. The AEO rubric rewards answer-first writing, clean heading hierarchies and comparison data in tables. These help readers and are worth doing, but Google says its AI features need no special formatting or schema, so treat the score as a readability rubric, not a citation forecast.

“The biggest readiness gains come from the simplest fixes: crawler access in robots.txt and basic structured data. These are infrastructure problems, not content problems.”

Lloyd Pilapil, Pixelmojo

What This Means for Your Business

A score below 50/100 means your site has technical gaps worth fixing. It does not tell you whether ChatGPT, Perplexity, Claude or Google AI Overviews cite you; only the citation checks measure that.

This is a different competitive dynamic than traditional search. In Google, you compete for 10 blue links. In AI search, you compete for 1 to 3 cited sources. Fewer sources appear in each answer, so each citation counts for more.

Three immediate actions based on the benchmark data:

1. Check your robots.txt for AI bot access. Run a free crawl check to see if GPTBot, ClaudeBot, and PerplexityBot can access your site. If the search crawlers are blocked, add explicit Allow directives.

2. Decide on llms.txt. It is optional: Google says Search ignores it, and no major AI engine documents reading it, though Radar's current score still counts it. If you publish one, the llms.txt validator checks it.

3. Add structured data to your key pages. At minimum: Organization schema on your homepage, Article schema on blog posts, and FAQPage schema only where the page shows the same Q&A. The AI Readiness Score checks these.

Watching a competitor get cited while you do not? Walk through My competitor is in ChatGPT and I am not, what do I do? and Why does Perplexity cite my competitor instead of me? for the diagnostic + fix sequence.

How We Collected This Data

The benchmark data comes from real audits run on the Radar AI Visibility Platform. Radar evaluates domains across 6 dimensions using automated tools that test crawl accessibility, parse robots.txt directives, validate llms.txt files, audit schema markup, and analyze page structure for AEO signals.

The original 62 domains spanned 6 core industry categories: Enterprise Tech, SaaS, Healthcare, Travel, Retail, and Services. The full 82-domain dataset has expanded to include additional niche categories (consulting, financial services, payment processing, and others). All domains were audited by real users on the platform. No domain names or identifiable data are published. All statistics are aggregated.

The full report with interactive data visualizations, complete methodology, and detailed industry breakdowns is available at pixelmojo.io/labs/state-of-ai-visibility-2026.

“The most underrated AI visibility fix is the simplest one: make sure the crawlers that fetch pages for AI answers can actually reach yours. Most businesses have never checked.”

Lloyd Pilapil, Pixelmojo

The Opportunity in the Gap

Just 1 A grade across 82 domains is a striking finding. It is an opportunity signal. Closing the technical gaps removes blockers. Whether AI engines then cite you is a separate question that only the citation checks answer.

Traditional SEO took years to become competitive. AI visibility is still early. The technical checks are simpler (robots.txt, crawler access, structured data). The tools are available (free AI readiness audit). The benchmark data shows the bar is low.

The question is not whether AI search will matter. It already does. The question is whether your domain will be cited when it does.

Ready to see where you stand?

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