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

The Data Nobody Else Has

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 63 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 63-domain dataset 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. Most businesses are invisible to AI search engines and do not know it.

This is the benchmark data backing Step 4 (E-E-A-T signals) of the GEO Playbook: How to Get Cited by ChatGPT, Perplexity, Claude & Gemini. 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 90/100 threshold, and only 11% have reached a B grade or higher. Even the best-performing domains have significant gaps in their AI visibility infrastructure.

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.

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 standards 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 do implement it score significantly higher overall because it signals intentional AI communication.

Answer engine optimization (25/100) is the worst-performing dimension because it requires content-level changes, not just technical fixes. AEO means restructuring content with answer-first formatting, adding FAQ sections with proper schema, using tables for comparison data, and ensuring heading hierarchies are clean. Most content was written for human readers scanning a page, not for AI systems extracting passages.

What This Means for Your Business

If your domain scores below 50/100 on AI readiness, your content is partially or fully invisible to ChatGPT, Perplexity, Claude, and Google AI Overviews. When a potential customer asks an AI assistant about your industry, your competitors who score higher will be cited. You will not.

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. The bar for being cited is higher, and the reward for being cited is disproportionate: AI citations carry implicit endorsement that links do not.

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 they are blocked, add explicit Allow directives. This is the fastest fix.

2. Implement llms.txt. Create a plain-text file at /llms.txt that describes your business, products, and expertise in a format AI systems can parse. Use the llms.txt validator to check your implementation.

3. Add structured data to your key pages. At minimum: Organization schema on your homepage, Article schema on blog posts, and FAQPage schema on pages with Q&A content. The schema audit tool checks all of 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 63 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 scoring model was calibrated against Pixelmojo's own documented journey from 0/4 LLM citations to 4/4 citations over 6 months, with 516 git commits as the audit trail.

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 Opportunity in the Gap

Just 1 A grade across 82 domains is a striking finding. It is an opportunity signal. The businesses that close this gap first will have disproportionate AI visibility in their industries. When most competitors score below 50, getting to 70 makes you the default citation.

Traditional SEO took years to become competitive. AI visibility is still early. The infrastructure is simpler (robots.txt, llms.txt, 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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