What the AI Visibility Index Reveals
If you think ranking #1 on Google means your business will be recommended by ChatGPT, think again. A new study from Fractl analyzed how consistently AI models like ChatGPT, Gemini, and Claude recommend brands, and the results show a major disconnect between traditional search authority and AI visibility. The short answer: organic authority still matters, but it’s not the whole map.
More than 9 in 10 brands in the dataset behaved the way most SEOs would expect: stronger traditional search authority generally tracked with stronger AI visibility. But the outliers are where the findings get interesting. About 5% (471 brands) were underexposed in LLMs—companies with high domain ratings, very high organic traffic, and a deep keyword portfolio that still drew almost no references from the models. On SEO infrastructure, they look like market leaders. On LLM references, they look like companies nobody has ever written about.
This matters because AI answers are becoming the new front door for customer discovery. If your brand can’t crack the top five to 10 names the model recalls for your category, ranking well in Google may not be enough to get you into the AI-generated consideration set.
Why Traditional SEO Authority No Longer Guarantees AI Recall
The competitive set inside an AI answer is much smaller than the competitive set on a search results page. In travel, for example, Booking.com (285 mentions), Airbnb (227), and Expedia (215) accounted for roughly 20% of the sector’s total mention volume. Three brands accounted for one out of every five recommendations. In HealthTech, Teladoc (275) led Amwell (220) by roughly 25%. The models have settled on a few default answers.
But the most striking finding is that some brands with massive SEO footprints barely appear in AI answers. Microsoft and Spotify topped the list in FinTech—they’re giants of traditional visibility, but the models didn’t categorize them as fintechs when prompted with fintech queries. That’s not a bug. It’s a categorization gap. The models have decided what counts as a fintech, and Microsoft wasn’t on the list.
Similarly, legacy insurance carriers like Aetna, Cigna, Humana, and Liberty Mutual all sat at DR 80+ with millions of monthly visits, yet the models barely cited them. Instead, Lemonade (213) ranked above State Farm (172), and Root Insurance (165) ranked above Progressive (114). The models cited the digital-native carriers first and treated the legacy ones as the alternatives, rather than the default.
What a brand says about itself matters less for AI recall than what the rest of the web has repeatedly said about it. Owned content still matters. Technical SEO still matters. But for AI visibility, the corroboration layer is becoming harder to ignore.
The AI Overperformers Show the Playbook
On the flip side, about 4% (377 brands) were AI overperformers—brands that the models referenced far more often than their modest traditional signals would suggest. Monday.com was the biggest overperformer in the dataset, with an AI Visibility Score of 0.71. Monday.com already has a lot of traditional traffic, close to 1 million organic visitors, and more than 50,000 ranked keywords. Its overperformer status tracks directly with how much more often the models referenced it than their SaaS peers did.
Six of the top 15 overperformers were in education: Stanford, MIT, Khan Academy, LinkedIn Learning, IBM Data Science, and Google Career Certificates. The models cited these for their credibility, not because they run high-Domain-Rating commercial platforms.
The common thread isn’t just “good SEO.” It’s category-level presence in other people’s content. That pushes AI visibility work closer to digital PR, content distribution, and category authority building than many teams want to admit. If you want to know why a competitor with less than half your organic traffic shows up in the model’s answer while you don’t, the question to ask isn’t, “What’s their SEO strategy?” It’s, “What’s their press coverage, and how did they earn it?”
Most Brands Exist in Only One Model’s World
Only 900 brands in the dataset, or 11% of the total, were referenced by all three models. Another 12% appeared in two. The overwhelming majority, 77%, were referenced by only one model. That’s a major measurement problem. A brand can win in ChatGPT and lose in Gemini. It can show up in Claude and disappear from ChatGPT. It can own one model’s answer set while barely registering in another.
Each model also had a different fingerprint. Claude leaned more heavily toward SaaS and insurance brands, with Notion, Linear, and Lemonade appearing more often in those categories than in the other two models. Gemini leaned toward travel and healthcare, with Booking.com, Teladoc, and Livongo appearing at a much higher rate. ChatGPT was the most consensus-driven of the three, with the most overlap between its top brands and the other models.
The gaps weren’t always subtle. Notion was cited 17 times more often by Claude than by Gemini. Whoop appeared almost four times more often in Claude than Gemini. State Farm drew almost half its mentions from ChatGPT and only about a quarter from Gemini.
This is why aggregate AI visibility scores can mislead senior teams. If a dashboard says your “AI visibility” improved, your first question should be: Where? A single blended score can hide the actual problem. You may not have an AI visibility problem broadly. You may have a Gemini problem. Or a Claude problem. Or a category prompt problem. Or a source problem. Measure each model separately. Track prompts by category and intent. Look at which sources appear repeatedly. Then decide where the gap is worth closing.
Some Sectors Have Already Been Re-Sorted
The disconnect between traditional authority and AI recall wasn’t evenly distributed across industries. Some sectors still look a lot like Google, while others have been almost completely re-ranked by the models.
Insurance was the least represented, at 8%. Legacy carriers like Aetna, Cigna, Humana, and Liberty Mutual were replaced by digitally native carriers. If you’re an established carrier, you have the largest gap between your Google authority and your AI recall. Education had the highest overperformer rate, at 7%, with Stanford, MIT, Khan Academy, IBM Data Science, Google Career Certificates, and LinkedIn Learning dominating. HealthTech was neutral (underrepresented by 5%, overperforming by 6%), with a shift underway from pharma and medical device companies to consumer-facing digital health. Travel had the smallest disconnect (4% under, 4% over) because LLM recall and traditional awareness lined up closely. In travel, Booking.com, Expedia, and Airbnb are the best-known names to both Google and the models. SaaS and FinTech sat between 3% to 4% over and 6% under. That’s because in these categories, the story is one incumbent replacing another (Stripe vs. PayPal, Notion vs. Microsoft) rather than new entrants entirely displacing the old guard.
The pattern across sectors mirrors the broader finding. Where the legacy authority of a category was built primarily on SEO and traffic, the models have re-sorted. Where the category was built around brand stories that circulated in third-party media, the alignment is tighter.
What This Means for Your Business
If you’re a business owner or marketing leader, this study has five practical takeaways you can act on today.
Measure traditional search authority and AI recall separately
Domain rating, organic traffic, and keyword rankings still matter. They just don’t tell the whole story. A brand can rank well on Google and still fail to show up in AI answers. Another brand can have a smaller traditional SEO footprint and still become a default recommendation. Track both. Measure where you rank in Google and where you appear in AI responses. Then look for mismatches. The mismatches are where the strategy lives.
Build third-party validation, not just owned content
The brands that outperform in AI responses tend to appear repeatedly in other people’s content. That includes roundups, best-of lists, comparison articles, expert reviews, analyst pages, podcasts, YouTube transcripts, community discussions, and category-specific publisher coverage. Owned content can help clarify your positioning, but it won’t replace external corroboration. If AI systems are building answers from the broader web’s understanding of your brand, digital PR becomes one of the most practical levers for AI visibility.
Track each model separately
Only 11% of brands were referenced by all three models. That should kill the idea that “AI visibility” is one clean metric. Measure ChatGPT, Gemini, and Claude separately. Break prompts out by product category, buyer intent, and comparison set. Track whether your brand appears, how it’s described, which competitors appear with it, and which sources seem to support the answer. A model-specific miss gives you a much clearer action plan than a blended visibility score.
Fix categorization before you chase volume
If your brand has strong overall awareness but weak recall in your target category, the issue may be categorization. The model may know who you are. It just may not think you belong in the answer set for the queries that matter. That requires a different strategy. You need more category-specific proof: media coverage, comparison content, partner references, customer stories, awards, reviews, and third-party pages that repeatedly connect your brand to the category you want to own.
Move before the default-answer slot hardens
Some categories are still wide open. Wellness, lifestyle, and parts of HealthTech still have room for new default answers. Others are already consolidating. Travel, major SaaS categories, and parts of FinTech are much harder to break into because the models already return a tight set of familiar names. The earlier you build category association, the easier it is to influence recall. Once a model repeatedly sees the same brands attached to the same category, dislodging them gets harder.
Bottom Line
The AI visibility index is a wake-up call. Your next brand visibility audit shouldn’t stop at your site. It should map the sources that teach models who belongs in your category: roundups, comparisons, expert lists, publisher coverage, reviews, partner pages, and community discussions. That’s where the overperformers in this study separated themselves, not by being the biggest brands in organic search, but by being repeatedly reinforced in the places AI systems use to understand, compare, and recommend companies.
FAQ
The study found that 5% of brands with high domain ratings and traffic were underexposed in AI models. The issue is often categorization—the model may not associate the brand with the category being queried, or the brand lacks third-party content that reinforces its category membership.
Focus on building third-party validation: get featured in roundups, comparison articles, expert lists, and industry publications. Also, ensure your brand is consistently associated with your target category across the web. Track your presence in each AI model separately to identify specific gaps.
No. Only 11% of brands appear in all three major models (ChatGPT, Gemini, Claude). Each model has its own biases and preferences. For example, Notion was cited 17 times more often by Claude than by Gemini. You need to measure and optimize for each model individually.
