The 181-Brand AI Visibility Study

How invisible, exactly?

The first pass also scored each brand from 0 to 100 on how strongly it surfaced. The mean was 18.4 out of 100, and 61 brands (34%) scored a flat zero. Only three brands in the entire sample scored above 50.

Methodology

How the sample was built

We pulled advertisers from the Meta Ad Library between 2026-06-15 to 2026-06-17, looping a list of consumer product keywords and keeping brands that ran their own online store. We removed tracking domains, duplicates and large-retailer subdomains. That left 181 clean United States ecommerce and DTC brands. Advertising was the qualifying signal, because a brand paying for traffic has already decided that being findable is worth money.

What the sample looked like

Category is the brand's own Facebook page label across 69 distinct values, and it is weak data: 46 of the 181 brands used a label that describes nothing, such as "Brand", "Website" or "Product/service". We report it for shape only and drew no conclusions from it.

What we asked the engines

For each brand we wrote one buying question in that brand's own category, phrased the way a shopper would type it, in the shape of "best [category]". We did not use the brand's name. The point was to see whether an engine reaches for the brand unprompted when a customer is choosing what to buy, which is the moment the sale is decided.

How each answer was classified

• Cited: the brand's own domain appeared as a source behind the answer.
• Mentioned: the brand was named in the answer text but its site was not cited.
• Absent: neither. The engine answered the question and the brand did not exist in it.

Four engines were queried for every brand between 2026-06-18 to 2026-06-25: ChatGPT, Perplexity, Google AI Overviews and Gemini. A brand counts as absent overall only when all four returned absent.

Why we quote 72% when this page says 87%

The first pass, run 2026-06-15 to 2026-06-17, used a single engine and found 131 of 181 brands absent, which is the 72% figure we have quoted publicly. Going back over the same 181 brands with all four engines moved the number up to 158 (87%). Two brands that looked absent turned out to be visible; twenty-two that looked visible turned out to be absent once we checked properly. We have kept quoting the lower number because a conservative claim you can defend beats a bigger one you have to walk back.

Limitations

Stated plainly, so nothing here reads as more certain than it is.

• This is not a random sample of ecommerce. Every brand was advertising on Meta, in the United States, in June 2026. That selects for businesses with a budget. Do not read 87% as a fact about all online stores. It is a fact about these 181.
• One question per brand per engine. Visibility is query-specific. Several brands that were absent for the broad category question turned out to be cited for a narrower one when we followed up. A different question would produce a different number.
• One run per question. AI answers are not deterministic. We did not sample the same question repeatedly to measure variance, so treat individual brand verdicts as a snapshot rather than a settled fact.
• June 2026, and engines change fast. Any of these results could move within weeks of publication.
• Gemini is the weakest leg. It was queried on a free tier subject to rate limiting, and a rate-limited response can come back empty. An empty response misread as silence would inflate the absent count, so the collector was changed to stop rather than record a false absent. It is still the engine we would trust least here.
• Category labels are self-reported and messy, as described above. Eight of the 181 were also later found not to be genuine online sellers.
• We are not neutral. We sell the fix for this problem. That is exactly why the method is written out in full: so you can disagree with it, or run it yourself.

What we take from it

The finding that surprised us was not the size of the number. It was that brand size and ad spend did not predict visibility. Brands with real revenue, genuine press and clean SEO sat in the absent column next to brands with a hundred Instagram followers. What separated the 15 cited brands was not scale. It was having content an engine could extract an answer from, and corroboration somewhere other than their own website.

If you want to check your own brand against this, the free AI visibility audit runs the same method on your category, or you can follow the steps in how to run an AI visibility audit yourself and do it by hand. The mechanics of getting cited are covered in how to get cited by ChatGPT.

Cite this study

Published under CC BY 4.0. Reuse the figures anywhere, including commercially, with attribution.

Questions about this study