Original research · July 2026
The State of AI Visibility in B2B SaaS
We asked four major AI engines to name the best tool in 63 B2B SaaS companies' own categories. 94% were named by none of them. On average, a company was named by just 0.16 of the four engines — while a small set of established incumbents took nearly every slot.
The pitch for AI-search visibility usually rests on an assumption: that most brands aren't showing up in AI answers. We wanted a real number, so we measured one.
For each of 63 small-to-mid-sized B2B SaaS companies, we identified its product category and asked four engines — ChatGPT, Perplexity, Google AI Overviews and Gemini — a buyer's question: "best [that category] 2026." Then we checked, engine by engine, whether the company was named in the answer. The result was stark: 59 of 63 (94%) were named by none of the four engines, and not one had its own website cited as a source.
Who gets named instead
The answers weren't empty — they were full. They just named someone else. A small set of established vendors and category leaders absorbed the recommendations across the sample:
Most-named brands across the 63 answers. A mix of enterprise incumbents (Microsoft, Oracle, SAP) and established category leaders (Greenhouse, Workable) — the recognizable names an engine can retrieve and corroborate most easily.
The engines don't even agree
Being named isn't one problem — it's four. Each engine surfaced a slightly different set, and the few brands that broke through rarely did so everywhere:
| Engine | Companies named | Answered |
|---|---|---|
| Google AI Overviews | 2 | 42 of 63 queries returned an AI Overview |
| ChatGPT | 2 | 63 |
| Gemini | 4 | 63 |
| Perplexity | 2 | 63 |
That's why AI visibility can't be a single score. A brand can squeak into Gemini and be absent from ChatGPT and Perplexity — so visibility has to be measured engine by engine, which is exactly how a real GEO audit works.
Methodology & caveats
Sample: 150 B2B software companies were drawn from an outbound prospecting list (US-based small-to-mid-sized vendors); 63 were confirmed as genuine B2B SaaS with a clear product category from their own website and included in the analysis. Non-software and unreachable sites were excluded.
Method: for each company we ran one category query ("best [category] 2026") against Google AI Overviews (via a search-data API) and ChatGPT, Gemini and Perplexity (via their live, web-connected models). "Named" means the brand appeared in the answer text (word-boundary match) or its domain was cited as a source. Competitor names were extracted only where they appeared verbatim in the answer.
Caveats: this is a directional snapshot of SMB B2B SaaS from July 2026, not a census, and skews toward smaller vendors. AI engines are non-deterministic and change constantly, so exact figures will move. We're publishing the method so it can be scrutinized and repeated. It is not a claim that any product is better or worse — only that it is, or isn't, being named.
What it means
If you sell B2B software and you're not a household name, the base rate is brutal: the AI engines your buyers now ask are almost certainly recommending someone else — and they don't agree with each other about who. The good news is that this is measurable and improvable. The names winning today aren't winning on product alone; they're winning on being the most retrievable, best-corroborated, most clearly-defined entity in their category. That's a gap you can close.
94% invisible isn't a verdict on the products. It's a measure of how much room there is to be named — and how few brands are doing the work to claim it.
Common questions
What did the study measure?
For 63 B2B SaaS companies, we asked four AI engines — ChatGPT, Perplexity, Google AI Overviews and Gemini — for the best tool in that company's own product category, then checked whether the company was named. 94% were named by none of the four.
Is this sample representative of all B2B SaaS?
It's a snapshot of small and mid-sized B2B software vendors, not enterprise incumbents. The finding is that smaller SaaS brands are largely invisible in AI answers while a handful of established names dominate. It's a directional signal, not a census, and engines are non-deterministic so results shift over time.
Does being unnamed mean the product is worse?
No. AI engines name what they can most easily retrieve and corroborate — well-defined entities, extractable content and third-party citations — not necessarily the best product. That gap is exactly what Generative Engine Optimization addresses.
Which side of 94% are you on?
Get a free one-page snapshot: one of your buyer queries, run across the same four engines, showing whether you're named — and who is named instead.