The GEO Benchmark: How Concentrated Is AI Search Visibility in B2B SaaS?

We asked the three leading AI engines, ChatGPT, Gemini, and Perplexity, to recommend the best tools across twelve B2B SaaS categories, then recorded which brands each one cited. The result: across 205 brands named, only 2.6 per category were recommended by all three engines, and 69% of brands that got cited appeared in just one engine’s answer. AI search visibility is far more concentrated, and far more fragile, than most teams assume.

Key findings

• Across 12 B2B SaaS categories, the three engines cited 205 distinct brands in total.
• A single engine names about 9 brands per category, but the three together produce roughly 19 distinct names, so the engines disagree more than they agree.
• Only 2.6 brands per category were recommended by all three engines. That consensus set is the real shortlist.
• 69% of all brand citations were fragile, meaning the brand appeared in only one of the three engines.
• Just 14% of citations were consensus picks named by all three engines. The other 86% depend on which engine the buyer happens to open.
• Cross-category authority is rare: only a couple of brands were consensus leaders in more than one category.

Why this matters

B2B buyers increasingly start research by asking an AI engine for recommendations. If your brand is not in the answer, you are not on the shortlist, and you never find out you were skipped. This benchmark shows that the answer a buyer gets depends heavily on which engine they use. A brand can dominate ChatGPT and be absent from Perplexity, and the buyer would never know the difference. The brands that win consistently are the small consensus set that all three engines agree on.

How fragile is AI search visibility?

The clearest finding is how few citations are durable. We counted every time a brand was named in a category, then grouped those citations by how many engines agreed.

More than two thirds of the brands that earned a citation earned it in only one engine. That visibility can vanish the moment a buyer switches from ChatGPT to Gemini. Durable visibility, the kind that survives the buyer’s choice of tool, is the 14% that all three engines agree on.

How small is the consensus shortlist?

Every category has dozens of credible vendors, yet the engines converge on a handful. The table below shows the brands named by all three engines in each category, the true AI shortlist for that market as of this benchmark.

Category	Consensus brands (named by all 3 engines)
Project management	Asana, ClickUp, Jira, Smartsheet, monday.com
E-signature	Adobe Acrobat Sign, DocuSign, PandaDoc, SignNow
CRM	HubSpot, Salesforce, Zoho
Expense management	Expensify, Ramp, SAP Concur
Product analytics	Amplitude, Heap, Pendo
Sales engagement	HubSpot Sales Hub, Outreach, Salesloft
Marketing automation	ActiveCampaign, HubSpot
HR software	ADP Workforce Now, BambooHR
Accounting	Xero, Zoho Books
Applicant tracking	Greenhouse, Lever
Customer support / helpdesk	Intercom
Email marketing	ActiveCampaign

In some categories the consensus is a single brand. In customer support and in email marketing, only one brand was named by all three engines, which means one company effectively owns the safe answer while everyone else competes for a fragile, single-engine mention.

Do the engines agree with each other?

Less than you would expect. Each engine names around nine brands per category, but the three only overlap on about three. The other names are spread across a long tail that differs by engine. Practically, this means there is no single AI search result to optimize for. A brand that wants durable visibility has to earn it across ChatGPT, Gemini, and Perplexity at once, because each one is reading and trusting a different slice of the web.

What this means for your GEO strategy

The benchmark points to three priorities. First, find out where you actually stand across all three engines rather than assuming, which you can do in two minutes with our free AI Search Visibility Checker. Second, treat consensus as the goal: a citation in one engine is fragile, so the work is to be named by all three. Third, recognize that this is winnable, because consensus sets are small and most categories have room for one or two more durable names. The brands that move now will define the shortlist before it hardens.

The mechanics of getting there, structuring content for extraction, building citability, and earning the off-site mentions engines trust, are covered in our GEO strategy guide and our complete guide to generative engine optimization. If you want it run for you, see how SearchLever approaches GEO.

Methodology

We selected 12 common B2B SaaS categories and asked each of three AI engines, ChatGPT (GPT-4o mini), Gemini (2.5 Flash), and Perplexity (Sonar), the same buyer-style question per category: which products or companies it would recommend. We then extracted the specific brand names from each response and recorded which engines named each brand. A brand counts as a consensus pick for a category when all three engines named it. This is a point-in-time snapshot using one prompt per engine per category, so it captures the shape of AI recommendations rather than a definitive ranking, and results will shift as engines update and as content across the web changes. The value is in the pattern, which is consistent across every category we tested: concentrated consensus, and a long fragile tail.

Frequently asked questions

What is a GEO benchmark?

A GEO benchmark measures how visible brands are inside AI-generated answers. This one asked ChatGPT, Gemini, and Perplexity to recommend tools across 12 B2B SaaS categories and recorded which brands each engine cited, to show how concentrated and consistent AI recommendations are.

How many brands do AI engines recommend per category?

A single engine named about nine brands per category in this benchmark, and the three engines together produced about 19 distinct names. Only 2.6 brands per category, on average, were recommended by all three engines.

Why do ChatGPT, Gemini, and Perplexity recommend different brands?

Each engine retrieves and trusts a different slice of the web, so their answers diverge. In this benchmark, 69% of brand citations appeared in only one of the three engines. Durable visibility requires earning citations across all three rather than optimizing for one.

How can I check my own brand’s AI search visibility?

Run your category and buyer questions through the major engines and record whether you are named. Our free AI Search Visibility Checker automates this across ChatGPT, Gemini, and Perplexity and returns a citation score plus the competitors cited instead of you.

Is being cited by one AI engine good enough?

It is fragile. A single-engine citation disappears the moment a buyer uses a different tool, and 69% of the citations in this benchmark were single-engine. The durable position is the consensus set named by all three engines.