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 12 B2B SaaS categories, then recorded which brands each one cited. The result: across 185 brands named, only 3.3 per category were recommended by all three engines, and 65% 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.

Last updated July 2026. This benchmark refreshes monthly.

Key findings

• Across 12 B2B SaaS categories, the three engines cited 185 distinct brands in total.
• A single engine names about 8.8 brands per category, but the three together produce roughly 17.0 distinct names, so the engines disagree more than they agree.
• Only 3.3 brands per category were recommended by all three engines. That consensus set is the real shortlist.
• 65% of all brand citations were fragile, meaning the brand appeared in only one of the three engines.
• 20% of citations were consensus picks named by all three engines. The rest depend on which engine the buyer happens to open.

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.

Most brands that earned a citation earned it in only one engine. That visibility can vanish the moment a buyer switches engines. Durable visibility, the kind that survives the buyer’s choice of tool, is the 20% 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 software	Asana, ClickUp, Jira, Smartsheet, Wrike, monday.com
CRM software	HubSpot, Microsoft Dynamics, Pipedrive, Salesforce, Zoho
customer support and helpdesk software	Freshdesk, Help Scout, HubSpot Service Hub, Intercom, Zendesk
product analytics tools	Amplitude, Fullstory, Heap, Mixpanel, Pendo
sales engagement platforms	Apollo.io, HubSpot Sales Hub, Outreach, Salesloft
e-signature software	Adobe Acrobat Sign, DocuSign, PandaDoc
email marketing software	ActiveCampaign, HubSpot, Marketo
marketing automation platforms	HubSpot, Marketo, Oracle Eloqua
HR software	Rippling, Workday
accounting software	NetSuite, Zoho Books
expense management software	Expensify, SAP Concur
applicant tracking systems	(none)

Do the engines agree with each other?

Less than you would expect. Each engine names around 8.8 brands per category, but the three only overlap on about 3.3. The other names are spread across a long tail that differs by engine. 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 reads and trusts a different slice of the web.

What changed since June 2026

Since June 2026, the average consensus set moved from 2.6 to 3.3 brands per category, and single-engine (fragile) citations went from 69% to 65% of the total. The pattern of concentrated consensus and a long fragile tail held.

New consensus picks (now named by all three engines): Microsoft Dynamics (CRM software), Pipedrive (CRM software), Rippling (HR software), Workday (HR software), NetSuite (accounting software), Freshdesk (customer support and helpdesk software), Help Scout (customer support and helpdesk software), HubSpot Service Hub (customer support and helpdesk software), Zendesk (customer support and helpdesk software), HubSpot (email marketing software), Marketo (email marketing software), Marketo (marketing automation platforms).

Dropped from consensus (no longer named by all three): ADP Workforce Now (HR software), BambooHR (HR software), Xero (accounting software), Greenhouse (applicant tracking systems), Lever (applicant tracking systems), SignNow (e-signature software), Ramp (expense management software), ActiveCampaign (marketing automation platforms).

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 mechanics, 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 for July 2026 using one prompt per engine per category, refreshed monthly, so it captures the shape of AI recommendations rather than a definitive ranking. Results shift as engines update and as content across the web changes. The pattern, concentrated consensus and a long fragile tail, has held in every category we have tested.

Frequently asked questions

What is a GEO benchmark?

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

How many brands do AI engines recommend per category?

A single engine named about 8.8 brands per category in this benchmark, and the three engines together produced about 17.0 distinct names. Only 3.3 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, 65% 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 65% of the citations in this benchmark were single-engine. The durable position is the consensus set named by all three engines.