We asked an AI assistant to recommend suppliers, the way a buyer might, for 1,917 US industrial B2B companies. Each question used that company's own category and state. The assistant named the company in 41% of its answers.
The conditions matter as much as the number, so they come first. It was one assistant, Claude Haiku, allowed up to two web searches per question, asked between July 30 and August 1, 2026. Each company was asked about once, and answers vary between askings, so 41% describes the group, not the odds for any single company. The companies came from a list of established US industrial businesses rather than a random sample of all of them. And the method shaped some of what came back, which the next section explains.
What we asked, and how that shaped the answers
Each question matched what the company does. A manufacturer's read: "I'm looking for a company that manufactures warehouse conveyor systems in Wisconsin. Who are the best companies to consider?" A distributor's asked for "a supplier or distributor of factory automation components in Georgia." Services businesses and manufacturers' rep agencies each had their own wording too.
Two instructions were added to every question. The assistant was told to name specific companies. It was also told to label each company it named as either national or global, meaning large brands, public companies and household names, or local and regional, meaning independent firms a buyer in that state would realistically call. Every question also named a state, which tends to pull answers toward companies in that state.
We asked 2,000 questions. For 83 the answer didn't include a real list of companies, so those are left out, leaving 1,917.
Bigger names were usually in the answer
Seven in ten answers, 69.8%, included at least one company other than the one we asked about that the assistant labelled national or global. Among answers that left the company out, the figure was 78.0%.
When another national or global name was present, the company we asked about was named less often: 34.1% of the time, against 57.0% when none appeared. The study doesn't show why, and several other things moved with it. National names turned up more often in answers for distributors (88.3%) than for manufacturers (65.0%). In 241 answers the assistant labelled the company we asked about as national itself. And answers that listed six or more companies named it less often (28.8%) than shorter answers (50.2%). Within those longer answers, the gap between having a national name present or not nearly disappears, at 28.3% against 30.5%. So treat it as a pattern worth knowing about, not proof that big names push smaller suppliers out.
The answers weren't all big brands. Most individual names were labelled local or regional, which is what you'd expect when every question names a state, and a typical answer mixed the two. Asked about a Wisconsin maker of hydraulic cylinders and pumps, the assistant named Perfection Hydraulics, Hader Industries, Hydraulic Solutions of Wisconsin, Morse Hydraulics and Simplex, alongside The Oilgear Company, which it labelled national. It didn't name the company we'd asked about.
One caveat on all of this: "national" and "local" are the assistant's own labels, applied using a definition we gave it, not a measured company size.
Does anyone actually buy this way?
Fair question, and the honest answer is that we have two data points rather than a pattern.
One industrial client closed a deal we could trace back to ChatGPT. Another, a company that produces physical marketing materials for CPG brands, keeps seeing inbound traffic with ChatGPT as the referrer, though nothing there has been traced to a closed deal yet.
Two companies is an anecdote, not a trend. What it does settle is a narrower question: whether a buyer can find and contact a company this way at all. They can, because it has happened.
Why a company might not get named
This study didn't test why one company was named and another wasn't, so this isn't a finding from it. It draws on separate research and on what we see when we look at sites.
When an assistant searches the web to answer a question like this, it often relies on what companies' own websites say, alongside directories and other pages. Our separate study of more than 55,000 B2B websites found that 82% aren't structured for AI search to read them: only 9% use structured data, and about half keep their specifications inside PDFs rather than on the page. Copy like "quality solutions for a variety of industries" doesn't name a product, a material or a state, so it gives an assistant little to match against a specific question. The same vagueness keeps companies out of ordinary search results, as covered in why isn't my website showing up on Google.
That's a plausible explanation for some of the 59%, and it wasn't measured here. Making a site readable to these tools is usually called answer engine optimization, and there's a practical walkthrough in how to get cited by ChatGPT.
The reason this is hard to notice
You can't see it happen. There's no record of a buyer who asked an assistant, got an answer that didn't include you, and contacted someone else. It doesn't reach your CRM or your phone. From the inside it looks like a week when fewer people were looking.
A test you can run in about five minutes
Open an AI assistant that searches the web and ask the question a buyer would ask:
> I'm looking for a company that makes [the specific thing you make] in [your > state]. Who are the best companies to consider?
Notice whether you're in the answer, who is, and whether any of them are much larger than you.
Then ask twice more, once with different wording and once naming an industry you serve instead of a product. Answers vary, so one asking tells you little.
It's also worth checking your analytics for referrals from `chatgpt.com`, `perplexity.ai` and `gemini.google.com`. Some visits sent by an assistant show up there, though many arrive with no referrer at all, so an empty report doesn't mean nothing is happening.
If you'd like a second opinion on what your own homepage gives an assistant to work with, we'll put together a complimentary breakdown. It's yours to keep either way.
