How do you measure whether AI engines are recommending your B2B company?

Here's a ritual I watch founders perform, and I've seen a lot of it this month. They open ChatGPT, type "best [their category] for [their kind of buyer]," read the answer, and either feel great or feel a little sick. Then they close the tab. That's the whole program. One check. One engine. One logged-in account that already knows exactly who they are. One afternoon.

And they'll tell you, straight-faced, that they're "tracking their AI visibility."

The question underneath it is real, and it's a good one. If buyers now ask ChatGPT, Claude, and Perplexity who to hire before they ever hit your website, you'd want to know whether the machines are naming you. That instinct is right. The way most people act on it is broken. What they're doing isn't measurement. It's a mood check dressed up as a metric.

Measuring whether AI engines recommend you isn't hard. It just isn't what founders think it is. You measure it the way you'd measure anything that matters to the business: a baseline, a fixed set of inputs, and a repeatable cadence. Not a vibe on a Tuesday.

Why isn't typing your name into ChatGPT actually measuring anything?

Call it the vanity spot-check. You type your own name into one engine, in your own account, once, and read whatever comes back. It feels like data. It isn't. Four things are wrong with it, and each one alone would sink it.

There's no baseline, so you can't tell better from worse next month. There's one input, when your buyers ask dozens of different questions. The results are personalized to you, the logged-in founder who's visited your own site a thousand times, so the model is practically reading over your shoulder. And you asked about you, not about your category, which is the only question a real buyer types. Nobody in-market opens ChatGPT and searches your company name. They ask for the best option for their problem, and they watch which names show up.

That last one is the whole game. The buyer never types your name. So checking your name tells you almost nothing about whether you'll get recommended. You're measuring the one query that doesn't matter.

Why does measuring AI recommendation matter more now than a year ago?

Because the shortlist now forms inside the model, before a human visits a single site. AI brought the cost of content to zero, so every category is flooded with the same averaged-out claims, and buyers cope by asking a machine to pre-filter the field. G2's 2026 research found 51% of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% a year earlier. The room where you get shortlisted moved. This is the surface AEO governs, and if the difference between AEO, GEO, and SEO is still fuzzy, start here. If you're not measuring what happens in that room, you're flying blind on the exact surface where deals now begin.

It gets worse. The answer isn't fixed. It's different for every user and it changes week to week as the engines re-crawl and re-rank their sources. Digitaloft found that 76.4% of the pages ChatGPT cites most were updated within the last 30 days. So a spot-check you ran in April is not just thin, it's expired. A number you took once and never took again isn't a measurement. It's a souvenir.

This is the same shift I wrote about in why doesn't AI cite my B2B company when buyers ask for recommendations. The difference here is you can't fix the citation gap until you can see it, on a graph, moving or not moving. Measurement comes first.

What should you actually measure? Track these five things.

Forget your name. Build a set of 15 to 20 questions your best-fit buyer would really type, the "best [category] for [buyer]" and "who should I hire to fix [problem]" shapes. Run that same set across the engines, on a schedule, in a clean session. Then track these five numbers, not your feelings about the answers.

1. 1Named-or-not rate. Across your full prompt set, in what percentage of answers does your company get named at all? This is the single most important number. It's a clean 0-to-100 you can watch climb or stall. Everything else is detail underneath it.
2. 2Share of voice. When an answer lists vendors, how many of the slots are you versus your named competitors? Getting mentioned once in a list of eight is not the same as being one of the two names the model leads with. Count the slots.
3. 3Description accuracy. When you are named, does the engine describe you correctly? Or does it mislabel what you do? I've seen a model confidently file a positioning firm under "slide design." A wrong description is worse than a missing one, because it actively steers the right buyer away. Log exactly how you're characterized.
4. 4Citation source map. Note which third-party pages the engine pulls from when it builds the answer for your category. Review sites, comparison posts, other people's articles. Those are the sources you have to show up in, because brand is the new backlink: the model recommends the names other people describe clearly, not the names that only describe themselves. When you do publish your own answer, structure it so it's actually quotable, which is its own discipline covered in how to write a blog post AI engines will quote. This metric tells you where the work is.
5. 5AI referral traffic. In GA4, segment referrals from chatgpt.com, perplexity.ai, gemini.google.com, and the rest. It's a small number today for almost everyone, but it's the closest thing to a direct-outcome metric, and its trend line is the one that eventually shows up in pipeline.

You can run the first three by hand in an incognito window for free, which is the honest floor every founder should hit before spending a dollar. Above that floor sit the dedicated AI-visibility trackers, Searchable, Profound, Otterly, Peec, and others, which run your prompt set across every engine automatically and chart the movement so you're not copy-pasting into a spreadsheet at midnight. The tool matters less than the discipline. A monthly spreadsheet beats an expensive dashboard nobody opens.

What do the numbers look like across most B2B companies right now?

Grim, and not for the reason founders assume. The Walker Sands B2B AI Search Visibility Benchmark studied 828 enterprise B2B companies across more than 45 million queries and found the median company gets cited in just 3% of AI answers, even on the keywords where it already ranks well on Google. Ranking number one and getting named by the model are two different achievements, and most companies have exactly one of them.

That's the gap the spot-check hides. Your homepage can rank fine, your traffic can look healthy, and you can still be nearly invisible in the answers your buyers actually read. If you only ever check your own name, you'll never see it, because your name query is the one place you probably do show up. The blind spot is the category question, and it's where the deals are. This is also why ranking on Google doesn't get you cited in AI search: the two systems reward different things.

What does this look like for a real company?

A $31M cybersecurity company came to us convinced they had an AI problem. They'd done the ritual, typed their name into ChatGPT, gotten a decent paragraph back, and couldn't square that with a pipeline that had gone quiet. The paragraph was fine. The measurement was the issue.

We built a 20-question prompt set from their real buyer language, ran it across three engines in clean sessions, and logged the five metrics. The baseline was brutal and clarifying: named in 3 of 20 category answers. Share of voice near zero when they did appear. And in two answers, described as a compliance-reporting tool, which is not what they sell. Meanwhile the engines were pulling their category answers from a handful of third-party roundups that named two competitors and never mentioned them.

None of that was a product problem. It was an entity-consistency problem, the same one I break down in why does AI recommend our competitors and not us. We locked their narrative to one clear, repeated description of who they're for and what they fix, then seeded that same answer across the surfaces the model was already reading. We re-ran the identical prompt set every month. About 90 days later they were named in 12 of 20, described correctly in all of them, and starting to appear in the roundups. The point isn't the jump. The point is they could see the jump, because they'd measured the floor first.

What should you do this week?

Stop checking your name and start measuring your category. You don't need budget or a tool to begin. You need a fixed set of questions and the discipline to ask them the same way, on a schedule, and write down what you see. Here's the whole starter kit.

1. 1Write your prompt set. List 15 to 20 questions your best-fit buyer would really type, in their words, not yours. "Best [category] for [kind of company]," "who fixes [the problem you solve]," "[competitor] alternatives." This list is the instrument. Save it, because you'll reuse it forever.
2. 2Take the baseline today. Open an incognito window, run every question across ChatGPT, Perplexity, and Google's AI answers, and log four things per question: are you named, who else is named, how you're described, and which sources it cited. That grid is your before picture.
3. 3Set a cadence and commit to it. Monthly at a minimum, weekly if you're actively pushing on AEO. Re-run the exact same set so the numbers are comparable. If the copy-paste gets old, that's when a tracker like Searchable earns its keep. What you can't do is measure once and call it knowing.