A 70% AI visibility score can hide 0% on buyer queries

A healthy blended percentage on the dashboard, and a buyer shortlist with no name on it.

Enterprise buyers already ask ChatGPT to build their vendor shortlists. The research section below walks through who ends up on those lists.

That is not a hypothetical. On a Deeep Dive marketing podcast episode, the hosts described Sandler Training, a B2B sales training company whose enterprise buyers were asking ChatGPT to put together vendor shortlists, while Sandler had zero visibility into whether it appeared on them. Zero. Not low. Zero. The same hosts called treating mentions and citations as one thing a major mistake.

Here is the trap. So ask your tracker one blunt question before the next meeting: on the prompts where a buyer asks which tool to pick, how many times were we named? Plenty of practitioners already treat these scores as a directional benchmark, not standalone proof of impact. Fine. But a benchmark that averages a definition question with a request for the best tool for the job is a weather report for the wrong city, read out loud with a straight face.

This piece takes one of those blended numbers apart, prompt by prompt. Clean and fast, the way a coroner opens a locked room, and just as surprised by what falls out of the closet.

How is an AI visibility percentage actually calculated?

An AI visibility percentage counts how many answers from a fixed prompt list mention your brand, then divides by total prompts. Every prompt counts the same, whatever it asks.

Before you trust the figure on your dashboard, take it apart on the table:

1. Pull the full prompt list behind the percentage.
2. Sort each prompt by what it asks: a "what is" question, a "how do I choose" question, or a "best X for Y" request.
3. Recount the mentions inside each pile separately.
4. Throw out every prompt that already names your brand.

The math underneath is street arithmetic. You build a fixed prompt list, run it through ChatGPT, Gemini and Perplexity, and count. One common explainer gives the textbook case: 6 mentions out of 10 prompts is 60% share of voice. The same explainer admits that counting mentions alone can mislead, because it does not give the full picture. Nobody reads that second sentence. They read the 60.

That is how our own category prompts printed a fat, healthy number while the tool-intent prompts printed a dead zero. Same engine. Same domain. Same week. The percentage did not lie, exactly. It just averaged a prizefighter and a corpse and announced that the household was in fair health.

Practitioners have caught the same con. In an October 2026 r/aeo thread, a marketer reported that their brand showed up on broad informational prompts while competitors took the use-case, comparison and recommendation prompts, and the score still "looked fine." One commenter nailed the defect in a single line: a blended score treats "what is AEO" and "best AEO tool for a 20-person agency" as equal, "and they're not even close in value." Over on r/SEMrush, a user warned that a rising score may only mean "the brand is appearing more often in the dataset Semrush is measuring."

Conventional wisdom says a bigger visibility percentage means a stronger brand inside AI answers. It only means a bigger count on whatever list somebody wrote. In 2025, one vendor pushing a rival metric made the point with grim comedy: a "700% increase in AI visibility" could be a move from 1% to 7% of answers. Hot headline, cold body.

A review of 5 sources, two Reddit threads, two metric explainers and our own audit record, lands in the same alley. The denominator decides the story. Change the prompts and the brand changes right along with them.

AEO Content has run 26,577 real AI-visibility audits, and I'll say it straight: the arithmetic is never the hard part. Picking the prompts is. If you want the measurement vocabulary nailed down before you go digging, our AEO knowledge base is a good place to start. Then go back to that prompt list, because the prompts that carry buying intent need a count of their own.

Which AI prompts actually signal buying intent?

Decision prompts signal buying intent: requests for the best tool for a job, head-to-head comparisons and searches for alternatives. Informational prompts teach. Nobody signs a contract off them.

The cleanest split I know of came from a commenter in that same r/aeo thread on confusing visibility scores. Three buckets, three jobs, and a crude weight on each:

The same commenter said even that rough weighting "changes the picture a lot." I believe it. Run those weights over a report that wins the first row and loses the third, and the healthy blended number caves in like a cheap hat in a hard rain.

Then strip out the branded prompts. Another commenter in the thread reported that when a company is named in the prompt, the AI is "super likely" to mention it. Of course it is. Ask a bartender whether Joe drinks here and he'll tell you Joe drinks here. A branded mention proves nothing about whether a stranger asking for a tool ever hears your name.

Decision prompts in this market read like buyer questions, not textbook questions:

• What companies specialize in answer engine optimization (AEO)?
• What are the best AI search optimization (AEO) companies?
• What are best services can help me rank in ChatGPT?

Those are the prompts where a shortlist gets written. One agency founder who sells AI search work said it flat out: "your AI visibility score is only as honest as the prompts behind it." Vendor talk. The arithmetic still backs it.

The buyers are already in the room. In 2025, Forrester reported that 95% of B2B buyers planned to use generative AI in at least one area of a future purchase. Over half said it led them to consider more or different vendors while saving them time. That second finding is the hot one. A shortlist that widens is a shortlist a challenger can crash.

The common assumption holds that AI answers will simply hand the business to whoever is biggest. Some practitioners argue the opposite, that early-stage startups can win in AI answers right away, where SEO takes years. If that holds, the decision bucket is the one unlocked door in the building. I'd argue the pages that walk through it need evidence a model cannot write for itself, which is the logic behind research-driven content built on evidence. First, though, you have to find out which of those doors you are standing outside of, and how long you have been out there in the cold.

How do you audit AI visibility on buyer queries specifically?

Score decision prompts on their own, apart from the blend. Sample each one several times, mark recommended versus merely listed, and compare runs over weeks instead of trusting one snapshot.

1. Split the prompt list into informational, consideration and decision piles before you average anything.
2. Move every branded prompt into a separate report.
3. Give the decision pile its own number on the dashboard.
4. Mark each answer as recommended, listed or absent, and write down the position.
5. Run each prompt several times per engine and record the rate.
6. Re-run the same set on a fixed schedule and compare the runs.
7. For every lost decision prompt, note who won it and which pages got cited.

The prompts themselves have to sound like buyers. A Deeep Dive marketing podcast episode put the gap in hard numbers: the average US query in traditional search runs about 3.37 words, the average ChatGPT prompt runs 23 words, and commercial prompts often run to hundreds of words. A five-word tracker prompt is a wax dummy of a buyer. It stands in the window looking alive and never buys a thing.

The hosts also described AIClicks, at about $79 a month, grouping thousands of user queries into intent-based prompt clusters instead of tracking single keywords. That's the right direction. You can do the crude version by hand with a spreadsheet and a free afternoon, right up until the prompt count gets ugly and the afternoon turns into a month.

Run-to-run noise is the other trap. One analytics vendor advises sampling each prompt three to five times per engine in a logged-out session and recording the rate, not the outcome, because the same prompt can come back with two different answers. A brand that shows up on one lucky run is a rumor, not a result.

Then separate the outcome types. Being named somewhere in a list is not the same as being recommended. Position matters too, because third place in an answer and fifth place in an answer live in different neighborhoods. Track both columns per prompt. The blended number loses its hiding place.

At AEO Content we have scored 11,000+ domains across 15 sectors and 28 categories, and our team includes a full-stack engineer and content infrastructure architect with 20 years of building enterprise systems. I bring that up for one reason. Splitting a report by intent is not exotic engineering. It's bookkeeping. Any dashboard can do it, so ask yours to.

If your team keeps arguing over what counts as a mention, a citation or a recommendation, the AEO FAQ on AI search optimization is a quick place to settle the vocabulary before the audit starts. Once the decision column exists, the next fight is over what a zero in that column actually costs you.

What should replace the blended visibility number?

A decision-prompt count should replace it: reported on its own, sampled across runs, and split into recommended versus merely listed. The blend can stay on the dashboard as the weather.

Our own February 2026 ChatGPT reports make the whole case in one breath. The same domain was the pick on 14 of 20 category prompts and nowhere at all on 20 tool-intent prompts. Seventy percent and a dead zero, same engine, same week.

I'll admit the split is not free. Sampling every decision prompt several times per engine multiplies the work, and one practitioner called manual competitor checks "pretty annoying" at 50 to 100 prompt variations. Annoying beats blind.

The hosts of the Deeep Dive podcast argued that AEO moves measurement away from top-of-funnel sessions and toward assisted conversions and influenced revenue. That is where this is heading. Then tie that count to something a sales team can feel: AEO Content has taken a quiet site to 10 booked calls in 3 weeks, and booked calls are the yardstick a decision-prompt number should answer to.