Before her first meeting she asked four engines which vendor they would recommend in her category. The competitor came back in all four answers. Her own brand came back in one, near the bottom, after a company she has never lost a deal to.
The board meets Thursday. “We should look into that” was the answer last quarter.
Four Engines On One Morning Is A Signal, Not A Baseline
Take the first half seriously, because it is real. Four engines is not one, the competitor appeared in all of them, and anybody who tells her that proves nothing is wrong.
What it is not yet is a number. Answers are personalized and probabilistic, so the same question put to the same engine tomorrow returns a different set of names in a different order, and that gap has nothing to do with anything her team shipped.
The difference between a signal and a measurement is repetition and a written-down method. Thursday needs the measurement, and three things separate the morning check from something a board can act on:
The Number You Are Asking For Is Two Numbers
Visibility is the share of buyer questions in which an engine names the brand at all. Share of voice is weighted by where in the answer the brand sits, because being named first in a list of three is a different outcome from being named fourth in a list of eight.
| Second of three brands | Second of eight brands | |
|---|---|---|
| Your position | Second | Second |
| Visibility counts it as | Named | Named |
| Share of voice | About a third of that answer | About an eighth |
| What moved | Nothing your team did | How crowded the answer was |
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Which is the useful part. The gap between the two numbers is the diagnosis: named often and ranked low is a different problem, with a different lever, from not being named at all.
We Counted Both Numbers On One Category
The table above is the argument. Here is the count behind it, run on 13 October on one software category with eighteen questions that named no brand at all, so nothing was handed back to a brand by the question itself.
One thing to be straight about first. The second number as defined above is weighted by position in the answer. What we counted is cruder: each brand’s share of all the brand mentions, and how many times it was mentioned in the answers that named it. It is reproducible in an afternoon with no tool, and it shows the same split.
| Brand | Named in | Visibility | Share of all mentions |
|---|---|---|---|
| ClickUp | 15 of 18 | 83% | 24.4% |
| Asana | 13 of 18 | 72% | 12.9% |
| Trello | 11 of 18 | 61% | 11.0% |
| Jira | 6 of 18 | 33% | 10.0% |
| Monday.com | 9 of 18 | 50% | 9.1% |
| Linear | 3 of 18 | 17% | 6.2% |
Never subtract these columns. They have different denominators: visibility is a share of eighteen answers, share of voice a share of two hundred and nine mentions. A difference between them in points is not a quantity. Rank against rank is the comparison, and so is the third number below.
Read the Trello and Jira rows together. Trello was named in nearly twice as many answers as Jira and came away with about the same share of the conversation. On a visibility slide Trello is comfortably ahead. On a share-of-voice slide they are level. Both slides are honest.
The third number that explains the first two
The brand named in the most answers is not the brand that gets talked about most. Divide mentions by the answers that named a brand and you get how much airtime it gets when it shows up:
[figure: chart-01]
Trello and Asana get 2.1 mentions in each answer that names them. Linear gets 4.3 and Jira 3.5, in a fraction of the answers. Trello is a name on a list. Linear is a paragraph.
Which sorts the work, and this is the diagnostic the two headline numbers cannot give on their own. A brand with wide reach and thin airtime is being listed rather than recommended, and its problem is the quality of what a reader finds when they look it up. A brand with narrow reach and deep airtime is convincing whenever it is reached for, and its problem is simply being reached for less often.
Our own limits, which are real. Eighteen answers, one engine, one category, one day, one pass each. A rank difference of two places at this size is noise rather than a finding, and the questions were built to vary query composition rather than to measure share of voice. It shows the two numbers can order a category differently. It does not say how often they do.
What A Small Sample Looks Like When Somebody Prints It
Both of her figures might be honest and still not comparable, and the reason is usually the sample. A practitioner auditing one tool’s exported client report in August found this:
“One competitor had 1 mention total and got reported as ‘1% share of voice.’ One observation, printed as a percentage.” — u/MinedZio, r/SEO, Aug 2026
Our own experience of running these studies says the same thing less dramatically. A visibility measurement built on a handful of tracked questions swings, and the difference between a small run and a large one is close to an order of magnitude.
So the rule that follows is cheap and almost nobody applies it. The sample size goes on the slide, next to the number, every time. A figure quoted without it cannot be compared with the figure from last quarter, including by the person who produced both.
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The Engines Do Not Agree With Each Other
One number for “AI search” is also hiding a second problem. A practitioner published a four-engine study in April, 3,837 calls across sixteen SaaS brands, and reported that more than half of the pairwise comparisons between engines came out significantly different:
“Averaged ‘AI visibility’ scores are misleading. If you’re ConvertKit checking only ChatGPT (54%), you think you’re fine. But you’re nearly invisible on Claude (18%), Gemini (16%), and Perplexity (12%).” — u/EddyYosso, r/SEO, Apr 2026
His figures are his, and they carry a limitation worth stating. He queried the engines through their APIs, and an API is not the interface a buyer uses. There is no personalization, no localization, and often no live web search behind the answer. So the study shows four APIs diverging rather than exactly what four buyers would see.
The direction survives the caveat. And the same thing happens across borders, because engines localize. The same question in another market surfaces different brands and different local-language pages, so a number with no market attached is not a number either.
Which Lever, Chosen By Which Number Is Broken
The two numbers sort the work, which is the part a flat list of tactics cannot do. Under roughly a third visibility, the brand is not in enough conversations for position to matter yet, so getting into the room comes before improving the seat. High visibility with a low share of voice is the opposite problem, which is being found and then outranked.
Empact Partners works those two readings in an order inside Generative Engine Optimization, the workstream we run on whether a buyer’s question comes back with the brand’s name in it. Three pillars, taken in the order the numbers point at:
The measurement behind the sorting rule is Qvery, our sister company. Both halves are ours, so weigh the recommendation as you would from anybody selling it, and ask us to show you the answers the numbers came from.
What To Say On Thursday
Not the two figures side by side, because they are two different measurements and she would be defending an artifact. The honest version takes an afternoon:
If the honest first reading is a zero, that is a legitimate thing to present. A zero recorded deliberately, with the method beside it, is a baseline, and the next reading means something because of it.
Expect the board’s next question to be attribution, and know that a page cited inside an answer can shape a decision without ever showing up as the last click.
What We Would Do With Thursday Behind Us
Take the reading, name the lever, work one pillar rather than three. The part a lean team can hold on its own is the first reading. What it rarely holds is that reading repeated across markets and engines for a quarter with the questions kept still, which is what makes the second number mean anything.
That repetition is the work rather than the insight, and it is where most visibility programs quietly lapse back into screenshots.
So the quit point is worth naming before the quarter starts. If nobody will keep the questions still and run them again, do not commission the tracking. Run the count once, write the method beside it, and treat it as a dated photograph rather than a program. A dashboard nobody trusts is neither honest nor useful, and it costs more. That is also the argument in what a query set is made of and in sorting a mention list by what engines cite.
If your brand came back in one answer out of four this morning, Thursday goes better with a method than a screenshot. Take a slot on my calendar and a first call will tell us both quickly whether this is work for Empact Partners.
