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 the gap between this morning and tomorrow morning has nothing to do with anything her team shipped.
The difference between a signal and a measurement is repetition and a written-down method. She has the signal. Thursday needs the measurement, and the first thing in the way is that the number she is reaching for does not exist as one number.
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.
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.
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:
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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.
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.
