What is the ASOV formula?

ASOV = (sum of your weighted appearances / sum of weighted appearances for every brand in the same answers) x 100. One sampled answer is one prompt, run once, against one assistant, so total samples equal prompts x models x runs per prompt. Hold all three constant between cycles or the numbers are not comparable.

How is ASOV different from classic share of voice?

Classic share of voice counts impressions or mentions across channels you can largely buy into, and it measures exposure. ASOV counts presence inside answers a model generates on the buyer's behalf, which you influence only through the sources the model trusts. Exposure can be purchased; answer presence has to be earned.

How is ASOV different from Share of Model?

Share of Model is a yes-or-no frequency metric: the percentage of sampled answers that name you at all. ASOV is competitive and weighted: of everything an assistant said in those answers, how much of it was about you compared with the other brands named alongside you. Share of Model tells you whether you show up; ASOV tells you how much room you take.

How many runs are needed for a reliable ASOV?

More than one, always. Model output is stochastic, so the same prompt can return a different brand set minutes apart. Sample every prompt several times per cycle and average. Single-run readings produce swings that look like performance changes but are variance.

Should ASOV be tracked per assistant or blended?

Track it per assistant and report the blend second. ChatGPT, Claude, Gemini, and Perplexity return materially different brand sets for the same question and update on different schedules, so a blended figure can hold steady while your position collapses on the one assistant your buyers actually use.

Definition

What is AI Share of Voice (ASOV)?

How much of the answer space your brand owns when AI assistants answer your buyers' questions, and the four-step framework Monroya uses to measure it.

Updated: September 2026

By Jeremy Unruh, Founder, Monroya · Reviewed September 2026

ASOV, share of voice, and Share of Model

The three terms get used interchangeably and mean different things. The distinction that matters is what each one counts and whether you can buy your way into it.

	What it counts	How you move it
Classic share of voice	Impressions or mentions across media channels	Largely purchasable: spend, placements, PR
Share of Model	Percentage of sampled AI answers that name you at all	Earned: be a source the model trusts on that question
AI Share of Voice (ASOV)	Your weighted presence inside those answers versus every brand named alongside you	Earned and competitive: own more of the answer than rivals do

Share of Model asks whether you showed up. ASOV asks how much of the answer was yours once you did. A brand can hold a steady Share of Model while its ASOV falls, because competitors are being named more prominently in the same answers.

The formula

One sampled answer is one prompt, run once, against one assistant. Weighting comes from step 3 of the framework below: a recommendation in the opening sentence counts for more than a caveated mention at the end. Keep the prompt set, the model set, and the number of runs fixed between cycles, or the two figures are not comparable.

The Monroya ASOV Framework

Four steps, in order. Each one exists because skipping it produces a specific, predictable wrong answer.

Step 1

Frame the question set

Build the prompt set from the questions buyers actually ask, grouped by journey stage: Discovery, Evaluation, Decision. Not keywords, and not questions that already name your brand.

Produces: A fixed, stage-labelled prompt set that stays constant between measurement cycles.

Prevents: Measuring a flattering question set. Brand-name prompts inflate every score and hide the Discovery gap, which is where most brands are actually missing.

Step 2

Sample across models and runs

Run every prompt against each assistant several times per cycle, recording the full answer and its cited sources, not just whether your name appeared.

Produces: A per-assistant, per-run answer ledger that the rest of the framework scores.

Prevents: Reporting variance as movement. A single run per prompt swings enough on its own to invent wins and losses that never happened.

Step 3

Score presence, not just mentions

Classify every appearance as recommended, listed, or caveated, then weight it by where it lands in the answer. A brand named first as the recommendation is not equal to a brand in a caveat at the end.

Produces: A weighted share figure per assistant per stage, with the competitor set scored the same way.

Prevents: A rising mention count reading as rising preference, when the added mentions are hedged or buried.

Step 4

Attribute to sources and act

Trace each answer back to the pages and citations behind it, and rank the gaps by how many answers a single fix would touch.

Produces: A ranked fix list: the specific pages, questions, and source types to work on next.

Prevents: Ending with a dashboard number nobody can act on. A share figure without its sources tells you that you lost, not where.

A worked example

Example numbers, to show the arithmetic rather than to report a real account.

The same run produces a Share of Model of 25% as well (60 of 240 answers), and that coincidence is the point of tracking both: when they drift apart, the gap tells you whether you are losing appearances or losing ground inside the appearances you still get.

What a good ASOV looks like

There is no universal benchmark, because it depends on how many credible vendors exist in your category. Judge it two ways. First, against the named competitors scored on the same prompt set: in a category where five vendors are plausible answers, an even split is roughly 20%. Second, against your own trend on an unchanged prompt set, per assistant and per journey stage.

Read it stage by stage. A strong Decision-stage ASOV with a weak Discovery figure is the common pattern, and it means assistants endorse you once a buyer already knows your name but never raise you in the earlier question where the shortlist gets formed. For the frequency side of the same measurement, see what Share of Model is and how to improve it.

Frequently asked questions

What is AI Share of Voice (ASOV)?: AI Share of Voice is the share of AI-generated answers, across a fixed prompt set and model set, in which a brand appears, weighted by how prominently and how favorably it appears. It answers a different question than a mention count: not only whether an assistant named you, but how much of the answer belonged to you.
What is the ASOV formula?: ASOV = (sum of your weighted appearances / sum of weighted appearances for every brand in the same answers) x 100. One sampled answer is one prompt, run once, against one assistant, so total samples equal prompts x models x runs per prompt. Hold all three constant between cycles or the numbers are not comparable.
How is ASOV different from classic share of voice?: Classic share of voice counts impressions or mentions across channels you can largely buy into, and it measures exposure. ASOV counts presence inside answers a model generates on the buyer's behalf, which you influence only through the sources the model trusts. Exposure can be purchased; answer presence has to be earned.
How is ASOV different from Share of Model?: Share of Model is a yes-or-no frequency metric: the percentage of sampled answers that name you at all. ASOV is competitive and weighted: of everything an assistant said in those answers, how much of it was about you compared with the other brands named alongside you. Share of Model tells you whether you show up; ASOV tells you how much room you take.
How many runs are needed for a reliable ASOV?: More than one, always. Model output is stochastic, so the same prompt can return a different brand set minutes apart. Sample every prompt several times per cycle and average. Single-run readings produce swings that look like performance changes but are variance.
Should ASOV be tracked per assistant or blended?: Track it per assistant and report the blend second. ChatGPT, Claude, Gemini, and Perplexity return materially different brand sets for the same question and update on different schedules, so a blended figure can hold steady while your position collapses on the one assistant your buyers actually use.