AI Share of Voice: How to Calculate It, and What a Good Number Looks Like

Start with a fixed set of buyer questions, run on each AI surface you care about. Then, for every answer, record which brands appear and which are actually chosen. Two shares come out of that:

• Presence share: your appearances divided by all brand appearances. An appearance is any answer that names you in its recommendations or its lists.
• Recommendation share of voice: answers that select you for a stated case, divided by all brand recommendations across the same answers.

Count each brand once per answer. A brand named five times in one answer is still one appearance. That rule alone removes most of the noise in homemade dashboards.

These figures come from our published teardowns: 100 buyer questions per category, ChatGPT with web search, one fixed rulebook (codebook v1.5).

Two things stand out. First, no brand dominates: even the strongest held about a quarter of the recommendations, and between 32 and 42 brands appeared in each category. Second, recommendations are more concentrated than appearances. In eight of nine categories the top three took a bigger share of the picks than of the mentions. AI answers name a wide field and then choose from a narrower one.

• Counting mentions instead of picks. A brand listed in every comparison table can lead on presence and trail on recommendations. See our brand mentions guide for real examples.
• Counting the brand in the question. In "alternatives to X", X is the thing being replaced. Counting it as a recommendation inflates the incumbent. We code it separately and exclude it.
• Changing the question set. Add or drop questions and the share moves for reasons that have nothing to do with your brand. Keep a fixed core set and add new questions as a separate group.
• One run per question. AI answers vary between runs. Treat single-run numbers as a snapshot and look at trends.

Share of voice tells you where you stand. The useful work is in the breakdown: which question types you lose, which competitor takes them, and which sources those answers cite. That is the analysis behind every AI SEO engagement we run. For the full measurement method, including citation share, see AI visibility.

What is AI share of voice?

It is your brand's share of all brand appearances, or all recommendations, in AI answers to a fixed set of questions about your category. It works like classic share of voice, with AI answers in place of ads or rankings.

How do you calculate share of voice in AI search?

Count the answers in which each brand is recommended across the same question set, then divide your count by the total for all brands. Do the same with appearances (recommended plus listed) to get presence share. Report both.

What is a good AI share of voice?

In our nine software categories, the most-recommended brand held between 13 and 25 percent of all recommendations. Above 20 percent put a brand at the very top of its category. Compare yourself with named competitors on the same questions rather than with a universal target.

Why do different tools report different share of voice?

They run different questions on different schedules and count mentions differently. A share is only comparable when the question set, the surfaces and the counting rules stay fixed.

• High Salience research: nine software teardowns with datasets
• High Salience: Benchmark 01 methodology

Every High Salience engagement starts with the Category Salience Brief: your commercial questions run across Google and the AI surfaces, competitors side by side, and a ranked list of what to fix first.