How to Calculate AI Share of Voice

To calculate AI share of voice, divide the number of times your brand is named, cited or recommended across a fixed set of AI prompts by the total number of brand mentions in that same set (yours plus every competitor's), then multiply by 100. The formula is: AI SoV = (your brand's mentions ÷ total brand mentions across the prompt set) × 100. Unlike Google rank tracking, there is no API that reports this number directly — you have to run the prompts yourself and count the answers.

Step 1: Build a fixed prompt set

Start with 50–500 real buyer-intent prompts in your category — the kinds of questions a prospect would actually type into ChatGPT or Perplexity ("best project management software for remote teams", not generic head terms). The prompt set has to stay fixed run over run, or your share-of-voice number stops being comparable month to month.

Step 2: Run every prompt against every engine you track

Send each prompt to the AI answer engines that matter for your audience — ChatGPT, Perplexity, Claude, Google AI Overviews and AI Mode are the common set. In our own benchmark run of 500 prompts we logged 2,000 total model calls, because we queried across multiple engines and reran prompts to check consistency.

Step 3: Record every brand mention, not just yours

For each answer, log every competitor brand named, cited or recommended — not only your own. Share of voice is a ratio, so the denominator (everyone's mentions combined) matters as much as the numerator.

Step 4: Classify the mention, don't just count it

A brand can be named outright, cited via a linked source, or merely described without being named (see our companion glossary page on mention rate for that distinction). Track these separately — a raw mention count that blends "named and recommended" with "described but unnamed" will overstate weaker signals.

Step 5: Divide and track the trend, not a single snapshot

Compute AI SoV = your mentions ÷ total mentions, per engine and blended. A single run is a snapshot; the useful signal is the trend across repeated monthly runs, since AI answers are non-deterministic and can shift with model updates. In our dataset, the average AI answer cited 9.6 sources, and share of voice only becomes stable once you're averaging across dozens of prompts per engine, not one.

Worked example

Say you run 100 prompts about "CRM software" across ChatGPT and Perplexity (200 total calls). Your brand is named in 34 of those answers; four named competitors combined appear 146 times. Total brand mentions = 180. Your AI SoV = 34 ÷ 180 × 100 ≈ 18.9%. Rerun the same 100 prompts next month — if your count rises to 40 while competitors hold flat, your AI SoV moved to roughly 21.5%, a real, comparable gain because the prompt set didn't change.