AI share of voice: calculate it without hiding the sample.
AI share of voice compares a company’s observed presence with relevant competitors across a defined set of AI answers. The calculation must state which questions, assistants, markets, dates and completion rules were used. It is a directional visibility measure, not a market-share or revenue metric.
Researched and reviewed by Eli · 9 August 2026
The question this page answers
Calculate AI search share of voice
Choose one transparent unit
A simple approach counts eligible answers in which each company appears, then divides by all company appearances in the same sample. Another approach reports the percentage of eligible questions where each company was named. Choose one method, document it and do not switch definitions inside a trend line.
Control the sample
Commercial and educational questions behave differently. Segment discovery, comparison, evaluation and branded prompts so a large awareness set does not hide losses near the buying decision. Record locale and provider coverage, exclude failed requests and publish the number of completed checks beside the result.
- Question portfolio
- Assistant mix
- Market and language
- Eligible-answer count
Use the metric to select work
Inspect the exact questions where a competitor appears and your company does not. Then inspect the sources behind those answers. The best intervention may be a clearer product page, a missing comparison, refreshed evidence or legitimate independent coverage. Remeasure the same segment after the verified work is live.
Questions buyers ask next
What is a good AI share of voice?
There is no universal benchmark. Compare your result with direct competitors inside the same category, question set and period.
Should citations count more than mentions?
They can be reported as a separate weighted view, but keep the unweighted underlying counts visible so the score remains auditable.
Can share of voice predict sales?
Not by itself. Compare AI visibility with qualified visits, conversions and CRM outcomes as separate evidence layers.
Primary sources
Find the buyer question you are losing
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