AI Share of Voice
TL;DR: What is AI Share of Voice?
AI share of voice is the percentage of brand appearances across AI-generated answers that belong to you versus competitors, measured over a consistent set of prompts. It adapts marketing's classic share-of-voice metric to the surfaces where buyers now ask their questions.
AI Share of Voice explained
Traditional share of voice measured your slice of a market's advertising presence; the AI version measures your slice of its answers. Across a defined prompt set, recommendations, comparisons, category questions, you count brand appearances (mentions, citations, or both, depending on methodology) and compute each competitor's share of the total.
The metric family here is young and overlapping, and the honest map helps: AI share of voice typically counts appearances across answers; share of model frames the same competitive question, sometimes with more emphasis on the model's learned preferences; citation share isolates the source-credit slice specifically. Vendors blend these differently, so the number matters less than the method being consistent: same prompts, same engines, same counting rules, tracked over time.
What makes the metric worth running is what it reveals that traffic analytics cannot. AI answers shape buyer shortlists upstream of any click, so a competitor with dominant answer-share is winning consideration you never see leaving. Trend lines against named competitors turn that invisible loss into a managed number, and the measurement itself is still rare enough that running it consistently is an advantage: most markets have exactly zero brands tracking who wins their category's answers. The metric's job is not precision; it is making an invisible competition visible enough to manage.
In practice
I report share of voice and citation share side by side for clients, because together they tell the story one number cannot: how present you are, and how trusted you are. Both matter for AI visibility. The discipline I insist on is a locked prompt set and a monthly cadence, because these systems vary between runs, and a single-day spot check will mislead you in either direction. Trends are the signal; snapshots are noise with confidence.
Common misconception
People often chase a single definitive share-of-voice number. Actually, answers vary by engine, prompt, and day; the metric is only meaningful as a consistently measured trend against competitors, not as a one-time score.
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