What Is AI Share of Voice?

AI Share of Voice explained

Traditional share of voice tracked 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 can't. 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 isn't precision; it's making an invisible competition visible enough to manage.