Share of AI Voice

What is share of AI voice (SAV)?

Share of AI voice (SAV) is a metric that measures how often a business is cited, recommended, or mentioned across a defined set of queries and AI engines, relative to the total number of opportunities to appear. It answers a specific question: out of every chance an AI engine had to mention this business, how often did it actually happen?

The formula: SAV = (Queries where the business is cited) / (Total queries × engines) × 100

Example: a restaurant tests 20 queries across 4 AI engines, 80 total query-engine combinations, and is cited in 24 of them. SAV = 24/80 × 100 = 30%.

SAV as a competitive metric

SAV is most useful measured against competitors, not in isolation. Calculating SAV for the same query set and the same engines across several competing businesses produces something close to an AI market share view:

Restaurant A holds 30% SAV. Restaurant B holds 45%, the market leader in AI search for this query set. Restaurant C holds 18%. The goal isn't an abstract target, it's closing the gap with whichever competitor holds the highest share, then holding that position once reached.

Breaking SAV down by engine and query type

SAV in Google AI Overviews can differ sharply from SAV in ChatGPT, Perplexity, or Gemini for the same business. A clinic strong in AI Overviews but nearly invisible in ChatGPT has an engine-specific gap worth investigating separately, since the fix for one engine (structured schema, GBP completeness) doesn't necessarily move the needle on another (which may weight independent reviews or forum mentions more heavily).

SAV also varies by query type. Recommendation queries ("best dentist in Koramangala"), informational queries ("how much does root canal cost"), and service-specific queries ("emergency dentist open now") each pull from different content and different signals. A business might hold strong SAV on informational queries because its blog content is thorough, while its SAV on recommendation queries lags because its review profile is thin. Treating SAV as one number hides this; breaking it out by query type shows where the actual work needs to go.

How SAV differs from share of local voice

Share of local voice measures presence in the traditional local pack across a geo-grid. Share of AI voice measures the newer, parallel question of whether AI engines cite a business at all. A business can hold strong SoLV, ranking well in Google Maps across its service area, while holding weak SAV, rarely surfacing in ChatGPT or Perplexity answers, because the two systems weight different signals. Increasingly, tracking both side by side gives a fuller picture than either alone.

Why SAV tracking matters now

Most businesses, in India and elsewhere, have never measured this at all. There's no established baseline to compare against, no industry benchmark to check a number against, which makes measuring it now, before it becomes standard practice, a genuine advantage rather than a routine reporting task. A business that starts tracking SAV in 2026 knows where it stands before its competitors do; one that waits finds out only after a competitor has already built the lead.

Common mistakes

Treating a single spot-check, asking ChatGPT one question once, as a measurement is the most common error. AI engines don't return identical answers on repeat queries, so SAV needs a defined query set tested consistently over time, not a one-off screenshot. Another mistake is testing only branded or near-brand queries ("angryturtle reviews") rather than the unbranded, intent-driven queries a prospective customer would actually type, which is where citation actually matters for new business.

Related terms: Prompt Coverage → · Share of Local Voice → · Citation Frequency → · Grounding (AI) → · AI Search Visibility →

Example: Angryturtle tracks SAV monthly for managed AEO clients, 25 target queries across 4 AI engines, 100 data points per client per month, so month-on-month movement is visible rather than assumed.

Angryturtle's SAV tracking methodology and rollout are described further at Share of AI Voice Tracking →, and it runs alongside the broader AEO Services → engagement for clients who want both AI citation and traditional local pack presence measured together.