What is AI share of voice?
Traditional share of voice measures ad impressions or branded search volume as a percentage of the total category. AI share of voice measures brand mentions in AI-generated responses as a percentage of total brand mentions across your tracked competitor set.
AI SoV calculation example
Query set: 50 category queries tracked across ChatGPT and Perplexity
Total mentions: 200. Your AI SoV: 62/200 = 31%
Why AI SoV matters more than mention rate alone
Your mention rate is your absolute performance. AI SoV is your relative performance. Context changes everything. A mention rate of 40% looks strong in isolation - but if your top competitor has a 70% mention rate, you're losing the AI search channel to them by a wide margin.
AI SoV is also the metric that resonates most in client conversations and board-level reporting. "We appear in 40% of queries" is meaningful. "We have 31% share of AI recommendations in our category vs. Competitor B's 39%" is a business metric.
Framing-weighted AI SoV
Raw mention-based SoV treats all mentions equally. Framing-weighted SoV adjusts for mention quality - a strong positive mention counts more than a neutral mention, and a hedged mention may count less than zero.
| Framing tier | Weight | Why |
|---|---|---|
| Strong positive | 1.0 | Maximum buyer impact - full credit |
| Positive | 0.75 | Good buyer pull - most of the credit |
| Neutral | 0.4 | Weak buyer pull - partial credit |
| Hedged | 0.15 | May deter consideration - minimal credit |
| Negative | 0 | Actively hurts - no credit |
Breaking down AI SoV by query type
Aggregate AI SoV masks important patterns. A brand might have 50% SoV on category queries but only 20% SoV on comparison queries - meaning they win awareness but lose when buyers get closer to a decision.
Category query SoV
"Best [category] software" - measures awareness-stage AI presence. High category SoV = brand enters most consideration sets.
Use case query SoV
"Software for [specific use case]" - measures niche positioning strength. Brands with deep use-case SoV win buyers with specific needs.
Comparison query SoV
"Brand A vs Brand B" - measures competitive framing. High comparison SoV = winning the AI verdict on direct comparisons.
Problem query SoV
"How to [solve problem]" - measures solution authority. Brands that dominate problem queries capture intent-rich buyers before they know which vendor to evaluate.
How to improve AI SoV
Improving AI SoV requires closing the gap between your brand and whoever holds the most mentions in your category. The fastest levers depend on the gap type:
Lower review volume than competitors
Systematic G2/Trustpilot review campaigns targeting their count, not just a flat volume target.
Missing from editorial roundups where competitors appear
Map the specific articles ranking for your target queries. Pursue inclusion - not just backlinks but actual mention in the ranked list.
Analyst recognition gap
Pursue Gartner/Forrester/G2 analyst recognition in your categories. Each tier upgrade creates persistent framing improvement.
Comparison query underperformance
Build "vs." content and ensure third-party comparisons include accurate, favorable framing about your strengths.
AI SoV as a client reporting metric
Agencies reporting AI SoV alongside traditional share of voice metrics create a richer competitive picture. This is the part most agencies haven't added to their reports yet. Traditional SoV (ad impressions, branded search) + AI SoV (ChatGPT/Perplexity recommendations) gives a full view of category presence across all search and discovery channels.
ArtificialPulse calculates both raw and framing-weighted AI SoV, tracks it weekly, and includes it in white-label client reports with competitor comparison and trend lines.
Calculate your AI share of voice
Free audit shows your AI mention rate and competitor comparison - the foundation for calculating your AI SoV.