The one-line definition.
AI Share of Voice is the percentage of your target AI-engine queries where your brand appears as a cited source, measured across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude.
AI SoV = (queries where your brand is cited ÷ total target queries probed) × 100Track weekly. Report monthly. Compare to 3–5 competitors in the same query set. This is the GEO equivalent of Google SEO Share of Voice — and the only metric that correlates directly with the AI-referred traffic channel growing 527% year-over-year (Semrush AI Search Study, 2025).
Why it diverged from Google Share of Voice.
Three structural reasons the two metrics stopped correlating in late 2025:
- Only 17–38% overlap between Google top-10 rankings and AI Overview citations (Ahrefs, March 2026; BrightEdge measures as low as 17%). A page ranked #1 on Google can be entirely absent from AI answers, and vice versa.
- 56% of AI citations come from off-site sources (AirOps LLM Brand Citation Study, 2026). Google SoV rewards your own domain ranking. AI SoV rewards Wikipedia, Reddit, LinkedIn, YouTube, press coverage, and directory profiles where your brand is mentioned.
- Only 11% of domains are cited by both ChatGPT and Perplexity (Lantern AI Citation Report, Feb 2026). The engines themselves don’t overlap — aggregate SoV requires separate per-engine measurement.
The benchmark table.
| AI Share of Voice | What it means | What to do |
|---|---|---|
| 0–5% | Invisible — default state for the 88% of companies without a GEO strategy | Audit + Monitor tier |
| 5–10% | Emerging — brand mentioned occasionally, usually only 1–2 engines | Growth tier — content + off-site |
| 10–20% | Competitive — cited across multiple engines, usually behind top 2 competitors | Growth → Dominate |
| 20–40% | Established — cited on most commercial-intent queries | Dominate — defend position |
| 40%+ | Category-dominant — a recognizable entity to every engine | Dominate — expand query surface |
Benchmarks above are for B2B SaaS and professional services — category-specific variance is high. DTC e-commerce tends to see lower raw SoV because Amazon/Shopify product queries bias toward marketplace domains. Enterprise healthcare runs lower still due to Claude’s aggressive citation-filtering.
Per-engine variance — it’s not one number.
Aggregate SoV hides the distribution. Report and track per engine because the variance between engines is often larger than the variance across weeks:
| Engine | Typical weekly σ | Response-over-response overlap |
|---|---|---|
| ChatGPT | 2–4 pp | ~35–45% |
| Google AI Overviews | 5–9 pp | ~15–25% |
| Perplexity | 4–7 pp | ~20–30% |
| Gemini | 3–6 pp | ~25–35% |
| Claude | 1–2 pp | ~55–65% |
A single-day SoV reading on Perplexity or AIO is a coin flip. The rolling 4-week mean ± 1σ is the only honest report. Google AI Mode responses overlap with themselves just 9.2% when the same query is tested 3 times (Growth Memo, 2026).
Why AI SoV is worth more than Google SoV.
Per dollar of pipeline:
- 5× conversion rate. AI-referred traffic converts at 14.2% vs 2.8% for traditional organic (Semrush, 2025).
- Per-platform B2B conversion (Seer Interactive benchmark): ChatGPT 15.9%, Perplexity 10.5%, Claude 5%, Gemini 3%, Google Organic 1.76% (ALM Corp / Seer, 2026).
- Every percentage point of AI SoV on a commercial-intent query is worth roughly 5× the same point of Google SoV at the same keyword volume, because the downstream visitor is further down the funnel when they arrive.
- AI referral sessions grew 527% YoY in 2024→2025 (Semrush, 2025). Meanwhile organic CTR on queries with AI Overviews dropped 61% (Dataslayer, 2026). AI SoV is still compounding; Google SoV is decaying.
How to measure it (the operational stack).
- Define the query set. 30–200 target queries per brand, matched to how real buyers phrase questions — not keyword-research-derived SEO queries.
- Probe each query × engine on a cadence. Daily for top 20 commercial-intent, weekly for the long tail.
- Detect citations.For every probe, parse the answer + cited source URLs; match against the brand’s domain and named-entity variants (branded terms, product names, founders).
- Aggregate. Weekly: cited queries ÷ total queries = current SoV. Split by engine and by query category (commercial vs informational).
- Report. Current vs rolling 4-week mean, per-engine breakdown, delta vs 7 days ago, top 5 gains, top 5 losses, competitor SoV for context.
Cited runs every step above automatically. The free audit probes 50 queries across 3 engines to show your starting SoV; retainer tiers probe continuously and ship the off-site content that moves the number.
Common mistakes.
- Reporting a single-day reading. Variance is too high on AIO and Perplexity. Always rolling mean.
- Aggregating across engines without per-engine splits. Hides the fact that you might be winning ChatGPT but losing Perplexity.
- Ignoring competitor SoV. Your brand at 15% SoV looks great until you learn the category leader sits at 42%.
- Using Google keyword volume to pick the query set. Only 17–38% of AI queries overlap with Google rankings — your SEO keyword list will under-represent how buyers actually ask AI.
What good looks like.
A production GEO program shows three things on the AI SoV dashboard every week:
- Overall AI SoV rolling 4-week mean, trending up or holding above variance band.
- Engine-by-engine breakdown with at least 3/5 engines ≥10% SoV.
- Competitor SoV comparison where the brand is either leading or narrowing the gap at >1pp/month.
If all three hold for 90 days, the program is working. If any one falters, the content pipeline needs a re-read. Cited’s dashboard wires these three exactly.