About this report
Benchmark data from ArtificialPulse audits across brands in each category as of Q1 2026. AI Visibility Scores reflect mention rate and framing quality across ChatGPT, Perplexity, and Google AI Overviews for a standardized set of category queries. All scores are on the 0–100 scale.
Overall AI visibility picture
The median AI Visibility Score across all categories in Q1 2026 is 31. Category leaders average 67. That gap is widening. Early-moving brands compound their signal advantages while everyone else falls further behind. 40% of tracked brands score below 20 - invisible or near-invisible in AI search. Frankly, most brands don't know where they stand.
Leaders (top 10% by category) (10% of brands)
Consistently recommended, positive framing, strong third-party signals
Established (top 25%) (15% of brands)
Regular mentions, mostly positive framing, some signal gaps
Emerging (middle) (45% of brands)
Inconsistent mentions, often hedged, visible but not recommended
Invisible (bottom 40%) (40% of brands)
Rarely or never mentioned in AI responses for category queries
Benchmarks by industry
| Industry | Leader | Average | Dominant signal |
|---|---|---|---|
| B2B SaaS | 65–80 | 30–50 | G2 rating + editorial roundups |
| Consumer financial services | 60–78 | 25–45 | J.D. Power + Trustpilot |
| E-commerce / retail | 55–75 | 25–45 | Wirecutter + Amazon reviews |
| Healthcare / medical devices | 50–68 | 20–40 | Clinical citations + accreditation |
| Travel / hospitality | 60–80 | 30–50 | Condé Nast + TripAdvisor |
| Professional services | 45–65 | 20–38 | Chambers/Vault + client reviews |
| Automotive | 60–78 | 35–55 | J.D. Power + Car and Driver |
| Insurance | 55–72 | 25–45 | J.D. Power + AM Best |
| Restaurant / food service | 55–72 | 25–45 | Yelp + Michelin + press |
| Technology (hardware/infra) | 65–80 | 35–55 | Tech press + Gartner/IDC |
| Wellness / fitness | 45–62 | 20–38 | Wirecutter + Well+Good + reviews |
| Real estate | 45–62 | 18–35 | Zillow ratings + press |
| Education / edtech | 48–65 | 22–40 | Rankings + review platforms |
| Crypto / fintech | 45–65 | 18–35 | Trustpilot + Forbes/NerdWallet |
| Nonprofit / social impact | 40–58 | 15–32 | Charity Navigator + press |
What separates leaders from average brands
Across all categories, AI visibility leaders share three characteristics that average brands don't have:
1. Presence in category-defining editorial content
Every category has 2–5 editorial sources that AI models retrieve most frequently for category queries. Leaders are in these sources - not just mentioned, but featured prominently with positive framing. Being in the Forbes "Best of" list, the Wirecutter recommendation, the G2 Category Leader badge.
2. Review volume and rating above category thresholds
There are threshold effects in review signals. Below ~100 reviews, a brand has minimal AI signal. Above 500 reviews at 4.3+, AI models consistently cite the review platform signal positively. Leaders typically have 3–5x the review volume of average brands in their category.
3. Accurate and complete entity data
Leaders have accurate Wikipedia articles, complete Wikidata entries, and structured data markup that matches their current positioning. When AI models describe them, the descriptions are accurate - because the entity data is maintained.
Score improvement timelines
| Action | Score impact | Lag to AI framing change |
|---|---|---|
| Review volume campaign (0→500+) | +8–15 pts | 4–12 weeks |
| Inclusion in major "best of" editorial | +5–12 pts | 2–8 weeks (Perplexity), months (ChatGPT) |
| Wikidata/Wikipedia entity update | +3–8 pts | 4–16 weeks |
| Wikipedia article creation | +8–15 pts | 3–9 months for training, faster for Perplexity |
| Analyst recognition (Gartner/Forrester) | +5–10 pts | 1–6 months |
| Own-site content alone | +0–3 pts | Minimal impact for AI recommendations |
See how your brand benchmarks in your category
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