Based on Scope platform data and industry analysis of AI recommendation patterns across thousands of business queries, Q1 2026.
AI visibility has moved from a speculative concept to a measurable business metric. The question is no longer "does AI search matter?" — it's "how does your business compare, and what are the highest-leverage improvements?"
This report presents benchmark data on AI visibility scores across industries, citation source effectiveness, schema implementation gaps, and platform distribution — giving businesses a data-driven baseline for evaluating their AI search performance.
Executive Summary
- The average AI Visibility Score across all industries analyzed is 42 out of 100
- Enterprise software and financial services lead all sectors; hospitality and home services lag significantly
- 74% of businesses have critical NAP inconsistencies across their top citation sources
- Google Business Profile is the single highest-impact citation source across all verticals
- ChatGPT handles the largest share of AI business discovery queries (38%), followed by Perplexity (27%), Gemini (22%), and Claude (13%)
- Businesses with 5+ schema markup types score 2.3× higher on average than businesses with no schema implementation
AI Visibility Scores by Industry
AI Visibility Scores measure how consistently and prominently a business appears in AI recommendations across relevant queries. Scores are normalized 0–100 based on mention rate, recommendation position, sentiment, and query coverage.
| Industry | Average Score | Top Quartile | Bottom Quartile | |----------|--------------|-------------|----------------| | Enterprise Software / SaaS | 61 | 82 | 38 | | Financial Services | 58 | 79 | 34 | | Healthcare (providers) | 54 | 74 | 31 | | Legal Services | 51 | 71 | 28 | | Marketing / Advertising Agencies | 49 | 68 | 27 | | E-commerce / Retail | 46 | 65 | 24 | | Restaurants & Food Service | 44 | 67 | 19 | | Real Estate | 41 | 62 | 22 | | Professional Services (Consulting) | 39 | 61 | 21 | | Automotive | 37 | 58 | 18 | | Home Services / Contractors | 31 | 54 | 14 | | Hospitality / Hotels | 29 | 52 | 13 |
Why Enterprise Software Leads
SaaS companies score highest because they've been building the citation infrastructure AI systems rely on for years — G2 and Capterra profiles, Product Hunt listings, analyst coverage, tech press reviews. The SaaS industry's content-marketing culture produces exactly the type of dense, authoritative, multi-source presence that AI systems reward.
Why Hospitality and Home Services Lag
Hospitality and home services businesses score lowest due to three compounding factors: high NAP inconsistency rates (frequently changing ownership, addresses, contact information), lower rates of proactive citation management, and a heavier reliance on platform-specific discovery (Booking.com, Angi) that doesn't translate as well to general AI recommendation queries.
Top Citation Sources by Industry
The sources that most frequently contribute to AI recommendations differ meaningfully by vertical.
| Industry | #1 Source | #2 Source | #3 Source | |----------|-----------|-----------|-----------| | Restaurants | Google Business Profile | Yelp | TripAdvisor | | Healthcare | Healthgrades | Google | Zocdoc | | Legal | Avvo | Google | Martindale-Hubbell | | SaaS / Software | G2 | Capterra | Product Hunt | | Home Services | Google Business Profile | Angi | Yelp | | Real Estate | Zillow | Realtor.com | Google | | Hospitality | TripAdvisor | Google | Booking.com | | Financial Services | NerdWallet | Bankrate | Google | | Agencies | Clutch.co | Google | LinkedIn |
Note: Google Business Profile appears in or near the top position for all local business categories, confirming its status as the foundational citation across all verticals.
Platform Distribution: Where AI Business Queries Are Asked
Not all AI queries reach the same platform. Understanding the distribution helps businesses prioritize their optimization efforts.
| Platform | Share of AI Business Discovery Queries | Primary Query Types | |----------|----------------------------------------|---------------------| | ChatGPT | 38% | Research, comparison, recommendations | | Perplexity | 27% | Research, local business, product discovery | | Gemini / Google AI | 22% | Search-integrated queries, local | | Claude | 13% | Research, professional recommendations |
Key insight: ChatGPT's dominance reflects its user base size, but Perplexity's 27% share is disproportionately high relative to its user base — suggesting Perplexity users engage in more business-discovery query behavior per user than ChatGPT users. This makes Perplexity a high-efficiency optimization target.
Gemini's 22% reflects the integration of AI into Google Search via AI Overviews, which now appear for a significant share of business-related queries.
Schema Implementation Gaps
Schema markup is one of the highest-ROI improvements for AI visibility, but it's also one of the most commonly neglected. Our data shows:
- 68% of businesses have no schema markup on their primary business page
- 82% of local businesses are missing
LocalBusinessschema - 91% of businesses have no
FAQPageschema despite having FAQ content on their site - 73% of SaaS companies are missing
SoftwareApplicationschema - Businesses with 5+ schema types score 2.3× higher than those with none
Most Impactful Schema by Business Type
Local businesses: LocalBusiness (with full address, phone, hours, geo coordinates), AggregateRating, FAQPage
SaaS / Software: SoftwareApplication, Product, FAQPage, Organization, Offer
Professional services: ProfessionalService, Person (for key personnel), FAQPage, Organization
Restaurants: Restaurant, Menu, AggregateRating, LocalBusiness
Healthcare providers: MedicalBusiness, Physician or MedicalOrganization, FAQPage
NAP Consistency Analysis
NAP (Name, Address, Phone) consistency is a foundational requirement for AI entity recognition. The data reveals a widespread problem:
- 74% of businesses have at least one major NAP inconsistency across their top 5 citation sources
- 41% have inconsistencies in business name formatting (abbreviations, LLC vs. omitting LLC, etc.)
- 38% have at least one citation with an outdated phone number
- 29% have at least one citation with an outdated address
- Businesses with perfect NAP consistency score 1.7× higher on average than those with inconsistencies
The most common NAP inconsistency patterns:
- Abbreviation differences (Street vs. St., Suite vs. Ste.)
- Legacy phone numbers from before a business move or number change
- Old addresses after a business relocation
- Business name variations across platforms (DBA vs. legal name)
Review Volume and AI Recommendation Rate
Review data shows a strong correlation between review volume and AI recommendation frequency:
| Review Count (Google) | Avg. AI Mention Rate | |----------------------|----------------------| | 0–10 reviews | 8% | | 11–50 reviews | 19% | | 51–100 reviews | 31% | | 101–250 reviews | 48% | | 251–500 reviews | 63% | | 500+ reviews | 71% |
The data suggests a threshold effect: businesses below 50 reviews struggle to achieve consistent AI visibility regardless of other optimization efforts. Businesses above 250 reviews achieve reliable baseline visibility in their category.
Review quality also matters. Businesses whose reviews average more than 50 words per review score 1.4× higher than businesses with short reviews — because longer reviews provide richer training data about what makes the business worth recommending.
Competitive Implications
The data reveals a significant opportunity for businesses willing to take AI visibility seriously now. Average scores across all industries are below 50 — meaning most businesses are leaving the majority of their potential AI visibility unrealized.
In every industry analyzed, top-quartile businesses score 40–50 points above bottom-quartile businesses — a dramatic spread that reflects the early-mover advantage available to businesses that build their AI presence systematically.
Early-stage industries like home services and hospitality represent the highest-opportunity sectors: because average scores are low, the gap between "doing nothing" and "doing the basics well" translates into dramatically higher relative AI visibility.
Get your business's AI Visibility Score →
FAQ
Q: How is an AI Visibility Score calculated? A: Scope's AI Visibility Score synthesizes four dimensions: mention rate (what percentage of relevant queries result in your business being mentioned), recommendation position (first, second, or later in a response), sentiment analysis of mentions (positive, neutral, or negative framing), and query coverage (how many distinct query types and phrasings result in your appearance). The composite score is normalized to 0–100 for comparability.
Q: How does my industry benchmark change optimization priorities? A: Industries with low average scores typically have a foundational citation problem — businesses haven't claimed and completed their key profiles. For these industries, the priority is getting to baseline: complete GBP, complete industry-specific platforms, NAP consistency. Industries with higher average scores are more competitive at the baseline level, meaning the margin is won by editorial citations, schema implementation, and review volume rather than just directory completeness.
Q: Is the 38%/27%/22%/13% platform distribution stable or changing? A: This distribution is a Q1 2026 snapshot and is actively changing. Perplexity's share has been growing quarter-over-quarter. Gemini's share is tied to Google AI Overviews adoption, which continues to expand. Claude's share reflects Anthropic's growing enterprise and consumer deployment. We expect ChatGPT's share to compress as the other platforms grow, and Perplexity's share to continue increasing.