How many brands per industry are included in these benchmarks?

Benchmark data is derived from the brands sampled in our 2026 study. The exact number varies by vertical, but each benchmark represents patterns observed across a sufficient sample to be directionally meaningful. We do not publish exact sample sizes.

Can niche industries use these benchmarks?

The 9 verticals covered represent broad categories. If your industry falls between categories — such as legal tech or health-tech — consider averaging benchmarks from the most relevant verticals as a starting point and refining based on your own tracking data.

How often are industry benchmarks refreshed?

These benchmarks are a snapshot of our 2026 study and are not updated on a fixed schedule. AI engine behaviour evolves with model updates, so benchmarks that are more than two quarters old should be treated with caution.

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Research

AI Visibility Benchmarks by Industry

Share of Model, citation rates, and visibility scores benchmarked across 9 industry verticals.

Last updated: 2026-10-02

Quick answer

Not all industries experience AI visibility equally. Our 2026 study suggested that citation rates, Share of Model distributions, and competitive dynamics vary dramatically between verticals — from heavily regulated sectors like healthcare and finance to high-volume categories like ecommerce and travel.

Key findings

Data at a glance

~34%

Avg. SoM — SaaS leaders

Our 2026 study suggested that top-performing SaaS brands achieve approximately 34% Share of Model for their primary category queries.

~12%

Avg. citation rate — Healthcare

Based on data collected in our 2026 study, healthcare brands see roughly 12% citation rates due to heightened YMYL caution in AI engines.

~47%

Ecommerce SoM variance

Our 2026 study suggested that ecommerce brand visibility varies by up to 47 percentage points between the most and least favourable AI engines.

~22%

FinTech avg. SoM

Based on data collected in our 2026 study, FinTech brands average approximately 22% Share of Model, with compliance-focused content driving the strongest results.

~5.2x

Travel citation gap

Our 2026 study suggested that the gap between the most-cited and least-cited travel brands is roughly 5.2x, one of the widest among verticals.

~8%

Legal SoM floor

Based on data collected in our 2026 study, even well-optimised legal firms rarely exceed approximately 8% Share of Model due to AI engines' caution around legal advice.

~29%

Education visibility score

Our 2026 study suggested that leading education brands achieve brand visibility scores around 29%, driven by strong authority signals and structured programme data.

~3.8x

Agency self-visibility gap

Based on data collected in our 2026 study, digital agencies that actively practise AEO are approximately 3.8x more visible than agencies that only offer it as a service.

Why benchmarks matter for AEO strategy

Setting AI visibility targets without industry context leads to either complacency or unrealistic expectations. Our 2026 study suggested that a 15% Share of Model might represent dominance in legal services but mediocrity in SaaS. Benchmarks provide the contextual frame that turns raw metrics into actionable intelligence.

Industry benchmarks also reveal structural factors that affect AI visibility. Based on data collected in our 2026 study, industries with higher content density, more comparison queries, and stronger third-party review ecosystems tend to produce higher absolute visibility scores. Understanding these structural factors helps brands identify whether a visibility gap is due to their own content strategy or to the inherent characteristics of their vertical.

Finally, benchmarks help prioritise effort across engines. Our 2026 study suggested that some industries see dramatically different visibility profiles across engines — a brand might benchmark well on ChatGPT but poorly on Perplexity. Engine-specific benchmarks enable targeted optimisation where it matters most.

High-visibility verticals: SaaS, Ecommerce, and Travel

Three verticals consistently show the highest absolute AI visibility scores: SaaS, ecommerce, and travel. Our 2026 study suggested this is driven by the abundance of comparison content, product reviews, and structured data available to AI engines in these categories.

SaaS benefits from a rich ecosystem of review sites (G2, Capterra), comparison blogs, and feature documentation. Based on data collected in our 2026 study, SaaS brands that maintain active profiles on major review platforms and publish detailed feature comparisons see 2-3x higher citation rates than those that rely solely on their own marketing content.

Ecommerce and travel brands benefit from transactional intent queries where AI engines actively recommend specific products or destinations. However, the competitive intensity is also highest in these verticals, meaning that visibility gains require sustained, strategic effort rather than one-off content campaigns.

Regulated verticals: Healthcare, Legal, and FinTech

Regulated industries face unique AI visibility challenges. Our 2026 study suggested that AI engines apply heightened caution when generating responses about health, legal, and financial topics, reflecting YMYL (Your Money, Your Life) principles inherited from traditional search quality frameworks.

This caution manifests as lower citation rates, more hedged language, and a preference for institutional sources over commercial brands. Based on data collected in our 2026 study, healthcare brands see citation rates roughly 40% lower than comparable SaaS brands for equivalent query volumes. Legal firms face even greater constraints, with AI engines frequently declining to recommend specific providers.

The strategic implication is that regulated brands need to invest more heavily in authority signals — peer-reviewed publications, regulatory credentials, professional certifications, and institutional partnerships — to achieve visibility that would come more easily in unregulated verticals.

Methodology

How this research was conducted

Industry benchmarks are derived from data collected in our 2026 study covering brands sampled across 9 verticals. For each vertical, we ran a standardised query bank of category-relevant prompts across all major AI engines and analysed the responses for brand mentions, citations, and recommendation placement.

Benchmark ranges represent the interquartile range (25th to 75th percentile) of observed scores for the brands sampled. Outliers at both extremes are excluded to provide representative ranges. All figures are estimates and should be used as directional benchmarks rather than absolute targets.

FAQ