Do AI engines agree on the best tool?

Rarely. In our category measurements no single vendor leads on every engine; a name that dominates one engine is often absent from another. AI visibility is engine-specific.

Where do AI answers get their sources?

Roughly 95% of the citations behind these answers come from third-party pages — comparison posts, review sites, and community threads — not the vendor's own marketing.

Research · Report

State of AI Search: the engines disagree

When buyers ask AI for the best tool in a category, the answer depends on which AI they ask — and the sources behind it are mostly not the vendor's own site.

Print / save as PDF →

TL;DR. Across the categories we measure, no single vendor leads on every engine, and roughly 95% of the citations behind AI answers come from third-party pages (Otterly, State of AI Search). AI visibility is engine-specific and source-driven — it has to be measured, and worked, per engine.

The finding

We run the same buyer-prompt set against ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews — each prompt repeated 10+ times per engine, because a single AI answer is noise, not data. We record which vendors are named and how often, then compute each vendor's share of model: the percentage of qualifying answers that mention them, with a confidence interval.

No single vendor led on all engines. A name that dominated one engine was frequently absent from another. The "best tool" a buyer hears depends less on the product and more on which assistant they happened to ask — and on which third-party sources that assistant tends to cite.

What the leaderboards show

Measured category leaders (2026-06-20) from our AI Visibility Index — median of 10+ runs/engine across 5 engines, Wilson 95% CI. See each leaderboard for the full ranked table and per-engine spread. Other categories are illustrative previews until their first live run.

Category leaders · 5 engines · measured 2026-06-20Updated monthly

Category	Leader	Share of model	SoM	95% CI
CI/CD platforms	GitHub Actions		21.3%	19.4–23.4%
Observability	Datadog		17.7%	16.1–19.5%

Why it matters

If your visibility is strong on one engine and invisible on another, you lose shortlist spots you will never see in your analytics. The flip side: because the inputs are knowable — structured data, entity signals, and the specific third-party sources each engine pulls from — this is fixable. It just has to be measured per engine and worked per engine.

Method & honesty

Share of model = the % of qualifying answers that name a brand, measured as the median of 10+ runs per buyer prompt across 5 engines (ChatGPT · Perplexity · Gemini · Claude · Google AI Overviews), with a Wilson 95% confidence interval. Consumer apps and APIs differ (system prompts, tools, browsing) — we treat each edition as a point-in-time measurement and re-run on a cadence. We publish the date and method with every figure.

Last reviewed: June 19, 2026. We re-check figures on a monthly cadence because AI engines change continuously.

Get the full dataset

The per-category CSV/JSON datasets (CC BY 4.0) plus the methodology notes. Tell us where to send them.

Logan Adams · Founder, Clear Cited

Writes on how AI answer engines pick what to recommend, share-of-model methodology, and reproducible AI-visibility measurement. About Clear Cited →