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Which incident management platforms do AI engines actually recommend?
As of June 23, 2026, PagerDuty leads with a 25.5% share-of-model — it appears in 25.5% of all incident management platforms recommendations across 5 major AI engines.
See your product's share-of-model — free How we measured thisThe leaderboard — Incident management platforms by share-of-model
Which incident management and on-call platforms do AI engines recommend when SRE and platform teams ask what to use for alerting, on-call and incident response? We ran 10 buyer prompts × 12 runs across 5 engines (Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI)) — 361 total AI answers. Last updated June 23, 2026.
PagerDuty leads our AI Visibility Index for Incident management platforms at 26% share-of-model across 4 AI engines.
Share of model = % of all recommendations (across every prompt and engine) that named the product. Per-engine columns show how often each engine recommends the product. CI = Wilson 95% confidence interval on share-of-model.
Methodology — reproducible, not vibes
Buyer "money prompts"
10 real buyer questions a person would ask an AI when choosing incident management platforms (e.g. "best incident management platforms for a Series A startup"). Each prompt is run 12+ times per engine.
5 engines, measured separately
We query Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI) independently, because the engines disagree — being strong in one says nothing about the others. Per-engine columns expose that spread.
Share-of-model + Wilson CI
For each product we report its share of all recommendations in the field, with a 95% Wilson confidence interval — the honest way to summarize a small, noisy sample.
Heuristic detection, disclosed
A product is "recommended" when its name or a known alias is named in the answer (boundary-aware); a citation is counted when its own domain appears in the answer's sources. A mention is not always a positive endorsement — we say so.
API answers approximate, but do not exactly replicate, the consumer apps (different system prompts, tools, browsing defaults). We treat each edition as a point-in-time measurement and re-run on a cadence.
FAQ
Which incident management platforms do AI engines recommend most?
As of June 23, 2026, PagerDuty leads the Clear Cited AI Visibility Index for Incident management platforms with a 25.5% share-of-model — meaning 25.5% of all product recommendations across 5 AI engines named it. See the full ranked table above.
What is share-of-model?
Share-of-model = the percentage of all product recommendations, across every buyer prompt and engine, that name a given product. It answers: when an AI recommends something in this category, how often is it this product?
How is the AI Visibility Index measured?
Each buyer prompt is run at least 12 times per engine across Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI). We detect which products each answer recommends, compute each product's share-of-model, and report a Wilson 95% confidence interval. The method is reproducible — the same one Clear Cited uses in its paid AI-visibility audits.
Why do different AI engines recommend different products?
AI answer engines draw on different training data, retrieval sources, and ranking — so the 'best tool' a buyer hears depends heavily on which assistant they ask. That is why the Index measures and reports each engine separately.
Is this ranking sponsored or pay-to-play?
No. The AI Visibility Index is free, independent original research. Products are not charged to appear and cannot pay to rank higher. It reflects what AI engines actually say, measured transparently.
Want your product's real share-of-model?
We measure where ChatGPT, Perplexity, Gemini, Claude & Grok send buyers in your category — reproducibly — and map the fastest way in.
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