Data as of Aug 25, 2026 · Based on 1,625 AI responses · See how Parse measures this
Incident Response Automation Platforms
Parse
https://parse.gl
The incident response automation leaderboard is highly contested between Rootly and , with both platforms trading the top spot in AI recommendations. AI models frequently position for Slack-native workflow automation while relying on for enterprise-grade alert routing and escalation.
The market map
Recommended by need
| # | Brand | What AI says | Mention rate |
|---|---|---|---|
| 1 | Remains the canonical choice for enterprise escalation policies and complex alert routing. | 64% | |
| 2 | Leads AI recommendations for Slack-native incident orchestration and automated war rooms. | 59% | |
| 3 | Frequently recommended for chat-first incident coordination and rapid MTTR reduction. | 45% | |
| 4 | 41% | ||
| 5 | 28% | ||
| 6 | 28% | ||
| 7 | 23% | ||
| 8 | Recommended for observability-native AI triage and end-to-end incident copilots. | 23% | |
| 9 | 13% | ||
| 10 | 13% | ||
| 11 | 13% | ||
| 12 | 12% | ||
| 13 | 11% | ||
| 14 | 10% | ||
| 15 | 10% | ||
| 16 | 9% | ||
| 17 | 9% | ||
| 18 | 8% | ||
| 19 | 8% | ||
| 20 | 8% | ||
| 21 | 5% | ||
| 22 | 5% | ||
| 23 | 5% | ||
| 24 | 4% | ||
| 25 | 4% |
Who wins on each AI
The same market, seen by two models.
Sources AI cited
rootly.com is the page AI reaches for most here, cited in 64% of analyzed answers.
Shifted in April 2026 from being the sole solution to being paired with Slack-native tools.
Frequently cited in April 2026 for agentic alert grouping and Slack-native automation.
“Slack integration tool” → “AI-native incident command center”
Emerging in April 2026 recommendations for pre-investigation and automated troubleshooting.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| 73% | 74% | ||
| 64% | 65% | ||
| 54% | 50% | ||
| 44% | 19% | ||
| 23% | 27% |
The two models disagree most about xMatters (ChatGPT #8, Google #20) and Prometheus (ChatGPT #19, Google #10).
The incident response automation leaderboard is highly contested between Rootly and PagerDuty, with both platforms trading the top spot in AI recommendations. AI models frequently position Rootly for Slack-native workflow automation while relying on PagerDuty for enterprise-grade alert routing and escalation.
Across 1,625 AI responses, PagerDuty is mentioned most, named in 64% of them, followed by Rootly (59%) and Incident.io (45%).
Parse measures each brand's mention rate — the share of answers naming it — across 1,625 AI responses to this market's buyer questions. Answers are collected daily and the ranking is published weekly.
Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.
AI models emphasize codifying repeatable patterns into executable workflows rather than relying on static documentation. Recommendations heavily feature event-driven automation and conditional logic, with platforms like Rootly and
Red Hat Ansible Automation Platform cited for orchestrating multi-step remediations.
Brands mentioned
How can an SRE automate incident response playbooks for common outages?
AI models emphasize codifying repeatable patterns into executable workflows rather than relying on static documentation. Recommendations heavily feature event-driven automation and conditional logic, with platforms like Rootly and
Red Hat Ansible Automation Platform cited for orchestrating multi-step remediations.
Responses consistently highlight PagerDuty and
as the most mature options for granular, multi-level escalation chains. AI models also surface newer alternatives like and for teams seeking flexible, time-based routing without enterprise complexity.
Which incident response platforms allow for defining custom escalation policies?
Responses consistently highlight PagerDuty and
Atlassian
Opsgenie as the most mature options for granular, multi-level escalation chains. AI models also surface newer alternatives like
ilert and
Squadcast for teams seeking flexible, time-based routing without enterprise complexity.