AI Incident Co-Pilot: Intelligent Incident Response

The Challenge

Context: the Co-Pilot is an accelerator (live prototype). This page covers the problem it addresses, how it is designed to work and what the prototype measured.

During major incidents, two things consistently slow resolution: the time it takes responders to correlate observability signals from tools such as Dynatrace and Splunk into a clear picture of what is actually happening, and the manual effort of drafting and sending stakeholder communications while the incident is still active.

Both tasks compete for the same responder's attention at the worst possible time — in the middle of an active P1. The result is incidents that take longer to resolve not because the technical fix is hard to find, but because the surrounding process of analysis and communication consumes time that should go toward resolution.

The Approach

AI Incident Co-Pilot sits alongside the existing incident response process rather than replacing the responders running it. Given observability signals (the design targets Dynatrace and Splunk), it correlates them using AI-driven analysis and surfaces a prioritised, context-rich summary of likely causes and recommended next steps in real time as an incident unfolds.

In parallel, it auto-drafts stakeholder communications — incident summaries, status updates and resolution notes — from the same real-time context, so the responder reviews and sends rather than writing from scratch under time pressure. Every AI-generated recommendation and draft goes through human review before action is taken, keeping a person in control of the final call.

The Architecture

AI Incident Co-Pilot layers AI-driven correlation and drafting on top of existing observability tooling, feeding directly into the human-led incident response process rather than operating independently of it.

The Outcomes

In the prototype, incident resolution time was reduced by 20% through AI-enabled triage and escalation workflows designed around Dynatrace and Splunk signals — a result of responders spending less time on manual correlation and communication drafting. This is a prototype benchmark, not a named-client result.

Business Impact

Faster, more consistent incident resolution directly reduces the customer-facing impact of outages and degradations — which matters disproportionately for SLA-bound managed services accounts where missed targets carry contractual and reputational cost.

Because every recommendation and draft is reviewed by a human before action, the design avoids the adoption resistance that fully automated 'black box' AI incident tools often encounter.

What it is built with

The headline is the workflow, not the model: observability in, human-reviewed guidance and communication out.

What other enterprises can learn

• The saving comes from removing correlation and drafting overhead during a P1, not from a faster technical fix.
• Put the co-pilot beside the incident process, with a human reviewing every recommendation and draft before action.
• Human review builds operator trust faster than fully autonomous incident tooling.
• Design the incident process so it still works when the AI is off — see AI Resilience by Design.