// Case Study · Intelligent Incident Response
AI Incident Co-Pilot: Intelligent Incident Response
Manual incident analysis and stakeholder communication were slowing down resolution times. AI Incident Co-Pilot gave operations teams real-time analysis and auto-drafted communications — reducing MTTR by 20%.
// Challenge
The Challenge
During major incidents, two things consistently slowed down resolution: the time it took responders to correlate observability signals from Dynatrace and Splunk into a clear picture of what was actually happening, and the manual effort of drafting and sending stakeholder communications while the incident was still active.
Both tasks competed for the same responder's attention at the worst possible time — in the middle of an active P1. The result was incidents that took longer to resolve not because the technical fix was hard to find, but because the surrounding process of analysis and communication consumed time that should have gone toward resolution.
// Approach
The Approach
AI Incident Co-Pilot was built to sit alongside the existing incident response process, not replace the responders running it. It ingests observability signals from Dynatrace and Splunk, correlates them using AI-driven analysis, and surfaces a prioritized, 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 — based on the same real-time context, so the responder reviews and sends rather than writing from scratch under time pressure. Every AI-generated recommendation and communication draft goes through human review before action is taken, keeping a person in control of the final call throughout.
// Architecture
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.
Dynatrace / Splunk Signals
→
Anomaly Correlation
→
AI Incident Co-Pilot Analysis
→
Resolution Guidance + Draft Comms
→
Human Review & Action
→
Stakeholder Notification
// Outcomes
The Outcomes
Real-Time
Incident Analysis
Incident resolution time was reduced by 20% through AI-enabled triage and escalation workflows integrated with Dynatrace and Splunk — a direct result of responders spending less time on manual correlation and communication drafting.
Over the course of the engagement, SLA adherence improved from 96% to 99.8% through intelligent monitoring and AI-driven workflow automation working in tandem with the Co-Pilot's incident-level support.
// Business Impact
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 the Co-Pilot's recommendations and drafts are always reviewed by a human before action, the tool built trust with operations teams quickly rather than facing the adoption resistance that fully automated 'black box' AI incident tools often encounter.