AI-Driven Incident Detection & Response
Deploy AIOps to detect incidents before users notice, automatically diagnose root causes, and reduce mean time to resolution by 65%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Operations teams rely on static threshold alerts that generate thousands of notifications daily — 90%+ are noise. Incidents are detected by users reporting issues, not by monitoring. Root cause analysis is manual, requiring engineers to correlate logs across dozens of services. MTTR (Mean Time to Resolution) averages 4-6 hours for P1 incidents.
After
AI monitors all telemetry (metrics, logs, traces) and detects anomalies before they become user-impacting incidents. Automated root cause analysis correlates signals across services in seconds. Runbooks are triggered automatically for known issues. MTTR drops to 30-60 minutes.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Consolidate Observability Data
3 weeksEnsure all services emit metrics, logs, and traces to a unified observability platform. Standardise logging formats and metric naming. Establish baseline performance profiles for all critical services. Map service dependencies.
Deploy Anomaly Detection
4 weeksImplement ML-based anomaly detection on key metrics (latency, error rates, throughput, resource utilisation). Use unsupervised learning to establish normal behaviour patterns. Configure seasonal models to handle daily/weekly traffic patterns. Set up intelligent alerting that groups related anomalies.
Build Automated Root Cause Analysis
4 weeksTrain AI to correlate anomalies across services and identify the originating cause. Use topology-aware analysis that understands service dependencies. Build pattern matching against historical incidents. Generate human-readable explanations of probable root causes.
Implement Auto-Remediation
3 weeksFor known incident patterns, build automated runbooks: scale up resources, restart services, roll back deployments, reroute traffic. Start with low-risk remediations and expand as confidence grows. Maintain human approval for high-risk actions.
Continuously Improve
OngoingFeed incident post-mortems back into AI models. Build a library of incident patterns and their resolutions. Track MTTR, MTTD (Mean Time to Detect), and false positive rates. Expand auto-remediation coverage as reliability improves.
Tools Required
Expected Outcomes
Reduce MTTR from 4-6 hours to 30-60 minutes
Detect 80% of incidents before users are impacted
Reduce alert noise by 90% through intelligent grouping
Auto-remediate 30-40% of known incident types
Improve service availability from 99.9% to 99.95%+
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Most AIOps platforms need 2-4 weeks of baseline data to establish reliable anomaly detection. Seasonal patterns (weekly/monthly cycles) may take longer. Start with known-good periods and gradually expand the training window. You can accelerate learning by providing labelled historical incidents.
Yes. Modern AIOps platforms are designed for multi-cloud and hybrid environments. The key requirement is unified telemetry collection — all services, regardless of where they run, need to emit metrics, logs, and traces to a central platform. Most major observability tools support multi-cloud ingestion.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.