Typical uses include event grouping, anomaly detection, dependency inference, capacity forecasting, probable-cause suggestions, ticket enrichment, and recommended or automated remediation. The label does not define a standard architecture, model quality, autonomy level, or security capability.
An AIOps workflow may combine metrics, logs, traces, topology, changes, incidents, service records, and user experience data. Its output should enter established service-management and engineering processes with named owners, verification, access controls, change governance, and feedback from outcomes rather than bypassing them.
Key points
Define the operational goalTie each use case to a measurable service outcome, known data sources, acceptable error cost, response authority, and comparison baseline.
Engineer trustworthy inputsMaintain telemetry coverage, timestamps, service ownership, dependency maps, change context, retention, privacy controls, and protection against missing or manipulated data.
Control actionSeparate observation, recommendation, approval, and execution; constrain automation by service criticality; preserve logs; and test rollback, degraded operation, and escalation.
Important limitationCorrelation or generated explanation does not establish root cause. Drift, topology gaps, shared failures, biased incident history, adversarial inputs, or feedback loops can produce confident but harmful recommendations and automation.