AIOps vendors pitch autonomous remediation — infrastructure that heals itself without a human in the loop — and for most organisations that's not where the near-term value actually is. What AI genuinely does well in IT operations today is correlation and noise reduction: taking the hundreds of alerts a busy infrastructure estate generates and identifying which handful are actually related to one underlying event, rather than autonomously fixing anything.
That noise reduction alone is worth a lot. An on-call engineer paged forty times during a single incident, once for every affected service, learns to triage by exhaustion rather than by genuine priority. An AIOps layer that groups those forty alerts into 'one incident, likely caused by X' turns a chaotic page storm into something a human can actually act on quickly.
Anomaly detection is the second area where it earns its place — catching a metric drifting outside its normal pattern before it crosses a hard threshold that would trigger a traditional alert. This is genuinely useful for catching slow degradations, a disk filling gradually or a memory leak building over days, that a static threshold alert would miss until the problem is already acute.
Autonomous remediation — the system fixing itself with no human approval — is worth approaching cautiously and only for the narrowest, best-understood scenarios: restarting a known-flaky service, scaling a resource that's predictably under load. Handing broader remediation decisions to an automated system before you trust its judgement on the narrow cases is how an AIOps rollout produces its own incident rather than preventing one.
