AIOps is a reasoning accelerator, not an auto-remediation system

The pattern

Where AI adds real value in incidents:

Alert storm (200 events)
       │
       ▼
  [AI correlation]  ──▶  3 likely root causes (ranked)
       │
       ▼
  [AI runbook retrieval]  ──▶  Relevant steps surfaced
       │
       ▼
  Human validates hypothesis  ──▶  Takes action
       │
  Auto-remediation only here ──▶  After human confirms

The insight

The orgs getting real value from AIOps aren't the ones automating remediation — they're the ones using AI to compress the signal-to-hypothesis gap. The hard part of incidents isn't fixing things. It's knowing what to fix, in what order, with what confidence.

The non-obvious part

AI that remediates without evidence is hallucination-as-a-service. The models that earn trust are the ones that show their work: here's the metric spike, here's the correlated trace, here's the similar past incident. Evidence first, action second.

My rule

→ Use AI for hypothesis ranking and runbook retrieval. Keep remediation behind explicit human approval. Trust is earned incrementally — don't give it away in the initial design.

Worth reading

• ▸ OpenTelemetry — consistent signal foundation for AI correlation (opentelemetry.io)
• ▸ Blameless RCA templates — 'did AI help or mislead?' as a standard post-incident question