LinkedIn Draft — Insight (2026-05-15)
Something I wish someone had told me five years earlier:
AIOps is a reasoning accelerator, not an auto-remediation system
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.
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 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
If you disagree, I want to hear it. The best version of this thinking comes from pushback.
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