Useful work AIOps can take on
Incident queues fill with duplicate pages, weak correlations, and alerts that point at symptoms instead of the failing dependency. AIOps tools can group related events, surface likely related changes, and highlight anomalies across metrics that humans would take longer to compare by hand.
That support matters when on-call load is high and telemetry is spread across many systems. Faster triage leaves more time for the recovery decisions that still need a person.
Where the autonomous story breaks
I do not present AIOps as autonomous incident resolution. Production recovery often needs context the model does not have: customer commitments, partial degradations that look healthy in aggregate, risky remediations, and political ownership boundaries.
Auto-remediation without clear bounds can turn a contained issue into a wider outage. Restarting the wrong service, scaling the wrong tier, or rolling back the wrong release is still an incident action, even when software triggers it.
How I evaluate AIOps work
I ask whether the tool reduces time to a trusted diagnosis, whether responders can inspect why it grouped events, and whether any automated action has a reverse path. If the product only adds another dashboard and another alert stream, it has not earned a place in the incident path.
DevOps, DevSecOps, and AIOps sit on the same journey: shorter feedback loops, safer change, and clearer operations. Treat each as a working discipline with limits, not as a label that replaces fundamentals.