What problems does AIOps actually solve?
Volume first, then correlation. Containerized, multi-cloud estates emit data volumes that impede a team’s ability to identify and resolve incidents (TechTarget’s definition, read 26 October 2026), and static dashboards and rule thresholds do not keep up. An AIOps layer runs anomaly detection over that stream, deduplicates and groups related alerts into incidents, and ranks probable root causes from dependency data. The deliverable is a shorter queue with causes attached, not a smarter dashboard; teams measure it in alert reduction and time to resolution.
Where does automation fit in AIOps?
Behind an approval boundary. Mature platforms act on what they detect, restarting a service, scaling a pool, rolling back a deploy, which is intelligent automation applied to operations, and increasingly the same pattern agents follow in agent incident response. The sane rollout is graduated: detection only, then proposed remediations a person clicks, then auto-remediation for the actions with proven safety and an audit trail. Let the platform earn write access the way a new hire would, runbook by runbook, and keep the rollback path manual until the automation has history.
How should a buyer evaluate an AIOps platform?
On your telemetry, against your incidents. The useful trial replays a quarter of your own history and asks: how many of our real incidents would it have surfaced earlier, how much of our alert noise does it collapse, and does it integrate with the AI observability and ticketing you already run rather than demanding replacement. Watch for the classic traps: models that need months of your data before they help, “root cause” that is correlation with confidence, and per-node pricing that explodes with the infrastructure it is meant to tame. The adjacent pattern for business processes rather than infrastructure is workflow automation; the evaluation discipline is the same.