In real environments, we use AIOps to sit on top of our monitoring stack and continuously analyze logs, metrics, and events from applications, infrastructure, and networks. The platform learns normal behavior for services and then flags anomalies, correlates related alerts, and groups them into single, meaningful incidents for the on-call team. This has reduced alert noise, shortened MTTR, and helped us spot patterns—like gradual performance degradation or recurring configuration issues—that were hard to see manually. The main challenges have been data quality, initial tuning of thresholds and models, and building trust so engineers rely on AIOps insights instead of treating them as “black box” outputs. Regular feedback loops, clear incident postmortems, and phased automation (recommendations first, auto-remediation later) have helped us get real value from AIOps.