In this video I explained AI in IT Operations (AIOps) and how it actually works in real IT environments. AIOps means using AI, machine learning (ML), and big data to enhance and automate IT operations, because traditional monitoring tools can’t keep up with today’s complex infrastructure—servers, applications, networks, and cloud services running together. The main idea is simple: AIOps collects huge amounts of operational data in real time (logs, metrics, events, traces, alerts) and then analyzes it to detect issues, predict problems, and even take action automatically. I broke the workflow into three key parts. First is data ingestion and correlation—AIOps pulls data from multiple sources and connects “unrelated” events to build one unified picture (like CPU spike + failed login + slow database response pointing to a bigger issue). Second is anomaly detection and root cause analysis—AIOps learns normal behavior over time and flags unusual patterns faster than static thresholds, then pinpoints the component causing the problem. Third is automation and remediation—once an incident is identified, it can trigger workflows like restarting services, scaling cloud resources, notifying teams, or creating tickets automatically. The big takeaways: faster incident response, less alert fatigue, better uptime, smarter planning, and cost savings through automation.