The DevOps and IT Operations landscape is evolving rapidly, with increasing pressure to ship software faster, more frequently, and with greater reliability. However, this shift brings new challenges, including a wider surface area to monitor and react to, increased data volume, and response fatigue. To address these issues, AIOps (Artificial Intelligence for IT Operations) has emerged as a technology category that puts AI and machine learning in the hands of on-call teams to prevent more incidents and respond to them faster. AIOps platforms analyze data generated by software systems to predict possible problems, determine root causes, and drive automation to fix them, enhancing monitoring, service management, and automation tasks. By providing an intelligent feed of incident information alongside telemetry data, AIOps augments the value of monitoring, enabling teams to pinpoint issues faster, understand why they occurred, and take proactive action. The use cases for AIOps include proactive anomaly detection, event correlation and noise reduction, intelligent alerting and escalation, and automated incident remediation, which can help DevOps, SRE, and on-call teams detect problems before they cause outages or performance issues, diagnose incidents more efficiently, and resolve them faster.