COMMON POWERUp 2026

Traditional IBM i monitoring often stops at surface-level events and alerts, such as CPU spikes or disk space warnings, leaving IT teams to diagnose the root cause. But what if you could predict issues well before they even show symptoms? By learning how your system behaves when healthy, AI-powered algorithms can Observe signals, detect subtle “heartbeat” patterns, and correlations between workload and processor-level time signatures. This enables IT teams to truly Understand the system behavior and Act proactively, addressing risks before they escalate into outages. In this session, we’ll go beyond theory and share a real-world example of how an IBM i environment leveraged AI/ML-driven observability to move from reactive alerts to predictive insights. You’ll learn practical steps to apply these techniques in your own organization, reducing business risk and keeping your IBM i running smoothly, well ahead of trouble.

Learning Objectives:

Understand the limitations of traditional IBM i monitoring and why component-level alerts are not enough. Learn how AI-powered observability applies the Observe, Understand and Act framework to predict and prevent issues. Explore a real-world case study demonstrating the transition from reactive monitoring to predictive insights. Identify practical steps to implement AI-driven observability in your IBM i environment to reduce operational risk.