Discover how IBM Z OMEGAMON AI Insights uses Predictive AI and machine learning models to detect anomalies and improve system performance, capacity and cost controls—without needing ML expertise.

(5min read)

TL;DR

Ever spent days chasing a performance issue that turned out to be a silent CPU spike? A situation where static thresholds creates too many false positive or maintenance? IBM Z OMEGAMON AI Insights 2.1.0 brings Predictive AI to your mainframe monitoring team. As a copilot, it helps you detect performance issues early—like CPU spikes or slow response times—without needing to be a data scientist.

This blog breaks down how it works and what it means for your operations.

🔍 Explore Real Use Cases - See how others are using OMEGAMON AI to solve real problems

IBM Z OMEGAMON AI Insights 2.1.0 Use Cases Summary 

What Problems Does It Solve?

• Performance issues that go unnoticed for days or weeks
• Manual threshold tuning that doesn’t adapt to changing workloads
• Alert fatigue from static monitoring rules and simple thresholds breach
• Hidden anomalies in CPU Time, Network traffic, CICS activity, and more...

How Does It Help?

• Dynamic baselines: Learns normal behavior from historical data
• Real-time alerts: Flags anomalies as soon as the divergence is significant
• Visual clues: Clearly highlight spikes, drifts, and broken patterns
• Explainability: Alerts and visuals you can trust, with built-in logic that explains why they matter
• Cost effective: Predictive AI and curated use cases and metrics, tailored to maximize your ROI
• Seamless integration: AI infused in your analytical stack integrated in your existing environment

Real-World Example

We took few weeks to detect CPU overconsumption manually. With OMEGAMON AI Insights, the issue was flagged on day one, saving up to €60,000 per incident.  — Mainframe Domain Lead, Large European Bank 

FAQs or Common Pitfalls

Do I need to know machine learning?

No. The AI models are built-in and work out of the box.

What’s the difference between AI Insights and traditional monitoring?

AI Insights use dynamic thresholds based on historical data. Traditional monitoring uses static rules that don’t adapt.

Can I customize what’s considered an anomaly?

Yes. You can tune the alerting layer to reduce noise and focus on what matters and control the models' predictions through a curated set of comprehensive parameters.

Final Thoughts

IBM Z OMEGAMON AI Insights helps you move from reactive to proactive monitoring. It’s designed for engineers—not data scientists—integrates seamlessly into your existing tools like Grafana, automation workflows and complementary with OMEGAMON Situations.

We want to hear from you!
Have you faced hidden performance issues? Curious how AI could help?
👉 Share your story or request a demo today.

What next?

💬 Join the discussion on LinkedIn or IBM TechXchange AIOps Group, follow this article for updates and comment bellow

📖 Continue reading: Don't miss the complete set of Use Cases of IBM Z OMEGAMON AI Insights 2.1.0

👉 Share your own story: Customer Use Cases in IBM Idea Portal or related readings on OMEGAMON

🛠️ Explore the product: IBM Z OMEGAMON AI Insights official documentation

#monitoring, #ArtificialIntelligence(AI), #IBMZ, #OMEGAMON, #AnomalyDetection

@Ash Mahay, @Jim Porell, @Anna Murray, @Fabien Gautreault