In today’s hybrid and distributed IT ecosystems, enterprises generate massive amounts of operational data. But when it comes to mission-critical workloads, the most valuable data still lives on the mainframe—IMS, Db2, VSAM, logs, SMF, performance traces, and more.

As AI adoption accelerates, one principle is becoming increasingly clear:

AI delivers the most accurate insights when it runs closer to the data.

Here’s why this matters, especially for the IBM Z platform:

1. Zero Data Movement = Faster, Safer Insights

Extracting mainframe data into external systems introduces:

• Latency

• Copy management overhead

• Security and compliance risks

• High infrastructure cost

By placing AI models near the source—on-platform or on secure adjacent systems—organizations get instant, real-time intelligence without the risk of data leaving the mainframe.

2. AI Understands Context Better When It Sees the True Picture

Mainframe datasets carry decades of historical patterns:

• Batch cycles

• Transaction peaks

• Storage growth

• Database access behaviors

• Resource utilization signatures

Running AI close to this data allows it to learn from rich, contextual signals that external systems often miss or flatten.

3. Intelligent Automation Becomes Practical

With insights generated locally, automation can be:

• Faster — immediate detection and action

• Safer — knowing the exact state of the system

• Accurate — fewer false alarms, more predictive guidance

Examples include:

• Predicting database space shortages

• Dynamic thresholding for performance metrics

• Identifying anomalies in IMS or Db2 logs

• Automated tuning recommendations

• Intelligent workload routing with zIIP or WLM insights

4. AI + SMF + System Logs = A Goldmine of Operational Intelligence

The mainframe already produces the best operational telemetry in the industry.
AI can turn this telemetry into:

• Forecasts

• Root-cause analysis

• Trend visualizations

• Optimization recommendations

Without exporting gigabytes of SMF data to other platforms.

5. Agentic AI Makes the Mainframe More Accessible

With on-platform AI interfaces—like ChatOps, GenAI copilots, and agent-based automation—users can ask:

“Why did CPU spike at 9 PM yesterday?”
“When will this IMS database run out of free space?”
“Which jobs delayed the nightly SLA?”

AI becomes the bridge between complex mainframe internals and everyday users.

The Future: Intelligent Mainframe Operations

When AI runs closer to data, enterprises gain:

• Real-time insights

• Lower operational overhead

• Predictive intelligence instead of reactive firefighting

• Safer, more compliant data usage

• A modern mainframe experience powered by natural language

The mainframe already runs the world’s most critical systems.
Now, with AI integrated directly where the data lives, it becomes smarter, faster, and more autonomous.