Let us know how we can help
Sales & Pricing
Speak to a rep about your business needs
Help & Support
See our product support options
General inquiries and locations
Contact us
BMC AMI Tech Talk: Seeing the Signals Sooner: How AI-Driven Insight Is Changing IMS Operations
For decades, IMS operations have relied on experienced specialists to make sense of signals spread across queues, logs, regions, and online and batch workloads. As environments grow more complex and expertise becomes harder to sustain, relying solely on manual interpretation is increasingly difficult.
Speakers
Gary Turner Solution Engineer, BMC
Gilles Robert Sr Principal Solution Engineer, BMC
Steven Dzikowski VSE, BMC
event summary
Why AI-driven operational intelligence is redefining resilience in complex IMS environments
As operational complexity accelerates and expertise gaps widen, enterprises are shifting from reactive monitoring to AI-driven insight. Industry practitioners explore how predictive intelligence is transforming how IMS operations detect, diagnose, and resolve issues faster.
core insights
From reactive monitoring to predictive operations
Organizations are rethinking operations as AI enables earlier detection, deeper context, and faster resolution in increasingly complex environments.
1
Monitoring alone no longer explains system behavior
Traditional monitoring surfaces symptoms but lacks root cause clarity. As system complexity grows, teams can no longer rely on threshold-based alerts alone. AI-driven insight introduces contextual correlation across workloads, enabling faster identification of underlying issues.
2
Proactive detection replaces reactive troubleshooting
Static thresholds only trigger alerts after problems manifest. AI models instead learn “normal” behavior and detect subtle deviations earlier. This shift enables teams to identify emerging risks before outages occur, significantly improving operational resilience.
3
Skills gaps are driving automation-led intelligence
Manual troubleshooting depends on deep institutional knowledge that is increasingly scarce. AI reduces dependency on long-tenured expertise by guiding teams toward probable causes. This democratizes operational decision-making, allowing less experienced teams to act with greater confidence.
WHAT THIS MEANS
Turning insight into operational advantage
AI-driven insight reshapes how teams prioritize, diagnose, and act—shifting focus from alert management to outcome-driven operations and faster decision execution.
Key takeaways:
- Adopt predictive analytics to identify anomalies before they impact performance or availability
- Correlate multiple signals to reduce noise and focus only on actionable events
- Prioritize issues by impact to align responses with business-critical workloads
- Leverage AI-assisted root cause analysis to accelerate mean time to resolution
- Enable continuous learning models to adapt to evolving system behaviors and workloads
“While monitoring shows symptoms, insight is going to pinpoint the actual causes."
Bring order to complex workflows
Control-M orchestrates workflows across applications, data platforms, and business systems; not just individual processes.