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BMC AMI Tech Talk: Optimize Mainframe Cost and Capacity with BMC AMI Ops
Mainframe cost pressures continue to rise—but reacting after the bill arrives or adding capacity “just in case” isn’t sustainable. In today’s hybrid environments, teams need earlier visibility into cost drivers, stronger forecasting, and practical ways to optimize—without putting performance or SLAs at risk.
Speakers
Glenn Everitt Principal Product Manager, BMC
Rachel Clevenger Solution Engineer, BMC Software
Jeremy Hamilton Big Iron Guy, BMC
event summary
Mainframe optimization shifts from cost cutting to data-driven capacity intelligence
As enterprise workloads grow more volatile, organizations are rethinking mainframe strategies around optimization—not reduction. Industry experts explore how data-driven planning enables smarter cost-performance balance in modern IT environments.
core insights
From static planning to continuous optimization
Rising workload complexity and evolving pricing models are forcing organizations to rethink how they manage mainframe cost and capacity.
1
Optimization replaces migration as strategic priority
Organizations are no longer focused on moving off the mainframe but optimizing within it. This shift reflects the realization that staying on-platform is often more cost-effective, requiring smarter resource utilization instead of system replacement.
2
Volatile workloads redefine capacity planning complexity
Workloads are increasingly unpredictable, making static capacity plans ineffective. Balancing cost, performance, and demand now requires continuous monitoring and adaptive decision-making rather than fixed forecasting models.
3
Skill gaps drive need for guided intelligence
A shrinking pool of experienced capacity planners is creating operational risk. Organizations must adopt guided workflows and tools that embed institutional knowledge to enable faster analysis and better decision-making across teams.
WHAT THIS MEANS
TURNING INSIGHT INTO OPERATIONAL ADVANTAGE
Modern IT teams must shift from reactive cost control to proactive optimization, where decisions are guided by data, simulations, and real-time visibility into workload behavior.
Key takeaways:
- Adopt continuous planning models to align capacity with dynamic workload demand
- Leverage historical data insights to inform infrastructure and investment decisions
- Simulate “what-if” scenarios before executing workload or capacity changes
- Identify and optimize high-cost workloads to improve overall system efficiency
- Embed expert workflows to scale knowledge across teams and reduce dependency on specialists
“If you want to make good decisions, you got to have good data.”
Jeremy Hamilton, BMC
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