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BMC AMI Tech Talk: From AI Awareness to AI Advantage on the Mainframe
A practical roadmap for progressing through the four stages of AI readiness
Speakers
Liat Sokolov Sr Manager, Product Management, BMC Software
Tim Ceradsky Director, Software Consulting, BMC Software
Nick Guillemette Solution Engineer, BMC Software
event summary
From AI experimentation to enterprise-scale impact: redefining mainframe innovation and workforce capability
As AI shifts from experimentation to enterprise deployment, organizations must rethink how they operationalize knowledge, manage complexity, and drive productivity. Industry leaders outline a pragmatic path to scalable AI adoption.
core insights
From experimentation to enterprise AI value
Mainframe organizations are moving beyond curiosity-driven AI use toward structured, scalable adoption that delivers measurable business outcomes.
1
Experimentation alone no longer delivers competitive advantage
Early AI adoption often begins with isolated experimentation, but real value emerges only when initiatives scale across the enterprise. Organizations must transition from individual use to coordinated deployment and measurable outcomes.
2
Institutional knowledge is becoming a strategic asset
Workforce changes are accelerating knowledge loss, especially in complex legacy environments. Capturing and embedding institutional knowledge into workflows is critical to maintaining continuity and enabling new talent to contribute quickly.
3
Governance and readiness define AI success
AI adoption is constrained not by technology, but by readiness—data, governance, and operational alignment. Establishing guardrails, policies, and structured workflows is essential to scaling AI safely and effectively.
what this means
Turning readiness into enterprise impact
AI adoption is reshaping how organizations prioritize investments, manage risk, and drive efficiency. Success depends on aligning technology with governance, workflows, and measurable value creation.
Key takeaways:
- Assess current AI maturity, including data, tools, and governance readiness
- Start with high-value, low-risk use cases such as code explanation or root cause analysis
- Establish clear governance models to protect data and ensure controlled adoption
- Embed AI directly into workflows to enhance productivity rather than disrupt processes
- Measure outcomes continuously to validate ROI and secure further investment
“Humans and agents will work together as a coordinated system… agents handle lower-risk workflows while humans remain accountable for higher-risk decisions”
Mainframe code explained. Finally.
See how BMC AMI DevX Code Insights uses AI to explain complex code, map runtime behavior, break down monoliths, and generate EARS-format specifications so your team can modernize with confidence.