11971: Modernizing Mainframe Applications: Unlocking Value With AI and Machine Learning
Project and Program:
New and Innovative Technologies,
Machine Learning/AI,
Enterprise Architecture Databases DevOps Hybrid Cloud Performance & Capacity Management Operations Management Systems Management & Automation Security & Compliance
Tags:
Proceedings,
SHARE DC 2025,
2025
As businesses continue to modernize their legacy mainframe systems, AI and
Machine Learning (ML) are transforming how organizations unlock value from their
core operations. By incorporating AI and ML, businesses can enhance operational
efficiency, reduce costs, and improve decision-making through automation,
predictive analytics, and real-time insights. Modernizing mainframe applications
with AI and ML provides an opportunity to future-proof critical systems and
deliver greater value to the business. AI and Machine Learning Capabilities in
Mainframe Modernization • AI-Driven Automation: Streamline repetitive tasks
within mainframe environments, reducing manual interventions, and improving
speed and accuracy. Automate routine processes such as data extraction,
validation, and application monitoring. • Machine Learning: Derive insights from
historical mainframe data, applying predictive analytics to enhance performance,
detect anomalies, and make informed business decisions. Forecast system
behavior, identify potential failures, and recommend optimizations to improve
both application performance and resource utilization. Key Use Cases of AI and
ML in Mainframe Modernization 1. Predictive Maintenance and System Health
Monitoring: o Monitor mainframe systems in real-time to predict hardware or
software issues before they occur, reducing downtime and improving system
reliability. 2. Workload Optimization: o Analyze patterns in workload and
resource usage to optimize application performance and resource allocation on
mainframes. 3. Automating Code Refactoring: o Assist in analyzing legacy code,
automating code refactoring processes, and suggesting optimizations to simplify
and speed up modernization efforts. 4. Real-Time Fraud Detection: o Detect
anomalies in transactional data on mainframe systems to identify potentially
fraudulent activities, enhancing security and compliance. 5. Intelligent Data
Insights: o Analyze large datasets stored on mainframes to provide actionable
insights that drive business decisions, improve customer experiences, and
enhance operational efficiency. Real-World Examples Explore case studies
demonstrating successful AI and ML implementations in mainframe modernization.
Learn how organizations reduced operational costs, improved system performance,
and created new revenue opportunities through data-driven insights. The session
will also cover integrating AI tools into existing mainframe architectures and
best practices for deploying ML models that leverage historical data to
future-proof core operations.
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