Mainframe modernization explained: COBOL and AI

2026-02-27 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cybersecurity & Data Privacy · Depth: Advanced, extended

Summary

This episode of "Mixture of Experts" explores critical topics in AI adoption and security, featuring insights from IBM product and research leaders. The discussion begins with mainframe modernization, highlighting that it often involves optimizing existing COBOL systems rather than migration, with AI assisting in understanding undocumented legacy code. The conversation then shifts to global AI adoption, revealing that a vast majority of the world's population has not yet used AI, underscoring a massive market opportunity but also immense compute and infrastructure challenges. Experts suggest a future with smaller, more targeted AI models and hybrid approaches to overcome these barriers. Finally, the episode addresses security by design for AI agents, emphasizing the need for accountability, transparency, robust evaluation, and policy enforcement to manage the risks associated with autonomous agents, particularly in enterprise environments like mainframes.

Key takeaway

For CTOs and VPs of Engineering navigating AI strategy, recognize that widespread AI adoption necessitates a dual focus: optimizing existing infrastructure like mainframes with AI-driven modernization and preparing for a future dominated by smaller, specialized AI models. Prioritize "security by design" for AI agents, establishing clear accountability, robust evaluation, and policy enforcement to mitigate risks as these powerful tools become more autonomous and integrated into core business processes.

Key insights

AI's future hinges on mainframe modernization, scalable compute, and secure, accountable agent deployment for broad adoption.

Principles

• Mainframe modernization prioritizes in-place optimization over migration.
• AI adoption requires a hybrid approach of small and large models.
• Security for AI agents demands multi-layered, embedded controls.

Method

Mainframe modernization leverages AI for static analysis and prompt engineering to understand complex, undocumented COBOL applications, enabling incremental updates and integration with modern DevOps toolchains.

In practice

• Use AI to analyze and document legacy COBOL applications.
• Implement hybrid AI models for cost-effective, scalable solutions.
• Tie AI agent actions to user credentials for security and accountability.

Topics

• Mainframe Modernization
• COBOL Programming
• AI Agent Security
• Global AI Adoption
• AI Compute Requirements

Best for: CTO, VP of Engineering/Data, Executive, AI Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.