Mainframe systems running COBOL have long been the backbone of global finance and insurance, yet their massive complexity makes modernization a notoriously risky endeavor. AWS is now tackling this challenge with AWS Transform, a platform that uses generative AI to bridge the gap between ancient codebases and modern cloud architectures.

The core insight from AWS’s work with over 400 enterprises is that AI cannot simply "read" source code to understand a system. Because legacy programs are huge and rely on specific compiler behaviors not visible in the text, AWS first uses deterministic models to map out dependencies. By creating a "platform-aware" context, the AI can generate precise technical specifications that reflect how the code actually behaves in production, rather than just how it looks on the screen.

This methodology ensures "traceability," a critical requirement for highly regulated industries like banking and government. Every piece of logic the AI extracts is mapped back to the original system, providing an auditable trail for regulators to prove nothing was missed. The results are significant: automotive giant BMW Group reported a 75% reduction in testing time, while financial services leader Fiserv shaved over a year off their modernization timeline.

Ultimately, the strategy moves AI from a simple coding assistant to a comprehensive transformation agent. By automating analysis, test planning, and refactoring, AWS aims to turn once-stalled legacy migrations into scalable, predictable engineering projects that finally move mainframes into the modern era.

Mainframe systems running COBOL have long been the backbone of global finance and insurance, yet their massive complexity makes modernization a notoriously risky endeavor. AWS is now tackling this challenge with AWS Transform, a platform that uses generative AI to bridge the gap between ancient codebases and modern cloud architectures.

The core insight from AWS’s work with over 400 enterprises is that AI cannot simply "read" source code to understand a system. Because legacy programs are huge and rely on specific compiler behaviors not visible in the text, AWS first uses deterministic models to map out dependencies. By creating a "platform-aware" context, the AI can generate precise technical specifications that reflect how the code actually behaves in production, rather than just how it looks on the screen.

This methodology ensures "traceability," a critical requirement for highly regulated industries like banking and government. Every piece of logic the AI extracts is mapped back to the original system, providing an auditable trail for regulators to prove nothing was missed. The results are significant: automotive giant BMW Group reported a 75% reduction in testing time, while financial services leader Fiserv shaved over a year off their modernization timeline.

Ultimately, the strategy moves AI from a simple coding assistant to a comprehensive transformation agent. By automating analysis, test planning, and refactoring, AWS aims to turn once-stalled legacy migrations into scalable, predictable engineering projects that finally move mainframes into the modern era.