Real-world mainframe modernization with AI: A safe, scalable path from mainframe to cloud

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For too long, enterprises with legacy mainframe estates have been faced with a high-stakes dilemma: continue maintaining their mainframes, essentially kicking the modernization can down the road (they know they will need to deal with it eventually), or perform a dangerous "big bang" migration with many unknowns and risks.  At Google Cloud, we propose an alternative: a modernization strategy that leverages the power of AI, agility of the cloud and allows for iterative and continuous modernization. This approach recognizes a fundamental truth: mainframe modernization isn’t a pure code-to-code conversion problem. Sure, modernizing a single, isolated and small application is relatively easy, especially with recent advancements with AI and large language models. The real challenge lies in modernizing at real-world scale without breaking the intricate web of dependencies and legacy data formats you find in a large global enterprise, all while ensuring functional equivalence. For example, some of these “real world” challenges include:

Application logic is tightly fused directly to legacy and proprietary databases and record schemas.

Non-relational formats that are inaccessible by AI Agents, such as VSAM, flat files, IMS hierarchical structures.

Transaction monitors like CICS and IMS TM deliver highly integrated transaction management. A single transaction scenario can consist of millions of lines of code. 

Intricate sequential workflows with complex conditional step logic and dependencies.

Internal/external boundaries utilizing proprietary protocols like CTG, IMS Connect, …

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