Generative AI is moving from coding assistants to enterprise transformation. It enables organizations to analyze and modernize complex legacy systems. Traditionally, legacy modernization faced challenges like skills gaps and outdated tech stacks. But with AI in the loop, these need to be rethought. The reliance on COBOL for legacy systems is a major issue. Estimates suggest billions of lines of COBOL code exist. Many organizations are turning to GenAI for mainframe modernization.

GenAI is integrated in different stages of the software development lifecycle, enhancing efficiency and quality. It shines in building new features, generating test cases, and automating deployments. However, its usage in dealing with complex, outdated systems is still a challenge.

There are two main reasons for the struggle in automating legacy IT modernization despite AI tools. The backlog is architectural, operational, and data-centric. And there is a heavy reliance on COBOL.

GenAI can fill this gap by extracting knowledge, mapping inventory and dependencies, generating tests and harnesses, translating and refactoring at scale, and supporting cutover validation.

The step-guide for utilizing GenAI for modernization includes value mapping, setting up controls and guardrails, running a discovery sprint, implementing phased modernization attempts, scaling with an assembly line, and finalizing and realizing value.

C-suite executives should consider GenAI for legacy modernization as it makes the process quicker and more effective. But true transformation requires more than just technology. A proper retraining, recruiting, and knowledge transfer strategy is needed. Collaborating with a reliable service provider can maximize modernization ROI.