Generative AI-Driven Legacy System Modernization: Transforming Enterprise Infrastructure Through Automated Code Translation and Refactoring

Abstract

Legacy systems continue to form the operational backbone of numerous enterprises despite presenting significant challenges, including scalability constraints, integration limitations, and escalating maintenance costs. Generative artificial intelligence, particularly large language models trained on extensive code repositories, offers unprecedented capabilities for automating critical aspects of legacy system modernization. These AI-driven solutions enable automated code translation between programming languages, comprehensive documentation generation, test case creation, and intelligent refactoring recommendations. Real-world implementations across financial services, insurance, and government sectors demonstrate substantial reductions in modernization timelines and resource requirements while maintaining functional integrity. The technology facilitates the transformation of monolithic architectures into modern microservices, bridges skill gaps created by retiring legacy experts, and enables continuous modernization rather than disruptive system replacements. However, successful implementation requires careful consideration of model hallucination risks, security protocols, and organizational readiness. A phased implementation framework combining AI capabilities with human expertise and robust governance structures emerges as the optimal path forward. This convergence of generative AI and legacy modernization represents a fundamental shift in how enterprises approach digital transformation, offering a more efficient, cost-effective, and sustainable alternative to traditional modernization methodologies.