AI-Assisted Legacy Modernization: Automating Monolith-to-Microservice Decomposition

Legacy systems are still critical business operations in many industries – but they are becoming roadblocks to innovation, agility, and scalability. As enterprises increasingly pressure themselves to modernize their aging infrastructures, strategic implementation of a transition from monolithic to microservices is gaining ground. Transforming this type of complex monolith into microservices is not a trivial task. It presents technical and organizational challenges, including bureaucratic service boundaries embedded in legacy codebases that tightly couple the service's functionality. The topic of this article is how artificial intelligence (AI) can help automate the decomposition of monolithic systems into decomposed, scalable microservices. By using machine learning, natural language processing, and clustering algorithms, AI tools can analyze source code, runtime data, and interactions between system components to determine intelligent service boundaries. A detailed methodology for AI-assisted decomposition is presented, along with real-world tools such as IBM Mono2Micro and AWS Microservice Extractor. A practical case study involving a global e-commerce company is included to illustrate applied outcomes. Additionally, the article addresses key challenges such as data inconsistency, domain misalignment, and organizational resistance. How it works outlines best practices to support successful implementation, including incremental migration patterns, domain-driven design, and DevOps integration. The article concludes with strategic recommendations and a forward-looking perspective on how AI will further change the modernization process. When done right, AI improves organizations’ ability to create agile, future-prepared software ecosystems.