Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Mainframe modernization has emerged as a critical imperative for enterprises seeking to leverage decades of valuable legacy data for advanced analytics and artificial intelligence initiatives. Legacy mainframe systems, while reliable for transaction processing, create substantial barriers to modern data science applications through architectural limitations and isolated data repositories. Cloud migration strategies utilizing specialized extraction tools and phased implementation approaches enable organizations to transform these historical information assets into accessible resources for contemporary analytics platforms. The integration of mainframe data with cloud environments dramatically accelerates enterprise AI adoption by exposing previously inaccessible information to data science teams, enhancing model accuracy and enabling comprehensive business insights. Large language models and other advanced AI systems, designed for cloud-native operation, gain substantial performance improvements when provided access to the rich historical context contained within mainframe repositories. Emerging technologies such as AI-driven extraction methodologies and retrieval-augmented generation systems further streamline the modernization process by automating complex mapping tasks and enabling immediate analytical capabilities without requiring complete re-platforming