AI-ENHANCED DATA MIGRATION STRATEGY FOR LEGACY SYSTEMS

Abstract

This article presents an AI-enhanced data migration strategy for legacy systems that leverages artificial intelligence technologies to address the significant challenges organizations face when modernizing outdated infrastructure. It explores how legacy systems with obsolete formats, proprietary databases, and inadequate documentation create substantial barriers to successful migration, resulting in high failure rates for traditional approaches. This article presents a detailed analysis of an innovative framework that incorporates machine learning, natural language processing, and automated reasoning, demonstrating how AI can transform the migration process by automating schema discovery, intelligent mapping generation, data transformation, validation, and continuous learning. A financial services case study illustrates the practical implementation of these techniques, revealing significant improvements in accuracy, efficiency, and cost-effectiveness. While acknowledging current limitations in training data requirements, specialized system handling, business logic extraction, and explainability, the article concludes by exploring promising research directions including zero-shot learning, multimodal approaches, temporal intelligence, and edge case management that will further advance the field.