Legacy information systems continue to underpin critical operations across industries such as banking, government, manufacturing, and large-scale enterprises. While these systems provide stability and institutional memory, they also introduce significant challenges related to scalability, security, interoperability, maintainability, and innovation velocity. Recent advances in artificial intelligence, including machine learning, predictive analytics, and generative AI, have emerged as transformative forces capable of re-engineering legacy systems rather than merely replacing them. This research article presents an in-depth, theory-driven and practice-oriented examination of AI-enabled legacy system modernization, with particular emphasis on augmented intelligence, quality assurance transformation, and human–AI collaboration. Drawing strictly on the provided academic, industry, and thought-leadership references, the study synthesizes multidisciplinary perspectives from software engineering, digital transformation, cybersecurity, organizational strategy, and human–computer interaction. The article develops a conceptual framework explaining how AI techniques support incremental migration, automated code abstraction, predictive quality assurance, and continuous system evolution, while simultaneously addressing governance, data privacy, cybersecurity, and ethical considerations. A qualitative methodological approach is adopted, integrating comparative analysis of existing frameworks, thematic synthesis of case-based insights, and interpretive evaluation of reported outcomes. The findings reveal that AI-driven modernization is not a purely technical endeavor but a socio-technical transformation requiring augmented intelligence models that balance automation with human judgment. The discussion critically evaluates limitations, including algorithmic bias, skills gaps, and integration complexity, and outlines future research directions related to resilient architectures, explainable AI, and adaptive governance. The article concludes that AI-enabled legacy modernization represents a foundational pathway for sustainable digital transformation when aligned with human-centric design and strategic oversight.
