The acceleration of digital transformation across global industries has precipitated an urgent need to modernize legacy information systems. While conventional migration strategies emphasize infrastructural upgrades or manual QA interventions, the emergence of artificial intelligence (AI) has enabled the conceptualization of AI-augmented pipelines capable of automating quality assurance (QA) and system validation processes. This study provides a comprehensive examination of automation-driven methodologies for legacy system transformation, with particular emphasis on AI-integrated QA frameworks. Grounded in contemporary literature and empirical evaluations, the research delineates theoretical foundations, historical developments, and practical implications of AI adoption in software migration. Through a synthesis of prior studies and emerging technological insights, the investigation demonstrates that AI-based pipelines facilitate accelerated validation, improved accuracy, and adaptive testing capabilities, surpassing the limitations of conventional QA paradigms (Tiwari, 2025). Furthermore, the analysis explores the socio-technical challenges associated with migration, including workforce adaptation, regulatory compliance, and interoperability concerns, proposing a multidimensional blueprint for successful transformation. The findings contribute to a deeper understanding of strategic, operational, and ethical dimensions of AI-assisted QA, offering actionable guidance for researchers, practitioners, and policy-makers seeking sustainable digital evolution.

Legacy Systems, Artificial Intelligence, Digital Transformation

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