Articles
| Open Access | Redefining Legacy Quality Assurance: AI-Driven Automation For Sustainable Digital Transformation
Magnus Stergaard , Department of Information Systems, University of Helsinki, FinlandAbstract
The accelerating pace of digital transformation has compelled organizations to re-examine the sustainability, scalability, and economic viability of legacy software ecosystems, particularly those anchored in traditional quality assurance and testing paradigms. While cloud migration and legacy system modernization have long been treated as primarily infrastructural or architectural challenges, contemporary scholarship increasingly recognizes the pivotal role of automation and artificial intelligence in reshaping the epistemic and operational foundations of software quality assurance. This study advances a comprehensive theoretical and methodological framework that integrates automation-driven digital transformation with cloud-based legacy modernization, placing special emphasis on the reconfiguration of quality assurance pipelines into AI-augmented, self-optimizing systems. Drawing on an extensive body of cloud migration, software modernization, and testing literature, and grounded in the automation blueprint articulated by Tiwari (2025), the article articulates how the convergence of artificial intelligence, cloud-native architectures, and automated testing practices constitutes a paradigm shift rather than an incremental improvement.
The research is designed as a qualitative meta-analytic and conceptual synthesis of authoritative scholarly sources, enabling a deep examination of historical trajectories, contemporary challenges, and future trajectories of legacy modernization. The methodology employs systematic comparative interpretation across multiple streams of literature, including cloud economics, risk management, performance optimization, enterprise IT strategy, and software engineering modernization frameworks. By embedding Tiwari’s AI-augmented quality assurance blueprint within the broader discourse on legacy system migration, the study demonstrates how automation is no longer an auxiliary tool but a central organizing principle of modern digital infrastructure.
The results reveal that organizations which treat automation and AI as integral components of migration planning achieve not merely technical uplift but structural transformation of governance, cost management, and innovation capacity. Rather than replicating legacy inefficiencies in cloud environments, AI-driven testing pipelines reconstitute quality assurance as a continuous, adaptive, and predictive discipline. The discussion situates these findings within ongoing scholarly debates about risk, cost, organizational resistance, and technological lock-in, arguing that AI-augmented quality assurance is the decisive differentiator between superficial cloud adoption and genuine digital transformation. Ultimately, the study contributes a theoretically grounded and practically relevant model for integrating automation, artificial intelligence, and cloud migration into a unified modernization strategy capable of sustaining enterprise competitiveness in the digital era.
Keywords
Legacy system modernization, Cloud migration, Artificial intelligence
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