The accelerating pace of digital transformation has fundamentally altered how organizations design, execute, and optimize their business processes. Among the most influential developments in this transformation journey is Robotic Process Automation (RPA), which initially emerged as a pragmatic solution for automating repetitive, rule-based tasks. Over time, however, the limitations of traditional RPA—particularly its dependence on structured data and deterministic logic—have prompted both scholars and practitioners to explore more advanced paradigms. This evolution has given rise to Intelligent Process Automation and, more recently, hyperautomation, a holistic approach that integrates RPA with artificial intelligence, machine learning, natural language processing, process mining, and low-code development platforms. This research article provides an extensive, publication-ready examination of the theoretical foundations, technological enablers, organizational implications, and future trajectories of hyperautomation. Drawing strictly from the provided literature, the study synthesizes insights from academic research, industry analyses, and conceptual frameworks to construct a comprehensive narrative of how automation is transforming from task-level efficiency tools into enterprise-wide intelligence systems. The article elaborates on the conceptual transition from RPA to hyperautomation, explores the role of enabling technologies such as generative artificial intelligence and process mining, and analyzes implementation frameworks and challenges in diverse organizational contexts. Methodologically, the study adopts a qualitative integrative review approach, enabling deep theoretical elaboration and cross-comparison of perspectives. The findings highlight that hyperautomation is not merely a technological upgrade but a strategic and organizational paradigm shift that redefines decision-making, workforce roles, governance models, and value creation mechanisms. The discussion critically examines limitations related to ethics, scalability, skills gaps, and governance, while also identifying promising avenues for future research and practice. By offering a deeply elaborated, theoretically grounded, and systematically structured analysis, this article contributes to the growing body of knowledge on intelligent automation and provides a robust foundation for both academic inquiry and managerial decision-making in the era of hyperautomation.

Robotic Process Automation, Hyperautomation, Intelligent Process Automation, Artificial Intelligence

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