Hyperautomation has emerged as one of the most transformative paradigms in contemporary digital transformation discourse, extending far beyond the traditional boundaries of task-based automation. Unlike earlier generations of automation technologies that focused narrowly on executing predefined, repetitive tasks, hyperautomation represents an integrated, multi-layered approach that seeks to automate entire jobs, decision chains, and adaptive processes by combining robotic process automation, process mining, artificial intelligence, data analytics, cyber-physical-social systems, and human-centric design philosophies. This research article develops an extensive theoretical and analytical exploration of hyperautomation as a job-fulfillment paradigm rather than a task-execution mechanism, grounded strictly in the existing body of literature provided. Drawing on foundational works in business process management, Industry 4.0, circular economy, affective computing, talent management, and innovation systems, the study conceptualizes hyperautomation as a socio-technical system that reshapes organizational roles, governance structures, trust mechanisms, and value creation logics. The article adopts a qualitative, interpretive research methodology based on deep literature synthesis and theoretical integration, enabling a holistic understanding of how hyperautomation redefines work, management, and technology alignment. The findings reveal that hyperautomation operates at the intersection of digital process intelligence, organizational learning, and human-machine collaboration, challenging conventional assumptions about efficiency, control, and labor displacement. Instead of eliminating human agency, hyperautomation reallocates cognitive, emotional, and strategic responsibilities, requiring new competencies, ethical frameworks, and institutional arrangements. The discussion elaborates on implications for business process management, product-service systems, sustainable industrial development, and workforce transformation, while also addressing limitations related to technological maturity, data governance, and socio-cultural resistance. The article concludes by positioning hyperautomation as a critical enabler of resilient, adaptive, and human-centered organizations in the era of Industry 4.0, and outlines future research directions for advancing theory and practice in intelligent automation.

AI Governance, Ethical AI, Regulatory Frameworks, Explainability

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