The accelerating convergence of artificial intelligence, big data analytics, robotic process automation, and agentic systems is reshaping contemporary socio-technical systems across healthcare, energy, smart cities, education, finance, and supply chains. This transformation, increasingly conceptualized under the umbrella of intelligent hyperautomation, extends beyond traditional automation by embedding adaptive intelligence, explainability, autonomy, and ethical governance into operational workflows. Despite rapid adoption, significant theoretical fragmentation persists across domains, with limited integrative scholarship connecting technical architectures, human-centered considerations, regulatory dynamics, and responsible AI principles. This research develops a comprehensive, publication-ready theoretical framework that synthesizes intelligent hyperautomation with explainable, agentic, and generative AI paradigms, grounded strictly in the provided scholarly references.

The study systematically elaborates how artificial intelligence augments process intelligence through demand forecasting, decision support, behavioral analytics, and autonomous learning systems, drawing insights from healthcare analytics, electric vehicle infrastructure optimization, smart city platforms, and enterprise hyperautomation models. It examines the evolution from rule-based robotic process automation toward intelligent process automation and further into agentic and multimodal AI systems capable of goal-oriented reasoning and cross-domain coordination. Particular emphasis is placed on the socio-technical implications of AI adoption, including psychological well-being, workforce engagement, organizational trust, and institutional accountability.

Responsible AI emerges as a foundational enabler rather than a regulatory constraint. Explainable AI, employee engagement mechanisms, regulatory competition, and ethical governance are analyzed as mutually reinforcing pillars that determine sustainable AI value creation. The article further explores how generative and multimodal AI systems redefine knowledge work, learning ecosystems, and decision autonomy, while simultaneously intensifying concerns related to opacity, bias, and systemic risk.

Methodologically, the research adopts an integrative conceptual synthesis approach, combining cross-domain theoretical analysis with descriptive interpretation of empirical findings reported in the referenced studies. The results articulate a unified model of intelligent hyperautomation that balances technical sophistication with human agency, regulatory alignment, and societal legitimacy. The discussion critically examines limitations, including scalability challenges, governance gaps, and uneven global regulatory maturity, and proposes future research directions focused on adaptive regulation, human-AI symbiosis, and explainability-driven system design.

By offering an extensive, deeply elaborated theoretical contribution, this article advances academic understanding of intelligent hyperautomation as a holistic socio-technical transformation rather than a purely technological evolution. It provides a rigorous foundation for researchers, policymakers, and practitioners seeking to design, govern, and deploy AI-driven systems that are not only efficient and autonomous, but also transparent, ethical, and socially sustainable.

Intelligent Hyperautomation, Responsible Artificial Intelligence, Explainable AI, Agentic Systems

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