Articles
| Open Access | Reconceptualizing Hyperautomation in Financial and Cyber-Physical Workflows: Integrative Perspectives from Generative Artificial Intelligence, Process Mining, and Intelligent Automation Ecosystems
Dr. Lukas van der Meer , University of Amsterdam, The NetherlandsAbstract
The accelerating convergence of hyperautomation, generative artificial intelligence, process mining, and intelligent automation frameworks represents a profound structural transformation in how contemporary organizations conceptualize, execute, and govern complex workflows. Across financial services, cyber-physical systems, and digitally mediated customer environments, automation has evolved beyond rule-based scripting into adaptive, self-optimizing, and cognitively augmented systems. This research article develops an original, integrative theoretical framework that situates hyperautomation not merely as a technological assemblage but as an epistemic shift in organizational intelligence and operational rationality. Drawing strictly on the provided scholarly and practitioner-oriented references, the study synthesizes perspectives from intelligent process automation, robotic process automation, conversational artificial intelligence, Industry 4.0 sensor ecosystems, Internet of Behaviors paradigms, and metrological foundations of cyber-physical infrastructures. Particular emphasis is placed on the role of generative artificial intelligence and process mining as catalytic mechanisms that transform static workflows into dynamically learning systems, enabling continuous discovery, prediction, and optimization of organizational processes (Krishnan & Bhat, 2025).
The article advances three central arguments. First, hyperautomation must be understood as a multilayered socio-technical system in which data generation, behavioral interpretation, and decision orchestration are inseparably intertwined. Second, the integration of generative AI into automation architectures fundamentally reconfigures decision-making authority, shifting organizations from deterministic control toward probabilistic and adaptive governance models. Third, the operational success of hyperautomation is contingent upon infrastructural foundations—such as sensor reliability, dimensional metrology, and cyber-physical security—that are often under-theorized in automation discourse. Methodologically, the study adopts a qualitative, theory-building research design grounded in critical synthesis and interpretive analysis of existing literature. The results reveal that hyperautomation produces not only efficiency gains but also new organizational vulnerabilities, ethical tensions, and epistemological uncertainties. The discussion situates these findings within broader debates on Industry 4.0, intelligent automation, and digital transformation, offering a nuanced assessment of limitations and future research trajectories. By articulating a comprehensive conceptual model, this article contributes to academic scholarship and provides a theoretically grounded lens for practitioners navigating the complexities of next-generation automation ecosystems.
Keywords
Hyperautomation, Generative Artificial Intelligence, Process Mining, Intelligent Automation
References
Yakovenko, Y., & Shaptala, R. (2023). Intelligent process automation, robotic process automation and artificial intelligence for business processes transformation. Baltija Publishing.
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Internet of behaviours (IoB) and its role in customer services. Sensors International, 100122.
Bornet, P., Wirtz, J., & Ian, J. (2020). Intelligent automation. National University of Singapore.
G. S., D. H., & R. M. (2021). An eradication of malicious node attack using a priority aware frequency domain polling in cyber-physical systems. European Journal of Molecular & Clinical Medicine, 1134–1147.
Moona, G., & Jewariya, M. (2019). Relevance of dimensional metrology in manufacturing industries. MAPAN, 34, 97–104.
Krishnan, G., & Bhat, A. K. (2025). Empower financial workflows: Hyper automation framework utilizing generative artificial intelligence and process mining. SSRN.
Singasani, T. R. (2021). Pega and robotic process automation: Synergies for next-generation workflow management. European Journal of Advances in Engineering and Technology, 8(2), 134–137.
Park, S. C. (2018). The Fourth Industrial Revolution and implications for innovative cluster policies. AI & Society, 33(3), 433–445.
Tammana, P. K., Chen, Y., Tian, X., Zhu, W., Shao, Y., Zhong, J., Lu, Y., & Tan, T. (2024). Leveraging conversational AI for enhanced decisioning: Integrating ChatGPT with Pega’s adaptive decision manager. Journal of Software, 19(2), 42–51.
Varshney, A., Garg, N., Nagla, K. S., et al. (2021). Challenges in sensors technology for Industry 4.0 for futuristic metrological applications. MAPAN, 36, 215–226.
Chen, Y. (2021). Research on CNN image recognition algorithm based on computer big data. Journal of Physics: Conference Series, 1744(2), 022096.
Kester, K., Archer, T., & Bryant, S. (2019). Diffraction, transrational perspectives, and peace education. Journal of Peace Education, 16(3), 274–295.
ValueLabs. (n.d.). Automation beyond RPA and AI: RPA and cognitive ops. ValueLabs Blog.
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Copyright (c) 2026 Dr. Lukas van der Meer
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