Intelligent Hyperautomation Ecosystems: Integrating Robotic Process Automation, Intelligent Document Processing, Process Mining, and Generative Artificial Intelligence for Adaptive Enterprise Workflows
Keywords:
Hyperautomation, Robotic Process Automation, Intelligent Document Processing, Process MiningAbstract
The rapid evolution of enterprise digitalization has led organizations beyond traditional automation paradigms toward comprehensive hyperautomation ecosystems that integrate robotic process automation, intelligent document processing, process mining, business rules management, low-code platforms, and generative artificial intelligence. While early automation initiatives focused on task-level efficiency, contemporary enterprises increasingly require adaptive, end-to-end, and intelligence-driven process orchestration capable of responding to dynamic operational environments. This research develops an original, theory-intensive examination of intelligent hyperautomation by synthesizing insights strictly derived from established academic and technical literature on robotic process automation, intelligent document processing, machine learning, optical character recognition, business process analytics, intelligent business process management, and generative AI-enabled workflow optimization.
References
Chaudhuri, A., Mandaviya, K., Badelia, P., & Ghosh, S. K. (2017). Summary and future research. In Optical Character Recognition Systems for Different Languages with Soft Computing (pp. 241–245). Springer.
Chen, Y. (2021). Research on convolutional neural network image recognition algorithm based on computer big data. Journal of Physics: Conference Series, 1744(2), 022096.
Chowdhury, G. G. (2003). Natural language processing. Annual Review of Information Science and Technology, 37(1), 51–89.
Delias, P., Doumpos, M., & Matsatsinis, N. (2015). Business process analytics: A dedicated methodology through a case study. EURO Journal on Decision Processes, 3(3), 357–374.
Doguc, O. (2020). Robot process automation (RPA) and its future. In Handbook of Research on Strategic Fit and Design in Business Ecosystems (pp. 469–492). IGI Global.
El Naqa, I., & Murphy, M. J. (2015). What is machine learning? In Machine Learning in Radiation Oncology (pp. 3–11). Springer.
Gomes, R., Amaral, V., & Brito e Abreu, F. (2023). Combining different data sources for IIoT-based process monitoring. Proceedings of the International Conference on Information Technology and Applications, 614, 111–121.
Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic Markets, 30(1), 99–106.
Islam, N., Islam, Z., & Noor, N. (2017). A survey on optical character recognition system. arXiv preprint arXiv:1710.05703.
Juhás, G., et al. (2022). Low-code platforms and languages: The future of software development. Proceedings of the 20th International Conference on Emerging eLearning Technologies and Applications, 286–293.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
Kaelble, S. (2018). Robotic Process Automation For Dummies. NICE Special Edition.
Kandray, D. (2010). Programmable Automation Technologies. Industrial Press.
Koubâa, A. (Ed.). (2020). Robot Operating System (ROS): The Complete Reference (Vol. 5). Springer Nature.
Koumparoulis, D. N. (2012). Key performance indicators (KPI): A commentary. International Journal of Management Sciences, 1(1), 30.
Krishnan, G., & Bhat, A. K. (2025). Empower financial workflows: Hyper automation framework utilizing generative artificial intelligence and process mining. SSRN.
Kroll, C., Bujak, A., Darius, V., Enders, W., & Esser, M. (2016). Robotic process automation: Robots conquer business processes in back offices. Capgemini Consulting.
Lasso-Rodriguez, G., & Winkler, K. (2020). Hyperautomation to fulfil jobs rather than executing tasks: The BPM manager robot vs human case. Romanian Journal of Information Technology and Automatic Control, 30(3), 7–22.
Martínez-Rojas, A., et al. (2023). Intelligent document processing in end-to-end RPA contexts: A systematic literature review. Confluence of Artificial Intelligence and Robotic Process Automation, 335, 95–131.
Nelson, M. L., Rariden, R. L., & Sen, R. (2008). A lifecycle approach towards business rules management. Proceedings of the 41st Annual Hawaii International Conference on System Sciences, 113.
Puchovsky, M., Di Ciccio, C., & Mendling, J. (2015). A case study on the business benefits of automated process discovery. CEUR Workshop Proceedings, 1–16.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr. Alejandro Moreno
This work is licensed under a Creative Commons Attribution 4.0 International License.
