Exploring Adaptive RPA Models for Dynamic Exception Handling in Business Process Automation

Authors

  • Robert Glimn Preses, Independent Researcher Author

Keywords:

Robotic Process Automation, Adaptive RPA, Dynamic Exception Handling, Cognitive Automation, Business Process Automation

Abstract

The success of Robotic Process Automation (RPA) in streamlining business processes is increasingly challenged by exceptions—errors that deviate from expected behavior. Adaptive RPA, enhanced with cognitive capabilities such as reinforcement learning (RL), offers a dynamic approach to handling exceptions in real-time. This paper explores emerging models of adaptive RPA, focusing on the design, application, and benefits of intelligent exception handling in evolving business environments.

References

Aguirre, S., & Rodriguez, A. (2017). Automation in business processes: RPA vs traditional methods. Journal of Business Process Management, 23(2), 134–147.

Subramanyam, S.V. (2021). Cloud computing and business process re-engineering in financial systems: The future of digital transformation. International Journal of Information Technology and Management Information Systems (IJITMIS), 12(1), 126–143.

van der Aalst, W. M. P. (2018). Process mining and RPA: A perfect match. Computers in Industry, 100, 1–4.

Syed, R., et al. (2019). Enabling cognitive automation in business. MIS Quarterly Executive, 18(4), 275–289.

Willcocks, L. P., Lacity, M. C., & Craig, A. (2017). Robotic Process Automation: Strategic transformation lever for global business services? Journal of Information Technology Teaching Cases, 7(1), 17–28.

Lamberti, F., et al. (2018). Challenges of integrating AI in RPA. Journal of Artificial Intelligence Research, 63, 567–595.

Subramanyam, S.V. (2019). The role of artificial intelligence in revolutionizing healthcare business process automation. International Journal of Computer Engineering and Technology (IJCET), 10(4), 88–103.

Huang, M. H., & Rust, R. T. (2019). A strategic framework for AI use in marketing. Journal of the Academy of Marketing Science, 47(1), 30–50.

Meijer, R. J., et al. (2019). Adaptive workflows in robotic process automation. Procedia Computer Science, 164, 515–522.

Chien, S., et al. (2020). Intelligent automation through hybrid models. ACM Transactions on Intelligent Systems, 9(3), 22.

Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey. AI & Society, 29(4), 551–562.

Hofmann, P., & Samp, C. (2017). RPA implementation success factors. Journal of Organizational Transformation, 34(3), 213–231.

Arora, A., & Jain, R. (2018). Dynamic exception modeling in robotic systems. Robotics and Autonomous Systems, 102, 145–154.

Kulkarni, S. P., & Joshi, S. (2019). Real-time process correction in adaptive automation. IEEE Transactions on Automation Science, 14(2), 135–144.

Almeida, M. A., et al. (2017). A review of intelligent agents in RPA. Expert Systems with Applications, 92, 155–167.

Verma, N., & Kapoor, R. (2019). Exception-aware bot design. Journal of Intelligent Process Automation, 3(1), 101–110.

Downloads

Published

2021-11-22