From Robotic Process Automation to Intelligent Process Automation Emerging Trends
-
DOI:
https://doi.org/10.67228/30715725/IJIARE-2018PI9M3TPublished 04-04-2018
Robotic Process Automation (RPA), Intelligent Process Automation (IPA), Artificial Intelligence (AI), Machine Learning (ML), Business Process Automation (BPA), Automation Trends, Digital Transformation, Cognitive Automation, Process Optimization, Future of Work Issue
Section
ArticlesHow to Cite
[1]C. King, “From Robotic Process Automation to Intelligent Process Automation Emerging Trends”, IJIARE, vol. 1, no. 1, pp. 01–08, Apr. 2018, doi: 10.67228/30715725/IJIARE-2018PI9M3T.Abstract
The landscape of business process automation is undergoing a significant transformation with the integration of artificial intelligence and machine learning into traditional robotic process automation. This paper examines the evolution from RPA to Intelligent Process Automation (IPA), discussing how AI and ML technologies are disrupting and enhancing business processes. We analyze the current trends, applications, and future prospects of IPA, identifying key research challenges and opportunities at the intersection of AI and business process management. Our findings aim to provide insights for organizations considering the adoption of IPA and to stimulate further research in this emerging field.
References
[1] Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation: The next transformation lever for shared services. London School of Economics.
[2] Lacity, M. C., Willcocks, L. P., & Craig, A. (2015). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 14(1), 21–35.
[3] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
[4] Aguirre, S., & Rodriguez, A. (2017). Automation of a business process using robotic process automation (RPA): A case study. Applied Computer Sciences in Engineering.
[5] Leopold, H., van der Aa, H., & Reijers, H. A. (2018). Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions. BPMDS 2018.
[6] Issac, R., Muni, R., & Desai, K. (2018). Delineated Analysis of Robotic Process Automation Tools. 2018 International Conference on Advances in Electronics, Computers and Communications (ICAECC). https://doi.org/10.1109/ICAECC.2018.8479511
[7] Hyun, Y. G., & Lee, J. Y. (2018). Trends Analysis and Future Direction of Business Process Automation, RPA in the Times of Convergence. Journal of Digital Convergence, 16(11), 313–327. https://doi.org/10.14400/JDC.2018.16.11.313
[8] Kopeć, W., Skibiński, M., Biele, C., Skorupska, K., Tkaczyk, D., Jaskulska, A., Abramczuk, K., Gago, P., & Marasek, K. (2018). Hybrid Approach to Automation, RPA and Machine Learning: A Method for the Human-Centered Design of Software Robots. arXiv:1811.02213.
[9] Lacity, M. C., & Willcocks, L. P. (2018). Robotic Process and Cognitive Automation: The Next Phase. SB Publishing.
[10] Willcocks, L., Lacity, M., & Craig, A. (2018). Robotic Process Automation at Telefónica O2. The Outsourcing Unit Working Research Paper Series, London School of Economics.
Downloads
How to Cite
[1]C. King, “From Robotic Process Automation to Intelligent Process Automation Emerging Trends”, IJIARE, vol. 1, no. 1, pp. 01–08, Apr. 2018, doi: 10.67228/30715725/IJIARE-2018PI9M3T.
Similar Articles
- P. K. Iyengar, Smart Manufacturing Analytics Using Industrial Internet of Things , International Journal of Intelligent Automation & Robotics Engineering: Vol. 5 No. 2 (2022)
- Ken Iverson, David Parnas, Graph Neural Network-Based Motion Planning for Mobile Robots , International Journal of Intelligent Automation & Robotics Engineering: Vol. 8 No. 2 (2025)
- Dr. Arvind Kumar Singh, Dr. Lakshmi Narayanan, Adaptive Learning Rate Strategies for Efficient Machine Learning Training , International Journal of Intelligent Automation & Robotics Engineering: Vol. 3 No. 2 (2020)
- Narendra Karmarkar, Energy-Aware Embedded Intelligence for Smart Robotic Applications , International Journal of Intelligent Automation & Robotics Engineering: Vol. 5 No. 2 (2022)
- Narendra Karmarkar, P. K. Iyengar, AI-Assisted Dexterous Manipulation Using Multi-Finger Robotic Hands , International Journal of Intelligent Automation & Robotics Engineering: Vol. 3 No. 2 (2020)
- Ole-Johan Dahl, Kristen Nygaard, AI-Assisted Dexterous Manipulation Using Multi-Finger Robotic Hands , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 1 (2021)
- Dr. Pooja Agarwal, Dr. Rakesh Chandra, Adaptive Force Control Strategies for Collaborative Robotic Manipulation , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 2 (2021)
- Dr. Suresh Babu Reddy, Dr. Anita Verma, Integration of Artificial Intelligence and Robotic Process Automation Literature Review and Proposal for a Sustainable Model , International Journal of Intelligent Automation & Robotics Engineering: Vol. 1 No. 1 (2018)
- N. Seshagiri, H. N. Mahabala, Hybrid Deep Learning Frameworks for Robotic Object Recognition , International Journal of Intelligent Automation & Robotics Engineering: Vol. 8 No. 1 (2025)
- Amanda Davis, Kevin Taylor, Design of Energy-Efficient Actuation Systems for Industrial Robots , International Journal of Intelligent Automation & Robotics Engineering: Vol. 2 No. 2 (2019)
You may also start an advanced similarity search for this article.