Combining Robotic Process Automation and Machine Learning
-
DOI:
https://doi.org/10.67228/30715725/IJIARE-2018PI4K8NPublished 01-03-2018
Robotic Process Automation (RPA), Machine Learning (ML), Intelligent Process Automation (IPA), Automation Technologies, Business Process Optimization, Artificial Intelligence (AI), Digital Transformation, Operational Efficiency, Process Automation Challenges, AI Integration Strategies Issue
Section
ArticlesHow to Cite
[1]R. K. Sharma and P. Natarajan, “Combining Robotic Process Automation and Machine Learning”, IJIARE, vol. 1, no. 1, pp. 01–09, Jan. 2018, doi: 10.67228/30715725/IJIARE-2018PI4K8N.Abstract
The convergence of Robotic Process Automation (RPA) and Machine Learning (ML) has ushered in a new era of intelligent process automation. RPA excels at automating rule-based, repetitive tasks, while ML introduces the capability to learn from data, enabling systems to adapt and make informed decisions. This paper explores the synergistic potential of combining RPA and ML, examining their individual functionalities, the benefits of their integration, and the challenges organizations may face during implementation. Through a comprehensive analysis, we aim to provide insights into how this fusion can transform business operations, drive innovation, and offer a competitive edge in the digital landscape.
References
[1] Mary C. Lacity, Leslie P. Willcocks, & Andrew Craig. (2015). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 14(1), 21–35.
[2] Mary C. Lacity & Leslie P. Willcocks. (2016). Robotic process automation: The next transformation lever for shared services. London School of Economics.
[3] Thomas H. Davenport & Rajeev Ronanki. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
[4] Sergio Aguirre & Adrian Rodriguez. (2017). Automation of a business process using robotic process automation (RPA): A case study. Applied Computer Sciences in Engineering.
[5] Erik Brynjolfsson & Andrew McAfee. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W. W. Norton & Company.
[6] Ajay Agrawal, Joshua Gans, & Avi Goldfarb. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
[7] Thomas H. Davenport & Julia Kirby. (2016). Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. Harper Business.
Downloads
How to Cite
[1]R. K. Sharma and P. Natarajan, “Combining Robotic Process Automation and Machine Learning”, IJIARE, vol. 1, no. 1, pp. 01–09, Jan. 2018, doi: 10.67228/30715725/IJIARE-2018PI4K8N.
Most read articles by the same author(s)
- Dr. Rajesh Kumar Sharma, A Reconfigurable Industrial Robot Architecture for Smart Manufacturing , International Journal of Intelligent Automation & Robotics Engineering: Vol. 2 No. 1 (2019)
- Dr. Priya Natarajan, Dr. Suresh Babu Reddy, AI-Powered Motion Prediction Models for Mobile Robots , International Journal of Intelligent Automation & Robotics Engineering: Vol. 2 No. 1 (2019)
- Dr. Rajesh Kumar Sharma, Dr. Priya Natarajan, AI-Driven Adaptive Control Systems for Industrial Automation , International Journal of Intelligent Automation & Robotics Engineering: Vol. 3 No. 1 (2020)
- Dr. Rajesh Kumar Sharma, Dr. Priya Natarajan, Intelligent Terrain Adaptation Techniques for Mobile Robotic Platforms , International Journal of Intelligent Automation & Robotics Engineering: Vol. 6 No. 1 (2023)
Similar Articles
- Mr. Vikram Sethi, Edge Intelligence for Real-Time Industrial Automation Systems , International Journal of Intelligent Automation & Robotics Engineering: Vol. 8 No. 1 (2025)
- Niklaus Wirth, AI-Enhanced Process Optimization in Automated Production Systems , International Journal of Intelligent Automation & Robotics Engineering: Vol. 7 No. 1 (2024)
- Dr. Nandhini Ravi, Intelligent Robotic Pick-and-Sort Systems for Dynamic Production , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 2 (2021)
- Dr. Linda Martinez, Dr. Mark Richardson, Digital Twin-Based Performance Optimization of Industrial Robots , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 2 (2021)
- Louis Pouzin, Jacques Arsac, Agent-Based Machine Learning Frameworks for Autonomous Predictive Decision Systems , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 1 (2021)
- Narendra Karmarkar, Federated Learning Architectures for Distributed Robotic Intelligence , International Journal of Intelligent Automation & Robotics Engineering: Vol. 8 No. 1 (2025)
- Ole-Johan Dahl, Kristen Nygaard, Vision-Guided Robotic Assembly Using Deep Neural Networks , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 2 (2021)
- N. Seshagiri, Adaptive Embedded Control Architectures for Intelligent Mechatronic Systems , International Journal of Intelligent Automation & Robotics Engineering: Vol. 5 No. 2 (2022)
- Zdzisław Pawlak, Jan Łukasiewicz, Intelligent Embedded Vision Systems for Autonomous Machines , International Journal of Intelligent Automation & Robotics Engineering: Vol. 5 No. 2 (2022)
- Jean Bartik, Intelligent Motion Compensation Methods for Industrial Robotic Arms , International Journal of Intelligent Automation & Robotics Engineering: Vol. 4 No. 2 (2021)
You may also start an advanced similarity search for this article.