Combining Robotic Process Automation and Machine Learning
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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
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[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.
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[6] Ajay Agrawal, Joshua Gans, & Avi Goldfarb. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
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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.
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