Evolution of Automation to Hyperautomation: Leveraging RPA, AI ML, NLP for Optimal Operational Efficiency |
||
 |
 |
|
| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-3 |
||
| Year of Publication : 2024 | ||
| Authors : Swaroop Raj Gunisity | ||
| DOI : 10.14445/22312803/IJCTT-V72I3P111 | ||
How to Cite?
Swaroop Raj Gunisity, "Evolution of Automation to Hyperautomation: Leveraging RPA, AI ML, NLP for Optimal Operational Efficiency ," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 76-83, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P111
Abstract
Automation has emerged as a crucial factor in gaining a competitive edge in the constantly evolving business environment we find ourselves in today. Its significance goes beyond just improving efficiency. The fusion of Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), Process Mining, and Natural Language Processing (NLP) has given rise to what is now referred to as hyperautomation. This cutting-edge approach amalgamates these technologies' strengths to optimize operational processes and enable organizations to make real-time data-driven decisions. The transition from traditional automation to hyperautomation represents a major change in how businesses tackle operational workflows. Leveraging RPA, AI, ML, OCR, and NLP in unison empowers enterprises to streamline complex tasks, enhance predictive analytics, and fundamentally revolutionize how data is utilized. Businesses find integrating these technologies essential for gaining a competitive advantage. This paper seeks to investigate the various stages underlying the evolution of automation to hyperautomation, outline the conceptual hyperautomation architecture, and offer insights into how organizations can harness the power of Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to achieve optimal operational efficiency. Through measurable KPIs and benefits, the paper aims to underscore the profound impact of hyperautomation, shedding light on the transformative potential of this paradigm shift in automation. Overall, the paper serves as a valuable resource for understanding the stages and architecture of hyperautomation, shedding light on its potential implications for organizations seeking to achieve operational excellence.
Keywords
Artificial Intelligence, Hyperautomation, Intelligent Automation, Process Mining, Robotic Process Automation (RPA).
Reference
[1] Tathagata Chakraborti et al., “From Robotic Process Automation to Intelligent Process Automation: Emerging Trends,” International Conference on Business Process Management, pp. 215-228, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Manishkumar Gami et al., “Robotic Process Automation – Future of Business Organizations: A Review,” 2 nd International Conference on Advances in Science & Technology, Mumbai, India, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] AkhilSahai, A Small Step with RPA, A Giant Leap with Hyperautomation, 2021. [Online]. Available: https://www.datasciencecentral.com/a-small-step-with-rpa-a-giant-leap-with-hyperautomation/
[4] Somayya Madakam, Rajesh M. Holmukhe, and Rajeev Kumar Revulagadda, “The Next Generation Intelligent Automation: Hyperautomation,” Journal of Information Systems and Technology Management, vol. 19, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Century Software, Revolutionizing Workflows: How Robotic Process Automation is Changing Business, Medium, 2023. [Online]. Available: https://medium.com/@centurysoftware/revolutionizing-workflows-how-robotic-process-automation-is-changing-business-ff65ce053105
[6] Transforming Business Efficiency with Robotic Process Automation (RPA), Subscribed.fyi, 2023. [Online]. Available: https://subscribed.fyi/blog/transforming-business-efficiency-with-robotic-process-automation-rpa/
[7] Future of Automation: AI and RPA Consulting Insights, Redresscompliance, 2024. [Online]. Available: https://redresscompliance.com/future-of-automation-ai-and-rpa-consulting-insights/
[8] Lukas-Valentin Herm et al., “A Consolidated Framework for Implementing Robotic Process Automation Projects,” Business Process Management, vol. 12168, pp. 471-488, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Abid Haleem et al., “Hyperautomation for the Enhancement of Automation in Industries,” Sensors International, vol. 2, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Alpa Reshamwala, Dhirendra Mishra, and Prajakta Pawar, “Review on Natural Language Processing,” IRACST – Engineering Science and Technology: An International Journal, vol. 3, no. 1, pp. 113-116, 2013.
[Google Scholar]
[11] Bonan Min et al., “Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey,” ACM Computing Surveys, vol. 56, no. 2, pp. 1-40, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Michael Alles, Mieke Julie Jans, and Miklos A. Vasarhelyi, “Process Mining: A New Research Methodology for AIS,” CAAA Annual Conference, pp. 1-16, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] A. Martínez-Rojas et al., “Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature Review,” Confluence of Artificial Intelligence and Robotic Process Automation, vol. 335, pp. 95-131, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Matej Puchovsky, Claudio Di Ciccio, and Jan Mendling, “A Case Study on the Business Benefits of Automated Process Discovery,” CEUR Workshop Proceedings, pp. 1-16, 2015.
[Google Scholar] [Publisher Link]
[15] Pavlos Delias, Michael Doumpos, and Nikolaos Matsatsinis, “Business Process Analytics: A Dedicated Methodology through a Case Study,” EURO Journal on Decision Processes, vol. 3, no. 3, pp. 357-374, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Gabriel Juhás et al., “Low-Code Platforms and Languages: The Future of Software Development,” 20th International Conference on Emerging eLearning Technologies and Applications, Stary Smokovec, Slovakia, pp. 286-293, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] iBPM—Intelligent Business Process Management, pp. 353-365, 2015.
[Google Scholar]
[18] Matthew L. Nelson, Robert L. Rariden, and Ravi Sen, “A Lifecycle Approach towards Business Rules Management,” Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Waikoloa, HI, USA, pp. 113-113, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Dimitrios Nikolaou Koumparoulis, “Key Performance Indicators (KPI): A Commentary,” International Journal of Management Sciences, vol. 1, no. 1, pp. 30-30, 2012.
[Google Scholar] [Publisher Link]
[20] Rodrigo Gomes, Vasco Amaral, and Fernando Brito e Abreu, “Combining Different Data Sources for IIoT-Based Process Monitoring,” Proceedings of International Conference on Information Technology and Applications, vol. 614, pp. 111-121, 2023.
[CrossRef] [Google Scholar] [Publisher Link]