Skip to main content

Intelligent Automation Framework Using AI and RPA: An Introduction

  • Chapter
  • First Online:
Confluence of Artificial Intelligence and Robotic Process Automation

Abstract

Business organizations may now automate high-volume processes with the help of Robotic Process Automation (RPA), a rapidly developing automation technology that bridges the gap between Artificial Intelligence (AI) and Business Process Management (BPM). Tools for robotic process automation (RPA) may record the actions of a human user on a computer’s interface, allowing a software robot to do those actions in the user’s place. RPA procedures may be made more precise and efficient by the supplementary use of AI methods and algorithms for process optimization, forecasting, pattern recognition and data extraction. Since RPA is still a newer discipline, there is not a lot of research published on the subject just now. Hence, the purpose of this chapter is to examine how the research fraternity defines RPA and how thoroughly the implementation of RPA, state and trends have been explored in the literature. The authors also do a state-of-the-art literature evaluation of current studies about RPA. The authors have also made contributions to evaluating the benefits and drawbacks of this powerful instrument and making predictions about its potential future uses. This chapter offers many perspectives on cutting-edge technologies like RPA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Hildebrandt, T., van Dongen, B. F., Röglinger, M., Mendling, J.: Business process management. Lect. Notes Comput. Sci, 11675 (2019)

    Google Scholar 

  2. González Enríquez, J., Jiménez Ramírez, A., Domínguez Mayo, F.J., García García, J.A.: Robotic process automation: a scientific and industrial systematic mapping study. IEEE Access 8, 39113–39129 (2020)

    Article  Google Scholar 

  3. Weske, M.: Business process management architectures, pp. 305–343. Springer, Berlin Heidelberg (2007)

    Google Scholar 

  4. Mendling, J., Decker, G., Hull, R., Reijers, H.A., Weber, I.: How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? Commun. Assoc. Inf. Syst. 43(1), 19 (2018)

    Google Scholar 

  5. Reddy, K.N., Harichandana, U., Alekhya, T., Rajesh, S.M.: A study of robotic process automation among artificial intelligence. Int. J. Sci. Res. Publ. 9(2), 392–397 (2019)

    Google Scholar 

  6. Saha, O., Chakraborty, A., & Banerjee, J. S.: A fuzzy AHP approach to IT-based stream selection for admission in technical institutions in India. In Emerging technologies in data mining and information security (pp. 847–858). Springer, Singapore, (2019)

    Google Scholar 

  7. Saha, O., Chakraborty, A., & Banerjee, J. S.: A decision framework of IT-based stream selection using analytical hierarchy process (AHP) for admission in technical institutions. In 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix) (pp. 1–6). IEEE, (2017)

    Google Scholar 

  8. König, M., Bein, L., Nikaj, A., Weske, M. (, September). Integrating robotic process automation into business process 2020 management. In: International conference on business process management, pp. 132–146. Springer, Cham (2020)

    Google Scholar 

  9. Wright, D., Witherick, D., Gordeeva, M.: The robots are ready. Are you? Untapped advantage in your digital workforce. Deloitte, 28. (2017)

    Google Scholar 

  10. Frank, C.: Introduction to robotic process automation. In: Proceeding of. Institute Robot Process Automation, p. 35 (2015)

    Google Scholar 

  11. Lacity, L., Willcocks, M.: Robotic process automation: The next transformation lever for shared services. Technical Report No. 16/01, School Economy of Political Science, Outsourcing Unit Working Research Paper Series., London, U.K (2012)

    Google Scholar 

  12. Ansari, W.A., Diya, P., Patil, S., Patil, S.: A review on robotic process automation-the future of business organizations. In: 2nd International conference on advances in science & technology (ICAST), (2019)

    Google Scholar 

  13. Llewellyn Evans, G.: Disruptive technology and the board: The tip of the iceberg. Econ. Bus. Rev. 3(1), (2017)

    Google Scholar 

  14. Gami, M., Jetly, P., Mehta, N., Patil, S.: (2019, April). Robotic process automation–future of business organizations: A review. In: 2nd International conference on advances in science & technology (ICAST), (2019).

    Google Scholar 

  15. Gotthardt, M., Koivulaakso, D., Paksoy, O., Saramo, C., Martikainen, M., Lehner, O.: Current state and challenges in the implementation of smart robotic process automation in accounting and auditing. ACRN J. Financ. Risk Perspect, (2020)

    Google Scholar 

  16. Aguirre, S., Rodriguez, A.: (2017, September). Automation of a business process using robotic process automation (RPA): A case study. In: Workshop on engineering applications, pp. 65–71. Springer, Cham (2017)

    Google Scholar 

  17. Schatsky, D., Muraskin, C., Iyengar, K.: Robotic process automation. A path to the cognitive enterprise. Deloitte Consulting, New York (2017)

    Google Scholar 

  18. Jovanović, S.Z., Đurić, J.S., Šibalija, T.V.: Robotic process automation: overview and opportunities. International Journal Advanced Quality 46(3–4), 34–39 (2018)

    Google Scholar 

  19. https://www.lateetud.com/the-evolution-of-process-automation-technology Web. 29 Jan 2019

  20. Das, D., Pandey, I., Chakraborty, A., Banerjee, J.S.: Analysis of implementation factors of 3D printer: the key enabling technology for making prototypes of the engineering design and manufacturing. Int. J. Comput. Appl. 1, 8–14 (2017)

    Google Scholar 

  21. Das, D., Pandey, I., Banerjee, J.S.: An in-depth study of implementation issues of 3D printer. In: Proceedings of MICRO 2016 conference on microelectronics, circuits and systems, pp. 45–49 (2016)

    Google Scholar 

  22. Met, İ., Kabukçu, D., Uzunoğulları, G., Soyalp, Ü., Dakdevir, T.: Transformation of business model in finance sector with artificial intelligence and robotic process automation. In: Digital business strategies in blockchain ecosystems, pp. 3–29. Springer, Cham (2020)

    Google Scholar 

  23. https://www.kofax.com/Blog/2018/august/robotic-process-automation-rpa-past-present-and-future Web. 29 January 2019

  24. Banerjee, J., Maiti, S., Chakraborty, S., Dutta, S., Chakraborty, A., Banerjee, J.S.: Impact of machine learning in various network security applications. In: 2019 3rd International conference on computing methodologies and communication (ICCMC), pp. 276–281. IEEE (2019)

    Google Scholar 

  25. Chattopadhyay, J., Kundu, S., Chakraborty, A., Banerjee, J.S.: Facial expression recognition for human computer interaction. In: International conference on computational vision and bio inspired computing, pp. 1181–1192. Springer, Cham (2018)

    Google Scholar 

  26. Guhathakurata, S., Kundu, S., Chakraborty, A., Banerjee, J.S.: A novel approach to predict COVID-19 using support vector machine. In: Data Science for COVID-19, pp. 351–364. Academic Press (2021)

    Google Scholar 

  27. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., Banerjee, J. S.: A new approach to predict COVID-19 using artificial neural networks. In: Cyber-physical systems, pp. 139–160. Academic Press (2022)

    Google Scholar 

  28. Saha, P., Guhathakurata, S., Saha, S., Chakraborty, A., Banerjee, J.S.: Application of machine learning in app-based cab booking system: a survey on Indian scenario. In: Applications of artificial intelligence in engineering, pp. 483–497. Springer, Singapore (2021)

    Google Scholar 

  29. Biswas, S., Sharma, L.K., Ranjan, R., Saha, S., Chakraborty, A., Banerjee, J.S.: Smart farming and water saving-based intelligent irrigation system implementation using the internet of things. In: Recent trends in computational intelligence enabled research, pp. 339–354. Academic Press (2021)

    Google Scholar 

  30. Mandal, J.K., Misra, S., Banerjee, J.S., Nayak, S. (eds.).: Applications of machine intelligence in engineering: Proceedings of 2nd global conference on artificial intelligence and applications (GCAIA, 2021), September 8–10, 2021, Jaipur, India. CRC Press (2022)

    Google Scholar 

  31. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., Banerjee, J.S.:.South Asian countries are less fatal concerning COVID-19: a fact-finding procedure integrating machine learning & multiple criteria decision-making (MCDM) technique. J. Inst. Eng. (India): Ser. B 102(6), 1249–1263 (2021)

    Google Scholar 

  32. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., Banerjee, J.S.: South Asian countries are less fatal concerning COVID-19: a hybrid approach using machine learning and M-AHP. In: Computational Intelligence Techniques for combating COVID-19, pp. 1–26. Springer, Cham (2021)

    Google Scholar 

  33. Chakraborty, A., Singh, B., Sau, A., Sanyal, D., Sarkar, B., Basu, S., Banerjee, J.S.: Intelligent vehicle accident detection and smart rescue system. In: Applications of Machine Intelligence in Engineering, pp. 565–57. CRC Press (2022)

    Google Scholar 

  34. Nilsson, N.J.: Principles of artificial intelligence. Morgan Kaufmann Editors (2014)

    Google Scholar 

  35. Das, K., Banerjee, J.S.: Green IoT for intelligent cyber-physical systems in industry 4.0: A review of enabling technologies, and solutions. In: Applications of machine intelligence in engineering, (pp. 463–478). CRC Press (2022)

    Google Scholar 

  36. Ribeiro, J., Lima, R., Eckhardt, T., Paiva, S.: Robotic process automation and artificial intelligence in industry 4.0–a literature review. Procedia Comput. Sci. 181, 51–58 (2021)

    Article  Google Scholar 

  37. Bahrin, M.A.K., Othman, M.F., Azli, N.N., Talib, M.F.: Industry 4.0: A review on industrial automation and robotic. Jurnal Teknologi 78(6–13), 137–143 (2016).

    Google Scholar 

  38. Banerjee, J. S., Bhattacharyya, S., Obaid, A. J. & Yeh, W. C (eds.).: Intelligent Cyber-Physical Systems Security for Industry 4.0: Applications, Challenges and Management, CRC Press (2022)

    Google Scholar 

  39. Zheng, P., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., Xu, X.:. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2), 137–150 (2018)

    Google Scholar 

  40. Ustundag, A., Cevikcan, E.: Industry 4.0: managing the digital transformation. Springer Editors (2017). Available from https://www.springer.com/gp/book/9783319578699

  41. Banerjee, J.S., Mahmud, M. & Brown, D.: Heart Rate Variability-Based Mental Stress Detection: An Explainable Machine Learning Approach. SN COMPUT. SCI 4, 176 (2023)

    Google Scholar 

  42. Chakraborty, A., Banerjee, J.S., Bhadra, R., Dutta, A., Ganguly, S., Das, D., Kundu, S., Mahmud, M., Saha G.: A Framework of Intelligent Mental Health Monitoring in Smart Cities and Societies. IETE J Res (2023)

    Google Scholar 

  43. Bhattacharyya, S., Banerjee, J. S., & Köppen, M (eds.).: Human-Centric Smart Computing: Proceedings of ICHCSC 2022, Springer. (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jyoti Sekhar Banerjee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chakraborty, A., Bhattacharyya, S., De, D., Mahmud, M., Banerjee, J.S. (2023). Intelligent Automation Framework Using AI and RPA: An Introduction. In: Bhattacharyya, S., Banerjee, J.S., De, D. (eds) Confluence of Artificial Intelligence and Robotic Process Automation. Smart Innovation, Systems and Technologies, vol 335. Springer, Singapore. https://doi.org/10.1007/978-981-19-8296-5_1

Download citation

Publish with us

Policies and ethics