Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

  • Conference paper
  • First Online:
Business Process Management (BPM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12875))

Included in the following conference series:

Abstract

Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA.

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

Access this chapter

Log in via an institution

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Syed, R., et al.: Robotic process automation: contemporary themes and challenges. Comput. Ind. 115, 103162 (2020)

    Google Scholar 

  2. Herm, L.-V., et al.: A consolidated framework for implementing robotic process automation projects. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 471–488. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_27

    Chapter  Google Scholar 

  3. van der Aalst, W.M., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60, 269–272 (2018)

    Google Scholar 

  4. Chakraborti, T., et al.: From robotic process automation to intelligent process automation. In: Asatiani, A., et al. (eds.) BPM 2020. LNBIP, vol. 393, pp. 215–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6_15

    Chapter  Google Scholar 

  5. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT Press, Massachusetts (2016)

    Google Scholar 

  6. Janiesch, C., Zschech, P., Heinrich, K.: Machine Learning and Deep Learning. Electronic Markets Forthcoming (2021). https://doi.org/10.1007/s12525-021-00475-2

    Article  Google Scholar 

  7. 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. Finance Risk Perspect. 9, 90–102 (2020)

    Article  Google Scholar 

  8. Panetta, K.: Top strategic technology trends for 2021 (2020), https://www.gartner.com/en/publications/top-tech-trends-2021. Accessed 19 Jan 2021

  9. Agostinelli, S., Marrella, A., Mecella, M.: Research challenges for intelligent robotic process automation. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 12–18. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_2

    Chapter  Google Scholar 

  10. Imgrund, F., Fischer, M., Janiesch, C., Winkelmann, A.: Managing the long tail of business processes. In: European Conference on Information Systems, Guimarães, AIS (2017)

    Google Scholar 

  11. Haugeland, J.: Artificial Intelligence: The Very Idea. MIT Press, MA, Boston (1989)

    Book  Google Scholar 

  12. Le Clair, C.: The forrester wave™: robotic process automation, Q2 2018 (2018), http://rpa.innavatar.ca/The_Forrester_Wave_RPA_2018.pdf. Accessed 19 Jan 2021

  13. vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun. Assoc. Inf. Syst. 37, 205–224 (2015)

    Google Scholar 

  14. Schultze, U., Avital, M.: Designing interviews to generate rich data for information systems research. Inf. Organ. 21, 1–16 (2011)

    Article  Google Scholar 

  15. Asatiani, A., Penttinen, E.: Get ready for robots: why planning makes the difference between success and disappointment. J. Inf. Technol. Teach. Cases 6, 67–74 (2016)

    Article  Google Scholar 

  16. Noppen, P., Beerepoot, I., van de Weerd, I., Jonker, M., Reijers, H.A.: How to keep RPA maintainable? In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 453–470. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_26

    Chapter  Google Scholar 

  17. Syed, R., Wynn, M.T.: How to trust a bot: an RPA user perspective. In: Asatiani, A., et al. (eds.) BPM 2020. LNBIP, vol. 393, pp. 147–160. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6_10

    Chapter  Google Scholar 

  18. Abdulla, Y., Ebrahim, R., Kumaraswamy, S.: Artificial intelligence in banking sector: evidence from Bahrain. In: Proceedings of the International Conference on Data Analytics for Business and Industry (ICDABI), pp. 1–6. IEEE, Virtual (2020)

    Google Scholar 

  19. Mazurek, G., Małagocka, K.: Perception of privacy and data protection in the context of the development of artificial intelligence. J. Manag. Analytics 6, 344–364 (2019)

    Article  Google Scholar 

  20. Herm, L.-V., Wanner, J., Seubert, F., Janiesch, C.: I don’t get it, but it seems valid! The connection between explainability and comprehensibility in (X)AI research. In: European Conference on Information Systems (ECIS), Virtual Conference, AIS (2021)

    Google Scholar 

  21. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)

    Article  MathSciNet  Google Scholar 

  22. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  23. Heinrich, K., Zschech, P., Janiesch, C., Bonin, M.: Process data properties matter: introducing GCNN and KVP for next event prediction with deep learning. Decis. Support Syst. 143, 113494 (2021)

    Google Scholar 

  24. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)

  25. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46, 1–37 (2014)

    Article  Google Scholar 

  26. Wang, S., Nepal, S., Rudolph, C., Grobler, M., Chen, S.: Backdoor attacks against transfer learning with pre-trained deep learning models. IEEE Trans. Serv. Comput. 1 (2020)

    Google Scholar 

  27. Amors, L., Hafiz, S.M., Lee, K., Tol, M.C.: Gimme that model!: a trusted ML model trading protocol. arXiv preprint arXiv:2003.00610 (2020)

  28. Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96, 108–116 (2018)

    Google Scholar 

  29. Zschech, P., Fleißner, V., Baumgärtel, N., Hilbert, A.: Data science skills and enabling enterprise systems. HMD Praxis der Wirtschaftsinformatik 55, 163–181 (2018)

    Article  Google Scholar 

  30. Daugherty, P.R., Wilson, H.J.: Human+ Machine: Reimagining Work in the Age of AI. Harvard Business Press, Boston (2018)

    Google Scholar 

  31. Pantano, E., Pizzi, G.: Forecasting artificial intelligence on online customer assistance: evidence from chatbot patents analysis. J. Retail. Consum. Serv. 55, 102096 (2020)

    Google Scholar 

  32. Martins, P., Sá, F., Morgado, F., Cunha, C.: Using machine learning for cognitive Robotic Process Automation (RPA). In: Proceedings of the 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, pp. 1–6. IEEE (2020)

    Google Scholar 

  33. Marrella, A.: Automated planning for business process management. J. Data Semant. 8, 79–98 (2019)

    Article  Google Scholar 

  34. Schuler, J., Gehring, F.: Implementing robust and low-maintenance Robotic Process Automation (RPA) solutions in large organisations. Available at SSRN 3298036 (2018)

    Google Scholar 

  35. Koch, J., Trampler, M., Kregel, I., Coners, A.: Mirror, mirror, on the wall’: robotic process automation in the public sector using a digital twin. In: European Conference on Information Systems (ECIS), Virutal, AIS (2020)

    Google Scholar 

  36. Richardson, S.: Cognitive automation: a new era of knowledge work? Bus. Inf. Rev. 37, 182–189 (2020)

    Google Scholar 

  37. Seasongood, S.: A case for robotics in accounting and finance. Financ. Executive 31, 31–39 (2016)

    Google Scholar 

  38. Yatskiv, N., Yatskiv, S., Vasylyk, A.: Method of robotic process automation in software testing using artificial intelligence. In: International Conference on Advanced Computer Information Technologies (ACIT), pp. 501–504, IEEE (2020)

    Google Scholar 

  39. Lasso-Rodriguez, G.W.K.: Hyperautomation to fulfil jobs rather than executing tasks: the BPM manager robot vs human case. Rom. J. Inf. Technol. Autom. Control 30, 7–22 (2020)

    Google Scholar 

  40. Lamberton, C., Brigo, D., Hoy, D.: Impact of robotics, RPA and AI on the insurance industry: challenges and opportunities. J. Financ. Perspect. 4, 8–20 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Julius-Maximilians-Universität, Würzburg, Germany

    Lukas-Valentin Herm, Christian Janiesch & Franz Seubert

  2. HAW Landshut, Landshut, Germany

    Christian Janiesch

  3. Utrecht University, Utrecht, The Netherlands

    Hajo A. Reijers

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Hajo A. Reijers

Authors
  1. Lukas-Valentin Herm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Janiesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hajo A. Reijers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Franz Seubert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lukas-Valentin Herm .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Carlton, VIC, Australia

    Artem Polyvyanyy

  2. NICTA Queensland Research Lab, Queensland University of Technology, Brisbane, QLD, Australia

    Moe Thandar Wynn

  3. Ghent University, Ghent, Belgium

    Amy Van Looy

  4. Institut fur Datenbanken, Info System, Universitat Ulm, Ulm, Germany

    Manfred Reichert

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Herm, LV., Janiesch, C., Reijers, H.A., Seubert, F. (2021). From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management. BPM 2021. Lecture Notes in Computer Science(), vol 12875. Springer, Cham. https://doi.org/10.1007/978-3-030-85469-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85469-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85468-3

  • Online ISBN: 978-3-030-85469-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation