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Cognitive automation

Author

Listed:
  • Christian Engel

    (University of St.Gallen)

  • Philipp Ebel

    (University of St.Gallen)

  • Jan Marco Leimeister

    (University of St.Gallen)

Abstract

Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization. We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research.

Suggested Citation

  • Christian Engel & Philipp Ebel & Jan Marco Leimeister, 2022. "Cognitive automation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 339-350, March.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:1:d:10.1007_s12525-021-00519-7
    DOI: 10.1007/s12525-021-00519-7
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    References listed on IDEAS

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    1. Wil van der Aalst & Kees van Hee, 2004. "Workflow Management: Models, Methods, and Systems," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262720469, April.
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    3. Peter Hofmann & Caroline Samp & Nils Urbach, 2020. "Robotic process automation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 99-106, March.
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    5. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    6. Sebastian Lins & Konstantin D. Pandl & Heiner Teigeler & Scott Thiebes & Calvin Bayer & Ali Sunyaev, 2021. "Artificial Intelligence as a Service," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 441-456, August.
    7. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    8. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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    Cited by:

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    4. Rainer Alt, 2022. "Electronic Markets on platform dualities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 1-10, March.

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    More about this item

    Keywords

    Cognitive automation; Knowledge work; Artificial intelligence; Machine learning; Cognition; Automation;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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