IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v32y2022i1d10.1007_s12525-021-00519-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-021-00519-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12525-021-00519-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ogiela, Lidia & Ogiela, Marek R., 2014. "Cognitive systems for intelligent business information management in cognitive economy," International Journal of Information Management, Elsevier, vol. 34(6), pages 751-760.
    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    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.
    4. 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.
    5. 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.
    6. 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.
    7. Dorian Selz, 2020. "From electronic markets to data driven insights," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 57-59, March.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rainer Alt, 2022. "Electronic Markets on platform dualities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 1-10, March.
    2. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    3. Alfonso Renato Vargas-Murillo & Ilda Nadia Monica de la Asuncion Pari-Bedoya & Adriana Margarita Turriate-Guzman & Cintya Amelia Delgado-Chávez & Franshezka Sanchez-Paucar, 2024. "Transforming Justice: Implications of Artificial Intelligence in Legal Systems," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 13, March.
    4. Christou, Prokopis & Hadjielias, Elias & Simillidou, Aspasia & Kvasova, Olga, 2023. "The use of intelligent automation as a form of digital transformation in tourism: Towards a hybrid experiential offering," Journal of Business Research, Elsevier, vol. 155(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rainer Alt, 2021. "Electronic Markets on robotics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 465-471, September.
    2. Rainer Alt, 2021. "Electronic Markets on digital platforms and AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 233-241, June.
    3. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    4. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    5. Rainer Alt, 2020. "Evolution and perspectives of electronic markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 1-13, March.
    6. Sundberg, Leif & Holmström, Jonny, 2023. "Democratizing artificial intelligence: How no-code AI can leverage machine learning operations," Business Horizons, Elsevier, vol. 66(6), pages 777-788.
    7. Choi, Hee-soo & Kim, Sang-heon, 2017. "A content service deployment plan for metaverse museum exhibitions—Centering on the combination of beacons and HMDs," International Journal of Information Management, Elsevier, vol. 37(1), pages 1519-1527.
    8. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    10. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    11. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    12. Uklańska Anna, 2023. "Robotic Process Automation (RPA) – Bibliometric Analysis and Literature Review," Foundations of Management, Sciendo, vol. 15(1), pages 129-140, January.
    13. repec:ags:aaea22:335590 is not listed on IDEAS
    14. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    15. Paola Perchinunno & Massimo Bilancia & Domenico Vitale, 2021. "A Statistical Analysis of Factors Affecting Higher Education Dropouts," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 341-362, August.
    16. Najla Alharbi & Bashayer Alkalifah & Ghaida Alqarawi & Murad A. Rassam, 2024. "Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection," Future Internet, MDPI, vol. 16(10), pages 1-22, October.
    17. Ugur Karaboga & Pelin Vardarlier, 2020. "Examining the use of artificial intelligence in recruitment processes," Bussecon Review of Social Sciences (2687-2285), Bussecon International Academy, vol. 2(4), pages 1-17, December.
    18. Cui, Mianshan, 2022. "District heating load prediction algorithm based on bidirectional long short-term memory network model," Energy, Elsevier, vol. 254(PA).
    19. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    20. Georges Sfeir & Filipe Rodrigues & Maya Abou-Zeid, 2021. "Gaussian Process Latent Class Choice Models," Papers 2101.12252, arXiv.org.
    21. Abdulwahhab, Ali H. & Abdulaal, Alaa Hussein & Thary Al-Ghrairi, Assad H. & Mohammed, Ali Abdulwahhab & Valizadeh, Morteza, 2024. "Detection of epileptic seizure using EEG signals analysis based on deep learning techniques," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elmark:v:32:y:2022:i:1:d:10.1007_s12525-021-00519-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.