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Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?

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  • Leyer, Michael
  • Schneider, Sabrina

Abstract

Artificial intelligence (AI) has emerged as a promising and increasingly available technology for managerial decision-making. With the adoption of AI-enabled software, organizations can leverage various benefits of the technology, but they also have to consider the intended and unintended consequences of using the technology for managerial roles. It is still unclear whether managers will benefit from enhancing their abilities with AI-enabled software or become powerless puppets that do more than announce AI-enabled software results. Our research has revealed distinct ways in which organizations can use AI-enabled decision-making solutions: as tools or novelties, for decision augmentation or automation, and as either a voluntary or a mandatory option. In this article, we discuss the implications of each of these combinations on the relevant managers. We consider outcomes related to managerial job design and derive practical advice for organizational designers and managers who work with AI. Our outcomes provide guidance on how to deal with the conflict-riddled relationship between managers and technology with regard to capabilities, responsibilities, and acceptance of AI-enabled software.

Suggested Citation

  • Leyer, Michael & Schneider, Sabrina, 2021. "Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?," Business Horizons, Elsevier, vol. 64(5), pages 711-724.
  • Handle: RePEc:eee:bushor:v:64:y:2021:i:5:p:711-724
    DOI: 10.1016/j.bushor.2021.02.026
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    References listed on IDEAS

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    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    3. Øystein D. Fjeldstad & Charles C. Snow & Raymond E. Miles & Christopher Lettl, 2012. "The architecture of collaboration," Strategic Management Journal, Wiley Blackwell, vol. 33(6), pages 734-750, June.
    4. Barry Mitnick, 1975. "The theory of agency," Public Choice, Springer, vol. 24(1), pages 27-42, December.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Sam Ransbotham & Robert G. Fichman & Ram Gopal & Alok Gupta, 2016. "Special Section Introduction—Ubiquitous IT and Digital Vulnerabilities," Information Systems Research, INFORMS, vol. 27(4), pages 834-847, December.
    7. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
    8. Xu, Yingzi & Shieh, Chih-Hui & van Esch, Patrick & Ling, I-Ling, 2020. "AI customer service: Task complexity, problem-solving ability, and usage intention," Australasian marketing journal, Elsevier, vol. 28(4), pages 189-199.
    9. Oldham, Greg R. & Fried, Yitzhak, 2016. "Job design research and theory: Past, present and future," Organizational Behavior and Human Decision Processes, Elsevier, vol. 136(C), pages 20-35.
    10. Kirsten Martin, 2019. "Ethical Implications and Accountability of Algorithms," Journal of Business Ethics, Springer, vol. 160(4), pages 835-850, December.
    11. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
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    1. Plantec, Quentin & Deval, Marie-Alix & Hooge, Sophie & Weil, Benoit, 2023. "Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry," Technovation, Elsevier, vol. 124(C).
    2. Johnson, Prince Chacko & Laurell, Christofer & Ots, Mart & Sandström, Christian, 2022. "Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    3. Ion Popa & Marian Mihai Cioc & Andreea Breazu & Catalina Florentina Popa, 2024. "Identifying Sufficient and Necessary Competencies in the Effective Use of Artificial Intelligence Technologies," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 1-33, February.

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