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Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes

Author

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  • Emmanouil Papagiannidis

    (Norwegian University of Science and Technology)

  • Ida Merete Enholm

    (Norwegian University of Science and Technology)

  • Chirstian Dremel

    (Norwegian University of Science and Technology)

  • Patrick Mikalef

    (Norwegian University of Science and Technology)

  • John Krogstie

    (Norwegian University of Science and Technology)

Abstract

In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI’s governance in organizations and by uncovering the practices that underpin them.

Suggested Citation

  • Emmanouil Papagiannidis & Ida Merete Enholm & Chirstian Dremel & Patrick Mikalef & John Krogstie, 2023. "Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes," Information Systems Frontiers, Springer, vol. 25(1), pages 123-141, February.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:1:d:10.1007_s10796-022-10251-y
    DOI: 10.1007/s10796-022-10251-y
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    References listed on IDEAS

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    Cited by:

    1. Daniel Leuthe & Tim Meyer-Hollatz & Tobias Plank & Anja Senkmüller, 2024. "Towards Sustainability of AI – Identifying Design Patterns for Sustainable Machine Learning Development," Information Systems Frontiers, Springer, vol. 26(6), pages 2103-2145, December.
    2. Denis Dennehy & Anastasia Griva & Nancy Pouloudi & Yogesh K. Dwivedi & Matti Mäntymäki & Ilias O. Pappas, 2023. "Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI," Information Systems Frontiers, Springer, vol. 25(1), pages 1-7, February.
    3. Haochen Guo & Petr Polak, 2023. "Intelligent finance and change management implications," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.

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