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Integrating intuition and artificial intelligence in organizational decision-making

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  • Vincent, Vinod U.

Abstract

Artificial intelligence (AI) is fundamentally changing organizational decision-making processes. With the abilities to self-learn and to improve decision quality, AI is now taking over many decision responsibilities that were formerly assigned to humans alone. However, the effectiveness of AI for ill-structured and uncertain decision environments is still in question. In such decision contexts that have no precedent on which to base a solution, humans have historically relied on their intuition to make decisions. Yet intuition, too, has been found to have weaknesses that restrict decision quality. Therefore, this article introduces a decision-making model that effectively integrates the strengths of both intuition and AI while minimizing the vulnerabilities of each method. The model specifies when and how both modes should be combined for effective organizational decision-making. In addition, the article presents important future research considerations relating to AI for both practitioners and academics.

Suggested Citation

  • Vincent, Vinod U., 2021. "Integrating intuition and artificial intelligence in organizational decision-making," Business Horizons, Elsevier, vol. 64(4), pages 425-438.
    • Handle: RePEc:eee:bushor:v:64:y:2021:i:4:p:425-438
    DOI: 10.1016/j.bushor.2021.02.008
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    References listed on IDEAS

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    Cited by:

    1. Walsh, Christian & Collins, Jamie & Knott, Paul, 2022. "The four types of intuition managers need to know," Business Horizons, Elsevier, vol. 65(5), pages 697-708.
    2. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    3. Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.

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