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The potential of artificial intelligence adoption for managerial decision making: A rapid literature review

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  • Rafal Rydzewski

    (University of Economics)

Abstract

The article aims to investigate how Artificial Intelligence (AI) can support managers in making decisions. The methodology employed for this purpose involves a rapid literature review process. It is done in two stages that involve the graphic analysis of keywords, the systematic manner for rapid literature review and a directed search through online scientific article databases in pursuit of answers to the research questions. As a scientific basis for article selection, Scopus and Web of Science were chosen. The study indicates that AI supports decision-making processes by providing managers with more informed insights and predictions because of high data analysis capabilities. However, there is a lack of clear indication regarding the potential future applications of AI for decision-making purposes other than a supportive role. The study highlights the importance of AI in managerial decision-making processes. It particularly enhances analytical capabilities which led to the improvement of decision outcomes. It also stressed out the need for further research on future applications of AI in managerial decision-making contexts. The main concerns tackled challenges related to its adoption, such as reliability, accountability and ethical considerations. This article contributes to the literature by conducting a rapid literature review of the current state of research of AI applications for managerial decision-making. It highlights gaps in research on the future of AI for decision-making and emphasizes the need for additional research in this area.

Suggested Citation

  • Rafal Rydzewski, 2025. "The potential of artificial intelligence adoption for managerial decision making: A rapid literature review," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 26(1), pages 77-88.
  • Handle: RePEc:agh:journl:v:26:y:2025:i:1:p:77-88
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    File URL: https://journals.agh.edu.pl/manage/article/view/7431/3198
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