Decision-making in organizations: should managers use AI?

Purpose: Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers' attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption. Design/methodology/approach: The paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline. Findings: The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making. Research limitations/implications: The paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers. Practical implications: This paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process. Social implications: The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making. Originality/value: This study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers' attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making. [ABSTRACT FROM AUTHOR]