Decision factors for the selection of AI-based decision support systems—The case of task delegation in prognostics

Decision support systems (DSS) integrating artificial intelligence (AI) hold the potential to significantly enhance organizational decision-making performance and speed in areas such as prognostics in machine maintenance. A key issue for organizations aiming to leverage this potential is to select an appropriate AI-based DSS. In this paper, we develop a delegation perspective to identify decision factors and underlying AI system characteristics that affect the selection of AI-based DSS. Utilizing the analytical hierarchy process method, we derive decision weights for these characteristics and apply them to three archetypes of AI-based DSS designed for prognostics. Additionally, we explore how users' expertise levels impact their preferences for specific AI system characteristics. The results confirm that Performance is the most important decision factor, followed by Effort and Transparency. In line with these results, we find that the archetypes of prognostics systems using Direct Remaining Useful Life estimation and Similarity-based Matching best fit user preferences. Moreover, we find that novices and experts strongly prefer visual over structural explanations, while users with moderate expertise also value structural explanations to develop their skills further.