AI-empowered KM processes for decision-making: empirical evidence from worldwide organisations

Purpose - This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision- making? Design/methodology/approach - An explorative investigation has been conducted through semi- structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and forprofit organisations. Interviews have been analysed through a mixed thematic analysis. Findings - The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes. Practical implications - The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision- making model, the authors propose a six-step systematic procedure for managers. Originality/value - To the best of the authors' knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.