Generative AI and managerial decision-making: reconfiguring decision processes in organizations

The rapid advancement of generative artificial intelligence (GenAI) is transforming managerial decision-making by enabling the generation, synthesis, and evaluation of complex information in real time. However, existing research still provides only a fragmented understanding of how GenAI reconfigures managerial decision-making, including how decisions are structured, how authority is distributed, and how outcomes are evaluated within organizations. Building on bounded rationality, this study addresses this gap using a mixed-methods approach that combines a systematic literature review of 84 articles with qualitative insights from 20 semi-structured interviews with AI experts across Europe. The study develops an integrative framework that links organizational and individual antecedents to GenAI decision-making paradigms, ethical and governance mechanisms, and decision outcomes. In particular, the findings identify three Human–AI interaction patterns, namely: a) augmented, b) hybrid, and c) autonomous, that reflect different distributions of cognitive roles and decision authority between humans and GenAI. The study extends bounded rationality theory by introducing a new perspective on GenAI-driven decision-making and provides practical insights into how organizations can effectively integrate GenAI into their decision processes.