Optimising Human– <scp>AI</scp> Decision Performance: A Trust and Capability Framework for Knowledge Management

ABSTRACT Organisations struggle to optimise human–AI collaboration in knowledge‐intensive decision‐making. This paper proposes the Trust–Complementarity Model of Collective Intelligence (TCM‐CI), explaining how calibrated trust and complementary capability utilisation drive superior organisational performance. Through systematic synthesis of human–AI interaction and knowledge management research, we identify three core mechanisms: (1) calibrated trust maximises collective intelligence by balancing appropriate reliance with necessary oversight, (2) complementarity–trust interaction determines optimal performance when high capability utilisation combines with appropriate trust levels and (3) dynamic feedback loops create reinforcing organisational learning cycles. The framework provides practical guidance for executives designing human–AI teams, developing trust calibration training, and establishing performance metrics. By integrating psychological trust factors with cognitive capability optimisation, this model offers actionable insights for knowledge management practitioners implementing AI‐augmented decision systems while advancing theoretical understanding of human–AI collaboration effectiveness.