ABSTRACT This research investigates the extent to which business process management resilience is improved within China's electric vehicle supply chains by AI‐driven knowledge management systems. The research defines AI‐KM through machine learning, natural language processing, and predictive analytics, utilizing a qualitative exploratory case study of JAC Motors ( n = 5 expert interviews). Grounded in Complex Adaptive Systems theory, this study explains how AI‐KM functions as a negentropic force to reduce system entropy during disruptions. The primary findings suggest that the use of AI‐supported validation tools resulted in a 58% reduction in the delays of innovative components, while semantic search functionalities reduced the reliance on individual expertise. The study also identifies “Mianzi” (face‐saving) and “Guanxi” as substantial cultural barriers to knowledge sharing. These barriers can be mitigated by institutionalizing psychological safety through anonymized automated anomaly detection. The research suggests the implementation of a comprehensive AI‐KM‐BPM framework to enhance the continuity of production and recovery period of the supply chain. The EV sector's critical gap in organizational learning and CAS literature is bridged through this research, which presents a theoretical model for AI‐driven resilience.
Irfan et al. (Mon,) studied this question.