Leveraging AI-enabled green knowledge through workforce transformation for circular business model innovation across construction segments

In the era of artificial intelligence, translating AI-enabled insights into circular business model innovation (CBMI) remains a major challenge for construction firms because implementation depends on workforce transformation rather than technology adoption alone. Drawing on the dynamic capabilities view and the 4I organizational learning framework, this study develops a knowledge-to-skill conversion model linking AI capability (AIC), green knowledge management (GKM), labor skill transformation (LST), government support, and CBMI. Survey data from 498 Chinese construction firms were analyzed using PLS-SEM, multi-group analysis, necessary condition analysis, and fsQCA. The results show that AIC is positively associated with GKM, LST, and CBMI. After LST is included, the average direct relationship between GKM and CBMI becomes non-significant, whereas the serial AIC–GKM–LST–CBMI pathway remains significant, indicating that green knowledge is translated into innovation mainly through workforce enactment. Government support strengthens the conversion of both GKM and LST into CBMI by improving the institutional conditions for knowledge application. Segment analyses show that this mechanism is stronger in industrial construction and infrastructure/heavy civil engineering, while different construction segments reach high CBMI through distinct configurations of AI, knowledge, workforce, and institutional capabilities. Our findings offer practical insights for industry practitioners, demonstrating that building CBMI involves more than simply adopting digital technologies; it also encompasses micro-level mechanisms for reshaping the workforce’s skill structure—specifically, reskilling, work redesign and work adaptability—to enable the transformation of these technologies into innovation.