AI-powered risk mitigation and organizational sensemaking for supply chain resilience

Purpose This study aims to examine the integrated impact of artificial intelligence (AI)-powered supply chain risk management (AISCRM), autonomous resilience (AR), organizational sensemaking (OS) and supply chain resilience (SCR) on enhancing US manufacturing supply chain resilience during digital transformation, thereby addressing a critical gap in the current literature. Design/methodology/approach Utilizing a quantitative approach, the research collected data from 741 respondents across 27 US states, representing 100 manufacturers in automotive, aerospace, electronics and industrial manufacturing sectors. The study used systematic random sampling via SurveyMonkey, distributing questionnaires to 1,900 employees. Structural equation modeling was used to test hypotheses linking AISCRM, AR, OS and SCR, grounded in the dynamic capability view (DCV). Findings The results confirm that AISCRM has a positive influence on AR (H1); AR enhances OS (H2) and SCR (H3); OS has a positive effect on SCR (H4); and OS mediates the relationship between AR and SCR (H5). These findings highlight the synergistic role of AI-driven autonomy and human sensemaking in fostering resilience, with significant implications for diverse manufacturing contexts. Practical implications Manufacturers can leverage AISCRM and AR to enhance autonomous recovery, while OS can guide strategic adaptations to reduce the $50bn annual loss from supply chain disruptions. This provides actionable strategies for large, digitally intensive manufacturers to enhance their competitiveness. Originality/value To the best of the authors’ knowledge, this research is among the first to empirically integrate AISCRM, AR, OS and SCR, addressing a gap identified in recent studies, providing a holistic framework for resilience in the digital era, and contributing to both theory and practice in manufacturing supply chain management.