Unlocking the value of generative AI for supply chain resilience and crisis readiness: a complex adaptive systems perspective

The existing literature conceptualizes generative artificial intelligence (GenAI) as a knowledge infrastructure that facilitates organizational abilities to use changes. However, there is a dearth of studies to illustrate the core reasons why GenAI-enabled knowledge mechanisms thrive with unequal variations across various spheres of organizations. The purpose of the current research is to draw on complex adaptive systems (CAS) theory and investigate how GenAI-enabled information quality and GenAI governance maturity are associated with supply chain resilience and crisis readiness through two distinct knowledge-sharing mechanisms: GenAI-enabled tacit and explicit knowledge sharing. The authors develop a CAS-informed model that distinguishes between GenAI-enabled tacit and explicit knowledge sharing as complementary pathways linking GenAI-derived value to adaptive outcomes. The model is tested using survey data from 192 full-time employees in organizations adopting GenAI. Confirmatory factor analysis and structural equation modelling are conducted in SPSS AMOS. GenAI-enabled information quality positively influences both GenAI-enabled tacit and explicit knowledge sharing. GenAI governance maturity also positively influences both forms of knowledge sharing. GenAI-enabled explicit knowledge sharing yields considerable advancement in supply chain resilience and crisis readiness. GenAI-enabled tacit knowledge sharing enhances supply chain resilience smoothly, but its association with crisis readiness does not appear significant. The limitation lies in the cross-sectional, perceptual design, as it limits causal inference and fails to capture the temporal evaluation highlighted by the CAS framework. It implies that future studies use longitudinal or event-based research designs and objective indicators (e.g. documentation traces, usage logs) to examine how GenAI-enabled knowledge dissemination advances across disruption layers. It is imperative to support supply chain resilience and preparedness. Institutions should invest in GenAI governance maturity and information quality (timeliness, accuracy and interpretability); propel GenAI governance maturity by structured policies, regulation and incremental optimization; and intentionally frame processes that strengthen both tacit sensemaking and explicit artefacts. This study reframes GenAI as an adaptive infrastructure within CAS and demonstrates that GenAI value materializes through distinct tacit vs explicit knowledge-sharing pathways, clarifying why GenAI investments yield uneven outcomes, especially for supply chain resilience under disruption.