Braving technological turbulence: generative artificial intelligence can build digital resilience in human-centric supply chains

Purpose Human-centric supply chains (HCSCs) are increasingly vital as firms face growing disruptions while balancing efficiency with workforce well-being and collaboration. This study examines how generative artificial intelligence (GenAI) influences HCSC performance under technological turbulence. Anchored in Industry 5.0, the research positions digital resilience as a key organizational capability and investigates whether advanced AI technologies translate environmental uncertainty into people-focused supply chain outcomes through sociotechnical and dynamic capability perspectives. Design/methodology/approach The study adopts a quantitative, deductive design using survey data from 276 manufacturing firms in the United Kingdom and Canada. Data were collected in early 2025 from managers involved in supply chain and digital transformation activities. The proposed relationships among GenAI, technological turbulence, digital resilience and HCSC performance were tested using partial least squares structural equation modeling (PLS-SEM). Measurement reliability, convergent validity and discriminant validity were rigorously assessed. Findings The results reveal that GenAI has a strong positive effect on digital resilience. Technological turbulence does not directly enhance digital resilience but significantly drives GenAI adoption and directly improves HCSC performance. Digital resilience does not directly influence human-centric performance, and its mediating role is not supported. However, GenAI significantly mediates the relationship between technological turbulence and digital resilience, underscoring its central role in transforming environmental uncertainty into adaptive capacity. These findings indicate that technological capabilities alone are insufficient to achieve people-focused outcomes. Research limitations/implications The cross-sectional design limits causal inference, and reliance on single-informant perceptual data may introduce bias. The focus on manufacturing firms in two advanced economies may restrict generalizability. Future research should employ longitudinal designs, incorporate multiple respondents, and examine additional socio-organizational mechanisms, such as governance, culture and skills development that link AI adoption to human-centric supply chain outcomes. Practical implications Managers should view GenAI as a strategic capability for building digital resilience rather than solely as an efficiency tool. While GenAI enhances adaptive capacity, achieving human-centric performance requires complementary organizational practices, including workforce upskilling, participative decision-making and transparent governance. Firms operating amid technological turbulence can leverage uncertainty to accelerate the adoption of responsible AI aligned with people-focused supply chain objectives. Social implications The findings suggest that digital resilience alone does not ensure improved collaboration, trust or workforce well-being. Achieving human-centric supply chains requires aligning AI adoption with social and organizational practices. Policymakers and industry leaders should promote responsible use of GenAI, workforce development and inclusive innovation to ensure that digital transformation contributes to sustainable livelihoods and equitable value creation. Originality/value This study advances the literature by distinguishing generative AI from traditional AI and empirically examining its role in human-centric supply chains. It introduces technological turbulence as a contextual driver of AI-enabled capability development and challenges assumptions that digital resilience directly leads to human-centric outcomes. By integrating sociotechnical and dynamic capability perspectives, the study provides novel insights into AI-enabled, human-centric supply chains within Industry 5.0 contexts.