The study focuses on understanding how the use of generative Artificial Intelligence (AI) can beneficially result in circular supply chain transformation while embedding design intelligence, ethical intelligence, and predictive intelligence within socio-technical systems. This study proposes and validates a model that integrates generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness, which collectively affect circular supply chain resilience and socio-environmental value realization, mediated by Sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To test the hypothesis, data were collected from 264 professionals in supply chain and technology-related industries in the USA. As the findings suggest, generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness significantly enhance sustainable process reconfiguration capability, which drives AI-enabled stakeholder co-creation. A serial mediation model indicates that Generative AI capabilities affect circular supply chain resilience and socio-environmental value realization via sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To our surprise, the regenerative policy ambidexterity negatively moderates the relationship between AI-enabled stakeholder co-creation and the realization of socio-environmental value. The results provide actionable advice for managers implementing generative AI in sustainable supply chains. Instead of focusing solely on algorithmic efficiency, if an organization can develop reconfiguration capability and engage stakeholders, it would generate systemic sustainability benefits.

This study examines how AI innovation quality shapes national ESG performance, including its governance dimensions, across 82 countries from 2000 to 2023. AI quality is measured as citation intensity per capita, reflecting scientific influence and technological sophistication validated in prior macro studies rather than patent volume, as citations better capture deployable impact for policy applications like regulatory compliance monitoring and transparent decision systems. Building on resource-resilient world theory, we assess direct AI effects and conditional interactions with financial institutions, globalization exposure, human capital, corruption, economic complexity, and clean-energy transitions using robust multi-model econometrics including IV-GMM to address endogeneity. The findings reveal advanced AI innovation quality significantly improves composite ESG performance, with stronger effects in developed economies where institutional capacity amplifies technological capabilities. AI citation intensity translates scientific breakthroughs into practical governance tools, enhancing regulatory transparency (G pillar), emissions tracking (E pillar), and social compliance (S pillar), though developing nations face implementation barriers. The findings imply that high-quality AI innovation serves as a critical technological shock absorber, enabling nations to enhance regulatory transparency and manage resources more resiliently against external shocks. However, the divergence between developed and developing nations suggests that technological sophistication must be paired with institutional reforms and human capital development to avoid value destruction. Ultimately, strategically governing AI innovation quality is essential for aligning global technological trajectories with long-term ESG performance and sustainable development goals.