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This study examines the integration of artificial intelligence (AI) with circular economy (CE) principles in Thailand's tourism industry. It explores the interactions between AI‐Enhanced Predictive Waste Analytics (AI‐PWA), Regenerative Resource Integration (RRI), Dynamic Material Flow Optimization (DMFO), and AI‐Induced Resistance to Change (AIRC). Using a mixed‐methods approach, qualitative insights from industry stakeholders are combined with quantitative analysis via Partial Least Squares Structural Equation Modeling (PLS‐SEM). Findings reveal that AI‐PWA improves real‐time resource management, driving DMFO and supporting regenerative practices through RRI. However, AIRC moderates AI's effectiveness in sustainability transitions, with concerns such as job displacement, mistrust, and complexity hindering adoption. This study provides actionable strategies to mitigate resistance, enhance stakeholder collaboration, and scale AI adoption in resource‐constrained settings, contributing to SDG 12 and SDG 13. The findings offer practical insights for aligning AI innovations with sustainable development in high‐variability industries.
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Bang‐Ning et al. (2025) studied this question.
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