Critical success factors of artificial intelligence adoption in the renewable energy supply chain

ABSTRACT With rising global demand for renewable energy, artificial intelligence (AI) presents significant opportunities to optimize supply chain operations. This study investigates the key determinants of AI adoption in Bangladesh's renewable energy supply chain. After reviewing a wide range of literature and consulting with experts, fourteen key factors were identified that contribute to AI adoption. These factors are organized within the Technology-Organization-Environment-Human (TOEH) framework. The Neutrosophic Interpretive Structural Modeling (N-ISM) method is also used to analyze how these factors are interrelated and which ones have a strong influence. The analysis indicates that government regulation, support from top management, and specific organizational characteristics stand out as the most significant CSFs, demonstrating considerable influence over other elements. Conversely, elements like technological readiness, cybersecurity, and job security show a higher level of dependence and a comparatively diminished driving influence. These findings provide valuable insights for policymakers, energy planners, and industry professionals who are looking to integrate AI-driven technologies into renewable energy systems. This research offers a structured framework that helps prioritize and manage the essential components for integrating AI, thereby contributing to the creation of a more adaptable, efficient, and sustainable renewable energy supply chain in Bangladesh. The findings additionally provide a basis for forthcoming research and strategic planning in comparable contexts within developing countries.