Delphi-driven insights into artificial intelligence implementation in sustainable supply chains: navigating barriers and strategic enablers

Abstract

The growing adoption of Artificial Intelligence (AI) technologies offers significant opportunities to enhance the sustainability of supply chain operations. However, the practical integration of AI into Sustainable Supply Chains (SSCs) remains limited due to various barriers and implementation challenges. This study systematically investigates these barriers and challenges, as well as mitigation strategies, through a two-round Delphi study involving industry and academic experts. In Round 1, an open-ended questionnaire was used to collect expert insights, followed by a consolidation phase in Round 2, which utilized a 4-point Likert scale. The results highlight that data quality and unavailability issues, AI-related skill gaps, cybersecurity risk, complex AI-Enterprise Resource Planning (ERP) integration, and a fragmented AI approach are critical barriers. Moreover, the analysis identified key mitigation strategies, including AI awareness and change management, AI compatible ERP systems, considering data as a strategic asset, gradual AI integration, strong cybersecurity measures, and solid data management systems, to mitigate the barriers to implementing AI in SSCs. This research contributes to existing literature by empirically consolidating the insights on AI adoption barriers in SSCs. It provides practical recommendations to mitigate those barriers for organizations aiming to implement AI-driven sustainable practices.