The Key Opportunities and Barriers to AI Adoption in Sustainable Supply Chain Management

Abstract

As sustainability becomes a strategic imperative in global supply chains, artificial intelligence (AI) is increasingly viewed as a tool that can support not only efficiency but also environmental and social objectives. Yet, the extent to which AI directly contributes to sustainable supply chain management (SSCM) remains underexplored. This thesis investigates the key opportunities and barriers to AI adoption in SSCM, focusing on Swedish industries including automotive, chemicals, electronics, logistics, and consumer goods. Guided by the supply chain operations reference (SCOR) model and the technology–organization–environment–human (TOEH) framework, the study adopts a qualitative abductive approach based on 16 semi structured interviews with professionals across different supply chain functions. The findings show that AI offers clear benefits for forecasting, sourcing, production, delivery, and return processes—particularly through waste reduction, emissions control, and improved efficiency. However, most companies prioritize economic gains, while environmental and especially social dimensions of sustainability receive less attention. The study also highlights persistent challenges such as poor data quality, limited organizational readiness, regulatory uncertainty, and human resistance. This research contributes to a more holistic understanding of AI in SSCM and provides practical insights for firms aiming to integrate sustainability more meaningfully into supply chain operations.