Purpose – This paper explores the application of Artificial Intelligence (AI) in e-commerce supply chains, addressing the challenges that need to be overcome to successfully implement AI in supply chain systems. The aim is to identify and analyze the technological, organizational, operational, and external barriers that hinder AI adoption and to propose strategies that enable efficient, automated, and customer-centric supply chain management.

Design/methodology/approach – The study reviews literature and real-world examples of e-commerce firms that have adopted AI technologies in their supply chain operations. It focuses on understanding the impact of various barriers such as technological limitations, decision-making issues in real-time, organizational resistance, data handling difficulties, and legal constraints. The research further examines how firms have developed strategies and business models to overcome these barriers through effective data governance, workforce adaptation, and policy support.

Findings – The study identifies several critical challenges in the adoption of AI in e-commerce supply chains. These include technological limitations, inefficiencies in real-time decision-making, resistance to change within organizations, inadequate information management systems, and compliance with regulatory and legal requirements. Despite these challenges, several successful e-commerce firms demonstrate that the strategic application of AI enhances operational efficiency, automation, and customer satisfaction. The findings emphasize that overcoming these barriers requires an integrated approach involving organizational restructuring, regulatory support, and continuous technological innovation.

Originality/value – This research contributes to the growing body of knowledge on AI integration in e-commerce supply chains by summarizing existing challenges, highlighting best practices, and identifying research gaps. It provides insights valuable to industry practitioners and policymakers for designing frameworks that promote sustainable, ethical, and resilient AI-driven supply chains. The study also suggests directions for future research, including empirical validation, sustainability assessment, and ethical considerations in AI implementation.