Artificial intelligence in forward supply chains: insights from fashion industry

Purpose – This study investigates how fashion companies adopt Artificial Intelligence (AI) across their forward supply chain processes, and which factors influence this adoption. Design/methodology/approach – A Grounded Theory methodology was employed involving four rounds of semi-structured interviews with 27 professionals from 11 Italian fashion companies. The initial and subsequent rounds evaluated general adoption perspectives, active projects, and external category validation. The final stage assessed the relationship between technology and sustainability. All transcripts were analyzed applying open, axial, and selective coding. Findings – AI adoption in the Fashion Supply Chain (FSC) remains exploratory and fragmented. While driven by operational efficiency, advanced analytics, and market responsiveness, it faces technological, financial, and cultural barriers. Key enablers include leadership support, targeted training, and governance frameworks. The analysis also highlights differing perspectives between staff and management. Originality/value – This research offers an industry-based view of AI adoption in the FSC. It offers a conceptual framework that captures the relationship among technological potential, cultural identity, and organizational readiness, providing insights for practitioners and researchers.