Systematic literature review: adoption of artificial intelligence technologies for sustainability in the ready-made garment sector (2013–2024)

Purpose This research aims to analyze the utilization of artificial intelligence (AI) by Bangladesh’s Ready-Made Garment (RMG) sector to enhance sustainability over the past decade, providing guidance for practitioners and policymakers on developing AI-integrated sustainability strategies and directing future research. Design/methodology/approach This study employed systematic literature review procedures, along with inclusion and exclusion criteria, to identify relevant Web of Science articles and develop a theoretical framework based on previous research. Findings A review of 48 peer-reviewed studies identified 71 determinants of AI adoption across technological, organizational and environmental contexts, with eight propositions related to perceived benefits, costs, technology readiness, supply chain integration and competitive pressure. The findings show that both internal and external benefits influence the adoption of AI for sustainability. Data security, infrastructure, human resources and institutional support are key environmental and institutional factors that make AI adoption strategic rather than merely trendy. Research limitations/implications The suggested model is merely theoretical and hence necessitates empirical validation. Practical implications It offers concrete solutions for industry executives, governments and development agencies to enhance AI-driven sustainability and competitiveness in emerging countries. Transdisciplinary policies ensure sustainable RMG development with AI technology, skilled labor and infrastructure. In many cases, conceptual models need empirical validation through further study. Social implications It promotes AI- and sustainability-based human resource development Originality/value This study addresses a knowledge gap by combining sustainability and AI to improve sustainability in the RMG sector. It applies adoption theories to evaluate the internal and external benefits of AI adoption in sustainable RMG practices. Furthermore, this study incorporates human, technical and competitive pressure to explore AI-driven sustainability in the RMG sectors, thereby expanding academic discourse and supporting practical implementation.