Unlocking AI-driven innovations in textile design for a circular economy

Purpose This study explores how AI can support sustainable and efficient textile design, identify AI-based factors, analyse their interdependencies, and categorize them into cause-and-effect groups using a structured decision-making approach.Design/methodology/approach The study adopted a three-step process: (1) conducting a systematic literature review to identify AI applications in textile design, (2) applying the Fuzzy Delphi Method (FDM) to screen and cross-validate key factors based on expert opinions, and (3) using the Decision Making Trail and Evaluation Laboratory (DEMATEL) approach to analyse the cause-and-effect relationships between the screened factors.Findings The study found that AI-enabled generative Design for Material Efficiency and Predictive Colour and Trend Forecasting for Design Planning are primary causal factors influencing other AI-driven innovations, while AI-Powered Fit Prediction and Customization Algorithms and AI-Based Circular Design Recommendations are ranked as effect factors (i.e., they are more dependent on the impact of other factors). The results demonstrate the interdependent role of AI technologies in textile design and their influence on sustainability and efficiency in operations.Originality and implications The study provides new insight by mapping the influence of textile design AI technologies in a systematic way using an integrated FDM-DEMATEL framework, and the findings offer useful guidelines for designers, producers, and policymakers to set priorities for AI-based innovations and to make strategic investments into AI applications for increasing efficiency, circularity, and sustainability in the textile sector.