The Critical Role of AI and Big Data in Revolutionizing Apparel Design

Background and Aim: While Artificial Intelligence (AI) and Big Data demonstrate significant potential in revolutionizing apparel design, existing research lacks empirical validation of AI’s predictive efficacy and fails to systematically deconstruct the structural barriers to technological integration. This study aims to evaluate the effectiveness of AI in trend and demand forecasting, analyze the optimization mechanisms of Big Data in design decision-making, and construct a framework to address integration barriers. Materials and Methods: Grounded in a constructivist paradigm, this research employs a multi-method approach, including a systematic literature review, semi-structured interviews with 32 global fashion professionals, and a three-dimensional (technology-organization-creativity) case analysis of Zara, The Fabricant, and others. Thematic coding was conducted using NVivo. Results: The findings confirm that AI improves prediction accuracy by over 30% through integrating social media, sales data, and other real-time sources, while Big Data-driven "just-in-time production" models reduce inventory waste by 25%. For the first time, barriers are deconstructed across three dimensions: (1) techno-ecological (fragmented infrastructure), (2) human-AI collaboration (digital literacy gaps), and (3) institutional governance (ethical compliance risks). A Multidimensional Barrier Co-evolution Framework for Digital Creative Transformation (MBCF-DCT) is proposed. Conclusion: This study addresses critical empirical gaps and offers a closed-loop mechanism—data insight → creative translation → ethical calibration—to guide industry transformation. Future research should focus on blockchain-based copyright verification, algorithmic adaptation for non-Western aesthetics, and sustainable AI design models.