A generative artificial intelligence (GenAI) system for fashion design : a case study

Abstract

This study employs a design science research approach to propose a foundational information system design theory tailored for generative artificial intelligence (GenAI) applications in the fashion design process. It delineates meta-requirements and design principles that address both the transformative potential of GenAI, and the unique challenges faced by the fashion sector. To validate the practicality of the proposed design theory, a prototype system was developed and evaluated with feedback from 30 experienced fashion practitioners, confirming its feasibility and effectiveness. Insights from case studies conducted with two Hong Kong-based fashion companies further highlight the benefits and challenges of integrating GenAI into fashion design. While GenAI demonstrates promise in enhancing communication, accelerating design processes, and improving customer engagement and satisfaction, key challenges remain, including the need for high-quality datasets, significant computational resources, and ethical considerations related to AI-generated designs. The design principles derived from this study provide a structured guideline for system designers, offering a practical framework for developing GenAI systems that cater to the specific needs of the fashion industry. By contributing both theoretical and practical insights, this study advances understanding of how GenAI can drive innovation in fashion design and lays a foundation for future research in this domain. Managerial Relevance Statement—Managers and policymakers can gain considerable benefits from the findings of this study, especially in understanding how GenAI can streamline fashion design processes and enhance product innovation. By investing in GenAI technologies, they can empower designers to generate diverse and innovative garment designs quickly, reduce production costs, and enable rapid prototyping. The study’s insights also emphasize the importance of integrating GenAI in customer engagement strategies, as demonstrated through virtual try-on and fashion image manipulation, which allow customers to visualize products and personalize their shopping experiences. Managers can establish strategic plans for GenAI adoption, prioritize staff training, and ensure that GenAI workflows integrate seamlessly with existing fashion design process. For policymakers, the research highlights the need to set clear ethical guidelines, develop best practices, and address potential copyright issues associated with AI-generated designs. By doing so, managers and policymakers can responsibly drive technological innovation while meeting industry standards and enhancing customesr satisfaction.