Agenticness of Artificial Intelligence in the Fashion Industry : A Case Study Based on a Conceptual Expansion Structure

This study reconceptualizes “agentic artificial intelligence (AI)” in the fashion industry, shifting the focal point from individual performance metrics to a multidimensional structure of agency. Employing a qualitative case study methodology, the research analyzes 11 AI-driven fashion services through an analytical framework composed of five functional dimensions (autonomy, decision-making, problem-solving, interactivity, and planning/execution)mapped across three operational levels (behavior, structure, and system). Findings indicate that fashion AI does not converge on a single developmental stage but forms a multilayered ecosystem where agency is selected and expanded based on strategic goals and service contexts. The services were categorized into three distinct levels: (1) Conventional AI Agent: Represented by H&M Chatbot, focusing on behavioral automation and stable rule-based recommendations, (2) Potentially Agentic AI: Including Alta, AskRalph, Heuritech, MaIA, OneOff, and WGSN Fashion Buying, which expand into structural decision-making but remain in a transitional phase dependent on human oversight, and (3) Highly Agentic AI: Comprising Shopify, Gensmo, Daydream, and Stitch Fix, which implement system-level agency by orchestrating complex “agentic commerce” processes such as multimodal interpretation and multistep execution.<br/>These results demonstrate that AI in fashion is evolving beyond mere assistants toward becoming independent“agentic actors.” This study provides a diagnostic framework and a phased roadmap—from operational efficiency to organizational reorganization—to support fashion companies in their strategic AI integration.