46 2026 TSMA shortening campaign cycles. On the design side, generative models help create shoe upper textures, racket outlines, and apparel patterns, forming a data-driven creative pipeline. This shift enables smaller brands to compete internationally with limited resources, narrowing the gap with industry giants and fostering greater diversity in creative expression. Global sports brands are already integrating AI into generative design tools—not only for footwear patterns and garment prints but also for simulating consumer-preference scenarios and testing market responses (WEF, 2024a; McKinsey & Company, 2023b). For manufacturers, especially those in Taiwan, the implications are profound. The traditional brand-centric design hierarchy is gradually giving way to data-empowered manufacturing partners. Firms capable of mastering AI tools can now participate at the creative front end of the value chain, not merely its execution phase. Yet generative AI also presents new challenges: ownership and copyright, data ethics, design homogenization, and the delicate balance between efficiency and originality. These tensions are prompting the industry to establish new creative and ethical standards for AI-assisted design. In essence, generative AI is not merely an auxiliary design tool—it is a structural innovation turning creativity itself into scalable productivity. 3. RAG: Retrieval-Augmented Generation and the Rise of Knowledge AI While generative AI drives creative transformation, Retrieval-Augmented Generation (RAG) is revolutionizing how the industry manages and retrieves knowledge. RAG systems combine large-language-model reasoning with secure access to internal databases, enabling organizations to generate accurate, context-specific responses based on proprietary information (OECD, 2025). In manufacturing, RAG applications are increasingly used to manage engineering specifications, supplier data, and quality records. Instead of sifting through thousands of pages of technical manuals, engineers can now query the system in natural language: “What are the latest safety-testing standards for recycled TPU materials?”—and instantly receive consolidated, citation-backed results. Retailers and distributors are adopting similar architectures to connect sales analytics, logistics, and customer-service databases. This allows managers to identify real-time market anomalies or inventory gaps. The broader value of RAG lies in organizational learning: by integrating distributed data into an accessible knowledge interface, companies effectively convert institutional memory into operational intelligence.
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