As the wave of artificial intelligence sweeps through manufacturing, the textile industry—a sector steeped in tradition—stands at a delicate historical juncture. The Chinese Academy of Engineering has launched a key science and technology consulting project, “Strategic Research on AI Empowering High-Quality Development of the Textile Industry,” in Beijing, bringing together academicians, industry associations, universities, research institutes, and leading enterprises to chart a clear roadmap for the industry’s digital transformation. This move itself signals that AI adoption in textiles is no longer a piecemeal experiment but a systematic endeavor requiring top-level design.
Background
The project’s launch is set against the national “AI+” action plan. Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, presented the research framework in the kickoff report: from macro assessment and strategic positioning to integrated applications and transformation pathways, and then to differentiated enterprise models and a holistic support system, covering the digitalization needs of the entire value chain. Notably, the project explicitly proposes a phased implementation plan—an indication that policymakers recognize that AI adoption in textiles cannot be achieved overnight and must proceed in a step-by-step, tiered manner.
Academician Yu Jianyong, in explaining the project’s background, positioned the research as “an important study serving the cultivation of new quality productive forces in the textile industry.” The term “new quality productive forces” in the textile context points to using AI, big data, and other technologies to reshape production functions and enhance total factor productivity. For an industry long reliant on labor cost advantages, this may be the key to breaking through growth ceilings.
Industry Impact
Experts’ discussions directly targeted the pain points of digital transformation. Sun Ruizhe, president of the China National Textile and Apparel Council, almost provided a “problem list” in his speech: weak data infrastructure, missing standards, insufficient depth of large-model applications, and a shortage of interdisciplinary talent. Each of these issues is enough to trip up enterprises on their AI transformation journey. Data is the fuel for AI, but textile data is scattered across various processes, lacking unified standards and circulation mechanisms, leading to data silos that prevent AI models from generating truly valuable insights.
More notably, experts repeatedly emphasized “scenario-driven” and “differentiated strategies.” This means AI empowerment in textiles cannot be a one-size-fits-all approach. Large enterprises have the capital and technical strength to experiment with frontier applications like large models and digital twins; small and medium-sized enterprises need lightweight, low-cost solutions that first address pain points in specific processes such as quality inspection and production scheduling. This tiered approach is, in effect, a correction of the past years’ “technology for technology’s sake” misstep—technology only makes sense when it lands in specific production links and translates into efficiency and benefits.
Sun Ruizhe’s call for a “leap from tool-assisted digitalization to intelligent-native digitalization” is a thought-provoking judgment. Tool-assisted means AI is just a nice-to-have aid; intelligent-native requires enterprises to consider AI integration from the outset of business process design, making data-driven operations the underlying logic of the organization. This is not just a technological upgrade but a transformation of management philosophy and organizational structure. For textile companies accustomed to traditional production modes, this leap is akin to a self-revolution.