While most textile enterprises still confine digitalization to ERP systems and standalone automation, a top-level design led by academicians and covering the entire industrial chain has quietly kicked off. In August 2026, the Chinese Academy of Engineering's key strategic consulting project, "Strategic Research on AI Empowering the High-Quality Development of the Textile Industry," was officially initiated in Beijing, marking the industry's digital transformation from enterprise-level exploration to systematic national strategy.
Background
The project brings together the Chinese Academy of Engineering, industry associations, universities, research institutes, and leading enterprises, headed by academician Yu Jianyong. At the kickoff meeting, Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, outlined the research framework: macro assessment and strategic positioning, integrated applications and transformation paths, differentiated transformation models for enterprises, and construction of a total-factor support system, with phased implementation plans. This signals that the industry is no longer satisfied with scattered AI pilot applications but aims to draw a clear roadmap for digital upgrades across the entire value chain.
Notably, expert discussions at the launch revealed a strong pragmatism. Several academicians and experts emphasized that the research must be problem-oriented, addressing common pain points and focusing on key segments and production links to avoid disconnection between technology and industry. This pursuit of "grounded, implementable, and effective" outcomes reflects lessons from previous digital transformation projects that failed due to poor data quality and process adaptation.
Industry Impact
Sun Ruizhe, president of the China National Textile and Apparel Council, offered a sharp diagnosis: the industry's digitalization is still in a climbing phase, with prominent issues like weak data infrastructure, missing standards, insufficient large-model application depth, and a shortage of composite talents. These four points essentially define the major bottlenecks for AI implementation. For textile enterprises, this means that in the coming years, those who first solve data standardization, build proprietary industry models, and cultivate internal AI talent will gain a competitive edge.
Sun's call for a leap from "tool-assisted digitalization" to "native intelligent digitalization" carries profound industrial significance. "Native intelligence" does not mean overlaying AI tools on existing processes but reconstructing production organization, supply chain management, and product design with intelligence as the underlying logic. For example, in fabric defect detection, the tool-assisted stage is "manual inspection plus AI re-inspection," while the native intelligence stage involves AI directly driving production parameter adjustments to achieve closed-loop quality control. This leap will reshape core competitiveness and impose higher demands on equipment and software suppliers.
The project emphasizes "differentiated strategies" considering the varying digital foundations of large, medium, and small enterprises. This orientation could significantly impact the industrial ecosystem: large enterprises may deploy proprietary large models, while SMEs are more likely to access AI capabilities through industry cloud platforms. We may see the emergence of a "platform plus ecosystem" service model, where leading firms or third-party providers offer standardized AI modules that SMEs subscribe to on demand, lowering the barrier to intelligence.
Furthermore, the project highlights "highlighting textile characteristics and cultural heritage," suggesting that AI empowerment should not only pursue efficiency but also preserve the cultural attributes of the textile industry. From pattern design to craftsmanship inheritance, AI can amplify designers' creativity rather than simply replace it. This balance between technology and humanity may become a unique path for China's textile industry compared to other manufacturing countries.