When most textile enterprises still interpret digitalization as installing a few ERP systems or automated production lines, national-level strategic research has begun to redefine the underlying logic of this transformation. In August 2026, the Chinese Academy of Engineering launched a key strategic consulting project titled "Research on AI Empowering High-Quality Development of the Textile Industry" in Beijing. The project brings together academicians, industry associations, universities, and leading enterprises to answer a more fundamental question: how can artificial intelligence reshape the value distribution across the entire textile supply chain?
Background
At the kickoff meeting, project leader and CAE academician Yu Jianyong made it clear that this research is a critical step in implementing the national "AI+" action and cultivating new quality productive forces in textiles. The study will systematically map out strategic pathways for AI empowerment, providing scientific decision-making support to overcome transformation bottlenecks. Notably, the project team proposed a phased implementation plan, suggesting that digital transformation is not a one-shot effort but a systematic project spanning multiple cycles.
Sun Ruizhe, President of the China National Textile and Apparel Council, offered a more sober assessment: the industry's digitalization is still in a "climbing and crossing" stage, with four prominent problems—unstable data infrastructure, missing standards, insufficient deep application of large models, and a shortage of interdisciplinary talent. These four points outline the real water level of textile digitalization: most enterprises remain at the level of isolated tools, far from systematic intelligence.
Industry Impact
The research framework covers macro assessment, integrated applications, differentiated enterprise transformation models, and support systems. The emphasis on "differentiated transformation models" is particularly critical, indicating that policymakers recognize the gaps in digital foundation, capital, and talent between large enterprises and small factories. Therefore, a one-size-fits-all approach will inevitably fail. The future may see layered solutions: leading factories leap toward "intelligent-native" models, while SMEs rely more on platform-based, lightweight AI tools for gradual upgrades.
For buyers and foreign trade companies, the outcome of this strategic research will directly affect supply chain responsiveness. Sun's principle of "scenario-driven, focusing on practical results" means AI will not be deployed for its own sake but to solve specific pain points such as quality inspection, production scheduling, and inventory management. Once data sharing mechanisms are established, fabric buyers can optimize order allocation through more transparent capacity data, and foreign trade firms can shorten delivery cycles via intelligent customs declaration and demand forecasting.
However, the lack of standards remains the biggest bottleneck. Without unified data interfaces and identification protocols, AI systems across the supply chain cannot interconnect, limiting "intelligence" to individual factories. By incorporating standards into the research agenda, the project is essentially laying the infrastructure for a "data highway"—which holds more long-term value than any single technological breakthrough.