Textile Digital-Intelligent Transition: A Strategic Shift from Tool-Assisted to Native Intelligence

The textile industry is at a crossroads: on one side, overcapacity and squeezed margins; on the other, a digital-intelligent transformation that has been talked about for years but rarely breaks through the 'last mile.' The strategic research project 'AI Empowering High-Quality Development of the Textile Industry,' recently launched by the Chinese Academy of Engineering, targets this deep contradiction. It is not another concept promotion but an attempt to answer a sharper question: why does AI implementation in textiles remain like a veil?\n\n## Background\n\nLed by academician Yu Jianyong, the project brings together industry associations, universities, research institutes, and leading enterprises. The project leader explicitly stated that this is a key study to implement the national 'AI+' action and cultivate new quality productive forces in textiles. Notably, Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, presented the opening report on behalf of the project team, detailing the research framework and technical route. This indicates a strong 'implementation orientation' from the outset, not pure academic exercise.\n\nThe project addresses the digital-intelligent upgrade needs of the entire industrial chain, setting four research directions: macro assessment and strategic positioning, integration applications and transformation paths, differentiated transformation models for enterprises, and construction of a comprehensive support system. This 'layered breakdown' approach reflects the industry's fatigue with one-size-fits-all solutions—large enterprises and small workshops have vastly different digital foundations, and generic 'smartification' slogans often become icing on the cake for big firms and castles in the air for small factories.\n\n## Industry Impact\n\nSun Ruizhe, president of the China National Textile and Apparel Council, set the tone at the launch: the industry's digitalization is still in a 'climbing a slope' stage, with four major bottlenecks—weak data infrastructure, missing standards, shallow large-model application, and talent shortage. These four points map directly to real pain points across industrial clusters: Keqiao's fabric merchants struggle with ERP-MES integration, Shengze's weaving mills cannot find engineers who understand both processes and algorithms, and Nantong's home textile cluster sees general large models as 'out of reach.'\n\nExperts' warning to 'avoid disconnection between technology and industry' targets the 'technology for technology's sake' tendency in past smart manufacturing practices. Many enterprises installed automation and data collection systems, but equipment remains in silos, and data accumulates without informing production decisions. The emphasis on 'scenario traction' and 'tiered strategies' signals that future policies and solutions will be more tailored to SMEs' actual capacity, rather than pushing flashy 'unmanned factories.' For buyers and foreign trade firms, this is a positive sign: the next phase of digital transformation may shift from coercive 'change or die' to pragmatic, step-by-step advancement.\n\n## Practical Recommendations\n\n### For Buyers\n- Monitor follow-up industry-specific transformation guides; prioritize suppliers in 'native intelligence' pilot supply chains to reduce integration costs.\n- Gradually include digital clauses in fabric purchase contracts—data interfaces and quality traceability—to prepare for future AI-driven QC and supply chain collaboration.\n- Use industry public data platforms (e.g., CNTAC data services) to calibrate purchasing decisions and reduce information asymmetry risks.\n\n### For Foreign Trade Enterprises\n- Build 'AI+foreign trade' capabilities early, such as optimizing multilingual product descriptions and compliance documents with large models, but rely on vertical industry models to avoid legal risks from generic AI 'hallucinations.'\n- Track standards developments, participate in feedback on group or international standard drafts, and gain influence in cross-border data flow rules.\n- Include compound talent training in annual budgets; partner with universities or research institutes for short 'process+algorithm' courses to mitigate talent gaps affecting delivery efficiency.