While many textile enterprises still hesitate over cloud adoption, top-level design has already set its sights on a more ambitious 'intelligent-native' future. On August 10, the Chinese Academy of Engineering launched a key strategic study on 'AI empowering high-quality development of the textile industry' in Beijing, uniting academies, industry associations, universities, and leading enterprises to address a core question: what will drive the next phase of digital transformation?
Background
Project leader, Academician Yu Jianyong, stated at the kickoff that the study is a concrete implementation of the national 'AI+' action in the textile sector, aiming to provide scientific decision-making support for overcoming transformation bottlenecks. Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, presented the research framework, covering macro assessment, integration paths, differentiated enterprise models, and support systems, with a phased implementation plan.
Sun Ruizhe, president of the China National Textile and Apparel Council, highlighted the industry's current predicament: weak data infrastructure, missing standards, insufficient large-model application, and a shortage of interdisciplinary talent. These four shortcomings are not new, but they have been magnified by the AI wave. Previously, enterprise digitalization remained at the level of single-point tool substitution, while true intelligent-native transformation requires end-to-end data connectivity and model-driven operations—a gap that is now evident.
Industry Impact
For the textile supply chain, the significance of this strategic study lies not on paper but in resource allocation directions over the next three to five years. Experts repeatedly emphasized 'tiered and classified implementation'—large enterprises and small factories have vastly different digital foundations, so a one-size-fits-all approach is inadequate. This implies that future policies and funding may be more tailored by enterprise scale and technical capability, with SMEs likely to receive lightweight, low-cost AI toolkits, while leading firms take on the role of benchmark explorers.
Another noteworthy point is the emphasis on 'scenario-driven' approaches. Sun Ruizhe specifically warned against 'technology for technology's sake', urging that AI be translated into tangible gains in production efficiency and economic benefits. For buyers, this means intelligent upgrades will likely prioritize high-value segments such as quality inspection, production scheduling, and dyeing control, leading to improved delivery cycles and quality consistency. For exporters, response speed and flexible production capabilities will gain further weight, while data compliance and standard alignment will become new competitive barriers.