While most textile enterprises are still agonizing over the return on investment of point automation upgrades, a top-level strategic study led by academicians has quietly been launched. The project, 'Strategic Research on AI Empowering High-Quality Development of the Textile Industry', initiated in Beijing in early August, unusually brings together the Chinese Academy of Engineering, industry associations, universities, research institutes, and leading enterprises. Its goal is not to solve a specific pain point on a production line, but to calibrate the direction for the digital transformation of the entire textile industry.
Background
Listed as a key project of the Chinese Academy of Engineering's science and technology strategic consulting, the project is led by academician Yu Jianyong. The core signal from the kick-off meeting is that the industry's digital transformation has entered a critical 'climbing' stage, with systemic bottlenecks such as weak data infrastructure, missing standards, insufficient deep application of large models, and a shortage of interdisciplinary talents constraining progress. Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, presented the research framework covering macro judgment, integrated applications, differentiated transformation models, and a comprehensive support system, along with a phased implementation plan.
It is noteworthy that the project's launch timing is not accidental. With the 'AI+' action elevated to a national strategy, the textile industry's digitalization pace directly impacts the cultivation of new quality productive forces. If the industry's focus in previous years was 'whether to transform', this strategic study signals that policymakers have begun to systematically answer 'how to transform and what the end state should be'. For SMEs still in a wait-and-see mode, this sends a clear message: transformation is not an option, but a must.
Industrial Impact
The repeated emphasis by experts on 'avoiding disconnection between technology and industry' and 'avoiding technology for technology's sake' is essentially a reflective calibration of past digitalization explorations. In early years, many enterprises blindly adopted systems and equipment, resulting in data silos and a disconnect between algorithms and actual production. The study's emphasis on 'scenario-driven and effectiveness-oriented' means future policy resources and capital will focus more on links that can directly convert into production efficiency and economic benefits, such as intelligent scheduling, quality inspection, and supply chain collaboration.
Another key judgment is 'differentiated implementation'. The differences in digital foundation, capital strength, and talent reserves between large enterprises and SMEs are objective, and a one-size-fits-all approach has proven ineffective. Layered and precise solutions will directly impact the business model design of equipment and service providers—standardized products with industry-specific customization may become mainstream. For textile foreign trade enterprises, the flow and sharing of data elements and full-chain collaboration are expected to shorten sampling cycles and enhance quick-response capabilities, thus gaining more order initiative in global competition.
President Sun Ruizhe's proposal of 'leaping from tool-assisted digitalization to intelligent-native digitalization' paints a clear evolution direction for the industry. In the tool-assisted stage, AI is an 'external add-on' that assists human decision-making; in the intelligent-native stage, AI is embedded in every link of business processes, becoming the core of decision-making and execution. This leap will not happen overnight, but the launch of strategic research means top-level design will pave the way ahead of market practice, providing a basis for subsequent standard formulation, pilot demonstrations, and policy support. For the cluster development of textile industrial belts, this is also a positive signal—collaborative innovation and data sharing within clusters will receive clearer policy guidance.