While most textile enterprises are still weighing the ROI of piecemeal automation equipment, top-level design has begun to map out the next decade. In August 2026, the Chinese Academy of Engineering launched a key strategic consulting project titled "Research on the High-Quality Development Strategy of the Textile Industry Enabled by Artificial Intelligence" in Beijing, marking a formal entry into systematic strategic planning for the industry's digital transformation—rather than fragmented local attempts.
Background
The project is led by the Environmental and Light Textile Engineering Division of the Chinese Academy of Engineering, bringing together industry associations, universities, research institutes, and leading enterprises. Project leader, academician Yu Jianyong, stated at the kickoff meeting that this is an important initiative to implement the national "AI+" action and to foster new quality productive forces in the textile industry. It is not merely a technical project but aims to provide a viable strategic path and implementation plan for the entire industry.
Zhang Chuanxiong, director of the Textile Industry Science and Technology Development Center, presented the opening report, outlining the research framework with four core components: macro assessment and strategic positioning, integration applications and transformation paths, differentiated transformation models for enterprises, and construction of a comprehensive support system. These four areas cover the full logic chain from "why to transform" to "how to transform" and "what to rely on for transformation." Notably, the project explicitly plans phased work arrangements, indicating that results will be delivered in stages rather than as a single report.
Industrial Impact
Behind the project launch lie real pain points in the industry's digital transformation. Sun Ruizhe, president of the China National Textile and Apparel Council, candidly stated during the discussion that the industry is still in a "climbing over a ridge" phase, with prominent issues such as weak data infrastructure, missing standards, insufficient depth of large-model application, and a shortage of interdisciplinary talent. These judgments align with the frontline experience of many textile enterprises: equipment networking rates are not low, but data silos are severe; single-point intelligent applications are common, but systematic coordination is insufficient.
Based on this reality, experts proposed several key orientations. First, problem-oriented research focusing on key segments and critical production links to avoid disconnect between technology and industry—this implies that future policy resources will be more targeted at solving practical pain points rather than chasing concepts. Second, strengthening industry-academia-research collaboration to bridge the gap between technology and application, which for SMEs may mean more reusable solutions. Third, implementing differentiated strategies that consider the varying digital foundations of large, medium, and small enterprises—large companies can afford private deployments, while smaller factories are better suited for lightweight SaaS tools.
Sun Ruizhe emphasized a critical judgment: the industry must transition from "tool-assisted" digitalization to "AI-native" digitalization. This is not a change in wording but a fundamental difference. Tool-assisted means using AI as an auxiliary means to improve efficiency, whereas AI-native means integrating intelligent capabilities from the outset of business design, making data-driven approaches the default logic for production, management, and decision-making. For buyers and foreign trade enterprises, this means that supply chain responsiveness, quality stability, and flexible production capacity will be redefined, and digital capability will become a new competitive threshold.