When the textile industry meets the wave of artificial intelligence, the 2026 Textile Industry Digitalization Conference in Keqiao sent a clear signal to the entire sector: digitalization is no longer an option but a necessity for survival. Data released at the conference shows that AI applications in textiles are rapidly moving from proof-of-concept to large-scale deployment, yet the transformation paths for enterprises of different sizes are beginning to diverge significantly.
Background
Held on May 7, the conference, themed 'Smart Link Textile Capital, Digital Creates Future – Building New Quality Productive Forces for the Textile Industry,' focused on three core tracks: digital transformation of dyeing and printing enterprises, AI-based fabric inspection, and end-to-end enterprise digitalization. Yan Yan, Vice President of the China National Textile and Apparel Council (CNTAC), proposed three directions: promoting deep AI deployment by building a 'Textile Intelligent Large Model' alongside small models for vertical scenarios; deepening efficient collaboration across the entire chain by breaking data silos; and deeply integrating green manufacturing by constructing a full-chain carbon footprint tracking model.
As an international textile capital, Keqiao's industrial cluster response is particularly noteworthy. Sun Weigang, Deputy Secretary of the Party Working Committee of China Textile City, revealed that Keqiao is building a '1+4+N' comprehensive intelligent system, pushing market transactions, trend analysis, and supply chain services toward digital and platform-based transformation. It is also establishing a 'live streaming + platform + cross-border e-commerce + overseas warehouse' model to accelerate the full-chain digital trade link.
Industry Impact
A core judgment delivered at the conference is that digitalization paths are increasingly differentiated by enterprise scale. Hu Song, Director of the China Textile Information Center, explicitly proposed a five-step digitalization method: 'diagnose current status, select scenarios, supplement data, conduct pilots, and expand capabilities.' He pointed out that SMEs should adopt low-cost, quick-win paths, while large enterprises should shift from system construction to data-ecosystem leadership. This implies a growing 'Matthew effect' in future digital investment: leading companies accelerate the building of data moats, while SMEs rely more on lightweight, iterable solutions.
In the dyeing segment, Zhejiang Meixinda shared its 'shaping, soul-casting, wisdom-awakening' three-stage evolution path, arguing that smart factories should not be seen as cost centers but as order-creation centers. This judgment has direct implications for supply chain decisions: digital capability is becoming a hard currency for order acquisition, not a decorative add-on.
More noteworthy is the TDSD® (Textile Digital Smart Dyeing) process released by Hangzhou Huanyu Digital Intelligence Technology. Through a fully self-developed inkjet equipment, new ink materials, and an AI color management system, the technology achieves nearly 99% water savings, 33% carbon reduction, and 21% reduction in chemicals. This data means the green entry barrier for the dyeing process is being significantly lowered by technology, offering a potential breakthrough tool for export enterprises facing EU carbon barriers.
AI fabric inspection was another focus. Shanghai Kai Quan Intelligent's 'teachable' self-learning AI system and Nantong Julian Digital's multi-scenario solutions covering online defect warning, warp/weft density monitoring, all respond to the pain points of traditional inspection: recruitment difficulties, high miss rates, and delayed feedback. However, Luo Yucheng, Deputy General Manager of Shaoxing Keqiao Weaving and Dyeing Industrial Brain Operation Co., acknowledged that the industry still faces challenges such as poor algorithm adaptation for complex fabrics, system instability in complex environments, and high implementation costs for SMEs. Chen Baojian, Deputy Director of the CNTAC Textile Product Development Center, suggested building a unified industry defect standard database, which will be key infrastructure for AI inspection to move from pilot to scale.