The International Textile Manufacturers Federation (ITMF) and the Institut für Textiltechnik (ITA) at RWTH Aachen University have published a new white paper on artificial intelligence (AI) in the textile value chain. Released on October 15, 2026, the report identifies poor data integration, skills shortages and governance gaps as the main barriers to AI adoption in the textile and apparel industry.
Titled AI in the Textile Value Chain: A Strategic Guide to Artificial Intelligence for the Global Textile Industry, the paper calls for coordinated industry action to make AI more useful and trustworthy by 2030.
Key Findings on AI in Textiles
The report draws on the ITMF–ITA webinar series conducted between 2024 and 2026. More than 20 experts from companies, research institutes and policy bodies contributed across ten sessions. It also incorporates discussions held at the ITMF Conference 2025 in Yogyakarta, Indonesia.
The study identifies several areas where AI is already delivering measurable benefits. These include fabric and yarn inspection, predictive maintenance, energy optimisation and demand forecasting. In one example from a nonwovens production line, causal AI reduced scrap by 10%.
However, the authors highlight data quality and workforce skills as major challenges. Many textile companies have fragmented digital systems and less usable, structured data than they realise. The report notes that AI cannot correct poor data and may amplify existing problems.
AI is also moving beyond decision support. Agentic systems are beginning to coordinate multi-step tasks, including sourcing, while causal AI can adjust production settings under human supervision. The report estimates that end-to-end AI workflows could become realistic within two to three years.
Recommendations for the Textile Industry
The white paper recommends that companies invest in people and data infrastructure before expanding AI deployment. It also advises businesses to begin with clearly defined applications and scale up gradually.
Industry associations can support this transition through shared data standards, collaborative data spaces and sector-specific tools, including a controlled “Textile GPT.” These approaches could help small and medium-sized enterprises (SMEs) access AI capabilities while maintaining data ownership and security.
Trust remains central to adoption. Companies must protect sensitive information, strengthen cybersecurity, explain system limitations and preserve human accountability.
ITMF Director Olivier Zieschank said AI can help the industry manage complexity and make better use of limited expertise. He also highlighted the importance of knowledge exchange through industry associations.
Prof. Thomas Gries, Director of ITA at RWTH Aachen University, stressed the need for dedicated teams to integrate data into daily operations and strategic business processes.
The white paper is available at RWTH Aachen University Publications. Recordings of the ITMF–ITA webinar series are available to ITMF members through the ITMF Virtual Forum.
