The white paper titled AI in the Textile Value Chain: A Strategic Guide to Artificial Intelligence for the Global Textile Industry, was published by the International Textile Manufacturers Federation (ITMF) and RWTH Aachen University’s Institut für Textiltechnik (ITA) in October 2026.
Drawing on the ITMF and ITA’s AI in the Textile Value Chain webinar series, it argues the industry’s progress will depend on “coordinated, industry-wide capability-building".
RWTH Aachen University, Institut für Textiltechnik ITA director professor Thomas Gries explained: “Companies need a dedicated task force to embed data integration into their day-to-day and strategic technical and business processes. Partnering with external experts on a transparent assessment - a data or automation waste walk - and on effective implementation is not only decisive to future success, but essential to survive!”
The report describes textile manufacturing as “not under-digitalised, but digitally fragmented”, noting that decades of digitalisation have resulted in the accumulation of data across disconnected systems. It states that companies frequently overestimate the amount of usable, structured data available to support AI projects.
Pressure to adopt AI is being driven by demographic change, shortages of skilled workers, market volatility and increasing customer and regulatory demands for quality, traceability and sustainability.
Several AI applications are already producing measurable results, according to the report. These include computer-vision systems for fabric and yarn inspection, predictive maintenance, energy optimisation and demand forecasting.
The guide also points to a shift from AI systems that assist decision-making towards systems able to coordinate or execute defined tasks under human supervision.
Agentic AI is beginning to manage multi-stage processes such as sourcing, while causal AI can adjust production settings directly, according to the paper. It places the likely timeframe for end-to-end AI workflows at two to three years.
However, participants contributing to the research warned that AI can compound, rather than resolve, underlying problems in incomplete or poor-quality data.
The paper also argues that businesses should establish dedicated teams to integrate data into technical and commercial processes before attempting wider AI deployment.
Trust and accountability were also identified as requirements for adoption. Industry participants cited control of sensitive information, cyber security, transparency about system limitations and retained human responsibility as conditions for using AI in production and supply-chain activity.
ITMF director Olivier Zieschank added: “AI is not a shortcut around the structural challenges our industry faces, but it can help companies manage complexity and make the most of scarce expertise. Understanding where AI genuinely helps is a learning process. That is why exchange through associations like ITMF matters so much.’’
The paper recommends that companies begin with contained use cases rather than attempting broad implementation programmes.
It also proposes shared data standards, data spaces that allow originators to retain ownership, and sector-specific tools such as a controlled “Textile GPT”, particularly to support smaller and medium-sized businesses.
