A new industry white paper found that fragmented data systems and a shortage of skilled workers are the main obstacles to using artificial intelligence to cut waste.
The International Textile Manufacturers Federation (ITMF) and RWTH Aachen University’s Institut für Textiltechnik (ITA) have released a white paper named ‘AI in the Textile Value Chain: A Strategic Guide to Artificial Intelligence for the Global Textile Industry.’
The white paper pinpoints poor data integration, skills shortages and governance gaps as the main obstacles to AI uptake across textiles and apparel, rather than any shortcomings in the technology itself.
The study contends that the sector’s capacity to profit from AI will hinge increasingly on coordinated, industry-wide capability building.
The white paper draws upon the ITMF and ITA webinar series AI in the Textile Value Chain, run between 2024 and 2026. Across ten sessions, more than 20 specialists drawn from companies, research institutions and policy bodies shared their views.
The findings also weave in insights from a World Café discussion with industry practitioners staged during the ITMF Conference 2025 in Yogyakarta, Indonesia.
Structural Pressures Fuelling AI Uptake
According to the authors, AI is growing more relevant to the textile industry in answer to structural pressures rather than technological hype.
Demographic decline, stubborn skills shortages, market volatility and rising expectations around quality, traceability and sustainability are nudging companies towards AI-driven solutions.
Meanwhile, decades of digitalisation have produced substantial volumes of industry data spread across multiple systems and platforms.
Yet this data frequently remains fragmented and hard to exploit effectively. The white paper therefore describes the sector as “not under-digitalised, but digitally fragmented.”
AI Applications Already Delivering Value
The report spotlights several areas where AI is already yielding measurable results across textile manufacturing and related processes.
These span computer-vision systems for fabric and yarn inspection, predictive maintenance, energy optimisation and demand forecasting. One example drawn from a nonwovens production line showed causal AI cutting scrap by 10%.
The findings suggest companies needn’t necessarily start with sweeping AI transformations. Instead, clearly defined and measurable applications can offer a practical entry point for adoption.
Data and Skills Remain Critical Hurdles
A central finding of the study is that data quality and workforce capabilities pose greater constraints than the availability of AI algorithms.
Industry participants stressed that AI cannot make up for poor-quality data. Rather, flawed or incomplete datasets can magnify existing weaknesses within business and production processes.
Companies may also hold considerably less structured, usable data than they presume.
The white paper accordingly recommends investing in people and data capabilities before attempting to scale AI deployment.
From Decision Support to AI-Driven Action
AI applications within textiles are likewise starting to progress beyond decision support towards taking action within defined processes.
The report points to emerging agentic AI systems able to coordinate multi-step activities, including sourcing-related processes.
Causal AI is also starting to adjust production parameters directly, though such applications remain subject to human supervision.
The authors estimate that fully integrated, end-to-end AI workflows could become realistic within the coming two to three years.
Building Trust Around AI
Trust is flagged as a decisive factor in the successful adoption of AI across the textile value chain.
Industry participants underlined several requirements for responsible implementation, including control over sensitive data, robust cybersecurity, transparency about system limitations and clearly defined human accountability.
Rather than treating AI as a substitute for human expertise, the paper stresses the importance of retaining human oversight whilst using AI to tackle complexity and stretch limited expertise further.
Collaboration Can Speed Industry Adoption
The white paper recommends companies take up AI incrementally, starting from bounded and clearly defined use cases.
It likewise calls for data to be treated as shared infrastructure and spotlights the role of industry associations and pre-competitive collaboration, particularly in supporting small and medium-sized enterprises.
Potential areas for collaboration include shared data standards, data spaces allowing ownership to stay with the original data holder, and sector-specific AI tools such as a controlled “Textile GPT.”
Olivier Zieschank, Director, ITMF, said: “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,” he added.
Prof. Thomas Gries, Director, Institut für Textiltechnik ITA, RWTH Aachen University, said, “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!,” he too added.
