As the textile industry advances in investing in AI, new research from the International Textile Manufacturers Federation (ITMF) and the Institut für Textiltechnik of RWTH Aachen University (ITA) revealed challenges and successes so far. The research was derived from webinars held by the organizations over the past two years.

The recently published white paper, titled “AI in the Textile Value Chain: A Strategic Guide to Artificial Intelligence for the Global Textile Industry,” identified a disconnected industry structure. The authors found that the global textile industry is not under-digitalized but “digitally fragmented.” Decades of partial digitalization have created large volumes of data locked inside disconnected systems.

“Data is generated across machines, quality systems, logistics platforms and enterprise software, yet these streams are rarely fully interoperable,” the 49-page report noted. “This fragmentation has several consequences: decision-making remains distributed across organizational silos, information is duplicated, manually transferred or reinterpreted and opportunities for cross-functional optimization remain limited.”

The report’s authors said in many cases, digital systems are used primarily for documentation and reporting rather than for use in any active decision support. “Data is collected after the fact, not integrated in real time—limiting the industry’s ability to respond to demand variability, quality deviations or supply-chain disruptions.”

The research found the main barrier to AI adoption is poor data quality, not machine learning algorithms. Unstructured data and workforce skill gaps also play a role. The authors said AI does not fix bad data; it amplifies existing flaws.

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