ITMF White Paper Finds Data Gaps, Not Algorithms, Block AI In Textiles

The Common Question

What are the biggest obstacles to AI adoption in textile and apparel manufacturing?

The Lesson in This Case

ITMF and ITA research found that causal AI cut scrap by 10 percent on a nonwovens production line, one of the few concrete results the sector can point to so far. The broader finding is that fragmented data systems and skills shortages are stalling adoption far more than any limitation in the AI itself. Senior leaders should audit their data infrastructure before scaling AI investment, because poor data does not get corrected by better models; it gets amplified by them.

Originally reported by Fashion United. Read the full story here.