Introduction
Research conducted by CSIL on a sample of over 100 European upholstered furniture producers (which are major users of fabric and leather) indicates that one in two companies cite environmental sustainability as a top strategic priority. For many of them, material waste remains a persistent challenge. More innovative solutions that improve production yield while supporting sustainability targets are therefore increasingly important. By adopting AI technologies, businesses can streamline production and meet growing sustainability expectations from regulators and consumers.

How does AI-Driven and process-oriented solution work?
One example is nesting, which is the process of arranging pattern pieces on a fabric roll or leather hide. AI algorithms might optimise nesting by simulating vast numbers of placement combinations in seconds. These systems can account for fabric width, grain direction, and defects, producing layouts that often outperform manual approaches in terms of material yield.

A European company leading the adoption of AI in textile manufacturing
Lectra is a global player headquartered in France that develops Industry 4.0 solutions. It is active in the furniture, fashion and automotive sector. It employs approximately 3 000 people and reports turnover of EUR 527 million. Lectra leverages AI through cloud-based high-performance computing, automating processes for mass-scale manufacturing and enabling companies to reduce fabric consumption at high volumes by predicting precise material needs before the first cut is made. This optimisation reduces both waste and cost per cut part, directly addressing a top priority for today’s manufacturers: aligning sustainability with profitability.

Investment and workforce training 
While AI can materially improve resource and time efficiency, adoption is often constrained by three structural factors: high upfront investment, the availability of accurate, interoperable digital pattern data (which is challenging for non-standard materials), and the organisational effort required to reskill teams for automated, data-driven workflows. Crucially, upskilling is not a “replacement” of competence acquired at the company level: in most production contexts, digital adoption creates even higher value when tacit know-how (e.g. how to recognise defects, interpret material behaviour, and balance yield, quality, and aesthetics) is made legible and interoperable with digital workflows, so that it is retained and embedded rather than eroded. This is particularly important for non-uniform materials such as artisanal fabrics or natural leather, where variability, defects, and aesthetic still require expert judgement and competencies often rooted in long-established production clusters.

Conclusion
AI-Driven and process-oriented solutions offer a clear pathway to reducing raw material waste in Europe. Achieving impact at scale depends on balancing technology investment and skills development with long-term benefits in sustainability, productivity, and competitiveness.