To advance sustainability within the automotive industry, it is essential to develop manufacturing methods that are both environmentally responsible and economically efficient. The innovative company STILFOLD has introduced a novel sheet-metal folding technology, comparable to metal origami, that does not rely on heavy tooling and can significantly reduces component count and material consumption. This in turn translate to a scalable, sustainable technology. While the underlying folding technique is inherently simple and elegant, large-scale industrial deployment requires robust automation. Effective automation depends on accurate robot programming, process parameter optimisation, and high-precision control systems. These requirements introduce substantial technical complexity that must be addressed through advanced modelling and simulation capabilities. In particular, high-fidelity finite element method (FEM) simulations are necessary to accurately characterise material behaviour, deformation mechanics, and machine–material interactions throughout the folding process. The ODEN-AI project aims to address these challenges by integrating digital twin methodologies, machine learning (ML), and physics-informed neural networks (PINNs) with detailed material and process models. The project is a collaboration between Linköping University, Stilfold, Jernkontoret and Outukumpu.