However, as industrial AI becomes more central to competitiveness, the scale and intensity of workloads continue to increase. Use cases such as digital twins, robotics, and ADAS training demand sustained performance and fast iteration cycles, placing new pressure on infrastructure as models grow in size and complexity. At the same time, the sensitivity of engineering and operational data adds further complexity around security, data handling, and regulatory alignment, widening the gap between what industrial AI makes possible and what existing infrastructure can support.