“AI-powered digital twins mark a significant evolution in the longer term of producing, enabling real-time visualization of the whole production line, not only individual machines,” says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft. “That is allowing manufacturers to maneuver beyond isolated monitoring toward much wider insights.”
A digital twin of a bottling line, for instance, can integrate one-dimensional shop-floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling right into a single operational view of the whole production line to enhance efficiency and reduce costly downtime. Many high-speed industries face downtime rates as high as 40%, estimates Jon Sobel, co-founder and chief executive officer of Sight Machine, an industrial AI company that partners with Microsoft and NVIDIA to rework complex data into actionable insights. By tracking micro-stops and quality metrics via digital twins, corporations can goal improvements and adjustments with greater precision, saving hundreds of thousands in once-lost productivity without disrupting ongoing operations.
AI offers the following opportunity. Sircar estimates that as much as 50% of manufacturers are currently deploying AI in production. That is up from 35% of manufacturers surveyed in a 2024 MIT Technology Review Insights report who said they’ve begun to place AI use cases into production. Larger manufacturers with greater than $10 billion in revenue were significantly ahead, with 77% already deploying AI use cases, in response to the report.
“Manufacturing has loads of data and is an ideal use case for AI,” says Sobel. “An industry that has been seen by some as lagging relating to digital technology and AI could also be in the very best position to steer. It’s very unexpected.”
