“AI-powered digital twins mark a significant evolution in the future of manufacturing, providing a real-time view of the entire production line, not just individual machines,” says Indranil Sarkar, global chief technology officer, manufacturing and mobility industry at Microsoft. “This is allowing manufacturers to move beyond isolated monitoring to more comprehensive insights.”
For example, a digital twin of a bottling line can integrate one-dimensional shop floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. Many high-speed industries face uptime rates of more than 40 percent, estimates an industrial AI company that partnered with Microsoft and NVIDIA to turn complex data into actionable insights. By tracking micro-stops and quality metrics through digital twins, companies can target more health-related improvements and adjustments, which can save millions in one-time lost savings without disrupting ongoing operations.
AI presents the next opportunity. The government estimates that up to 50% of manufacturers are currently deploying AI in production. That’s up from the 35 percent of manufacturers surveyed in the 2024 MIT Technology Review Insights report that said they’ve started to put AI use cases into production. Large manufacturers with more than $10 billion in revenue were significantly ahead, with 77 percent already deploying AI use cases, according to the report.
“Manufacturing has a lot of data and is a great use case for AI,” says Sobel. “An industry that few people have talked about is digital technology and could be in a great position to lead AI. It’s very unexpected.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review. It was researched, designed and conducted by human writers, editors, analysts, and writers. This includes writing the survey and collecting data for the survey. The AI ​​tools that have been used were limited to secondary production processes that passed full human review.