What’s different now: the tech stack has caught up
While factories began using robots more than 50 years ago, machines that leverage human robotics and AI are relatively new to manufacturing. Manufacturers have begun using them in general-purpose work in human-designed spaces, with early use cases ranging from handling materials to tending to machine parts and performing inventory inspection. Robots have also been used in basic assembly where human-like reach and mobility offer an advantage.
Mobile robots — also known as AMRs (autonomous mobile robots) and AGVs (automated guided vehicles) — form the backbone of new enabling technology that drives a flexible, responsive internal logistics system. These machines are capable of physically moving materials — parts, pallets, totes — around inside a factory or warehouse, guided by vision/LiDAR navigation, precise docking and fleet orchestration that dynamically routes materials, kits and pallets.
As companies adopt this technology, deploying digital twins offers a safe bridge between concept and execution. Creating virtual replicas of production lines, warehouses and processes enables teams to test changes, train systems and forecast outcomes before entering production.
At the same time, advances in computer vision hold the potential to help physical AI mature into a practical workhorse. Modern 2D/3D vision models can now recognize parts, estimate pose and spot defects reliably.
Further, orchestrating this across both the manufacturing plant and cloud keeps everything current, allowing the system to continuously monitor and provide updates and safety checks. If something should go awry, the humans overseeing operations on the manufacturing team retain the option to roll back actions if necessary. To mitigate the possibility that adding autonomy will introduce unacceptable risks, safety and cybersecurity are embedded from the start. Organizations need to keep safety top of mind throughout the deployment, especially in warehouse settings where machines will share the same space with human workers.
Where physical AI proves its value: ROI signals that matter
When deployed responsibly, physical AI deployments can deliver impressive results, improving throughput as systems minimize minor stops, drive faster changeovers and maintain cycles. Quality and yields increase as vision checks identify issues early, adjusting to automatically stay within optimal ranges, reducing scrap and rework. This significantly improves productivity by automating repetitive transport and inspection tasks, while safety machines tackle more hazardous tasks.
Deploying physical AI will also enable organizations to improve working capital by synchronizing the tasks of making and moving products, improving inventory turns and reducing the need for large buffers. Organizations are building upon this to measure ROI by tracking common baselines such as equipment effectiveness, defect rates, changeover time, on-time delivery and safety incidents.
How manufacturers are using physical AI today
Most organizations have yet to deploy physical AI at scale due to initial implementation costs and the inherent challenges of integration with existing legacy systems. However, some manufacturing organizations are turning autonomous supply chains into reality. Here, we outline use cases across the make, move and deliver phases.
