Physical AI Moves From Warehouse Pilot to Baseline
The rush to deploy artificial intelligence in warehousing has shifted from generative software tools to physical applications. Sensor-connected intralogistics systems are generating massive data streams on the packaging line, offering immediate returns through bottleneck prevention and compliance tracking.
The Case for Physical AI
The commercial pull behind that shift is measurable. According to Global Market Insights, the warehouse automation market is on track to grow from about $26.5 billion to more than $115.8 billion by 2034, while intralogistics revenue is forecast to climb from $57.23 billion in 2025 to $140.73 billion by 2034, a compound annual growth rate of 10.4 percent. Analysts attribute the acceleration to the sensor-connected robotics and AI-driven material handling that Ranpak describes.
Warehouse operators are increasingly leveraging machine-level analytics to maintain continuous material flow. Ranpak, a provider of sustainable packaging solutions, views this data integration as a baseline requirement for modern fulfillment rather than a future concept.
Bryan Boatner, Chief Revenue Officer at Ranpak, replied in writing to an inquiry from Warehouse Insider detailing how machine learning drives immediate value.
"We see physical AI as the key to intralogistics not in the future, but now. The interplay of sensor-connected machines creating rich data with AI analyzing and recommending is a symbiotic relationship. What's happening on the packaging line can be used to troubleshoot or prevent bottlenecks, while each managed case becomes another example to train against and learn from. Today, that presents itself as proof of compliance with key regulations like PPWR and downtime avoided contributing to ROI. It can also eventually become iterative digital twin testing environments, agents with expert-level training to reference, and more. As quality data is collected, the use cases will continue to grow exponentially."
