Gartner has identified 10 misconceptions about physical AI. Achieving results in the field requires designing the entire system, including sensors, data and simulation, rather than only AI-equipped robots.
Gartner Japan defines physical AI as “AI that operates in the real world,” describing it as technology that links AI decisions to the operation of robots and equipment to improve quality, uptime and safety in the field. It also points out that many people mistakenly interpret physical AI as referring to robots themselves.
Physical AI applies not only to robots but also to a wide range of equipment, including manufacturing equipment, vehicles and drones. It refers to systems that recognize conditions in the real world, make decisions and connect those decisions to physical actions. Accordingly, implementation cannot be completed simply by “adding” AI to existing robots or equipment.
Among the 10 misconceptions identified by Gartner are the idea that adding AI to existing equipment makes it physical AI, the interpretation that physical AI refers to humanoids or robots, and the assumption that Japanese companies will naturally gain competitiveness from implicit knowledge embedded in their data. Gartner emphasizes the importance of overall system design, including technology, data, simulation and computing infrastructure.
Applying or advancing LLMs as they are is unlikely to be sufficient for controlling the real world, making elements such as World Models and VLA models important as well. Physical AI is expected to be used across a wide range of fields, including logistics, mobility and construction, as well as manufacturing, and is expected to affect corporate competitiveness in the 2030s.