Physical AI takes AI beyond chatbots and image generators by combining models with sensors, actuators and control systems, allowing machines to perceive, reason and act in the real world.

Unlike conventional robots built for fixed tasks, AI-enabled robots can draw on broad knowledge and specialised training, improve through use and work as part of larger systems such as factories, power grids and vehicle fleets.

To operate reliably in those environments, physical AI needs real-world, multimodal data—including sensor signals, images, video, speech and text—because synthetic data alone is not enough.

The push toward industrial deployments also reflects pressure on the AI sector to turn investment into revenue by finding practical business applications beyond consumer-facing software.

Companies are already applying AI to energy operations: Shell monitors more than 13,000 pieces of equipment for predictive maintenance, BP says real-time drilling analysis cut evaluation from months to days, reduced costs by 7% and lowered non-productive time by nearly 8%, and Chevron uses its ApEX tool for seismic interpretation and exploration review.

ADNOC has agreed with G42 and OpenAI to apply advanced AI across its energy operations, aiming to improve efficiency and tackle complex engineering challenges; OpenAI CEO Sam Altman called energy and AI a major opportunity for both industries.

In manufacturing, Foxconn uses AI and digital twins to simulate and automate delicate tasks such as tightening screws and inserting cables; the company says simulations have cut deployment times by nearly 50%, while AI-powered robots have improved cycle times by 20–30% and reduced errors by 25%.

Ani Kelkar of McKinsey estimates robotics and physical AI could generate at least $1 trillion in economic value by 2040, largely in manufacturing and logistics, through new products and processes and by shifting workers toward problem-solving roles rather than simply replacing them.