7 use cases for leveraging AI in the physical world

The next big AI wave won’t be a chatbot in your laptop, or an agent that works behind the scenes to turn meeting notes into project tickets, but AI that takes control of devices that move and interact with the environment. Physical AI can be defined as the integration of AI into autonomous systems, allowing them to perceive the environment around them and perform complex actions in the physical world. The physical AI market, currently valued at about $92 billion, is projected by PwC to surpass $489 billion by 2030. For many people, physical AI may conjure images of robots building widgets on a factory floor, or a self-driving car. Both examples are among the top use cases for physical AI, but physical AI is also being integrated into security cameras, traffic lights, inspection robots, medical devices, and more. What makes a strong use case for physical AI IT leaders thinking about how to use physical AI should think beyond the human-shaped robots that generate a lot of attention, says Adnan Masood , chief AI architect at digital transformation provider UST. “I usually have one caution for CIOs — skip the humanoid theater,” he says. “The near-term advantage is adaptive automation in variable environments where conditions change, humans share the space, and downtime is expensive.” The sweet spot for physical AI is when it can run safely and repeatedly and can be audited within existing safety and compliance regimes, he adds. For physical AI to make a big impact, a handful of conditions must exist, adds Vikram Venkat , investor in physical AI systems at Cota Capital. First, there should be a major labor component, such as existing or expected labor shortages or conditions that make the work dangerous for humans, he says. In addition, the environment should be relatively constrained, because physical AI platforms generally aren’t yet proficient at handling highly variable environments. Finally, the task should be repeatable, often at high volumes, and have clear measurable outcomes, he adds. In the short term, a couple of other conditions should exist, Venkat says. First, deployments should be simple, and require minimal changes to existing processes, additional infrastructure, or integrations into existing systems. Second, humans in the loop should be able to correct errors. Top use cases for physical AI Despite those constraints, physical AI’s potential is huge, says Albert Liu , founder and CEO of edge AI solutions vendor Kneron. “Most people think physical AI begins with robots, which is simply the example our minds go to since it has been the most visible until now,” he notes. “But physical AI isn’t just about the typical answer — machines that move — it’s about environments that become intelligent.” With several caveats in mind, here are seven promising uses for physical AI systems. Manufacturing robots When thinking about physical AI, many people may envision robots manufacturing cars or other products. That’s certainly happening, with several vendors offering builder robots for sale, and with the industrial robotics market valued at $54.3 billion in 2026, growing to $94.4 billion by 2031, according to Mordor Intelligence . One example of robots building products comes from car maker BMW, which has used a humanoid robot to weld parts together at a plant in the US. Quality inspection and predictive maintenance Physical AI deployed inside manufacturing environments isn’t just being used to assemble products. The technology is also being used for material handling and automated quality inspection and defect checking, with labor shortages and constrained environments driving use, notes Venkat. Predictive maintenance is also a sweet spot for physical AI in manufacturing. AI can be used to check that the software powering equipment is working correctly, says UST’s Masood. “Agentic pipelines now read hardware schematics and chip pinouts natively, generate the regression suites engineers once scripted by hand, and compare live equipment telemetry against digital twins to catch firmware regressions and signal-integrity faults before a production run,” he says. Boston Dynamics’ four-legged Spot is an example of a marriage between robotics and AI, with the company saying thousands of robots have been deployed across 40 countries at companies such as Intel, Chevron, Michelin, and Cargill . Spot is used to automate industrial inspections, conduct predictive maintenance, and go on security patrols. Boston Dynamics also sells Stretch, which automates the unloading of trailers and containers, and Atlas, a humanoid robot that can lift, sort, and assemble products. Physical AI embedded into cameras and sensors can provide quality control inspections on factory floors, notes Parm Sandhu , group vice president for enterprise AI, edge computing, and digital innovation at IT solutions provider NTT DATA. “They want to make sure the products built right the first time,” he says. “We use a foundation model, set up with cameras and trained in self-learning, so it very can very quickly learn standard operating procedure for one factory station.” Autonomous vehicles and drones The promise of self-driving cars entered the public consciousness several years ago, and the market, separate from the physical AI market, was worth more than $200 billion in 2025 , according to Global Market Insights. Autonomous taxis are also gaining momentum, with Waymo and Tesla launching robotaxi experiments in limited areas in 2025. Uber also has huge plans for robotaxis. But the autonomous vehicle market extends far beyond cars driving down the highway. Autonomous farm equipment, including tractors, harvesters, and drones , represent a growing market, with market size estimates varying wildly. Global Market Insights estimated the market to be worth $70.9 billion in 2025, with projections for it to reach $144.7 billion by 2035. Drones can also be operated by an AI, leading to all kinds of applications, including military uses and food and package delivery services. Amazon and other companies have experimented with drone delivery services in recent years, and DoorDash announced in late July that it would jump into the market . One use that staddles the autonomous vehicle and manufacturing use cases involves self-driving forklifts. NTT DATA has worked with forklift manufacturer Hyster-Yale to install self-driving capabilities into the vehicles, in part a response to labor shortages, Sandhu says. “If you think about manufacturing, pretty much everything you touch in that world was lifted by a forklift somewhere or components were lifted by a forklift somewhere,” he says. “But people don’t want to drive forklifts, and that’s a huge problem.” Fleet and warehouse coordination Physical AI, built into trucks and smart shelves, can track and better coordinate the movement of materials and products, from the warehouse to the end customer. Physical AI, installed in robots, can pick, sort, and transport goods. AI can use fleet telemetry to optimize routes in the shipping fleet. AI models can now orchestrate thousands of autonomous mobile robots across fulfillment networks, what UST’s Masood calls “air traffic control for robots.” The AI intelligence sits in the coordination layer that routes, sequences, and removes conflicts in the fleet, he adds. “It scales in ways single-robot programming never could,” notes. Physical AI has moved beyond pilots and is operating at enterprise scale in warehouses, according to Symbotic, a warehouse physical AI vendor. The company’s fleet of 22,000 autonomous mobile robots that traveled more than 200 million miles in 2025, with one robot traveling more than 52,000 miles, or more than twice the distance around the Earth, the company says. Surveillance and physical security Physical AI’s application to physical security includes roving robots like Boston Dynamics’ Spot, but it also allows organizations to connect video cameras and other security tools to provide an ever-vigilant view of the secured environment. Companies such as Artificial Intelligence Technologies Solutions and its subsidiary Robotic Assistance Devices are connecting several devices for a sort of security mesh across a campus or building. The companies’ Speaking Autonomous Responsive Agent (SARA) is an agentic AI platform designed to coordinate cameras, fixed security devices, autonomous patrol vehicles, lights, speakers, monitoring systems, and human security personnel. SARA can evaluate events from physical security systems, verify security events, communicate directly with people at the site, and initiate approved responses, the companies say. The automated response can save valuable time compared to human intervention, they claim. Another example of the use of physical AI for security involves smart metal detectors with AI embedded inside. Athena Security is one company that offers AI-powered body scanners that claim a high rate of detection for all kinds of weapons, including razor blades and small knives. Smart buildings and infrastructure Companies can use physical AI to monitor all kinds of metrics inside buildings and across utility grids and telecom networks, notes UST’s Masood. The AI can trigger alerts, safety interventions, or environmental controls. Hospitals are now using physical AI to coordinate care, and network operators are deploying AI-powered self-healing tools. Physical AI will create intelligent concierges at hotels, airports, and hospitals that provide directions, verify identities, and coordinate services, Kneron’s Liu says. Over the next decade, AI will be embedded in nearly all physical spaces, including drive-thru lanes, restaurants, factories, and offices, he predicts. “People will expect a security camera that understands intent instead of simply detecting motion, a hospital room that recognizes subtle changes in a patient’s condition before an alarm sounds, a retail shelf that manages inventory autonomously, or a building that continuously optimizes energy, security, and occupancy,” he adds. Smart cities Outside of traditional enterprise environments, cities are now embedding AI into traffic devices to monitor vehicle flow and into cameras to monitor community service needs. The AI-powered systems can improve traffic flow, monitor intersections, and make roadways safer without relying only on human observation. Lidar maker Ouster worked with the New Jersey Department of Transportation to install sensors at 42 intersections ahead of the World Cup tournament to assist with road and pedestrian traffic congestion, the company says. NTT DATA is working with Brownville, Texas, to set up a citywide alert system to send workers for incidents such as when a park’s garbage containers are full and to assist police officers in filling out reports, notes Sandhu.