Physical AI is moving beyond traditional robotics into real enterprise tasks, but enterprises still face big challenges integrating and scaling robotic systems.
Physical AI is finding new applications beyond conventional robotics as enterprises test and use AI-enabled robotic systems in data centers, manufacturing plants and warehouses.
These systems combine robotics with AI capabilities, such as computer vision, decision-making and world models -- AI models designed to represent physical environments and predict how they might change in response to different actions.
Meta, for example, is testing robots at several of its data centers for tasks traditionally performed by technicians, such as swapping network cables and reseating hardware components. At the social media giant's Altoona, Iowa, campus, dual-armed robots have been practicing network-cable swaps. At its newer Prometheus facility in New Albany, Ohio, Meta is testing robots to reseat hardware components, while also exploring whether a robotic arm can power-cycle servers.
Similar applications are emerging in other industries. Automaker Nissan is deploying AI-powered autonomous mobile robots to transport components in its Smyrna, Tenn., factory and logistics operators, such as DHL Supply Chain, are using robots to guide workers through warehouse picking.
While applications of physical AI vary widely, they share a practical focus: using robots for work that's repetitive, physically demanding or hard to scale with human labor.
Physical AI isn't the same as traditional robotics
Robots have worked at enterprises for decades. Traditional industrial robots typically execute predefined instructions in controlled environments. Physical AI adds perception and decision-making capabilities, enabling robots to respond to conditions they weren't explicitly programmed to handle.
That ability of robots to determine how to achieve a goal and adapt to changing conditions is emerging … but it isn't yet reliable enough to handle the full range of real-world conditions.
Bill RayGartner Analyst
Bill Ray, an analyst at Gartner, described the shift as moving from telling a robot how to perform a task to giving it a goal and letting it determine how to achieve it. An example of this is telling a robot that boxes need to be in a van rather than instructing it to pick up a box and put it there. However, Ray cautioned that this approach is still in the early stages.
World models can support this kind of adaptability by helping robots predict how the physical environment might change in response to different actions before they act.
"That ability of robots to determine how to achieve a goal and adapt to changing conditions is emerging in demonstrations and some deployments, but it isn't yet reliable enough to handle the full range of real-world conditions," he said.
AI-powered vision is one technology helping robots become more adaptable. Craig McDonnell, managing director, business line industries, at ABB Robotics, said AI is shifting industrial automation "from static, preprogrammed stations toward autonomous, adaptable and versatile robots." Robots can use vision to recognize products, detect defects and locate parts, then act on what they see. Meta is testing ABB Robotics' robots at Meta's Prometheus data center.
But that doesn't mean every AI-enabled robot is autonomous. Human oversight remains common, especially around expensive equipment, but greater perception and adaptability can expand the range of tasks robots can handle.
Physical AI also doesn't necessarily mean humanoid robots. Ray used the term polyfunctional robots to describe more general-purpose machines whose functions can change using software. He said wheels often make more sense than legs in enterprise environments where robots move materials across relatively structured spaces. Legged robots must constantly balance, he noted, which makes them power-intensive.
Similarly, humanoids aren't always necessary for enterprise tasks, said Anthony Jules, co-founder and chief innovation officer at Robust.AI, a robotics company developing AI-powered collaborative mobile robots for warehouses. "Ninety percent of activity in warehouses and material handling is done by things with wheels," he said.
Where enterprises are actually using physical AI
Physical AI is finding practical applications across the enterprise, although most deployments still focus on specific tasks. Early examples span data centers, factories and warehouses, where companies use robots for physical work that's repetitive, demanding or difficult to scale with human labor.
Data centers
Meta's experiments show how companies are applying robotics to AI infrastructure operations rather than conventional manufacturing or warehouse work. The newer pilots involve robots interacting directly with data center equipment, including network cables and server hardware.
Ray said data centers can be attractive environments for physical AI because many maintenance tasks are repetitive and facilities can be mapped in advance. At the same time, server halls contain dense cabling, varied equipment configurations and technicians working in the same space, creating more variability for robots than conventional automation typically handles.
Manufacturing
Factories have long used robots but usually for one fixed job. Physical AI moves beyond "the traditional limitation of 'one job per robot," ABB Robotics' McDonnell said. Some of the greatest opportunities for physical AI lie in applications that have traditionally been hard to automate because of high variability, he said, including mixed products, components in different positions and orientations, and varied lighting or environmental conditions.
ABB is addressing some of those challenges using AI-powered simulation. Its RobotStudio HyperReality system lets manufacturers train robots in simulated factory environments with variations in lighting, materials, colors and angles. The approach relies on digital twins, virtual replicas of products, robots and operating environments, so manufacturers can train and validate AI models in many conditions before physical deployment.
Simulation can also help robots make decisions before they act in the physical world. Gartner's Ray described a manufacturing example in which robots move screens between test stations, with each next stop depending on the previous test result. The task was too complex for conventional automation. Development using simulation took four months, but deployment took 36 hours with minimal disruption to the existing process, he said.
Warehouses and logistics
Warehouses have long used robots to move inventory, but newer systems are adding AI to help robots handle more variable tasks and work alongside employees. Amazon has built large-scale robotic infrastructure across its fulfillment network, using mobile robots to transport inventory and AI to coordinate its fleet.
Warehouses already use advanced specialized robots, so there's often little reason to replace them with humanoid ones, Ray said. Rather, the value comes from finding tasks for which flexible robots fit existing processes.
Other deployments focus on integrating robots into existing warehouse workflows. Robust.AI's work with DHL Supply Chain offers a more incremental example. "Our robots work alongside warehouse employees, helping them pick orders by moving between locations and directing workers where to go," Robust.AI's Jules said.
The deployment illustrates how physical AI can be added to an existing operation without requiring companies to replace the broader workflow. But making that approach work in production requires companies to address the systems, processes and people around the robot.
From capable robots to production-ready systems
For enterprise robotics, getting a machine to perform a task is only part of the deployment. The robot also must connect with existing IT systems and fit into the human workflows it's working alongside.
Enterprise robotics requires two kinds of integration, Jules said: linking the robot to systems such as ERP, warehouse management and manufacturing execution, and a second, often harder piece: "the change management piece." Poorly supported change leads to slow or no adoption, he said.
That makes the robot's fit within the existing operation just as important as its technical capabilities. Ray said enterprises should adapt robots to existing processes rather than redesigning processes around them, since robots will change substantially over the next five years.
Once a robot fits the workflow, it still must perform reliably and safely enough for production. "Even a fractional drop in performance is unacceptable in demanding industrial and commercial environments," McDonnell said.
Ray emphasized that safety grows more important as robots work around people and that protective infrastructure, such as cages, can cost more than the robot themselves. He expects more robots to be designed with built-in safety limits, such as lower speeds or reduced force, so they can't seriously injure someone even if something goes wrong.
Safety is only part of the technical challenge. The hardware itself can limit what robots can reliably do.
"Human fingertips are incredibly sophisticated at sensing force and pressure, and replicating that is really difficult," Ray, said. "Even getting a robotic fingertip to last three months can be a significant achievement, but that's still not long enough for practical enterprise use." Technical reliability, however, doesn't guarantee that an enterprise deployment will make financial sense. Jules described this as a "novelty trap": companies buying a few robots to experiment without a clear ROI. "A lot of things that people call deployments are actually just pilots," he said.
Beyond the technical and financial considerations, physical AI introduces another requirement.
That's trust.
Physical systems demand a different kind of it than software, Jules said, because "they're situated and working next to people, and anything that's like that, you need to touch and feel and really understand."
Kinza Yasar covers AI and emerging technology for TechTarget, with a focus on ethics, enterprise adoption, governance and business strategy. Before moving into journalism, she worked in IT and network support roles, giving her a systems-level perspective on how enterprise technologies are built, deployed and managed. Her reporting draws on conversations with industry experts, analysts and enterprise leaders to examine how AI is being adopted and operationalized in real-world environments.
