After years of innovation, physical AI is entering a more practical phase as rapid advances in AI make scaling projects more viable.
That’s according to Capgemini’s latest report, “Physical AI: Taking human-robot collaboration to the next level,” which highlights the growing momentum of physical AI -- defined as technology that gives machines more human-like perception and reasoning capabilities.
The report, which surveyed 1,678 executives in 16 countries, found the vast majority (80%) are already engaging with physical AI in some form, though only 4% said they're operating at full scale.
This gap between interest and implementation highlights a long-standing issue in robotic deployment, as the rapid advancement of technology often outpaces companies’ ability to integrate it.
However, the report argues that barriers to wide-scale deployment are decreasing, driven by better foundation models, simulation tools that compress training cycles, falling hardware costs, and advances in edge computing.
In addition, reindustrialization efforts are accelerating across Europe and the U.S., causing investment in physical AI to rise in tandem. Indeed, two-thirds of organizations now rank physical AI as a high priority in their automation agenda for the next three to five years.
More than half of business leaders cited autonomous mobile robots, industrial robotic arms and cobots as the fastest growing segments over this time period -- far ahead of humanoids. While cobots gain more media attention and hype, technical immaturity, cost and training challenges remain obstacles to the technology’s uptake.
Labor shortages are also playing a role. More than costs, the report found that the top investment driver in physical AI is difficulty finding workers, particularly in manual labor-intensive industries such as agriculture, warehousing and logistics.
“Robotics has a long history of overpromising, as early breakthroughs created expectations the technology could not yet meet,” Pascal Brier, chief innovation officer at Capgemini, said in a statement. “What is different today is not the hype, but the convergence of AI, data, and engineering maturity.”
While two-thirds of executives said they expect physical AI to reach scale within the next five years, the path there won't be straightforward. Integration challenges, unclear ROI and questions about public acceptance (particularly for humanoids) remain significant hurdles.
“The opportunity is real, provided we focus on what works at scale,” Brier said. “Deploying physical AI responsibly, safely, and progressively will be essential to building trust, with security by design, transparency, and human oversight at the core of sustainable human‑robot collaboration.”
