Capgemini finds 66% of organizations prioritize Physical AI despite scaling challenges

Capgemini Releases Major Physical AI Report Amid Growing Industry Interest

Capgemini Research Institute has published a comprehensive 140-page report titled 'Physical AI: Taking Human-Robot Collaboration to the Next Level,' marking the latest entry in what critics describe as an emerging hype cycle . The research reveals that 66% of organizations now rank physical AI as a high priority in their automation agenda for the next three to five years 3

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. This widespread recognition spans sectors from high tech, where 93% of executives see the opportunity, to warehousing and logistics at 69%, and agriculture at 59% 2

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Foundation Models and Simulation Technologies Drive Autonomous Real-World Action

Physical AI represents a shift in robotics from basic automation to autonomous real-world action in complex environments. According to the report, advances in foundation models are equipping robots with the intelligence needed to operate independently, while simulation technologies are compressing training cycles by enabling large-scale learning 2

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An emerging AI-robot-data flywheel is reinforcing this progress, as deployed systems generate real-world data that continuously improves performance and generalization 3

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. These gains are amplified by advances in edge computing and batteries, falling hardware costs, new commercial models such as robotics-as-a-service (RaaS), and connectivity breakthroughs including private 5G 2

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Labor Shortages and Reindustrialization Fuel Investment

Labor shortages, rather than labor costs, emerge as the top driver of investment in physical AI, especially in agriculture, retail, high tech, warehousing, and logistics sectors 3

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. The report indicates that 43% of executives say reshoring and reindustrialization are increasingly driving their interest in physical AI as a means to support domestic production at scale 3

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. Japan leads globally in prioritizing physical AI within automation strategies, with more than three-quarters of executives identifying it as a priority over the next three to five years, ahead of the US 3

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Intelligent, Physical Robots Face Significant Data Gap and Scaling Challenges

Despite the optimism, only 4% of organizations say they are already operating at scale, and nearly eight out of ten executives report that scaling physical AI remains a challenge, primarily due to a lack of technology and operating readiness 3

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. A critical issue highlighted by industry critics is the colossal data gap in intelligent robotics—equivalent to 100,000 years of human consumption—because usable data about the physical world needs to be 3D and gathered at source, unlike web-scraped textual data used for Large Language Models . Complex manual dexterity remains particularly tough to model virtually, creating actions that are approximate where they need to be pinpoint accurate .

Humanoid Robots Remain Long-Term Bet Amid Technical Barriers

While 60% of executives say that physical AI will enable robotics applications that were previously impossible or impractical, humanoid robots face significant barriers and remain a longer-term bet 2

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. Technical immaturity such as reliability and dexterity concerns deterred 72% of executives, while 63% cited high costs and 58% pointed to training challenges 3

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. More than half of business leaders cite autonomous mobile robots, industrial robotic arms, and cobots as the fastest growing robot form factors in their organization over the next three to five years, well ahead of humanoids 3

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Critical Voices Warn Against Hype Cycle Expectations

Pascal Brier, Chief Innovation Officer at Capgemini, acknowledges that "robotics has a long history of overpromising, as early breakthroughs created expectations the technology could not yet meet," emphasizing that deploying physical AI responsibly, safely, and progressively will be essential to building trust 3

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. Industry analysts warn that the current messaging around physical AI—suggesting something immediate and solvable—risks creating unrealistic expectations when the reality is a complex, expensive, and decades-long journey . Use cases span hazardous operations, micro-logistics, pick-and-place, and field inspection, as well as sector-specific applications in manufacturing, healthcare and eldercare support, and disaster-damage assessment in insurance 2

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. Nearly half of executives identify improved flexibility and operational resilience as key benefits, highlighting the ability to reconfigure production systems more rapidly than with traditional robotic automation 3

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