Physical AI - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026-2031)

The physical AI market size is projected to expand from USD 5.06 billion in 2025 and USD 7.11 billion in 2026 to USD 34.89 billion by 2031, registering a CAGR of 37.46% between 2026 to 2031. This report is Segmented by Component (Hardware, Software, and Services), Robot Type (Industrial Robots, Professional Service Robots, Personal and Household Service Robots, and More), Deployment (On-Device, Cloud-Based, and Hybrid), End-User Verticals (Logistics and Supply Chain, Manufacturing, Healthcare, Automotive, Agriculture, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Physical AI Market Trends and Insights

Rising Demand for Real-World Automation in Logistics and Manufacturing

Industrial robot installations in the United States rose 11% year over year to 38,000 units in 2025, showing that buyers were still adding automation capacity even outside a purely automotive cycle. The food industry alone posted a 30% increase to 3,000 installations, which showed that sectors with lower historical automation density were also moving faster. In the physical AI market, that matters because demand is no longer tied only to fixed factory robots; it is now spreading into mixed fleets that combine mobile systems, robot arms, and more adaptive control layers. Once operators pursue full workflow orchestration instead of isolated hardware purchases, software coordination and fleet management become more central to buying decisions. That shift supports the physical AI market because each new deployment increasingly depends on perception, planning, and control software that can work across different machine types.

Edge AI Inference Maturity for Low-Latency Robot Decision-Making

The release of the Jetson AGX Thor into general availability shows how quickly embedded compute capabilities are improving for robots that need immediate local reasoning. NVIDIA stated that the platform delivers 2,070 FP4 teraflops and 7.5 times the AI compute of its predecessor, which supports real-time multimodal processing at the machine level. Boston Dynamics is integrating the platform into Atlas, and Agility Robotics has adopted it for the sixth generation of Digit, which shows how silicon choices are becoming strategic design decisions rather than replaceable components. In the physical AI market, this creates long-term vendor relationships because edge processors selected during design often remain in place throughout an operating life. The result is a more defined split between hardware that executes time-critical decisions locally and software layers that continue to gain value through updates, orchestration, and model improvements.

High System Integration Cost and Long Commissioning Cycles

High deployment costs still slow the physical AI market even as robot hardware economics improve, because the full project typically includes tooling, safety systems, integration labor, infrastructure updates, and training. The input placed a mid-range collaborative robot cell at USD 50,000 to USD 90,000 before operators even begin to judge repeatability at scale. Integration labor of USD 100 to USD 140 per hour, added safety spending of USD 10,000 to USD 25,000, and training expense of USD 12,000 to USD 20,000 show why installation still feels like a system project rather than a product purchase. The problem becomes more serious when issues escape factory testing and appear during site acceptance, because every delay can disrupt line output or go-live schedules. This keeps the physical AI market from moving as fast as end demand alone would suggest, especially when buyers want a predictable time-to-value before expanding from one site to many.

Other drivers and restraints analyzed in the detailed report include:

• Human-Robot Collaboration in High-Variability Workflows
• Labor Scarcity in Unstructured Physical Work Environments
• Certification, Liability, and Functional Safety Complexity

Segment Analysis

Hardware held a 52.42% share in 2025, giving it the largest position in the physical AI market, as every deployment still starts with compute modules, actuators, sensors, and the robot body itself. The installed hardware base matters because it determines which models can run locally, how quickly a system can respond, and how much redesign is needed as capabilities improve. The physical AI market size at the component level still leaned toward hardware in 2025, reflecting the capital-intensive nature of robots that must operate in real environments rather than solely in software. Boston Dynamics integrated NVIDIA Jetson AGX Thor into Atlas, and Agility Robotics adopted the same platform for the sixth generation of Digit, demonstrating how early hardware decisions shape long-term supplier relationships. These design choices also affect serviceability, power consumption, thermal limits, and upgrade paths, meaning hardware still sets the operating boundaries for the rest of the stack.

Software is projected to grow at a 40.43% CAGR through 2031, indicating where more of the long-run value in the physical AI market is likely to shift. The shift is tied to world foundation models, simulation frameworks, and fleet management tools that improve performance across deployed machines without replacing physical assets. NVIDIA released GR00T N1.7 in early commercial access at GTC 2026, pointing to software licensing as a real revenue stream rather than only a feature embedded inside hardware sales. Services remain important because integration, commissioning, training, and optimization become more complex as fleets spread across sites and include machines from different OEMs. The physical AI industry is therefore moving toward a structure where hardware opens the door, but software and service layers capture more of the recurring value as performance improves through updates and orchestration.

Industrial robots held a 58.23% share in 2025, reflecting the large installed base already operating in automotive, electronics, and semiconductor settings. That position gave the physical AI market a strong foundation, as buyers could add smarter perception and control layers to proven hardware rather than replace entire systems. The established installed base also meant that even incremental AI upgrades could influence a wide set of production environments where throughput, quality, and labor flexibility matter. At the same time, the line between industrial and service robots is becoming less rigid, as warehouses and fulfillment sites now need machines that combine mobility, manipulation, and situational response. This is changing how suppliers define product categories, because a machine working in a warehouse aisle may now share traits with both an industrial arm and a service robot.

Professional service robots are projected to grow at a 39.72% CAGR through 2031, making them the fastest-growing robot type in the physical AI market. Growth is being driven by logistics, healthcare, and retail applications where operators want robots that can handle changing layouts, moving objects, and tasks that once required human judgment. The input also noted that humanoid and mobile manipulator platforms are moving into mixed-material handling, which explains why professional service robots have become a favored commercial entry point for general-purpose systems. Funding concentration around this category reinforces the view that suppliers and investors expect broader use beyond narrow pilots, even though the source-backed figures for some transactions were excluded here for source hygiene reasons. The physical AI industry is therefore balancing a mature industrial base with a faster-moving service robot segment that is expanding the addressable use case range well beyond fixed factory work.

Complete Report Scope:

• By Component
  • Hardware
  • Software
  • Services
• By Robot Type
  • Industrial Robots
  • Professional Service Robots
  • Personal and Household Service Robots
  • Other Robot Types
• By Deployment
  • On-Device
  • Cloud-Based
  • Hybrid
• By End-User Verticals
  • Logistics and Supply Chain
  • Manufacturing
  • Healthcare
  • Defense and Security
  • Automotive
  • Agriculture
  • Other End-User Verticals
• By Geography
  • North America
    • United States
    • Rest of North America
  • Europe
    • Germany
    • France
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held a 31.82% share in 2025, making it the largest regional market for physical AI. That lead reflected the concentration of AI infrastructure suppliers, strong commercial deployment sectors, and a defense environment that continues to support autonomy spending. U.S. industrial robot installations rose 11% to 38,000 units in 2025, reinforcing the region's broad automation momentum beyond software headlines. The Association for Advancing Automation has also pushed for a Federal Robotics Office and a national robotics strategy, indicating that policy discussions are moving toward deployment support, procurement alignment, and updated safety standards. Canada and Mexico add to the regional base through automotive and electronics production, and they benefit when cross-border deployment models spread from early adopters into broader supply chains.

Asia-Pacific is projected to grow at a 43.04% CAGR through 2031, making it the fastest-growing region in the physical AI market. The region combines manufacturing scale, robotics density, and policy support in a way that gives suppliers both local demand and production depth. The input linked this acceleration to China's 15th Five-Year Plan for 2026 to 2030 and to South Korea's robot density of 1,220 units per 10,000 manufacturing workers in 2026, which framed growth as structural rather than temporary. China also recorded 295,000 industrial robot installations in 2024 and held a 54% global share in that year, showing the scale of the regional robot base supporting future AI layering. For the physical AI market, this means Asia-Pacific is not only a demand center, it is also increasingly shaping supply economics, component ecosystems, and deployment speed.

Europe, South America, and Middle East and Africa show more varied adoption paths in the physical AI market, with performance tied to industrial structure, investment conditions, and standards maturity. Germany had 278,900 active industrial robots in 2024, or 40% of the EU factory robot base, yet the input also pointed to a projected 10% robotics and automation revenue decline in 2025 before a later recovery. Europe appears more deliberate because reliability and standards still weigh heavily on commercial rollout decisions in advanced settings. South America and Middle East and Africa remain earlier-stage opportunities, where logistics automation and inspection robotics are acting as the main entry points for wider physical AI adoption.

List of Companies Covered in this Report:

• NVIDIA Corporation
• ABB Ltd
• FANUC Corporation
• Yaskawa Electric Corporation
• KUKA AG
• Boston Dynamics, Inc.
• Agility Robotics, Inc.
• Figure AI, Inc.
• NEURA Robotics GmbH
• Universal Robots A/S
• Teradyne, Inc.
• OMRON Corporation
• Dexterity, Inc.
• Covariant, Inc.
• Siemens AG

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support