Trustworthy AI and safety. The smallest error rates can have cascading effects in physical systems, potentially leading to production waste, product defects, equipment damage, or safety incidents. If AI systems hallucinate, errors could be perpetuated and amplified across entire production runs, creating compounding downstream effects on costs and operations.
AI-powered machines can behave unpredictably even after extensive safety testing. The stakes rise significantly in public spaces, where autonomous systems must navigate unpredictable human behavior. To scale physical AI systems across various industries, comprehensive safety strategies that integrate regulatory compliance, risk assessments, and continuous monitoring are necessary.15
Regulatory environment. Companies must navigate overlapping and sometimes contradictory requirements across jurisdictions.16 As robots move from controlled factory environments into public spaces, regulatory bodies are likely to develop new frameworks for safety certification, liability, and operational oversight.
Data management. Organizations must capture and manage massive amounts of sensor data, 3D environmental models, and real-time information. High-fidelity digital twins of physical assets are essential for effective training and deployment, requiring extensive data on physical characteristics, object properties, and interactions. Organizations will also need to integrate multimodal data from disparate sources, ensure data security, and manage data infrastructure costs.
Human acceptance. While most workers are generally comfortable with predictable, rule-based robots, physical AI systems that learn and adapt introduce new uncertainties, especially worries about job displacement. However, experts predict that most roles will evolve toward collaboration rather than replacement.17 The goal is to create environments where robots handle repetitive or dangerous tasks while humans focus on creative problem-solving and complex decision-making.
Cybersecurity vulnerabilities. As discussed in “The AI dilemma,” physical AI systems create new attack surfaces that bridge digital and physical domains. Connected fleets increase cyber risks, with vulnerabilities potentially leading to unauthorized access, data breaches, or even malicious robot control. The stakes are even higher when security breaches can affect physical safety and operational continuity.
Robot fleet orchestration. As physical AI systems mature, organizations will increasingly deploy heterogeneous fleets of robots, autonomous vehicles, and AI agents from multiple vendors, each with proprietary protocols. This creates interoperability challenges that can lead to accidents, downtime, system congestion, and operational inefficiency.18 Autonomous fleet management and orchestration systems can help resolve these issues.
Over the coming 18 to 24 months, resolving these foundational issues will likely enable physical AI and robotics to expand beyond traditional industries. Warehousing and logistics may have served as physical AI’s proving ground, but sector boundaries do not limit the technology.
Crumbling sector boundaries
As leading organizations across the public and private sectors are laying the groundwork for physical AI at scale, adoption is accelerating exponentially. Applications are emerging wherever physical AI solves real problems.
In health care, a sector facing global staffing shortages, medical technology companies are developing AI-driven robotic surgery and digital imaging devices. GE HealthCare is building autonomous X-ray and ultrasound systems with robotic arms and machine vision technologies. Other medtech companies are designing intelligent robotic assistants that can help with patient care and automate surgical tasks.19
Restaurants are also deploying robots to help address labor shortages. Sidewalk-crawling delivery robots travel at pedestrian speeds; inside restaurants, robots handle tasks like flipping burgers and preparing salads, while service robots seat customers and serve food.
Naturgy Energy Group, a Spanish multinational natural gas and electrical energy utilities company, currently uses drones for inspection purposes. Rafael Blesa, Naturgy’s chief data officer, envisions an expanded role for physical AI as the technology hardens, particularly in dangerous field operations involving high voltage or open gas pipes. “Many operations related to grid maintenance could be performed by robots in the long term,” he explains. “My expectation is that in three to four years, we’ll have robots performing physical operations, which could save lives.”20
Similarly, the city of Cincinnati is using AI-powered drones to autonomously inspect bridge structures and road surfaces, reducing costs, keeping human inspectors out of hazardous situations, and condensing months of analysis into minutes. “This type of technology is going to be the nuts and bolts of what’s going to allow [mayors] to do their jobs better and provide better information, decisions, and cost efficiencies for their constituents,” said Cincinnati’s mayor, Aftab Pureval.21
In 2024, the city of Detroit launched a free autonomous shuttle service designed for seniors and people with disabilities whose mobility was severely limited by traditional transit systems. Known as Accessibili-D, the self-driving vehicles were equipped with wheelchair accessibility and a trained safety operator. Three autonomous vehicles operated within an 11-square-mile section of Detroit, offering 110 different stops.22
Regardless of the sector, these deployments share a common characteristic: They augment human capabilities in situations where safety, precision, or accessibility are most critical.
link