The real test for physical AI starts after the pilot

Physical AI’s challenge is not producing an impressive pilot. It is working reliably in the changing conditions of real-world operations, where decisions can affect safety, uptime, service delivery and costs. TechRadar’s Meagen Eisenberg reports that the main constraint is execution, not ambition or budget.

Companies expect to more than double AI spending in 2026, from 0.8% to approximately 1.7% of revenue, according to BCG’s 2026 AI Radar. Yet PwC’s 2026 AI Performance Study found that 74% of AI’s economic value is captured by 20% of organizations. In robotics, Capgemini found that 79% of organizations are engaging with physical AI, while 27% are deploying or scaling it.

The article argues that organizations should start with an operational outcome they want to change, then check whether the needed data is available and which decision or workflow could improve. Physical AI uses information from vehicles, equipment, cameras and sensors, but fragmented or delayed records can leave systems with only part of the picture. Shared operational data and workflows provide a foundation for deployment beyond a controlled pilot.

Value comes when an insight leads to an appropriate action. In a “sense, decide and act” model, systems gather information, evaluate it in context and prompt a person or system to respond. Examples include a vehicle health warning that starts a maintenance workflow and pothole detection that helps public teams prioritize repairs. The article says oversight should reflect potential consequences, with human review for decisions that could materially affect safety, employment or service delivery.