With physical AI, gunslingers and risk takers need not apply

""We need clear boundaries..., definitions, and rules," said Tianlan Shao, CEO of Mech-Mind Robotics, which makes industrial robots."

""Physical AI use cases...in controlled domains such as factories and warehouses tend to progress much faster than use cases in open, real-world environments, where the challenges and risks are far greater," Deloitte said in its study."

""The emphasis has been on deployment in constrained environments: logistics, agriculture, energy, and manufacturing, where labor shortages and efficiency gains are very real problems today," Martin-Rayo said."

Agentic and physical AI have rapidly advanced but produced failed projects and risky implementations, prompting calls for caution. Physical AI applications already appear in over half of companies and are expected to reach 80%, spanning robots, drones, inspection devices, intelligent cameras, and forklifts. Use in constrained environments such as factories and warehouses advances faster than in open real-world settings due to lower risks and clearer controls. Deployment emphasis focuses on logistics, agriculture, energy, and manufacturing to address labor shortages and efficiency gains. Clear definitions, rules, and monitored, controlled testing environments are necessary to prevent hazardous mistakes.

Read at Computerworld