Gartner urges coordinated systems for physical AI

In its October 1 analysis, Gartner argues that organizations should evaluate the system around machines, including continuing facility adaptations and software integration costs. It recommends setting return targets and failure criteria before deployment, while investing in reusable control policies and simulation for validation. These are the analyst firm’s recommendations for organizations planning physical AI deployments.

Connecting fleets

AI Business’s October 16 interviews explain what coordination means in practice. Gartner’s Bill Ray describes a software layer connecting manufacturers’ fleet management systems to each other and to wider infrastructure; those manufacturers retain fleet management. It coordinates tasks rather than communicating directly with every robot. Omdia’s Mark Beccue sees legacy infrastructure and missing expertise as bigger adoption obstacles than absent interoperability standards. Ray separately warns that replacing an orchestration vendor becomes harder with each additional integration.

The existing factory matters

An Accenture manufacturing article from August 2025 supplies background on these site constraints. It cautions that adding a robotic cell may disrupt an existing production schedule. Its digital twins are virtual representations of machines, lines or facilities, refreshed with sensor data. Accenture identifies startup and maintenance costs, scarce skills and changing workflows as barriers. Isolated twins can fragment data and hamper teams’ communication; cybersecurity and privacy also need attention.