Software-Defined Automation as the Bridge to Physical AI

This IDC Perspective examines the relationship between software-defined automation architectures and physical AI capabilities. There is talk of pushing AI capabilities deeper into physical processes executed by mechanical systems. In the industrial world, physical AI is about leveraging AI to automate more dynamic closed-loop control systems. To enable this shift, software-defined automation architectures will be necessary to virtualize and bridge between data-driven decision initiatives and real-time process execution systems while maintaining the strict requirements of these environments. "People are talking about physical AI as if it is a new set of capabilities," says Jonathan Lang, research director, IDC. He adds, "AI or various types have been used in closed-loop automation for a long time. What is new is the desire to bridge broader and more general-purpose AI outputs and initiatives into the system models and ultimately into the execution processes. This integration carries dramatic risks and significant technical limitations that only a shift toward more software-defined automation architectures can possibly resolve."