April 9, 2026 -
By Neeta Shenoy, EE Times
Historically, AI in industrial automation was separate from the machines responsible for productivity, reliability, and safety. Data was captured on the factory floor, analyzed elsewhere, and provided after the fact as insights for operators. This architecture worked in an era when AI focused on reporting, optimization, and longer-cycle decision support. That era is ending.
Today, AI is expected to operate continuously and in real time, detecting defects, performing counting and inspection tasks at line speed, and identifying safety hazards as they occur—flagging mechanical anomalies before equipment fails. These workloads are no longer confined to dashboards or centralized analytics platforms; they now sit adjacent to machines, where response time, power efficiency, security, and long-term reliability are essential to the effective use of AI.
As intelligence moves closer to the machine, the deployment challenge shifts. The question is no longer whether an algorithm is accurate in isolation but whether the underlying silicon and system architecture can deliver AI as a dependable, always-on function of industrial automation.