AI Moves Down the Engineering Stack
If you followed the congressional testimony in April 2026 on “Robots Made in America: Advancing U.S. Leadership in Manufacturing and Automation,” you saw many of the same forces shaping this year’s Automate 2026 show in Chicago. Although Jeff Burnstein of the Association for Advancing Automation (A3) and Evan Beard of Standard Bots approached the industry from different angles, one message came through clearly in both discussions: Automation is evolving beyond standalone components toward integrated, software-defined systems that can be deployed, scaled and adapted more easily.
That evolution was on full display on the show floor. Vendors such as maxon and Standard Bots leaned into pre-integrated “playbooks” designed to reduce deployment friction, while machine vision increasingly sat at the center of AI-enabled architectures, guiding how machines perceive and respond in real time.
What’s notable isn’t that robots are becoming more intelligent—that story has been unfolding for years. What has been easier to miss is where that intelligence is taking hold. The AI zeitgeist has focused largely on software intelligence, to the point that the transformation happening inside machines has received far less attention.
AI is much more than an add-on capability; it is becoming a foundational layer in machine design. Machines are learning rather than simply executing commands. Vision systems are beginning to reason, not just detect. Simulation is replacing trial-and-error. Intelligence, in other words, is moving down the engineering stack to the motors, gears and actuators that make these systems work.
As a result, the design challenge now is to build machines that can continuously adapt to the world around them.
Our post-show coverage examines how that shift is reshaping robotics, motion systems, machine vision and industrial automation.
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Rehana Begg, Head of Content, Machine Design
