Should companies deploy robots at their plant if they could virtually reprogram themselves to perform new and different tasks, asks Industry Week (May 13, 2026)? We’re nearly at the end of the AI hype cycle, when suggestions for how to leverage the technology become less flashy and more realistic.
Now Siemens has just revealed Eigen, an AI agent that can replace manual coding or programming for programmable logic controllers, distributed control systems, and robotics applications, updating code or instructions to reflect new priorities and goals.
Siemens says that engineering and reconfigurations constitute 70% of the entire lifecycle cost of a robot. If, however, an AI agent like Eigen can shorten the time needed to make these adjustments, it makes the robot more efficient, and small and medium-sized businesses might be better able to afford deploying the technology.
“There’s a kind of new age of automation arising, because with AI assistance to program robots and PLCs, it means you could suddenly automate much smaller lot sizes on a good return of investment,” says the firm’s CEO of its automation division.
Eigen can help manufacturers deal with a lack of coders and programmers. Another Siemens exec adds “We don’t attract the best of the programmers to the manufacturing floor. … So getting programmers to come and code our controllers or robotic systems? That was a scale up bottleneck. Bringing in AI to reprogram things, reprogram the whole process, will be more game changing in the U.S. than in Germany, where I see when people with Master’s degrees on the manufacturing floor, which is not the case in the U.S. Humans must always remain in the loop, however. Agentic AI is like an orchestra and humans the conductors.”
In short, Eigen acts as an AI-agent that handles the tedious, expensive back-end coding of robotics, making automation flexible, cheaper and more accessible to smaller firms.
Classroom discussion questions:
- What is Eigen‘s role?
- What is the roadblock to more robotic use in small manufacturers?
For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines (Ch. 9), digitized records and built predictive maintenance models (Ch. 17), all in the service of marginal gains in efficiency.
Manufacturing faces a dual disruption. AI, robotics and automation are reshaping production at unprecedented speed, while skilled labor shortages intensify when experienced workers retire, taking decades of knowledge with them.