From Machines to Minds: How AI Is Reshaping Manufacturing Talent
When manufacturers talk about AI, the conversation often starts with automation, productivity, and what happens to jobs. But at IMTS 2026, a panel of manufacturing and workforce leaders spent much of their time talking about something else: the questions employees don't ask, the expertise that disappears between shifts, and the people whose knowledge never makes it into a training manual.
The discussion, moderated by Murray Mentor AI CEO Paul Boris, brought together:
Matthew Leach, Vice President, North America Innovation, Essex Solutions USA
Mike Lewandowski, CIO, Lerman Enterprises
Dave Werblo, Vice President, Flexible Concepts
James Yoo, Senior Director of Business and Workforce Services, Jane Addams Resource Corporation
Their experiences offered a revealing look at how AI is beginning to change the way manufacturing employees learn, work together, and share what they know.
What happens when the knowledge walks out the door?
Every manufacturer has people who know exactly what to do when something goes wrong. They've spent years troubleshooting equipment, adjusting processes, and learning things that rarely make it into written procedures.
At Flexible Concepts, many of those experienced employees work first shift. When they're gone, employees on later shifts don't have the same access to their expertise.
AI-powered knowledge tools are helping close that gap by making years of accumulated tacit, or tribal, knowledge available to employees whenever they need it.
One panelist even described an experienced employee using the system to retrieve his own previous troubleshooting knowledge. Sometimes the person who knows the answer needs help remembering it, too.
The questions employees don't ask their coworkers
One of the more surprising observations came from Mike Lewandowski, who described employees asking AI questions they had been reluctant to ask coworkers. Why does a particular measurement matter? What happens when a machine setting changes? Why do we follow a certain procedure? These questions are part of learning a trade, but employees don't always feel comfortable asking them.
The panel also discussed how quieter employees, including those facing language or communication barriers, were contributing knowledge that might otherwise have gone unshared.
AI doesn't eliminate human collaboration. Used thoughtfully, it can help more people participate in it.
That collaboration extends beyond conversations with an AI assistant. Lewandowski described reviewing employee contributions during shift meetings, creating opportunities for teams to discuss and learn from one another's experience.
Who’s really resisting AI on the shop floor?
The panel challenged another common assumption: that younger employees would embrace AI while experienced workers resisted it.
Several panelists reported seeing the opposite. Some longtime employees were eager to share their expertise, while younger workers worried about how AI might affect their jobs.
There were also different views about how quickly manufacturers should move. Leach emphasized careful evaluation, particularly around intellectual property and data security. Lewandowski argued that waiting for perfection can mean missing valuable learning opportunities. Werblo advocated starting with a focused problem where AI could make a meaningful difference.
Their perspectives reflect the reality of implementing AI on the manufacturing floor. Companies need to protect their information, earn employee trust, and give people opportunities to discover what the technology can do.
What changes when everyone has access to the expertise?
As experienced workers retire and manufacturers work to attract new talent, transferring knowledge is becoming increasingly urgent. The panel offered encouraging examples of AI helping employees build confidence, develop skills, and access expertise that previously depended on knowing the right person or working the right shift.
It also raised questions worth continuing: How do manufacturers preserve foundational skills as more work becomes automated? How can they encourage employees to share what they've learned? And how do they make that accumulated knowledge useful to everyone?
Those are conversations worth having well beyond IMTS.