Every plant manager I know wants the same thing from a new AI system: prove it works, and prove it fast. I’ve seen predictive maintenance catch a bearing failure weeks before it would have shut down a line, and vision systems catch a defect before it ever left the plant. Manufacturing is in the middle of an AI revolution, and it is moving faster than almost any technology shift I have watched in my career. But here is what the vendor demo never shows you: the technology is rarely what decides whether the rollout actually works. What decides it is what happens on the floor.
AI adoption stalls on trust, not technology.
Most manufacturers I talk with are already using AI somewhere in the operation, often in more than one place. Far fewer have scaled it across a full plant or network. That gap usually has less to do with the technology than with the people running it, and it shows up as quiet resistance on the floor long before anyone sees it in a missed ROI number.
I have used digital twin technology myself. In a six-figure plant transformation I led, we modeled the new layout virtually before moving a single piece of equipment, and it saved us from mistakes that would have been far more expensive to fix once construction was underway. The technology worked exactly as promised. The team had to decide if they trusted its output.
Trust is the part leaders underestimate, and not just on a plant redesign. When AI shows up on the floor without explanation, training, or a chance to ask questions, people do not usually push back out loud. They go quiet, comply on the surface, and work around the system in private, waiting to see what happens to the first person who raises a concern before they decide whether raising one is safe.
This is where I always bring leaders back to MORE, the framework my book is built around. MORE stands for Meaning, Optimism, Relationships, and Excellence. It’s M, O, and R before E because you cannot demand excellence into existence. You design the conditions that make it more likely. That is just as true for an AI rollout as it is for a lean transformation.
Start with meaning. AI can either reinforce a worker’s sense of contribution or quietly erode it, and the difference is rarely the algorithm. It is whether the system gives people visible ownership over the outcome. An AI quality system that simply rejects a part and moves on teaches an operator that their judgment no longer matters. The same system that shows the operator what it caught, why, and asks them to confirm the call, teaches them their expertise still counts.
Then there is optimism, which on the floor mostly sounds like naming the real fear instead of talking over it. “Automation means layoffs” is one of the most common thoughts I hear when a new system shows up, even when nobody says it out loud in the meeting. Grounded optimism does not mean telling people not to worry. It means asking the honest question: what do we actually know, and what do we not know yet? From there, it’s real hands-on practice before go-live, enough that people feel capable of using the tool instead of just informed that it exists. Confidence with the tool, more than the tool itself, decides whether people lean in or quietly disengage.
And then relationships, which is where most AI rollouts are won or lost. Every new system creates a trust test the moment the first person raises a concern about it. Is it welcomed, ignored, or quietly brushed aside? People do not stop noticing problems with a new tool. They stop telling you about them, and that costs far more than the tool itself.
None of this is an argument for slowing down. Indiana manufacturers who wait on AI will not be rewarded for their caution. They will simply adopt later, at greater cost, with less time to learn from early mistakes. But the leaders who get real value from AI will be the ones who treat the rollout as a change in how people work together, not just a change in equipment. You cannot demand adoption into existence any more than you can demand excellence into existence. You design for it, one honest conversation and one kept promise at a time.
The next AI success in your plant may not come from the algorithm at all. It may come from the operator who was trained well enough, and trusted enough, to tell the system when it got something wrong.
About the Author: Kathy Miller, MAPP, MBA, ACC, is an executive advisor and board director who advises leaders of growing manufacturers on closing the people and culture gaps that cap performance during moments of major change: new technology, fast growth, and leadership transitions. She was a senior executive leading manufacturing operations at General Motors, Parker Hannifin, Rolls-Royce, and Vertiv. A Shingo Prize recipient and Women in Manufacturing Hall of Fame inductee, Kathy is the author of MORE Is Better: Leading Operations with Meaning, Optimism, and Relationships for Excellence and offers executive advisory engagements, keynotes, and the MORE Mentor tool for leaders who want stronger cultures and better operational outcomes. www.more4leaders.com
