Eaton's AI Leader Explains How to Scale Manufacturing Intelligence Across 200+ Facilities

What does it actually take to deploy AI across hundreds of factories? Tony Trim, Manufacturing Operations AI Leader at Eaton, breaks down the real constraint — and it is not the machine on the floor.

In the first installment of a three-part series published by CDO Magazine, Trim speaks with Stephen Hooper, SVP Operations Solutions at Autodesk, about what separates digital transformation that works from the kind that stalls. Trim's career spans oil and gas controls engineering, data engineering, and now manufacturing AI strategy across more than 200 Eaton facilities worldwide.

Key Takeaways

• The bottleneck is knowledge, not technology. Tribal knowledge locked in people's heads and scattered across disconnected systems is the real obstacle to effective AI deployment. Until that knowledge is captured and structured, every AI project starts from zero.
• A shared data foundation is non-negotiable. A unified namespace — where every signal has the same meaning across every facility — is the prerequisite for AI that scales. Without it, solutions built at one plant cannot transfer to another.
• Build products, not one-off projects. Organizations that win build solutions designed to scale across every site from the start. Proof-of-concept thinking produces pilots that never replicate.
• The definition of a digital factory is operational speed. Trim's working definition: a change on the floor reaches the right person and triggers the right action within minutes, not a shift or two. That standard is what separates digital capability from genuine digital execution.

For marketing and operations leaders thinking about AI at scale, Trim's framework reframes the question: the investment is not in more tools, it is in making knowledge portable and action immediate.

Read the full article on CDO Magazine