Manufacturers Are Starting Small with AI — And That's Exactly the Right Move
Insights from the TDMAW Dinner Event
At a recent TDMAW dinner, manufacturers across the region shared how they’re already using AI to capture institutional knowledge, speed up quoting, and make their shops more attractive to the next generation of talent. The common thread? Nobody waited for a perfect plan. They just started.
When Allis-Chalmers made the leap from steam-powered machinery to electric engines in the early 1900s, it wasn’t because the old way had stopped working. It was because the leadership recognized that clinging to what was familiar meant falling behind what was possible.
That same instinct was alive at North Star American Bistro last week, where manufacturers gathered for a TDMAW dinner event on a topic that’s moved squarely from buzzword to business reality: AI for Manufacturers — Practical Ways to Use AI Today.
Tyler Hansen led the evening’s presentation, walking the room through real-world applications that don’t require massive budgets, complex integrations, or a dedicated data science team. The core message resonated because it was honest: you don’t need to transform everything overnight. You just need to pick one problem and start solving it.
The Biggest Barrier Isn’t the Technology
For most manufacturers in the room, the challenge wasn’t skepticism about AI. It was uncertainty about where to begin. Which process? Which tool? What if it doesn’t work?
Tyler’s advice cut through that paralysis: start small, ask questions, and give yourself permission to experiment. AI adoption in manufacturing doesn’t have to look like a company-wide rollout on day one. It can look like one team automating one workflow — and learning from what happens next.
That framing shifted the conversation from “should we do this?” to “where do we start?”
Three Problems That Keep Coming Up
As the discussion opened up, a few operational pain points surfaced again and again — the kind that nearly every manufacturer in the room recognized immediately.
- Tribal knowledge walking out the door. Experienced employees are retiring, and decades of institutional know-how — how to set up a tricky job, why a particular process runs the way it does, what to watch for when a machine sounds off — is leaving with them. AI tools can help capture and structure that knowledge into searchable SOPs before it’s gone for good.
- Quoting that takes too long. Slow quotes mean lost deals. Manufacturers talked about how AI can pull from historical job data, material costs, and past estimates to accelerate quoting — getting proposals out faster without sacrificing accuracy.
- Processes that live in people’s heads. When the only documentation for a workflow is “ask Dave,” you’ve got a single point of failure. AI can help teams document, standardize, and improve processes so that work stays consistent regardless of who’s on the floor that day.
These aren’t futuristic use cases. They’re problems manufacturers are solving right now, with tools that already exist.
Technology as a Talent Strategy
The conversation also turned to a challenge that’s become impossible to ignore: attracting younger workers to manufacturing.
The reality is that today’s workforce spans two very different worlds. On one side, you have experienced professionals with deep operational knowledge built over decades. On the other, you have a new generation of workers who grew up on technology and expect modern tools in their workplace.
Manufacturers in the room made a compelling point: adopting AI and advanced software doesn’t just improve operations — it reshapes how the next generation perceives the industry. Manufacturing stops being “just” hands-on work and starts looking like what it actually is: technical, data-driven, and highly skilled.
That shift in perception is a recruiting advantage that compounds over time.
Moving Fast Without Being Reckless
Data security came up early and often, which makes sense. Manufacturers deal with proprietary processes, customer specifications, and competitive pricing data. Nobody wants to feed that into a tool they don’t trust.
The consensus in the room was practical: use secure platforms, keep data in controlled environments, and vet any AI tools for governance and compliance before you go live.
The point isn’t to avoid risk entirely — it’s to manage it intelligently so that security concerns don’t become an excuse to stand still.
Fresh Leadership, Fresh Thinking
One observation that stood out: manufacturing boards and leadership teams are getting younger. That generational shift is bringing new openness to emerging technologies and a willingness to challenge “the way we’ve always done it.”
The best outcomes seem to come from pairing that fresh perspective with the hard-won experience already on the team. When seasoned operators and tech-forward leaders are in the same room — much like they were at this dinner — the conversations get a lot more productive.
The Takeaway
Events like the TDMAW dinner make one thing clear: manufacturers aren’t sitting on the sidelines wondering about AI. They’re rolling up their sleeves, picking a starting point, and figuring it out.
As Tyler put it throughout the evening — you don’t need a massive budget or a complex system. You need curiosity, a willingness to experiment, and one good problem to solve first.