Manufacturing AI adoption is stalling, but the bottleneck is not the technology. It is the number of doors manufacturers have to walk through to find the right solution. Workforce programs, technology vendors, economic development agencies, and capital access organizations all compete for the same manufacturer's attention, each organized around their own offering rather than the problem the manufacturer is trying to solve. The result is cognitive overload. Companies know they need to act, but cannot figure out where to start.
Paul Lavoie, VP of Innovation and Applied Technology at the University of New Haven, leads the creation of a 133,000-square-foot research and development park focused on advanced manufacturing, cybersecurity, robotics, machine learning, and Industry 4.0/5.0 technologies. Previously, he served as Connecticut's Chief Manufacturing Officer, the only such role in a US state executive branch, where he coordinated statewide efforts to strengthen and modernize the manufacturing sector. He was named one of the top five Chief Manufacturing Officers in the world by Manufacturing Digital Magazine.
"Manufacturers don't have a lack of solutions. They have a lack of clarity. When everything's possible, nothing happens," says Lavoie.
Lavoie frames the adoption gap as a structural mismatch between how solutions are sold and how manufacturers think. "Solutions are organized by provider, not by manufacturer need. Supply is structured by silos. Demand is structured by real-world problems."
A manufacturer that cannot hire skilled workers does not think in terms of which workforce program to apply to. They need welders. But instead of being directed to two places that train welders, they get contacted by eight programs pitching their own offering. "These workforce organizations are calling them up, and everybody's like, stop. You're not solving my problem."
The pattern repeats across technology adoption, supply chain resilience, and capital access. "A company has to evaluate options, compare providers, understand funding, and integrate multiple solutions. That's not what they do. They end up with decision fatigue, and progress stalls."
Lavoie is building what he calls a manufacturing navigation hub at the University of New Haven's Elevation Center. The model starts with problem intake rather than solution pitching. A manufacturer describes their problem. The system triages and diagnoses. Curated providers are matched to the need. Implementation is coordinated. Outcomes are measured. "It's not about more solutions. It's about making the right solutions accessible, connected, and actionable."
AI plays a role in front-end triage, provider vetting, and ongoing performance monitoring. Providers are evaluated against prescribed qualifications, scored objectively, and continuously monitored through feedback loops. "If somebody doesn't qualify, we go back to them and say, here's why. It has to be that prescriptive."
The human layer remains critical. Lavoie is clear that the concierge function sitting between manufacturers and providers needs people who are agnostic to the solution and committed to solving the problem. "If you come to me wanting an AI solution, maybe you don't need AI. Maybe we can solve that cheaper and quicker."
Lavoie applies the same discipline to individual engagements. He describes a recent project where a manufacturer arrived with six priorities. His team spent a full day going through each one, evaluating ROI, efficiency gains, and emotional importance to the leadership team. They narrowed to two. "If we do six, we're going to overwhelm you and overwhelm us, and we're not going to deliver well. Let's do two, then the next two."
One of those two projects involves building an AI agent that automates a sales intake process where a human employee had been manually triaging emailed job requests. The company was quoting urgent $500,000 jobs while $5 million opportunities sat unquoted. The agent evaluates incoming requests against a defined set of criteria and routes high-value jobs directly to estimators.
"The problem wasn't that they couldn't get to all of the quotes. The problem was they were missing high-value opportunities. We used technology to solve that problem, but it required the organizational knowledge and change to drive that behavior."
The broader point is that AI adoption in manufacturing is less about the capability of the tools and more about the ability of the ecosystem to make those tools findable, relevant, and actionable. "It's never about the technology. It's about the organization's ability to adopt the technology and the leadership and prioritization required to do it. Maybe the story is that even with AI, it's not about AI. It's about how you use this tool to get demonstrable ROI and fully understanding the organizational changes needed to make it work."
