Turning AI Hype Into Hands-On Value for Manufacturers
As OEMs invest in AI, the National Association of Manufacturers warns that data, leadership, and workforce misalignment—not technology—pose the biggest roadblocks.
Research shows that those who aren't already embracing AI are falling behind their competitors.
Sean Riley/PMMI Annual Meeting
Artificial intelligence is no longer a futuristic vision for manufacturing, it’s on the plant floor and in the boardroom. But according to Steven Moskowitz, Senior Director at the National Association of Manufacturers (NAM), who spoke at PMMI’s Annual Meeting, too many companies are still struggling to connect experimentation with measurable business results.
“Everyone’s talking about ChatGPT and CoPilot,” Moskowitz said during his presentation, “but the manufacturers creating real value are the ones getting their data right and aligning AI with strategy, leadership, and workforce readiness.”
Leadership must get its hands dirty
Roughly half of the manufacturers NAM surveyed say they either lack a defined AI strategy or have one that isn’t connected to their data management efforts. “AI is often treated as a side project—something to ‘let people play with,’” Moskowitz explained. “But when AI isn’t tied to core business goals, its impact evaporates.”
Moskowitz argued that companies should treat AI as a top-level strategic function, not a supporting tool. That means embedding AI alongside technology and product investment decisions, rather than as an IT add-on.
“AI isn’t just supporting the business—it’s changing how we run it,” he said. “You can’t bolt it on after the fact.”
While senior executives are aware of AI’s potential, few factory leaders and frontline supervisors are using it directly. In fact, 50% of factory-level leaders NAM surveyed reported no involvement or awareness of AI tools in their daily work.
That gap between executive enthusiasm and plant-floor execution mirrors past industrial transformations, from robotics to lean manufacturing, but with higher stakes.
“Unlike previous technologies, AI changes how decisions are made,” Moskowitz said. “You don’t need to know how to build a robot to run a robotic line. But if you’re not personally experimenting with AI, you’re already behind.”
Moskowitz urged leaders to move beyond funding AI pilots and to personally engage with generative tools to understand how they can reshape operations, decision-making, and even management roles.
Building an AI-ready workforce
Predictive and preventative maintenance are the typical first steps for AI in manufacturing operations.Sean Riley/PMMI Annual MeetingThe manufacturing workforce challenge isn’t new, but AI adds another layer. “We’ve seen an explosion of reports—almost three new ones a day—on the workforce and AI,” Moskowitz noted. “Yet most manufacturers still don’t have programs that train people on what AI actually means in their jobs.”
NAM is developing training modules within its FAME (Federation for Advanced Manufacturing Education) program to integrate AI literacy into traditional lean manufacturing apprenticeships. Trainees will learn not just how to operate machinery, but also how to apply AI tools for predictive maintenance, problem-solving, and decision support.
“It’s not about replacing people,” said Moskowitz. “It’s about augmenting human intelligence with machine intelligence. The human still presses the button—but AI helps decide when and why.”
The human-in-the-loop
Moskowitz cited MIT professor Julie Shah’s framework for AI adoption: trust the technology as a digital assistant but always keep a human in control. In manufacturing, that means using AI to generate insights, not autonomous decisions.
Trust also extends to leadership. “If your AI isn’t transparent, and your leaders aren’t either, you’ll lose buy-in on both fronts,” Moskowitz said.
The future, he added, is neither “lights-out manufacturing” nor complete automation. Instead, it’s a collaborative ecosystem where every worker—from maintenance technicians to C-suite executives—has their own digital assistant, trained on the company’s unique data and processes.
From experimentation to integration
For most OEMs, the first step isn’t a major AI investment—it’s getting their data house in order. Manufacturers must identify where their data lives, who owns it, and how it connects across systems before layering AI on top.
“Don’t try to do it alone,” Moskowitz cautioned. “The landscape is too complex, and it’s changing too fast. Find trusted partners who can help you integrate securely and responsibly.”
As AI shifts from hype to infrastructure, success will hinge less on technology and more on alignment—across strategy, leadership, and workforce.
“AI isn’t artificial—it’s augmented,” Moskowitz concluded. “It’s the ladder that helps manufacturing climb faster. But only if we learn to climb together.”
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