September 23, 2026
AI in Manufacturing Is Bigger Than ChatGPT
How a broader view of AI can help manufacturers find better solutions to real business problems
Ask a group of manufacturing leaders about AI, and the conversation almost always starts in the same place—ChatGPT. Maybe someone on the team used it to draft an email. Maybe Copilot is already part of your Microsoft 365 tools. Maybe leadership is asking what generative AI means for the shop floor.
That’s a reasonable starting point. But according to Sanjay Mohan, Executive Director of AI Strategy with the MKE Tech Hub Coalition’s Synapse initiative, it’s also a limited one. “It’s like being handed a flashlight and told it’s a floodlight,” Sanjay explains. “You can see something—but you’re missing most of the room.”
Why ChatGPT Dominates the AI Conversation
It’s not surprising that most manufacturers equate AI with tools like ChatGPT and Copilot. Several factors are reinforcing that narrow view. First, access is easy. Many companies already have AI tools bundled into software they use every day. “When something shows up in your existing tools at no additional cost, the natural question is—why would I need anything else?” Sanjay says.
Second, most of the training and content around AI is focused on prompt writing and chatbot use. “There’s a lot of emphasis on how to talk to these tools,” he explains. “That’s useful—but it creates the impression that learning prompts is the same as learning AI. It’s not.”
Third, the vendor landscape reinforces the same message. “Most vendors lead with their product,” Sanjay says. “And because generative AI is the most visible right now, that’s what gets showcased—even when it’s not the right fit.” The result? Manufacturers are navigating a major technology shift with a very narrow view of what’s possible.
The 80% of AI Most Manufacturers Aren’t Seeing
Generative AI has its place—but it represents a small slice of the opportunity. “Tools like ChatGPT are well suited for maybe 15–20% of use cases,” Sanjay explains. “Mostly around documentation, communication, and knowledge work.” The rest—roughly 80%—looks very different. And it’s where the biggest operational gains are found.
Think about the challenges manufacturers deal with every day:
- Unplanned downtime
- Quality issues and scrap
- Slow or inconsistent quoting
- Production scheduling challenges
- Loss of tribal knowledge as experienced workers retire
“These are the problems that actually impact performance,” Sanjay says. “And they have AI solutions—but they don’t look like a chatbot.”
Instead, they rely on other forms of AI:
- Predictive maintenance using sensor data
- Computer vision to detect defects in real time
- Machine learning to improve forecasting and scheduling
- Data-driven quoting tools based on historical performance
“These technologies have been delivering results in manufacturing for years,” he adds. “They’re just not as visible as generative AI.”
When It Clicks for Leadership Teams
In workshops with manufacturers, Sanjay sees a clear turning point. “We walk through where generative AI fits—and where other approaches like machine learning are the better fit,” he says. “Then we apply it to real manufacturing problems.” That’s when the shift happens. “Leaders start to realize that their biggest operational challenges—the ones with the most financial impact—aren’t chatbot problems,” Sanjay explains.
A chatbot won’t predict when a machine will fail. It won’t detect a defect at production speed. It won’t tell you which RFQs are most likely to win. But when manufacturers see the full picture, something changes. “They realize they’re not behind,” Sanjay says. “They’ve just been looking at a small part of what’s possible.”
It’s Not One Tool—It’s the Right Combination
Another key insight: these technologies often work best together. “In a strong implementation, machine learning solves the problem—predicting a failure or identifying a defect,” Sanjay explains. “Then a language model can help explain the result so people understand and trust it.” That combination is powerful—but only if you know both tools exist. “If all you see is the flashlight,” he says, “you miss the rest of the room.”
What This Means for Your Business
Manufacturers don’t need to become AI experts. But they do need a broader view of what AI actually includes. If your definition of AI starts and ends with generative tools, you’re likely missing the opportunities that drive real performance. “Limiting AI to what’s most visible leads to incomplete decisions,” Sanjay says. “And incomplete decisions lead to incomplete results.” The good news is that once you understand the full landscape, the path forward becomes much clearer.
Bottom Line: Broaden Your View of AI—But Keep the Business Problem in Focus
AI in manufacturing is bigger than ChatGPT. Much bigger. The real value comes from applying the right type of AI to the right problem—whether that’s predictive maintenance, quality, scheduling, or quoting. But that starts with visibility. “You can’t navigate what you can’t see,” Sanjay says. “And right now, most manufacturers are operating with a very limited view.”