"We're already working on AI."
Every manufacturing executive says this. And they're not wrong—their teams ARE working on AI. They've got ChatGPT licenses, maybe some pilot projects, definitely some enthusiasm. But six months later, the results are... underwhelming. Sound familiar? Here's the thing: Your IT team isn't failing because they lack technical skills. They're failing because they're solving the wrong problem.
Your internal teams are doing exactly what smart technical people do. They see a cool tool (ChatGPT, Claude, some shiny new AI platform), they think about where it could plug into existing workflows, and they start experimenting. The problem? They're optimizing processes that shouldn't exist in the first place. While your data analyst is building an AI tool to generate reports faster, they're missing the fact that half those reports are never read. While IT is automating invoice processing, they're not asking why invoices need three approval levels or why vendors are still faxing documents in 2025.
They're too close to the current state. When you live with broken processes every day, you stop seeing them as broken. They become "just how we do things." Your accounts payable team has workarounds for workarounds, but they can't step back and see that the entire workflow is inefficient. They lack cross-departmental visibility. The biggest automation opportunities happen where departments intersect. Your warranty team manually enters data that your inventory team already has. Your pricing team builds spreadsheets with information that lives in three different systems. But IT sees systems, not business flows. They default to incremental improvements. Technical teams think in terms of "making this 20% faster." Business transformation requires asking "should we be doing this at all?"
Here's what we see when companies try the DIY approach:
The manufacturers seeing real AI ROI—we're talking tens of millions in savings, not percentagepoint improvements—start differently. They begin with business process assessment, not technology deployment. They map workflows across departments, identify bottlenecks that span systems, and find automation opportunities that employees don't even see because they're buried in daily tasks. Only then do they talk about AI tools. This isn't slower than the DIY approach. It's dramatically faster. Because when you know exactly which processes to automate and why, implementation becomes straightforward. You're not guessing, piloting, and pivoting. You're executing a plan.
Your team already tried AI because they're smart and motivated. But they're playing with one hand tied behind their back—trying to optimize a business they can't step outside of to truly see. The companies winning with AI aren't the ones with the best technical teams. They're the ones who took time to understand their processes before they started automating them. Your internal team can still lead your AI transformation. They just need someone to show them where to look first.
Ready to see what your team might be missing? Most manufacturers discover automation opportunities worth millions in processes they didn't even think were problems. Sometimes the biggest wins are hiding in plain sight.