Measuring AI ROI in Manufacturing: Start With the Problem, Not the Tool

Plenty of manufacturers have started using AI. Far fewer can say with confidence that it’s paying off. The most common gap is a missing starting point, because no one defined what the tool was supposed to fix. Without that, measuring AI ROI in manufacturing becomes guesswork.

During a recent live episode, Kevin Golden, CPA, shared practical insights on where manufacturers are getting real value from AI and how leadership can tell whether the investment is working. The discussion highlighted a simple principle: start with the business problem, then judge AI by whether it helps solve it.

Where AI Is Creating Real Value Today

According to Kevin, the clearest gains show up in what AI frees CFOs, controllers and operations managers to do. Less time goes to manual, repetitive work and more goes to problem solving and planning for where the business needs to be three or five years from now.

“That’s where we see people who said I didn’t have time to, AI is now allowing them to create that time to make those kind of more critical decisions,” Kevin said.

Start With the Problem You Need to Solve

Most manufacturers already have more data than they can use. Kevin’s advice is to narrow the focus first. Pick the problem that matters most, such as cash flow, and point AI at the information connected to it, whether that lives in your financials or your inventory system.

He’s clear about the role AI plays in that process. “It is not the solution itself,” he said. It sorts through large amounts of information quickly and points you toward where to look, so your team can apply its own judgment to the answer.

“Think of it like another coworker, just a very intelligent coworker that can pull from a lot of different areas to help you critically think through these areas,” Kevin explained.

Catching Profit Leaks Before They Grow

Kevin noted that “there are profit leaks everywhere,” and timing is often what turns a leak into a real financial problem. He pointed to the monthly close as an example. If financials aren’t ready until two or three weeks after month-end, “that’s way too late.” AI can help cut that turnaround to a few days, giving leaders current information they can act on.

How to Measure Whether AI Is Paying Off

Once the problem is clear, Kevin’s approach to measurement comes down to three things: what you put in, what you track and how often you check the results.

Count Your Time as Part of the Investment

When Kevin talks about investing in AI, he includes both money and time. Some AI tools are expensive, but many aren’t. The bigger investment is often the time your team spends learning and using them, and that belongs in the ROI conversation too.

Tie Your KPIs Back to the Original Problem

The right metrics depend on why you brought AI in. “I go back to the source of the problem, right? And is it delivering on that?” Kevin said. If margin erosion was the issue, is AI showing you where margins are slipping? If cash flow was the issue, is it helping you make decisions that actually improve cash flow?

Kevin recommends defining those measures at the start. “How are we going to know that we’re actually taking a step forward here and making progress? How are we going to measure that, right?” He added that AI itself can help you figure out what to track.

Keep Checking the Results

Kevin describes measuring AI ROI as a continuous improvement process. Weeks or months after rollout, leaders should ask whether cash flow is truly improving and whether the same problems keep showing up. If results fall short, he suggests digging into why: “Is it that AI didn’t do what it intended or did we instruct AI improperly or did we have an improper use of AI?”

Why Waiting Is Getting Harder to Justify

Kevin also pointed out how quickly expectations have changed. Early adopters once gained an edge simply by using AI. Now, he said, the industry is getting close to the point where manufacturers implement AI “just to keep up.”

That makes a measurement plan even more important. The manufacturers who define the problem first and track results against it will know exactly what their AI investment is returning.

Watch the full episode for Kevin’s complete discussion, including his practical tips for using AI to improve inventory management and map your processes from order to delivery.

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