Using AI to Improve Financial Performance: Where Manufacturers Still Need People
Originally published on October 16, 2026
“It’s going to tell you kind of where to look, but it’s not going to tell you the cure.” – Kevin Golden, CPA
In this live episode, Kevin Golden, CPA, discusses using AI to improve financial performance in manufacturing. He covers how to separate useful insights from data overload, what effective decision-making looks like and how AI is changing forecasting for revenue, expenses, working capital and cash flow.
Kevin also shares a real example of how AI helped spot slipping margins at a manufacturer. It took a closer look at the invoices to trace the problem back to an internal process that wasn’t being followed.
He closes with a look ahead at how AI will change the roles of manufacturing executives, financial leaders and operations managers. He expects more real-time feedback and more people across the organization helping solve problems.
Resources
Transcript
[00:02] Faith: Hi everyone and welcome to the James Moore channel. Today I am here with Kevin Golden. Hi Kevin, how are you?
[00:02] Kevin Golden: Hey Faith, I’m doing well. How about yourself?
[00:02] Faith: Good. It’s a good Thursday, almost Friday. It’s been a productive week. We’re getting back to school soon, so I’m kind of sick of that with the kids, but…
[00:02] Kevin Golden: Get in a routine, which is always good.
[00:26] Faith: Yeah, for work-life balance, I think it’s needed. Everything falls into place soon. So today we’re doing the second part of our series, From Data to Decisions: Using AI to Improve Financial Performance. And the first question today is, AI can generate enormous amounts of information within like 8 milliseconds. How do manufacturing leaders separate useful insights from data overload?
[00:51] Kevin Golden: I think that’s a great question. Especially in manufacturing, there’s data everywhere, right? So you can get as much or as little as you want. I think the first question is to anticipate, what is this going to help me solve? What is the problem? I said that before.
[01:15] Kevin Golden: I said that I think in the last stream and I’ll say it on this one. What is the problem, right? So, if cash flow is a problem, great. Now it’s just understanding what that metric or that data tells me that helps me with that cash flow problem. It’s not so much talking to your buddy or maybe another manufacturer and asking, “What are you looking at?” More important question is, “Why are you looking at that?” Right? “Oh, well, we were having problems with this and this.”
[01:37] Kevin Golden: “Oh, I’m having a similar issue, too. Great, maybe I should look at similar data to that.” If you’re not sure what that means, even ask AI sometimes, “Why would this be important to me?” Get its opinion and so forth. But again, go back to what is it helping me solve? What am I going to get? I think if you work backwards with that in mind, then look at the data and extract from there, now you’re looking at data that’s meaningful for you, and test that out.
[02:03] Kevin Golden: As you’re looking at this weekly, monthly, quarterly, whatever that cadence is, is this really helping to make better decisions? Do I actually see that I’m making some improvement there? If not, is it that we’re not utilizing the data in the right form, or are we looking at the wrong set of data?
[02:21] Faith: Absolutely. What does effective decision-making look like when AI becomes part of the reporting and analysis process? I mean, you kind of touched on that already.
[02:21] Kevin Golden: I did there, two in one right there. Again, I think effective decision-making is the regular cadence of measuring and evaluating, and then seeing, okay, is this actually improving? So go back to my, I use cash flow all the time, but go back to my cash flow issue.
[02:47] Kevin Golden: Okay, great, I’m having a cash flow issue. Do I have a better understanding now because of this data, and understand where those issues are, and more importantly, what I can do with that to make it better, right? Now, that doesn’t always mean it’s better tomorrow. These aren’t light switches we’re turning on and off, but are we making progress toward that? If we can’t measure that, then either A, we’re looking at the wrong one, or maybe we’re just interpreting incorrectly and making the wrong decisions.
[03:14] Kevin Golden: And something else I’ll hit on, too, is timeliness of it as well, right? So effective decision-making isn’t just what am I doing with it, how quickly am I able to respond. Now I’m able to work a lot more efficiently, a lot more effectively, than maybe previously when I had data that’s a month or two out of date.
[03:30] Faith: Absolutely. How has AI changed the way manufacturers forecast revenue, expenses, working capital and cash flow?
[03:30] Kevin Golden: One, it’s more real time, right? Think of how long some of these processes take to even get to some of those KPIs.
[03:51] Kevin Golden: Now, with good foundation, I can’t emphasize that enough, with good infrastructure and foundation, they can be generated almost instantaneously, right? So you can take your financials and feed them into AI to help generate a lot of these things that normally you would do manually or with some other much slower, more manual process.
[04:07] Kevin Golden: So one, it’s in real time. Two, you’re looking out much more frequently. So this isn’t, “Hey, I’m going to look out near the end of the year at the next year.” At any given point in time I can almost instantaneously forecast out and ask, wait, what did that same forecast look like last month? What does it look like now? One, two, five years down the road.
[04:30] Kevin Golden: Obviously variables may change, things like that, but we can look at it a lot quicker and even better, compare it to itself. Where were we this time last month? Where do we expect we should be next month, right? So again, I think it’s turning a lot of people much more proactive and on the spot, because the turnaround time on getting that data is a lot quicker.
[04:50] Kevin Golden: It’s more real time.
[04:50] Faith: And for you personally, because I think you and I are around the same age demographically, and we really were at the beginning of it, right? So how has it really helped you in your profession?
[04:50] Kevin Golden: Yeah, so speaking as a business owner, I’m a business owner in James Moore.
[05:11] Kevin Golden: I even use it with our own financials, right? There’s a lot to look at in there. Go look at your own set of financials for any manufacturer, right? There’s a lot in there. It’s a lot to look at at one time. It’s a lot to digest at one time, even for people like myself who are used to doing that. So what I needed was trends.
[05:32] Kevin Golden: I needed analysis and I needed more details to say, what does that mean for me as an owner? How are my actions, my team’s actions and my leadership’s actions going to change because of the knowledge I have now? How can we improve and how can we get better, right? So again, I think it’s spending more time on creative thinking and critical thinking, and less time trying to figure out what all this means. What do I even have here? It’s a lot more time on, okay, what am I going to do with it? How am I going to be a better business owner because of that?
[06:01] Kevin Golden: Same thing in manufacturing. It just looks a little different because we’re talking about inventory instead of people. That is my inventory.
[06:01] Faith: Yeah. Absolutely. I love that. I’m always interested to hear people’s stories because we were at the brink of everything.
[06:01] Kevin Golden: Even personally, I use it to project out cash flows. Not for my day-to-day, buying groceries, things like that.
[06:27] Kevin Golden: I use it for, “Hey, these are things I’m saving up for. Hey, that family trip I want to take every year. Hey, I’ve got to set aside for my own income taxes. I’ve got to set aside to pay the kids’ school tuition,” things like that. Great. I set aside money for that every month and throughout the year. Well, I cash flow that out.
[06:44] Kevin Golden: When is that going to run thin? I need to beef that up. When is enough? Something as simple as that. That’s a very small example of a cash flow projection. But looking at that 12 months out, AI can do that very quickly and I make changes on the fly without taking a lot of time. Now I make better decisions about, “Oh yeah, that’s coming up. I should probably cash flow plan for that 6 months out instead of 6 weeks out.”
[07:03] Faith: Absolutely. And so one of the questions I had mentioned to you before, I was really interested in your opinion on. What are some examples where AI might identify a trend or an issue, but leadership still needs financial context to make sure that’s the right decision?
[07:24] Kevin Golden: Again, you’ve got to remember, what is AI doing for us? It is a tool. Think of it like a super employee that just doesn’t sleep, doesn’t complain, doesn’t care if it gets paid or not, right? All those limitations we have as humans, AI doesn’t, right? It’s a tool. So what’s it going to do? It’s going to spit data right back to you without bias. It’s going to tell you, “Hey, here’s a trend.”
[07:48] Kevin Golden: So I’ll give you an example. There was a manufacturer we were working with whose margins were slipping. So AI and data like that spits out, “Hey, your margins are dropping,” and so forth, right? It even specifies, “Hey, here are some accounts that could be the culprit.”
[08:05] Kevin Golden: What it doesn’t do is tell you why. It can make a whole bunch of wild assumptions and say, “Hey, this may or may not be why that’s happening,” but it doesn’t really. So what did we do? We went back and started looking. In this case, it was a waste expense line item, their scrap, right? We went and looked, and it turns out there had been some internal process and procedure that wasn’t quite being followed that resulted in more scrap than normal.
[08:33] Kevin Golden: How did we notice that? Because of the trend AI told us. We went back and looked at the invoices, and on the invoices, we saw the charges by the waste company continued to increase. Why would those increase? Oh yeah, it’s by weight. Well, what is it weighing? All our scrap.
[08:51] Kevin Golden: Why do we have so much scrap? Oh, now we’ve identified a problem. So again, it’s going to tell you kind of where to look, but it’s not going to tell you the cure. It’s not the solution, right? I know people say that constantly, but you can’t just throw things into any sort of technology or AI and say, “Great, here’s my solution, copy paste, I’m done.”
[09:10] Kevin Golden: But it can synthesize a lot to say, “Look here, we think there’s an issue here.” Now, what does that mean? How does that tie back to my operations? AI can help you get there, but it can’t solve it for you.
[09:10] Faith: Absolutely. All right, so the last question is, looking ahead 5 years, how do you see AI changing the role of manufacturing executives, financial leaders and operational managers? Honestly, you could even do it a year from now because it’s ever evolving so rapidly. But what are your thoughts on that? What is the first thing that you think of?
[09:40] Kevin Golden: The first thing that comes to mind, and I think we spoke on this on the other podcast I did about the CFO’s role and how it’s changing, is it’s just going to be less of a historian and more predictive of the future, right? We need people in today’s environment to not just say, “Okay, what do you do and how do you do it?” It’s, “How can I make that better?” Critically think. How can I improve that? How can I make that better?
[10:11] Kevin Golden: By having real-time data and feedback that gives you real-time production results. So if you’re an operations manager, it may be less time sitting there trying to figure out, “Hey, how did we do last month? How did we do 2 months ago on production? What could we have done better in hindsight?” Now learn from that.
[10:36] Kevin Golden: It takes you a month or two to figure that out. So now it’s 4 months down the road before any real changes are made. Instead, you get that on a day-by-day, week-by-week basis and it’s, “How can we pivot in real time? What else can we do?” And even better, it’s going to start turning more and more of your workers, from top to bottom, into critical thinkers.
[10:53] Kevin Golden: So they’re all adding to that solution. Maybe you’re someone on the floor. Maybe you’re the operations manager. Maybe you’re a C-suite person. All of those people are now working in conjunction to say, “Here’s something we noticed. Let’s come up with a solution.”
[11:09] Kevin Golden: So again, I think it’s going to turn a lot of people from just doers into more critical thinkers, if they’re not already, because they’re going to say, “I’ve got a machine. I’ve got technology that can do it. What I need is for you to tell me how we can do that better. How can we critically think to make that better?” What does that actually mean in terms of us as a company, or the job that you do, or the product that you make?
[11:32] Faith: Absolutely. I love that. It’s happening. We’re here.
[11:32] Kevin Golden: We got involved with it.
[11:32] Faith: Well, thanks so much for coming on today, Kevin. I really appreciate it and I look forward to speaking to you again.
[11:32] Kevin Golden: Sounds good. Thanks, Faith.
[11:32] Faith: Thanks.
Hear the Full Conversation
Watch the full episode to hear Kevin Golden explain how manufacturers can turn AI-generated data into faster forecasts and better financial decisions. For more conversations like this one, subscribe to Moore on Manufacturing on YouTube.
All content provided in this article is for informational purposes only. Matters discussed in this article are subject to change. For up-to-date information on this subject please contact a James Moore professional. James Moore will not be held responsible for any claim, loss, damage or inconvenience caused as a result of any information within these pages or any information accessed through this site.
