Is AI Actually Creating Value for Manufacturers?

“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 Golden, CPA

Plenty of manufacturers are investing in AI, but fewer can point to clear results. In this live episode, Kevin Golden, CPA, shares where AI for manufacturers is creating practical value today and how leaders can tell whether their investment is working.

Kevin covers how AI can turn scattered operational data into focused answers, starting with a specific problem such as cash flow. He also explains how faster financial reporting can help catch profit leaks sooner and why the month-end close shouldn’t take weeks.

The conversation then turns to measurement. Kevin outlines how to tie KPIs back to the original problem, why time counts as part of the investment and what to do when results fall short. He closes with two practical ways AI can improve inventory management, including using your voice to build a process map from order to delivery.

Resources

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Watch: Is AI Actually Creating Value for Manufacturers?

Transcript

[00:01] Interviewer: Hi everyone and welcome to the James Moore channel. Today I am here with Kevin Golden. Hi Kevin, how are you?

[00:01] Kevin Golden: Hi. Doing great. How about you?

[00:01] Interviewer: Good, good. So, we’re actually doing a two-part series with you and the first part is AI on the factory floor, turning operational data into business results. And we’ll actually be doing our part two August 6th. So, if everyone is interested in what we’re talking about, which I think everyone will be, make sure you guys tune in August 6th for part two. So, the first question is, manufacturers are hearing a lot about AI, shocking. Beyond the headlines, where are you seeing manufacturers create real operational value today?

[00:20] Kevin Golden: Yeah, so you’re right. We do hear a lot about AI, but I think where we’re seeing that actually create value is in what it’s allowing CFOs, controllers, operation managers do. And we’re getting a lot away from what they traditionally have done and doing more critical thinking activities. Things that are moving the dial forward towards the future. So, less of what I’ll call busy manual work and that can look like a lot of different forms. More of critical thinking, problem solving, forward thinking. Okay, what does this mean for us now, in the near future, three years down the road, five years down the road? How do we solve those types of problems? How do we grow? 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.

[01:25] Interviewer: Absolutely. Many manufacturers already have more data than they know what to do with. We’ve talked about this in the past. How does AI help turn that information into something useful instead of creating more noise?

[01:25] Kevin Golden: Well, first of all, I think you’re right. There is data all over the place and we’ve mentioned that previously. So, one, you’ve got to really hyper focus on what is important, right? What do we need to be focused on? There’s a lot you could, but what should we be? So, depending on your problem again, AI can help synthesize a lot of information very quickly to get you to the meat of the problem to focus on. So, for example, maybe if you have cash flow problems, right? I always use that one because manufacturers are all, I mean, cash is king, right? In any industry, but especially manufacturing. Then, it can help synthesize whether it be your financials, whether it be from your inventory system or whatnot, synthesize a lot of that very quickly to point you in the right direction. It is not the solution itself, but it helps synthesize and get you down to point you in the right direction where you should be looking. Now, let’s focus, use your critical thinking skills on how to help solve that, right? And even help you think through it, right? Again, 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. So, again, it’s going to take a whole bunch of data, synthesize it really quickly, and then you can tailor it to focus on, “Hey, I have cash flow problems. Hey, I have inventory management problems,” and help point you in that right direction. You start from there, you cut out all the other manual processes.

[02:59] Interviewer: Absolutely. And I think, I’ve had this chat in the past with some of your coworkers too, but it’s the continuing education of learning new AI and how to understand that and read it and use it to your advantage. It really is like continuing education, I think, for all of us. Because it can get overwhelming and make more noise like we just said prior.

[03:23] Interviewer: Okay, so the next one is, what inefficiencies tend to be hidden inside manufacturing operations and how can AI help uncover them before they become financial problems?

[03:23] Kevin Golden: Right. So, I think first of all, there are profit leaks everywhere, right? I mean, no one’s a hundred percent efficient all the time. Whether you talk about your personal life, talk about your business, they’re just not, right? Again, it kind of goes back to the data. There’s a lot of areas you could look, but it kind of points you in where to look. So again, one being into your problems and then reiterating those problems to AI can help put AI in the right mindset of what to look for. Two, AI can help, again, think of these manual processes. Think of like a monthly close for maybe your CFO and your controller, right? Well, you may not get that done until two, three weeks after the end of the month. That’s way too late. Now those are already real problems. AI can help synthesize that and cut that down, even to a few days after the end of the month, right? And depending on your process and procedure, it can go about synthesizing that data really quickly to turn around to you what you need to make real-time decisions. So the timeliness of data and when it’s given to you, the format in which it’s given to you, AI can help out with all of that. That takes a lot of that out of it and spends more of your time on what does this mean for me, right? How does it inform me to make better decisions today rather than a month from today whenever I get those financials?

[04:55] Interviewer: Absolutely. If a manufacturer invests in AI, what operational KPIs should leadership monitor to determine whether the investment is actually delivering results?

[04:55] Kevin Golden: Right. So when we say invest in AI, I want to clarify two things here. One, that it can be a dollar investment. And by the way, it could be large, it doesn’t have to, right? Utilizing some AI, getting involved, it’s not always a crazy expensive AI, right? It can be though. But we’re also talking about investing your time, right? So when you say, “Well, what kind of KPIs should we look at?” I go back to the source of the problem, right? And is it delivering on that? Okay, so if cash flow is that problem, if margin erosion is that problem, when you initially set out, is it solving that problem of giving you real answers of, “Hey, here’s where some of those problems exist that are eroding your margins. Here’s what those profit leaks look like,” things like that? Is it actually helping you make decisions that then improve cash flow? And so again, it’s not a kind of one-and-done. Okay, AI did that. It’s a continuous, as you said earlier, continuous improvement process. Okay, now we’re two weeks, three weeks, three months down the road, is our cash flow truly improving? Are we having the same problems? And if so, why? And 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? Which comes back to the continuous learning. So really, anytime you go into any improvement process, but especially with technology that’s changing every day, there should be some clear defined, 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? Clearly defined at the beginning. Heck, you can even get AI to help you figure out what that should be and then use that to monitor.

[06:43] Interviewer: Yeah, absolutely. Our last question today is, inventory remains to be one of the largest uses for working capital for many manufacturers. How can AI improve inventory management, forecasting and carrying costs?

[06:43] Kevin Golden: Yeah, I think two ways. One, probably the easiest way is not everyone has a fancy inventory system, right? So, a lot of times a lot of this is being done manually, right? Through the use of basic things like Excel, Word and so forth, right? So, it can speed that up. It can take a lot of that manual work out, speed that up and do that automatically for you. So, think of anywhere that takes a lot of time, that is a bottleneck. Anything you get exhausted thinking about, “Oh, I got to go update this or that.” Let AI do that for you, right? So, that’s one way. The other way is even if you do have a really nice system, for a lot of people it’s hard to understand, especially if there’s a lot of bells and whistles. It’s hard to understand it fully, and how does that incorporate into what you’re currently doing? Your current process and procedure. Do I even understand my current processes and procedures fully from beginning to end, right? AI can help do that. You can even talk. Turn on your microphone when you’re talking with AI. Talk into it about what you’re doing in a process, maybe just from ordering all the way to delivering your product to a client, and if you don’t have a process map, it can build one for you, right? And even better, tell you what an inventory system, if you had one, can do and how it can do that. Sure, you’re still going to need the software experts on there to explain how to implement that best. But understanding what its full capabilities are in a way that you as a manufacturer can understand, AI can help out with a lot of that. I’ve used that several times to help people just understand what they have and how they can best utilize it. Now, let’s go and implement. Okay, great. We get a software person that’s an expert from that area or from that company to help us do that. But what can it even do? Gain that understanding. AI can help out with a lot of that as well.

[08:45] Interviewer: Absolutely. And I think this episode would be great for anyone who needs advice or is hesitant towards implementing AI. And you’re definitely the expert, so people can come talk to you and you would help guide them. Because it’s ever-evolving and it’s getting more intense. It’s not going away.

[09:02] Kevin Golden: Yeah. I said this earlier today on the podcast I was doing that it used to be when AI, and I say when AI, it makes it sound like it was so long ago. It wasn’t that long ago. But when AI first came, it’s like, “Oh, great. If you can implement it, you got a leg up.” We’re getting very near, if we’re not already there, to the point where you’re implementing AI just to keep up, not gain a leg up. So, yeah, it’s here definitely to stay, and if you’re not implementing in some way, you’re going to want to get on that bandwagon and start doing that because everyone else is, and before you know it, it’s mandatory in order to keep up in today’s environment.

[09:46] Interviewer: Absolutely. Good advice. Okay, so today was a great episode. Thanks for joining me. I really appreciate it and we will be back for part two August 6th where we will talk about from data to decisions, using AI to improve financial performance. So, excited to see you again. Have a great day.

[09:46] Kevin Golden: Thanks for the chat today.

[10:04] Interviewer: Thanks.

See Where AI Fits in Your Operation

Kevin’s advice comes down to starting with a real business problem and measuring AI against it. Watch the full episode to hear his complete discussion and subscribe to Moore on Manufacturing for more conversations on the issues facing manufacturers today.

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