Ep 68: Your Systems Are Costing You Millions

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Welcome to the Exit Engine Podcast, episode 68. Welcome to the Exit Engine Podcast, where we help founders, owners, and entrepreneurs turn their businesses into assets buyers fight for. If you're looking to grow smarter, reduce risk, and ultimately exit stronger, then this is the show for you. I'm your host, Joe Stapleton, and with me as always is my partner in value and salability, Mr. John Kniff. Hey, John.

Good morning, Joe.

So, John, today we're going to dive deep into a very crucial but often overlooked part of exit planning. And we talk about this all the time in our assessments. And in our framework. But this is about discovering and unlocking hidden value.

Right? I love it.

Hidden value obviously is, or it's also called latent value, right? There are things that are going on inside your organization, John, everybody's organization right now that you are unaware of.

Right?

There are bottlenecks. There are, there are issues with process. There are potentially issues with customers, there are issues with employees, there are— everybody has, every single business has some of these things going on, right?

Yeah. And it shows up in ways, you know, I have many examples, we can get into a couple. But, but yeah, there's so many ways that this sort of penetrates into organizations that are sort of just forgotten about or said, like, we can't solve this because it involves multiple parties or it's hard or it's complex or we tried to solve it before. We couldn't figure it out.

Absolutely. So, and again, there are hidden value includes things like, you know, again, management, right? Who's your chain of command? It's gonna be about recurring revenue models. It's gonna be about standard operating procedures, right? There's gonna be cultural alignment. There's gonna be customer concentration. There are so many areas in which these bottlenecks can occur, where there are issues with process. And again, when you fix some of these things, it's amazing what actually can happen because not only does it potentially change your top-line revenue and your profitability, but as we always talk, discuss on this podcast in particular, it's gonna grow your value and salability, right? So we're gonna talk and we're gonna get into— we've got a special guest today and we're gonna get into that. But first, John, let's talk about our new ExitEngine ecosystem.

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Absolutely.

Yep.

You get there now, and depending on when this launches, that you may find an email thing, or actually you may find, uh, uh, actually a lot more information there. So just go to myexitengine.com right now, wherever you are. You can start for free. It's really simple. And what we're— what you're gonna do is you're gonna meet people like our guest today. Okay? And our guest is, uh, is an expert in finding and improving hidden value. Frank Piak is the founder and chief consultant at Organization Renovation, which I love that name, by the way. First of all, this— I just love the— I love the sound of it. But Organization Renovation, he helps lower mid-market companies uncover high-leverage improvements that can lead to significant gains with minimal disruption. Frank's genius lies in identifying contradictions inside complex systems, places where outdated processes conflicting incentives, or siloed decision-making are holding the company back. So Frank is the— John, Frank is the kind of outsider who can see what insiders miss. Whether it's misaligned incentives, inefficient cross-departmental systems, or legacy policies creating friction, Frank helps business leaders diagnose root causes and turn them into simple, scalable solutions that accelerate enterprise value. Guys, today we're gonna talk in depth, okay, about how to surface How to surface invisible operational risk before it sabotages your valuation, what founders should do when their company outgrows their systems, the real cost of poor cross-functional communication, and how a few targeted tweaks can radically improve buyer confidence when you get to that stage. So if you're in growth mode or planning your exit in the next 1 to 5 years, this is a must-listen. Frank Piak, welcome to the Exit Engine Podcast. Glad to have you here.

Thank you, gentlemen. It's a pleasure to be here.

We're really excited today because, again, this is something, as we've talked about before, we talk about this stuff all the time. This is one of the first, and in our framework, it's the first thing that we're doing is trying to uncover the ways in which people are just completely oblivious, really, to really what's going on. And when you talk about stuff, you talk about really the— when we've talked before, you've got these 3 big areas that you find inside companies.

Yes. Right?

Can you talk a little bit about what those 3 areas are and what, what tends to happen as a result?

Well, the first one, and the one that's easiest to diagnose, and the easiest in most respects to solve, is misaligned incentives. If you're— to use manufacturing, because it's the simplest one to talk about— if your production manager is incentivized to keep cost per piece production low, he's incentivized to run large production runs. If your purchasing manager is incentivized to get cost per piece low, the easiest way to do that is to buy large quantities. And if your materials manager is incentivized to keep inventory low, he is incentivized to fight with the purchasing manager and the production manager.

Right.

They cannot all achieve their incentives. They cannot all achieve their KPIs without— they can't. They can't. One of them gets screwed.

Yep.

Or all 3 of them get screwed to some degree. There's some level of compromise that has to happen, but they're inevitably in conflict. And really, odds are that if you just give everybody the same incentive as the materials manager, the company will be better off. Because in any factory there is a limited number of bottlenecks, and the capacity of the factory is the capacity of those bottlenecks. And if you are getting costs down by producing large production runs in front of the bottleneck, you are building inventory that can never get through the bottleneck and will ultimately be scrapped. You are wasting money if you manage For the inventory, you're going to waste a lot less money. If you're, if you're putting a value of the labor and the material, the labor on top of the work in process, and putting the overhead of the CEO salary and the insurance for the, for the factory, all the other expenses of the business on top of that work in process inventory, you are attributing a large amount of value to that work in process inventory, which is absolutely— Actually garbage.

Not, not true.

It will, it will be thrown away, and all those costs are going to be written off. It's not profit. Your accounting system is lying to you because you've set it up that way. You don't know it's lying to you. It's not intended to lie to you, but you are attributing value to something that has no value, right?

And also, you're— yeah, and you're also tying up cash, right?

To your point, you're tying up cash. Yes, you're tying You've spent money on labor that has no value. You've spent money on overtime that has no value. You've spent money on materials that have no value. You've saved money in the quarterly income statement, and you have profit in the quarterly information statement, but in fact, the true value of most of that work-in-process inventory is zero. It's, you, you, you are misunderstanding reality. Right. Right.

So, so that's, so, so that's misaligned incentives. What's, what are the other 2?

Well, I've just actually given you another one, which is that the accounting system is following certain rules that have been built up over the last 200 years or the last 500 years really, because double entry bookkeeping is, comes from the Medicis, if I'm remembering correctly, 500 years ago. When in— when overhead rates were 30 or 40%, attributing, applying those costs to the materials was not a serious error. When overhead rates are 300 or 400%, it's a horrendous error. The marginal cost is much, much lower than the attributed cost, and the attributed cost has nothing to do with, with the actual money you put in. But the, but the, on the books, this stuff is valuable. Has nothing to do with the reality. And, you know, when I took accounting in business school, we, we talked about the fact that in inflationary times, you are undervaluing your balance sheet because the building you bought for $100,000 a year, 10 years ago is now worth $200,000. Or worth, you know, bought it, built it, bought it 20 years ago and it's worth a million.

Right.

Um, so you've got undervalued assets on your balance sheet and overvalued assets on the balance sheet. And it's by coincidence, it's possible they balance each other out, but the odds are no. The odds are that you're way off in some direction or another. And if you're making decisions based on those balance sheet values that are wrong, you're making decisions that inevitably are wrong. You can't make a good decision on bad information.

And at the same time, right, I mean, that's the kind of thing that, that, that, that will be seen by, by a buyer's team coming in there, right, and doing due diligence work. Right.

So you, you have this building that you're valuing at $1 million, and the buyer comes in and says, oh, that build— that land is worth $10 million, whether the building is there or not. Um, And these guys willing to sell us the whole business for $2 million, we're going to make a bundle on the real estate.

Yeah. And just to point out, this, this isn't fraud by any stretch of the imagination. This is, this is like, you know, this is— again, you put it very well, Frank— this is misvaluing the assets that your company controls.

Right. And you're following the rules.

Exactly. Yeah.

The rules, the rules have gotten out of whack. I mean, the worst example is hospitals, right? Everybody says hospital beds are expensive. We need to minimize them. Well, what does a hospital bed cost? Some of them cost $500 and some of them cost $20,000. You divide $20,000 by $365, 365 days a year, you've got a few hundred dollars a day. Why is the— why is the bed $3,000 a night? Because you've got a billion-dollar hospital with 1,000 beds.

And you're trying to cut that. You're, you're breaking the cost of the entire thing down per bed.

Well, yeah, you're— because those are the revenue produced. I mean, if you think about it, those are the revenue-producing assets that you have. So some cost accountant said, okay, we have 1,000 beds, we have to divide the cost by— and our costs are a billion dollars—

divide that cost by that. Oh God, beds are expensive. We better have fewer of them. We have to send people home before they're healthy. And then they have to come back.

Yeah, sorry.

You know, we're— I mean, you know, this actually can lead to life and death situations, right? You send people home prematurely, they get sick. If maybe they come back, they come back sicker than they left. You have procedures that cost a fortune. To save them. And sometimes you don't save them. Sometimes people die that would've been fine if they just stayed in the hospital 3 more days. Mm-hmm. And there was no surge. Why was the pandemic such a catastrophe? There was no surge capacity in the system.

Right.

There are literally— there are literally fewer hospital beds now than there were 20 years ago, and the population is significantly larger. Mm-hmm.

That's insane.

Right.

And, but it's all, it's all, all because of the attributed cost accounting that they—

All because of attributed cost accounting. And the fact is, if you doubled the number of beds, the cost per bed would go down. I mean, you'd have to build more buildings because we shrunk the hospitals, but you doubled it. If you could, it doesn't cost that much to build another wing. You double the number of beds, you have a low intensive care unit with fewer nurses and fewer orderlies, and The cost per bed goes way down. And you know what? You'll have fewer malpractice lawsuits, and your malpractice insurance will go down, and you will probably save— the cost per bed will probably be less than half.

And you'll provide better care.

And you will provide better care.

So why, Frank, why, why don't— why, why don't they know this?

I don't know. It's like, okay, I'm smart, but But I'm not that smart. We have people with PhDs and, and, and MDs who are, you know, geniuses running hospitals, and they're making this idiot mistake. And when I was a kid in New York City, there was an article in the New York Times said New York built a hospital to be the best hospital in New York. And then when it was finished, we decided we couldn't afford— it would be too expensive to run. And so there's this, this palace of a hospital that never got opened, right? Why? Because they— the accountants, the cost accountants and the financial accountants are following the rules that were created 500 years ago that are divorced from reality, right?

Right.

The entire accounting system needs to be rethought.

Yeah, well, because in your example before, it's like the building, and they're depreciating that building over time, right? So it's actually, you know, if it's a 20-year useful life or 27 years, whatever it is, I can't even remember, but they're depreciating it down further from that $100,000 down to nothing. Um, and it's, you know, it's not nothing, but, but even despite the land or the, the land value going up, so it's even more of a discrepancy than we talked about because The market price and the, um, and the accounting price are just completely different.

Right. Um, so, so we had, we had said 3. We've actually hit one that we didn't talk about, which was accounting, bad accounting, but incentives, bad accounting, um, information flows. You know, if you have a chain of command, The larger the chain of command, the more information gets filtered as it goes up, and the longer it takes to go up. And then you have a decision that has to affect two departments. So one department raises the issue; it goes up the chain of command. It takes a week, two weeks, a month. The decision's made at the top. It goes down the chain of command. It takes another week, two weeks, a month. And now it's two months later. A decision that Should have been trivial and should have been fast has taken 2 months, and what might have been an easily solved problem is now a big problem. Right.

It's out of control. It's worse than it was.

If you allowed the 2 people at the bottom of the chain of command who could solve the problem to talk to each other and agree on how to solve the problem, it could have been solved in a day. If— or it never gets solved at all because I'm at the bottom of the chain of command. I tell my boss, he says, okay, I'll, I'll tell my boss. He tells his boss, his boss has 300 things to decide amongst. He has to— he's sending a report to his boss. Your piece doesn't even get there. And so it never gets resolved. And so you have this ongoing problem that the CEO is oblivious to, the heads of the departments are oblivious to. And it, it's an inefficiency and it's a terrible inefficiency and it's an inefficiency forever because nobody who can solve it has ever known about it, even though there's some poor soul at the bottom of the chain of command screaming.

Yeah. And by the way, and also they are screaming now at each other, not, not screaming at each other, but they are together going, why can't these guys fix this thing? We, if we could just do this, we could do this. And now they're angry at management.

And this Yeah, this came, this came out actually, this exact thing came out in a franchise survey. So your scenario was exactly the way it worked out, Frank, and the result of it was it went, it went filtered up. There were 300 things. This never got solved. It never happened. And it came out in a franchise survey that this problem manifested itself with, with regard to logins for agents that had left the company. And we all knew about the problem. We all knew the solution to the problem, but no one ever made the decision. I shouldn't say nobody, but like our, our, the CIO of the company never made the decision because he had too many things on his plate. And it was an easily solvable problem that when we finally addressed it, we got a new CIO. I went into his office and we sat down and had the problem. He's like, why wasn't this fixed? Like, a lot of reasons. Can we get it fixed now? Let's get it fixed. And it took about a week of coding and we're done.

So The thing that— the 2 things that make me craziest, that make me most angry, one is the hospital thing. The other is customer service. Customer service is treated as a cost center to be minimized. And so you end up with undertrained clerks who, who don't know how to solve the problems or even understand the problems when you call them. Or you have a chatbot that's been coded to answer 6 questions. And you need— if you go to the website, you can figure out which— how to answer those 6 questions. You don't need the chatbot. You need the— you need customer service because the damn website doesn't talk about this.

Right. Yeah.

And you get pissed off. And if you can, you leave the— you leave and you find a new vendor. You're, you're reducing the lifetime value. You spend a fortune getting a new customer and you reduce the lifetime value very quickly because first time they have a problem, they get pissed off. Legitimately pissed off. You are, you are mistreating them, right? To save money? You're throwing money away, right? I may assign customer service to marketing. Tell marketing to spend half their market research budget on customer service, and you will get so much better information from well-trained customer service people than you will ever get from focus groups, right? Well, you will, you will, you will save an enormous amount of money. You will be able to find the problems in your products find the problems in your services, fix the problems in your products, fix the customers in your service— problems in your services, and have happy customers who stay with you for a long time, you make money hand over fist, and you stand out amongst a sea of companies that screw their customers as a matter of doing business, as an honorable company.

Well, yeah, I had the opportunity to talk to the CEO of Zappos a while ago, And his customer service philosophy was drive all the decision-making. We were talking about that earlier. Drive all the decision-making down to the customer service people, and they can make the decision. To your point, Frank, is it's much easier to retain a customer than to get a new one. And they would give them free shoes. If there was a screw-up, they would get— they would just— they had the power to make sure that they retain the customer pretty much at all costs. And they— the loyalty of that brand was amazing.

Look at L.L.Bean. I mean, it's a great example. Yeah, a company that's been around forever, right? And, and does well, right? And, and is profitable and all that stuff, right? And, and, and its entire philosophy is built around, you want to return these boots 15 years from now when they're worn out and whatever? Great, return them.

No one did it either, just so you know. No one ever does it. No one ever does it.

Yeah, the cost savings comes from We don't actually have to give people free stuff because they don't ask for it. We make quality things. We tell them that they can have them if they have new ones, if they want them. And then, and we, all we do is just count money, right?

The Four Seasons hotel chain, right? They, they say that anybody on the front line can spend $2,000 to make a customer happy.

There you go.

Yep. They make a lot of money. They're a very successful hotel chain. They're profitable. They do not lose money on that decision.

But that lifetime value of those customers who will only go to Four Seasons because they get, they could be, even if every time they go, they get $2,000 worth of extra value, it's still worth it because the nights they're spending, because they're spending $5,000 a clip in the, you know, in renting the rooms, right?

Right.

So it's interesting how though, again, when we talk about the value, overall value and salability, just to just bring it back to that for a second, there are major implications here, right? Like, major implications.

Major implications.

And what I want to just, again, just kind of shout out to everyone is that you may not see this stuff, and it's very possible that you don't. And by the way, that's okay because most people don't see this stuff. We've just talked about very large companies and very large examples of people who do not see this stuff, who we would assume were very, very smart people.

Right.

The, the, but the, the bigger problem is that there are people out there, they work for private equity companies and they've worked for, you know, for, and they are, they, they are high-end M&A folks who work for buyers, okay, who will spot this stuff.

Okay.

And as you're going through due diligence and they, they will uncover this stuff. All right. And what you'll see is that your valuation dropping precipitously as a result, even as they're finding opportunities to make themselves more profitable.

So the value to them goes up even as the value that they tell you you have goes down.

That's it. Because marketable inefficiencies, right? And now what I've got is a situation where I now know where the holes are. I know how to plug them. Thank you very much. I will take this to what is now ultimately almost a distressed asset, right? And take that, and I'm going to now turn that around and fix, plug all those holes. And now I'm going to make 30% in the next 3 years.

Right. Although there are private equity companies who buy things and destroy them.

That's true too. I just mean that the professional side of this, the idea that, right, that the 3 of us sitting here, right, aren't the only people in the world who understand what we're talking about and that a lot of those folks work for the buyer.

Right.

And so, yeah.

And so it's, you know, the, the other thing is simply you have a process, a procedure that made perfect sense 5 years ago. And the business has grown and you're following that same process.

Right.

And technology's changed and the customer base has changed and that process doesn't fit the way it did 5 years ago. And you are creating inefficiency that you're completely oblivious to because there's nothing, no specific pain that points at that process.

Right.

There's no specific pain that points at that procedure. There is a generalized discomfort. There's aggravation. There's friction. There's fights between executives and fights between department heads and their subordinates that they don't attribute to the underlying cause because the underlying cause is habit.

Frank, in those situations, do you advise, like, processes to be owned by people, or what's the solution to making sure that processes are updated with technology or with sort of some sort of cadence?

Tough question, because if processes are owned by people, then that's a new silo.

Fair point.

Yeah. You know, so I mean, basically silos tend to be well run by the criteria that the silos are run on. And as we've said, you know, accounting is There's 500 years of— I mean, accounting is the perfect example, right? It was brilliant 500 years ago when General Motors created the system that General Motors was running on 40 years ago. It was genius. But overhead rates were less than 100%, and the accounting errors were not even noticeable. If you looked for them, you would have found them, but they didn't hurt the business very much. When overhead rates are 300%, something that doesn't matter at 30% is incredibly important.

And it's also tied to regulatory things, right?

Regulatory things. We have the Accounting Standards Board, which talks to the SEC. And if you don't play by those rules, you could be in deep, deep trouble.

Right. Yeah.

But going back to the car industry, you know, or the manufacturing industry, again, as an example of where these things get fixed, though, Okay, is to look at, you know, we brought this example up before, but the BMW plant in South Carolina, right, where they basically, the bottleneck, the process, all that stuff is where they have built a factory around eliminating those things, right? So that—

Well, the role model is—

Just-in-time delivery of parts, the parts, the robotics up, the parts come right off the truck. They never even store the parts. They never warehouse them. They just come right off the thing and directly onto the car. They've got, you know, so the point is that there are people out there who have, Who do it.

Yeah, I've seen it. BMW was my— took Toyota as a role model, and Toyota spent 20 or 30 years figuring out how to do this, little tiny experiment by little tiny experiment. And so if I'm going into a client, I'm going to say, look, the performance measurements are relatively easy to figure out, and they cost you $0 to change. Yeah, you might have your lawyers write some documents, human resources might be spending some time, but these are people on salary. You're not actually laying out any new money to solve these Right, right. And you can afford to make experiments. You can afford to change some of the performance measurements and see what happens, the way Toyota would take a little bit of inventory out of the factory here and a little bit of inventory out of the factory there and see what happens.

Right.

So, so use that system that Toyota invented for manufacturing for the entire business. For the entire management process, make a limited number of experiments all the time. Never too many, because if you try to do too many things, the results get very confusing. You don't know what caused what.

Mm-hmm.

But you can make experiments on incentives. You can make experiments on reorganization.

Right.

I wouldn't make dramatic experiments on reorganization. I was in a company that reorganized frequently and it just was chaos.

Yep.

Right. But to say, let's make customer service part of marketing, it'll take a year or 2 for marketing to figure out how to do it right, probably.

Right.

Don't expect overnight success on that. But in 2 years, if your marketing customer— if your marketing research expenses are not down and the quality of your marketing is not up, Fire the head of marketing.

Right.

Yeah.

You know, because you're getting real valuable information from unhappy customers. Right. Yeah. But change performance measurements. That changes the culture. That changes the relationships. The people who are yelling at each other, you say, okay, Your bonus is based on corporate profits. Your your bonus is based on you guys figuring out how to solve problems together. They now have an incentive to talk to each other, not to fight with each other. Right, right.

And empowered them, right? Because that's that's the key thing is that that you've eliminated the bottleneck. You've given them the power. You've empowered them to be able to make decisions, which is a helping the business because it's happening faster and better. Right? They are the people on the front lines.

They probably know a and b.

You're stopping the fighting. You're stopping the people from—

You're aligning them, right, Jo? Right? You're aligning their—

Well, I mean, I say, you know, with a few exceptions, I say you start with the top people. You start with the department heads and what are the incentives for the department heads. And after you've figured that out, you've made a huge improvement in the quality of the business. And probably everybody goes home a little earlier at night and they're a little nicer to their wives and their kids and their wives and their kids are nicer to them. So they come back in better moods and everybody's a little happier. And, um, and then they're treating their subordinates a little better because they're not as stressed and they're not fighting with the other guy. And, and, and then they can have the freedom to ask their subordinates, how can we make this better?

Right.

And the subordinates will be able to tell them. And sometimes the subordinates will be wrong and sometimes they'll be right, but they'll be right a lot. So So, you know, there's a cascade and it takes a time and you, but you can't go too fast and you can't go too slow. And then you start thinking about those obsolete policies and procedures because now you have a little more headspace to look at them. But an outsider is invaluable for that as well.

Yes.

I mean, you, you've had this policy for 20 years. It's, it's, you're not going to think about it.

Right.

Somebody else can redo it. Why are you doing that? And you think about it and you say, oh my God, we shouldn't be doing it this way. But unless somebody gives you a reason to look at it, you've got lots of stuff to do. You've got those subordinates to give performance appraisals to. You've got those guys that you're not fighting with anymore, but you're still negotiating. You're still spending time figuring out how to solve problems. You're working on the stuff that you can see that you know how to solve, not the stuff that you can't see. And when somebody shows you what you can't see, then you know, then you probably can solve it without a lot of help. Maybe the outsider needs to help you, right? But just knowing it makes a huge difference.

At least, and again, depending on where you are in your exit journey, right? The reality is it may be too late Okay. You know, based on your timeline to make a major change that way, even if you discover it, right? But at least you can point out, it'll actually help you to point out the fact that I didn't get to fix this, but I've identified this issue. As we were talking about before, you know, the buyer comes in here, spots these things, right? They're not telling you, they're going to tell you they found these things and they're going to drop the valuation, but you're not thinking about how they're going to take that stuff and then turn that into profit.

Right.

But if you come to them and say, hey, by the way, you're gonna find this, right? And I didn't fix it, and I know it's there. Here's an opportunity for you that actually raises your value and salability in the eyes of the buyer.

Right. Yeah. I mean, your negotiating position is so much better if you know where the hidden value is, even if you haven't had the chance to realize it. That's because you know that they're gonna— okay, I could see that if I kept this business for 5 more years, I could double the profits. You're gonna— so I know you're gonna double it. You might be better at me, maybe you can triple it, but, but I'm not going to sell you a business where the profits are going to be worth twice as much as they are in 5 years as if they're going to be the same as in 5 years, right? I have, I have a, I have a negotiating position now that enables me to get a better buy, a better price.

Exactly right.

And it goes a long way to pointing out, as Joe's pointing out, it goes a long way with With buyers, trust me, buyers find stuff in due diligence that you don't know about. But to the extent that you can point those things out to them, or some of those things out to them, you're buying a lot of confidence from the buyer to say, hey, this person is disclosing all these things to me that I don't need to find in due diligence, A, and maybe I would never have found these things. Some of the things you're talking about never would have been found until after the buyer has that sort of Aha moment in 60 days, or when the problem comes up again, if it happens every, every month or something. So you're buying goodwill.

Uh, some of these problems— I mean, look, if the accounting is standard accounting and the accounting is screwed up, not every buyer, even very, very sophisticated buyers, are going to say, let's re-examine the accounting from ground up, right? They're trained in accounting in the same standard way. Those misvaluations, there's certain ones they're looking for. Okay, this is a This— the book value of this is nowhere near what the real value is, and the book value of this is way over what the book value is, uh, what the real value is. And they know how to— but gee, the KPIs are all because that's the way standard accounting works, right?

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Right. They're not gonna find that.

No, no one's gonna.

No, no, exactly.

So, right.

But again, but again, another, all the more reason, right, for you to actually point that stuff out if you can find it. And, and one of the things that, that, that we believe so strongly is, and I'm sure that, you know, you do this in some way too, because I want to talk next a little bit more, Frank, more specifically about actually your approach to things and how you actually work with clients and things like that. But we know that, that, you know, The first thing that we're obviously doing with anybody, with any client, right, is assessing their current state, right? This is the State of the Union. Here's where you are. Here are the things that we can look at. Here are the things, the techniques, and the processes of doing this ourselves that we can apply here so that we can uncover this stuff and show you what this stuff potentially is, because we know to look for the accounting that is right, except it's not.

Yes.

Right? So the, the, the— what I want to talk about a little bit about it with— for you is, let's start here. When you approach, when you, when you're talking with someone the first time, because I think, again, part of the problem that we probably all face here in our, in our work is getting people to understand that, that this exists, right? I mean, the entire conversation we're having right now is for the benefit of the listener to know That what you might be seeing may not be real, right? And what you want to do is you want to see the reality so that you can deal with the reality and you can adjust reality to your— in your favor and everything else. What is the— what do you find is the general mindset when you kind of show up and start talking with people at the beginning about their issues and things like that? Do you find it difficult to get people to kind of like accept and understand what you're saying and what you're seeing? Do they believe it? Like, what, what is— what does it look like when you, when you start working with people at the beginning?

My working assumption is I'm brought in because of a symptom.

Yep.

And I have to, I have to understand the underlying causes of the symptom. What is the real disease? So the first thing is fact-finding, and that is a lot of interviews.

Right.

And I'm looking for certain clues that I know some of them are there. I know that the incentives are not going to be perfect.

Right.

The best companies are having imperfect ones. Most companies have terrible ones.

Right.

And I know that that is a huge quick win. So that's where I start. If I get them a huge quick win, then it's okay, we have a huge quick win. Do you trust me to go deeper? And that's the next engagement, and it's a completely different negotiation.

Mm-hmm.

And at that point, it's let's map out the systems, let's find the bottlenecks, let's analyze how to, you know, there are bottlenecks that can be easily changed. But if you, if you break a bottleneck, another one pops up.

Right. Sure.

So sometimes the bottleneck you have is the one you should keep, and sometimes it's the one you should break.

Right.

Right. Um, so you analyze the bottlenecks and you say, okay, in 2 or 3 years, this is going to be a different company, and the analysis is not a permanent analysis. But for right now, let's maximize the value of this bottleneck. And get the most out of it, which means you improve the capacity of the whole system because this is the limiting factor. Let's find those 1, 2, 5 bottlenecks and let's optimize those. And that involves looking at policies and looking at procedures because the bottle— the policies and procedures are hiding the bottlenecks.

Right.

And that's a slower process, harder process than incentives and performance measurements.

Yeah, I can imagine, because you really have to know the entire system, Frank, right? Because when you look at the incentive structure, it's like, okay, let me look at how people are incented. And that's sort of not that simple, but it's a notable thing. With a system, it's like you really have to do interviews. You have to understand the complete system to understand where the problems lie, the process improvements, that type of thing.

Well, okay, I mean, I'm a soloist. I don't have the capacity to take on a $100 million company and understand it at that level of detail in any kind of reasonable period of time.

Okay.

So what I'm going to look for are sub-optimizations that are improvements. Let's, let's find this bottleneck. Let's optimize this bottleneck. Let's get this better. That will impact other parts of the system. And now we have new pain points, and that's the next place to go.

Right.

And we may undo something we just did because it's causing an impact down the road.

Right.

I, you know, I learned about bottlenecks from, from the book The Goal and Eliyahu Goldratt, who developed the theory of constraints. His second book was about warehouses. He said, okay, In the goal, my hero, with the help of Jonah Goldratt stand-in, fixes his factory. The warehousing system that was designed to cope with a dysfunctional factory now is coping with a functional factory and it can't cope.

Now it's broken.

Now it's broken. Now we have to rethink the entire distribution system. Okay, so you rethink the entire distribution system and now the sales team needs to understand how the thing works because now they have new opportunities for sales and they ought to improve the sales and the marketing has to change. And so every improvement has ramifications down the road. And, and the KPIs and the performance measurements that were appropriate with a dysfunctional factory for marketing and sales and customer service now need to change also. And by the time you've got all of these things solved in the 4 or 5 years it takes to solve all these things, the market has changed and new technologies come in and you have to rethink everything anyway.

Right.

I mean, I'm thinking about AI as a change agent, right? That companies are throwing incredible amounts of money at AI without a clue what they're doing. There's a terrible fear of missing out. And you have this incredibly potentially useful product that could potentially be a disastrous product. Right? You have a new toolkit that you don't understand, and you put— you use the tools in the wrong way in the wrong place, and things will blow up.

That's right. If I use the claw side of the hammer, right? If I've never seen a hammer before, and I pick up the hammer, and I start trying to bang something with the claw side of things, right? I'm just gonna be like, alright, great. I got a new tool, but I'm not using it Right.

Right. And so you need the same analysis that I would take to a factory, I would take to any AI project. I would say, before we start the project, let's map out the project, the product, the process that currently exists. Let's find the deficiencies and let's figure out if AI will help this, or we have to modify the process for AI to help this, or maybe just modifying the process will solve the problem. We don't need AI.

Yeah.

And let's do small AI projects so we learn the darn thing because it's incredibly complicated. And the vendors are dealing with an unprecedented product that they don't actually understand as well as we would hope they would. And the honest ones admit it. But they also use that as a shield. Well, we don't actually understand our product, so don't blame us when it when it blows up.

Right.

And so how do you, how do you protect yourself? How do you train your workers to know how to— your, your knowledge workers, right? It's not the people on the factory floor or the, or the, the people delivering things that are going to be that affected in the short run. Who knows in 5 years? But for right now, your, your Your financial analysts, your systems analysts, your product managers, the marketing processes, all of these things are— they're looking at AI. Yeah. And it's the promise. They're doing, doing things that look like they make sense, which may or may not make sense. And all these people talking about agentic AI, okay, we're gonna have And AI do things that people are currently doing now, and we're not gonna— how are we gonna supervise that, right?

The customer service example you said before, Frank, right? Like, if you train the AI bot on the 7 things or the 8 things, or I think you said 6 things earlier that you can glean from the website, like, that's what they're doing with these AI people. Like, do you really want the first customer interaction that you have with a customer or the second customer interaction Is by an AI bot as opposed to—

Actually, that would be a big improvement because these are not AI bots.

These are old, old chatbots.

These are, these are, these are very crude tools. And if you actually had the AI read the website and had the AI listen in on real conversations between humans and, and humans, right? If you gave the transcripts of all the customer service calls to—

That's different, right? Yeah, that's a different—

At that point, you're using AI. As opposed to some very crude tool that's been programmed with a set of yes or nos and if this, then that. That would be a huge improvement. And in fact, I think that I would say you want to save money on customer service, you spend a lot of money on customer service. Build these chatbots slowly and carefully and The first thing that every chatbot should say, and it can work for sales too, is, I'm a chatbot and my job is to find you the— find the solution to your problem. If I can solve the problem, I will, and if I can't, I will get you to the best qualified person. Your choice whether it's the best qualified person immediately available or the best qualified person. The best qualified person immediately available may be able to talk to you in 10 minutes. The best qualified person may type may not be available for 3 or 4 days or a week. But if the more information you give me, the better information I will give to the human being who will solve your problem. That would collect an enormous amount of incredibly valuable information that the chatbot could digest and turn into reports to engineering, reports to product management.

And you're being upfront with the customer, right? You're not, you're not putting, you're not putting a chatbot in front of someone and they're saying, Is this a real person or is this a chatbot? Like, I'm not quite sure.

Yeah, I'm a chatbot whose job it is to get your problem solved. And probably it's going to take a person to do it, but I want to get you to the right person, so I'm going to ask you a bunch of questions. Yeah, right. Yeah, I don't think there are very many people who would object to that.

Agreed. Because, because you're— again, they're calling in with a problem. No one likes to call customer service. No one likes to, to do something. I mean, they're not just calling because they want to interact with a chatbot. They have a problem that they want solved. And if you're upfront and honest with them to say it's going to take 13 minutes for Sally to be ready and she's the person you need to talk to, or, you know, I agree with you, people will interact with that.

People want— they're calling because they have a problem and they want the problem solved. However they get the problem solved is great, right?

I have dealt with chatbots that were completely useless. In fact, the one that pissed me off the most was with Anthropic's chatbot. Anthropic sells Claude, which is one of the best AIs out there.

Exactly.

There was a problem with Claude. Claude said, oh, this is a serious problem. You have to tell Anthropic. How do I tell Anthropic? Well, first it gave me complete disinformation about a page that didn't exist. And then it guided— Figured out there was a page where there was a customer service page. Customer service page had a chatbot that couldn't comprehend what I was saying, and after several minutes it said, "I will I will share what you are sharing with me with a human being who will get back to you promptly." And a week later, I mean, that was not the worst chatbot I've ever dealt with. The worst one was with Intuit. I had a problem with the Intuit website. And I went to the end of the chatbot, said, what product do you have a problem with? And the website wasn't one of them, right? It was Intuit Online, Intuit on the desktop, TurboTax. It would have been mildly helpful with any of those, but no, I have a problem with the website. It's telling me to do something I can't do. No way to get to a human being. It's like, oh my God. So—

But let's go back.

Actually, this is— Just so, so Claude, the Anthropic chatbot, says a human being will get in touch with you a week later. I get information that an email that basically says read the documentation, which had nothing to do with the problem. There's no documentation about it. And you can reply to this email, but no one will read it. It literally said that.

You can reply, but nobody will. Yeah, it just says don't reply because nobody will read it.

But yeah, but you can reply, but Okay, so you have this thing that's miraculous that I can ask a really complicated question for and get a lucid answer, and I— why isn't this your customer service? You know, clone it, have a specialty sub-thing that's not marketing.

But the marketing department is writing all its content with it, right? So I mean, the reality is that the AI discussion around this stuff, right, is that I actually, I think that Jen and I talk, you know, because we talk to so many people this way, AI comes up on every single podcast somewhere, right? And the reality is, is that what we, what we are all kind of seeing here is that there is, there is a very large movement towards, again, the things that I'm not necessarily good at, the things that are hard for me as a human being, getting started writing, you know, getting the ideas for things, right? Those are the kind of pain points that human beings have, and I think that's why this early adoption— I'm saying early because I think we are very much early on in this AI stuff, right?

Yes, sure.

Is going to be— is, again, it tends to kind of lean there. The next round, like you said, the agentic AI stuff, and again, even the agentic AI stuff makes it seem as if it's all about interaction with a person back and forth all the time, and that's not necessarily even true.

No, a lot of the agentic stuff I'm reading about is the machine's going to do it for you. And I'm saying, okay, Claude is very, very good when it's very, very good, but it's like the little girl with the curl on her forehead. When it's bad, it's horrid. Are you going to trust a machine that is at 2 sigma reliability?

You can't.

You're going to have this thing make the airline and hotel reservations? And it's gonna screw up 2 times out of 100, you're gonna have some really pissed off executives. Yeah. When, when they're in their hotel room, isn't, isn't available. Right.

To Jill's point, it's, it's starting things off, right? It's starting— I mean, the way that I, the way that I use AI, the way that I think about AI, it's a great starting point, right? It's a great thing.

It writes all my rough drafts.

There you go. And I think eventually, once it can craft a final version and take a rough draft that either you create or some other AI creates and make it refined and get that error rate down to 99.9999, whatever that is, the 5th sigma, is that when it's obviously used. But that's going to be a long, long time if we ever even get there.

But really, the best use of it right now— and by the way, guys, this is going on right now. And just specifically with what Frank's talking about in terms of the analyzation of Okay, so one of the best uses for agentic AI is going to be— and I'm not saying it's there yet— but is going to be exactly in the areas in which we are talking about right now on this podcast. Okay, the ability— and John, you and I are constantly talking to, you know, to people in the M&A space in particular, okay, whose job it is to build, who are building these things to do quality of earnings reporting.

Yeah.

To be able to look into this stuff and to be able to do this stuff, it's only gonna take an enterprising person like Frank, probably, to take that AI and go, okay, here's how we're gonna run— here's how I'm gonna examine your business with it, right? And be able to look at these things and to pull in case studies and to do stuff.

The problem is— I wouldn't trust it yet.

I know, exactly. You wouldn't trust it yet to do that. No, exactly correct. And again, all I'm saying is that— but that is obviously one of the promises to me about how helpful AI can be in that regard. It's not about— to me, it's not about the knowledge we know. It's about connecting the dots where humans can't see. And what we're talking about today is exactly that, a situation that human beings have created in general that is actually outside of their capacity to know. that they— that is actually hamstringing them in ways in which they don't know, just because they're just used to doing it a certain way.

Right.

If there was an AI, and there will be an AI agent that could, could take in those inputs and look through this process and discover where the bottlenecks were and at least give you clues or at least get you down a path so that you can say, hey, look, these are the 4 places I can see that there might be issues, right? I mean, That's gotta be, we've gotta be close to that.

Well, if, if you have, if you have a factory where you actually know the capacity of all the machines and you have all the stuff in an ERP system that you could run through an AI, it could in fact find bottlenecks. Right. And I, I would say that would be a really good starting point and a really good use for it. Right. But the agentic stuff, the way it's described is like the machine's just gonna take over for this human, this, this thing, this clerical work that human beings are doing, the machine will just do it for you. Yeah. And until it's hit at least 3 or 4 sigma, it terrifies me.

Agreed. Yeah, that's true.

Yeah.

Yeah.

And that's the truth. And I think that everybody should be wary of that. But again, I think it's also a matter of there's also, there's all kinds of fear baked into that and things like that. And people don't want to have that stuff and they're fearful of losing their jobs and the things and all that stuff. And the way that I always look at it is like, look, if you can be the guy inside your company that figures out how to use AI in this way to do the thing, right? This isn't about repl— this particular thing we're talking about isn't necessarily about replacing people's jobs, because what we're saying is there is nobody right now inside your company who can do this.

Well, Walmart is saying that they're not going to be, you know, that the, the total population of Walmart employees is not going to grow for several years, and I think that's incredibly naive and dangerous. And Amazon has literally laid people off saying they're being replaced by AI. And I'm pretty sure most of the— all of those, not the same people, but a lot of people will be hired to replace the people who were laid off because they're going to find out that no, in fact, they, they pretty much can't replace it.

Right.

But it isn't just, you know, that for sure that there are jobs that currently exist that will stop existing. And there are jobs that don't exist yet that will replace them. And new ones, right? Yeah. And, and in this case, it's very possible that unlike replacing, um, oxen with tractors, yeah, where you needed to know how to take care of oxen, now you need to know how to repair a tractor, and it's a very different skill set in some cases. As we did with assembly line automation, there's a deskilling thing going on. People who are not that well-educated will be able to do more sophisticated jobs because of AI. And it won't actually require people with college degrees to be able to use it. Totally.

Yeah, absolutely. No, I think that's going to be a whole other level. I think there's going to be— again, I don't know if it's an— I wouldn't call it an AI department because I don't think that's what's going to happen.

But I do— There's no electricity department except in public utilities.

There's no internet department in any company anymore, right? They were back in the day, but—

No, but there is IT. And I think that you're right, John. I mean, yeah, go ahead.

I guess what I was going to say is everyone's going to use AI. Like, everyone's using AI. And I think that's the beautiful thing about AI right now is there's all the experiments you were talking about earlier, Frank, is there's so much experimentation going on in companies and in industries. And unfortunately, the benefits accrue to The, the AI companies, right? Not a lot of that information is shared with users. Like, yeah, they solve their individual problem, but the AI companies are learning a lot about like how their, how their product is being used.

The AI companies are making it very difficult for themselves to find out how the products are being used because the customer feedback loops are disgustingly horrendous.

Oh, well, that's about—

that's not a product.

I mean, that's a, that's a feature, not a bug though, right?

To a degree. It's a choice, and it's a really bad choice. I agree with you, but it's horrifically bad management. Um, I, I cannot speak to how they market to enterprises because clearly they're doing an okay job marketing to enterprises, but marketing to consumers, the product is selling itself despite the marketing because the marketing is, is malpractice at best.

I agree with you.

Catastrophically bad at worst.

Yeah. No, it's fascinating that that's the case. It absolutely is. And again, what's crazy is when you think about it is that the nature of AI in itself, right, is actually all of that should actually be already built into it because getting in, taking the data, getting the information back, all that stuff, getting the results and all that stuff is right there. Right? There just needs to be There just needs to be a conduit to be able to getting that information. Now, one of the things is obviously we don't wanna take that stuff, and the people are very, very, very careful right now, especially the AI companies, of looking like it's Big Brother.

Right? Yeah.

So I saw a tool yesterday, Frank, that's gonna— that would— I can't even discuss it in too much detail right now, but it is so good at what it does in a way that it can help businesses of any kind do a specific thing. It's so good, it's actually frightening. And the fact is, is that they as a company are frightened by it. In other words, they are very concerned about how it's going to be perceived because it is so good at what it does that it's a little bit scary. And so, I mean, it's a, it's a real concern is my point. Right?

The, you know, I have, I have asked Claude and I have asked Copilot and I've asked Gemini, why is there no feedback loop? And it's, well, because the European privacy laws and the California privacy laws. It's like, put a radio button on the damn thing that says private, shared.

Yeah.

Give me some choices. Like, if I have a problem and I want this to go to the AI engineers who need this to make it a better product, I ought to be able to click a button and say, this is shareable with the engineers.

Seems simple.

It's an absolute opt-in, right?

I, I—

default is private.

Unless you do it every app on your phone, right? On your iPhone, you're saying it— can I share this information with you? Every time you did, every single time. Why can't they do the same thing?

Yeah, you know, it's just the default is private. The, the option is send this to the company. The company needs to know about it.

That's it.

Make this a better product. Totally.

Yep.

That is gonna be— and it's gonna come a day, you know. Um, and I mean, the first company that actually makes the damn thing user-friendly and gets publicity for making it user-friendly is gonna blow away the ones that don't do it.

And again, I, you know, define what user-friendly is and in what capacity. I think, again, it's a matter of it's always going to be results because, right, I mean, if I can do— let's just, just to bring it back into this thing, right, what we're talking about right now, which is hidden value, right? Being able to spot these things, these areas inside your company that are going to— that are the bottlenecks, that are the problems with incentivizing employees, the problems with the dissemination and the kind of the chain of information, right, which are those 3 big things. All of those are things that could be examined by, by, by, by, by AI without affecting anything negatively, right? I mean, just from the examination standpoint. But the ability— but it's still going to take, right, what AI is not going to do right now is make the changes, you know. And so it's still—

I was running— the AI would not make those changes. As long as I was in charge.

Right. And you wouldn't want it to.

Maybe, maybe, you know, in 10 years or in 20 years, it will have gotten to a point where I would actually trust it. That's right.

But, right.

Not, not, not, not, not, probably not. But the, but the point is that, again, so you're not gonna use AI, right, that to make those changes. Is there a way right now that, that some companies could be using AI to spot the, and, and, and find these, this latent hidden value?

Maybe. Right. Well, I, I did, I did actually have a conversation coincidentally on Sunday with a senior executive at a software company who said, I get these reports and it would take me 3 weeks to go through them and I don't have the time to go through them. And I run them through AI and it says, here are the 5 things you need to worry about.

Yeah.

In a few seconds. That's good news. That's very good news.

And that's probably, and that, that's probably pretty accurate.

Right. Yeah.

Well, he doesn't know. He doesn't know the ones that it's not telling him about.

He doesn't know.

He doesn't know what that's about. But at least he has, he has 5 things that he needs to worry about. And it might be a good idea for him to tell one of his people, look, go through this thing, spend the 3 weeks it takes to go through it and find the things that AI should have told me that it didn't.

And feed it back in and make sure AI finds that stuff.

See, these 6 things are important. For the next time I send you this report, make sure that there's 11 things that pop out of here as opposed to just the 5 you highlighted.

It's all about the prompt, man. It's all about the prompt at the end of the day. So, Frank, when— just in terms of recovery, we're running out of time here, but I want to get a couple things that I want to get out of you. One is, um, what is something that you could advise people to do right now? We did talk about those 3 things, but if there was something that you could give people right now that they could go and do right away that would make a substantial difference in terms of their systems and finding hidden value in the whole thing? Do you have any advice in that regard?

The first one is spend enough money on customer service so that you improve the lifetime value of your customers. That's the first one. Second one is look at the incentives. Are you confusing a KPI with a performance management Measurement, right? It makes all sorts of sense from a staffing point of view, from a, from a management point of view, from a forecasting point of view, to know how many— how— what the average phone call length is for the call center in customer service.

Mm-hmm.

It, it is completely counterproductive to tell the people that their performance review is going to be based on the number of calls they handle. Right? You want them to take as much time and you want to have a fair measurement so that you know what the staffing requirements are. Don't confuse the KPI, the thing you need for management, with the thing you need to managing people.

Right.

That is a profoundly different— those are separate issues. And if you compound them, you break them both. Then you get a game system and the information you're getting out of the system is of no value and the quality of the work that you're getting is of much less value. Separate them and both improve. Amazing.

Yeah, absolutely. So Frank, where can people get into— if they— people wanna reach out to you, Frank, what's the best way for them to get to, to reach you?

Organization-renovation.com. There's a dash in there. Organization-renovation.com. Uh, there are only 2 Piacks on LinkedIn. The other one's my sister. Um, so her name is Hope. If you get to Hope Piack, you keep looking, but there's only one Frank Piack on LinkedIn.

And Piack is spelled?

P-I-U-C-K.

Good.

Okay. There you go.

And it's frequently misspelled as P-U-I-C-K, but it doesn't rhyme with Buick. The I's in front of the U.

It's, it is easy to trans— it is easy. I've done it myself to transpose the U and the I just so that it's Piack. Not Puick, which is a completely different situation and a completely different family too, by the way. Nice people, but not the same.

You've met some? I haven't. My mother was a doctor. She used to get a magazine from a medical magazine to Dr. Pluck, Dr. Pick, Dr. Pink, Dr. Puck, Dr. Puick, and Dr. Payak.

So funny.

It's, uh, the, the name thing is always an interesting thing. But again, uh, and there's nothing hidden about that. What is hidden in your business though, somewhere, okay, is hidden value. There are things about— there are things hidden in your systems, in your operations, in your processes that are broken that you may not even realize are broken. And it's going to take some deep dive work to be able to discover what those are. But the reason this is important is not just because it makes you— finding these things is going to make you a healthy and sustainable business now, but because at that point at which you do go to sell, At some point, it's very possible that the people you sell to are going to find uncover these things, and you may not even know they're there. It's best for you to find out what those things are now. You can go talk to someone like like Frank by going to organization.renovation sorry organization-renovation.com. You can find him also on LinkedIn, Frank Piuk P I U C K, and also at Exit Engine. If you go to Exit Engine right now, myexitengine.com right now. You're going to be able to get in and start for free figuring out things like this. Okay? You're going to meet other people like Frank who can help you with specific areas of your business. You can find— you'll find our entire framework at ExitEngine, our entire EXIO framework that we've brought down to ExitEngine, that's going to help you start to examine your business in a way that a buyer would. So that before the buyer— before you're ever ready to buy or to sell, before you're ever— before a buyer ever walks in, certainly before you're sitting across from somebody who's looking to buy your company, you will know the things that are wrong and you'll know the ways to fix them. Even if you choose not to fix them, you'll know what they are, and that's really important. John, as always, man, thanks for being my partner in all this and stuff. I really appreciate it. Frank, it was great to have you on today. I hope we could— there's going to be more to come with Frank, we hope, as well. So stay tuned at myexitengine.com and follow us, by the way, on LinkedIn. I'm Joe Stapleton, J-O Stapleton, LinkedIn, and John is John Canniffe, C-A-N-N-I-F-F-E, LinkedIn as well. So, check us out there. Come to myexitengine.com right now, guys. It's gonna be, it's gonna be a benefit no matter where you are on the journey right now. This is gonna be worth your time to check out. You can start for free at myexitengine.com. Frank, so much, thanks so much again for being here today with us. We really appreciate it.

Thank you. It's been fun. Thanks.