AI Only Works When Leaders Know The Business

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Aloha, and welcome to another candid conversation.
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We're joined today by Metz Kramer, and we want to continue exploring artificial intelligence and where Met thinks it's going to impact us in the capital goods space, construction equipment, material handling, et cetera.
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And we'll just have another of these conversations.
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So, Mets, this one's going to be more complicated, but um, and it's more opinions than necessarily facts.
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Where do you think AI is going to take us in the capital in the equipment dealers that you serve so it's a really big question, obviously.
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Um and I can I see it from sort of two perspectives, right?
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One as the founder of a software company.
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We live with it day-to-day now.
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Um, and at the same time, I work with customers who are learning to apply it.
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Um, and then I have my history in the industry and the various areas of the dealership, um, and where I can start to see how it could be applied.
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Um from our side right now, which is probably one of the leading areas of AI use and probably one of the most functional areas, um, is is in the software development space.
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We we now do projects in a day that used to take us a week or two.
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So it is it's just radically different.
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Um what it what's changing from that perspective is um that people with a lot of um domain knowledge um can now do more and do things faster than before.
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Right?
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So, you know, while the AI on our one of the software development I can write code very fast, if you don't know what it is you're trying to build or trying to accomplish, you'll still not be that much faster at doing it because you're still leading on people with domain knowledge.
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Um, but if you have a lot of domain knowledge, now it can think alongside you.
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It can execute tasks for you that you know how to do, but formerly would have given to someone else to actually execute.
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So I think that's sort of a fundamental change.
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Um it was funny to hear when um who was it, Oracle, I think, they laid off a ton of people, um, like we're seeing in in the tech area, tech tech space, uh, and they let go a lot of senior people, uh, which I thought was kind of interesting because uh we sort of uh moved on a place to let people go, but um it would be the junior people actually who have no domain experience, they you know don't have a lot of they need a lot of guidance, and and that's where they're threatened.
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I think you kind of have to start looking at what does it actually do, and what does it do well, what does it not do well?
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Like you've heard the term like hallucinating, that it hallucinates.
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Yeah.
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It happens all the time.
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You know, you domain knowledge is important, and there's not very many quote operating people that understand what that means.
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So I'll go back and use reports as a similar situation because with AI they're they're looking to find things.
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So then I bring back to your reports that come out of your system.
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Do you have any idea what percentage of those reports actually get looked at?
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Oh, it's terrible.
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I'm like, Yeah, that's but I I believe that's gonna end up being the same thing, Matt.
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People are gonna find what they need to do their job, and that's as far as they're gonna go.
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Yeah.
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Yeah.
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And and and even more so, they're uh they're gonna believe what they get as their first answer.
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Yeah.
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Like the the parallel that we see in in say the the marketing space, because we do a lot of like e-commerce and websites, and uh so we're dealing with marketing all the time.
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Uh and one of the fundamental changes that's happened is people used to search something and see several results and then review some of the results, right?
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Like there's always this conversation you have land on the first page of Google, and then you'll get seeing or clicked on.
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Well, now that 50% or more of search is done with an AI chat, um, you're really only getting the first answer back, kind of thing, right?
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And and no one's going further.
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And yeah, you I think you're absolutely right.
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People have this uh do just what they need to do to get their job done.
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So it it narrows in some ways, narrows the thinking if you can't tell it how to rethink or how to approach the problem from a different perspective that you may see that it hadn't picked up yet.
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So it's like it's like a an infant with an infant amount of knowledge.
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Is what you're dealing with in a lot of cases when you're working with one of these AI tools.
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What bothers me most, I think, is this really is exposing, it's really exposing, I think that's the right word, how poorly we are led in this industry.
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The senior management in this industry is is very weak in leadership.
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And the other thing, and and maybe you disagree with this, but when you and I started, most of the leaders, most of the managers, most of the supervisors knew how to do every job they were leading.
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Today, hardly anybody knows how to do the job of the people that are leading.
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Interesting.
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Do you know do you notice that?
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Yeah, I think we've we've always had like um certain pathways into senior management, right?
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That and typically most senior management came out of sales.
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Yep.
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Which means they they didn't grow up.
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It was it was interesting, you know.
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Like my my old dealership, that current president came out of parts and service.
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So like he has that whole background and then became a branch manager and all that, and so he he knows all the details.
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But that's that's pretty uncommon.
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You know, uh when I started at Finning in 1978, we hired between 12 and 18 students between their junior and senior year undergrad or between master's degrees.
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And and they we gave they were led by the youngest manager when I was there, that was me.
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So here's these people that we're exploring hiring as to whether they're a fit to the company.
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And it's up to me how the hell I get I expose them.
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So the first month out of the four, I had them do nothing but warehouse work.
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Picking, packing, receiving, stocking shelves, all that stuff.
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Yep.
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And I lost about a third of them because it was too much work and it wasn't what they were anticipating.
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Yeah.
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The rest of the people, we had three months parts, three months service, three months selling, three months we didn't have rental or administration, three months leadership.
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And at the end of that, we said, okay, when you graduate, we have a job for you.
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And when they came in, we put them in an 18-month program, very, very specific, across all the departments again.
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They actually worked the counter, they actually did warranty reports, etc., etc.
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And then we gave them a small sales territory where they couldn't really screw anything up that was material, right?
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And and depending on how they went, the really successful ones ended up being a small store branch manager.
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Some of them got a bigger sales territory.
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So after when I got there, they'd been doing this 10 or 15 years.
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Everybody in the company who was a parts manager, a service manager, a salesman, a sales manager, had come through the company that way.
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Imagine that culture.
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I mean, that's almost impossible to break.
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It it it you people mis um or undervalue the impact of having that experience has on your decision making, right?
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And of the people's decision making.
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And I went through a similar program at Tormon as a management training program, and I did a few stints in a couple places, including like doing warranty claims for three months, you know.
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Um and that's a real investment.
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And people are scared to invest in people anymore.
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And so, you know, to sort of apprentice someone through the business in multiple roles, knowing that they're not going to be just in the parts department, but you're gonna people don't want to make that investment.
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And that that's gonna be that's gonna be exacerbated by AI.
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No question.
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Because those people without that background who haven't seen and trained it and now have a tool that can hallucinate and tell them how they think it should be done, um, are gonna head off in like some pretty wild tangents.
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Well, you know, again, because artificial intelligence is hallucinating, and that people believe that the answer they get back is correct when it isn't.
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Then they go down a rabbit hole that's just a complete waste of time.
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But you know, it's it was the early 90s where training, employee development became a discretionary expense, and it's bothered me ever since.
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That's actually that's how the reason I started into training when all the OEMs and all the associations stopped.
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It was an economic compression, that's fine.
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But they haven't brought it back anywhere.
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And you know, sales per employee has become such an odious measure, and people have figured out, oh, well, I can look better by just reducing the headcount, which is what we've been doing for the last 30 years.
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And here comes artificial intelligence that has the capacity and capability to really help us do our jobs, but there's hardly anybody that's gonna under, like you say, the domains is a starting point.
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And that's just a very small starting point.
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Yeah.
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Understanding how the business works is more important.
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You yeah, because then then you can interrogate it or send it in a different direction.
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I'll tell you something.
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I I do it, you know, a couple days a week.
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I do quite a bit of stuff now.
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I'll spend a good day on some projects.
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Um I'll I'll actually run multiple instances of Claude.
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Um but it's exhausting.
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Of course.
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Your brain, your brain is working fast.
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You have to concentrate, man.
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You have to think.
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You get like three minutes break while it's doing its work before you have to think about the next step of where you want it to go.
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So you can get a lot done.
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Um, but you have to work, you have to know more and work harder.
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You know I I like I like this.
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I like your statement about Claude, and I use Copilot a fair amount.
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I use Gemini a fair amount, and I I weave them all together.
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And I get different answers on all three.
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And that's really that really is sobering, which means, like you say, it's exhausting because now you have to read through and understand what the difference is.
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Where is it?
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Is it just English?
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It is it language, and in many cases it is.
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But if you don't know what the hell they're trying to do, understanding how they're saying to do it, that becomes rather interesting.
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The fact that you're doing projects, uh, that I think that's a reflection on the fact that AI allows senior executives to have the time because it's so much quicker to take a project from start to finish.
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Yeah.
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I've been training uh a couple of our customers.
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I've uh uh VP of sales in the Northwest, and so I've been training him in um not super technical, um, but once we connected it to the CRM um and taught him how to do stuff, um you do see them start to think differently and ask different kinds of questions.
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And if the AI has access to data, it will start to answer them and build them.
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And one nice thing was it kicked out a PowerPoint presentation for his meeting in three minutes that he didn't spend two hours building because he's not very good at working with PowerPoint.
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So, like um it it can eliminate waste that way.
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Um but it if if you don't understand the business, it could have told him things that are incorrect or added it up incorrectly.
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Um and then I don't know, you still have to be able to fact-check it against maybe, you know, if you say you say you use AI to pull a bunch of financial number reports together, then you still have to go to like the vetted report at some point and just pull that and just check the bottom number and be like, okay, yeah, we did make that much money according to this very boring financial report that no one knows how to read, but the bottom number matches.
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Yeah, what's what's interesting, you know, I think you've heard me talk about the DePont model is a chemical company.
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And what they they did exhaustive studies on what were the significant measures to move a company.
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And they came up with three.
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The revenue line, obviously, the gross profit line, obviously, and then the return on net assets.
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Just those three.
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And that was a pointer for them.
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If they could solve those three numbers, that was 80-85 percent of the dilemma the company had.
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So you could leave all the rest behind.
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So as an example, you know, many dealers in the product support world go after the top 80 percent of their customers volume-wise, which is you know the old 80-20 rule.
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Get me 20.
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Anybody buys more than X, I'm gonna include them in in a territory.
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And nobody really talks about the fact, oh, what about all those customers, the 80% we don't get being left for our competition.
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And then it comes back, well, gee, you know, there's a different personality required for somebody that doesn't give us a lot of their business than there is for a guy that gives us a lot of their business.
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One I call a hunter to get more business, the other I call a hugger.
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Some people call them farmers and you know, different terms.
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But it AI turns that on its head.
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We've we've got life cycle management now.
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So and we have GPS now.
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So every machine in the field that has GPS and is has got life cycle data, I can tell you with after a certain number of hours how much they've spent and how much the spending graph is gonna change.
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Yeah.
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So I know when to replace that machine today.
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Do you know any dealer that's doing that?
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Sorry, say that again.
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Do you know any dealer that's calling a customer up who's got a machine saying, you know, we should change that machine out about now because your cost per operating hour is going to start gr growing quickly.
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And no, I mean, I I I've seen some nominal attempts at that and have been involved in similar things, but to holistically change how you do that, you know, even like I work with some of the newer brand dealers, and um they they'll sell a machine to a customer and they don't know if they want to keep the the telematics from the factory on.
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And I'm like, you you that one machine you sold to that one new customer is the most important piece of telematics data you have.
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It's like, is it are they using it?
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They bought it.
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Did that do they use it?
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Oh, it's been parked for six months.
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Like they don't like my machine.
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Like, you know, that but you have to have that that thought, right?
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So there there is no I think we're a long way from having creative thought in in the AI that will introduce to you different ways to to look at data or a problem.
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It has like incredible domain knowledge in certain areas, you know.
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Um but to to come up with a novel new idea on how to look at your customer base using its intelligence, uh I haven't seen anything quite like that yet.
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Um, because it it has to exist somewhere.
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Like for it to know that that idea exists.
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Yeah, the the trick we we have classes on territory potential.
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Yeah.
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So we show dealerships, employees, how to calculate what the potential of a customer is for 2027.
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And then we have another one for territory management, and we say, okay, the potential is$100,000, you get$10,000 of that.
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You're getting 10% market share of their potential.
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Is that a higher probability of success than the guy who has 40%?
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So how do you set your territory up?
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And now we're looking at a completely different world on managing territories, aren't we?
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Yep.
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When I started the first product support for Caterpillar, here's the keys to the truck, here's the list of the customers, there's the door.
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Good luck.
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That was about as the extent that we went through.
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Yeah.
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Yeah, I I think in the that um that's really technical, right?
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Like you and I are fairly technical, information driven, and then with a lot of experience.
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And you know, that model is pretty complex.
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If you have a good background in all areas of the dealer, you can figure out how to do that.
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Most people at the dealerships don't have that same background and so can't approach it that way.
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But I think with that idea and and then uh a tool that is more interactive, you know, like classic problem on the sales side is people don't want to do anything on the computer because they don't like computers, they you know, they're not good at computers, and so they don't.
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Uh and that that can change, right?
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Like now, um like we have a like a spoken interface with our platform now.
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So we can talk to the the the integrated chat, and then it has access to data and can come back with answers.
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So I think with the ability to put those ideas in people's heads of what they could do, and then it being easier to execute, then you get like an AI assistant of like who who are not talking to, who are what how much has this guy been spending versus what he should be spending?
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And if it has those models, then it can start to help do that, right?
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It'll go as far.
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So originally, here comes CRM, and that allowed the managers to control how many calls the salesmen were making.
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So here comes territory potential and setting up territory assignments.
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Are we going to outsource that as well?
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Like here comes Stephanie Smith, she'll do the evaluation for us and she'll show us what she came to and we'll talk about it, and then we'll decide collectively?
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Or is this something that is going to be in the dealership to begin with?
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That's a great question.
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Well, it's it's supposed to, you know, I I say human resources is not an essential part of the business.
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So outsource it.
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Outsource recruiting, outsource performance reviews, outsource exit interviews, all that stuff.
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Yeah, well, most people outsource marketing.
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But but like now you're now you're talking about the part of marketing that no one wants to talk about.
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Correct.
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Right?
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Like everyone talks about marketing, they're really talking about typically, or if they have in-house marketing, they're actually advertising people.
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Yeah.
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Um, to have someone sit around and be a strategic marketer.
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Um it's a pretty rare role anyway, at most dealerships.
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Um so Well, what uh okay, shift gears.
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What about chief information officer?
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Is that becoming getting traction or no?
00:21:58.779 --> 00:22:02.059
I think that's that's a really that's the proper place for that.
00:22:02.299 --> 00:22:02.859
Oh, I agree.
00:22:03.339 --> 00:22:10.379
I know that like I'm I'm a I'm a believer that this CIO type role isn't a technical IT role.
00:22:10.619 --> 00:22:14.299
Yeah, that IT and IS are completely different things.
00:22:14.619 --> 00:22:18.539
Um IT should be outsourced hardware.
00:22:19.259 --> 00:22:25.980
That kind of stuff, but you you want to be strategic in an information leadership role and on how to lead the business with information.
00:22:27.339 --> 00:22:29.500
We were not a good technical person.
00:22:30.059 --> 00:22:37.339
When uh when I was taking my degree, one of the classes that I took or subject lines that I took was computer science.
00:22:37.819 --> 00:22:41.819
And in those days it was unit record equivalent, so you learn how to wire it.
00:22:42.220 --> 00:22:51.420
But there was a common comment made, and this is middle sixties roughly, that the programmers of today are the clerks of tomorrow.
00:22:54.700 --> 00:22:56.859
And that's turned out to be pretty true.
00:22:58.139 --> 00:23:10.619
Where we have an imbalance, systems analysts, design systems designers, they're rare because there's very few of them that understand the business.
00:23:11.579 --> 00:23:11.900
Yeah.
00:23:12.220 --> 00:23:19.740
So I go to your company, I go to DIS, I go to Constellation, I go to Vital Edge, I go to PFW, all of these different companies.
00:23:19.900 --> 00:23:24.619
I look at the people that are doing the coding, and they have never worked at a dealership.
00:23:25.259 --> 00:23:25.660
Yeah.
00:23:25.900 --> 00:23:28.139
I look at people that are designing systems.
00:23:28.299 --> 00:23:30.139
They neither have worked at a dealership.
00:23:30.220 --> 00:23:33.339
How the hell can they do that for the dealership?
00:23:34.059 --> 00:23:35.339
Interesting comments.
00:23:35.740 --> 00:23:42.539
Well, you get sort of like um a vibe-coded version of what you're trying to build, right?
00:23:42.779 --> 00:23:49.740
Like this is one of the things that um I've been grappling with over the last two months as all this stuff takes off.
00:23:49.819 --> 00:23:59.019
You know, you anyone can sit down, you've heard the term vibe coding, and they can build something in an afternoon that looks like a piece of software.
00:23:59.259 --> 00:24:05.500
Um, but if they don't understand what's in the background, then they don't know what just got built or how it thinks.
00:24:05.900 --> 00:24:32.539
And so like where I see, you know, an even growing value in existing code that like we've built from scratch is in is in the fact that it's a an architecture and a set of rules and a set of knowledge that if you wanted to do something in a vibey kind of way, you can point it at you know, this chunk of code and be like, that's how you do it properly.
00:24:32.700 --> 00:24:38.460
And then are like, oh, you know, I did this sort of like a website for a dealer and it just built a beautiful website.
00:24:38.619 --> 00:24:43.339
And then I looked at the the actual structure of the code, and I'm like, that's not built right.
00:24:43.420 --> 00:24:45.980
And so I'm like, look at this website over here that we built.
00:24:46.139 --> 00:24:47.500
We built that one properly.
00:24:47.740 --> 00:24:51.420
And it's the code, the AI just says, Oh, okay, I can do that.
00:24:51.660 --> 00:24:57.339
And then it just, you know, for four minutes, it's just it works and rewrites the whole thing and structured it the way you want.
00:24:57.579 --> 00:24:59.420
Like, oh, I thought you were just making something fun.
00:24:59.579 --> 00:25:02.220
I see that you're trying to get a production quality version.
00:25:02.379 --> 00:25:03.980
Like, yeah, like so.
00:25:04.059 --> 00:25:18.139
If you don't, if you don't have the exit access to that, then you end up with something, you know, that approximates what you think you want it, but you have no real way to know that it it has the right poems or the right logic in behind it.
00:25:18.299 --> 00:25:23.740
So um a good example on that is pivot tables inside Excel.
00:25:25.019 --> 00:25:28.299
There's not very many people that are good at drawing up pivot tables.
00:25:28.379 --> 00:25:31.900
And if you look at the personality of them, they're younger kids.
00:25:32.700 --> 00:25:34.460
They're more adaptable.
00:25:36.460 --> 00:25:42.139
It's it's a it's a it's a really interesting question to me.
00:25:42.940 --> 00:25:59.339
And, you know, part of my age is I don't know how, you know, ten years from now I I might not be here, so I might not see what the real transition is, which is a pain in the butt not to be able to, you know, I can guess and I can hope that this is where we're gonna go.
00:25:59.500 --> 00:26:04.220
And I I really would like to see the chief information officer become some something necessary.
00:26:04.379 --> 00:26:10.299
Like AED should be having people talking about that at every bloody convention and dealer meeting they operate.
00:26:10.940 --> 00:26:12.859
Cybersecurity is another one.
00:26:13.900 --> 00:26:14.379
Yeah.
00:26:15.259 --> 00:26:22.859
Do you know what the number of days is from the time that somebody gets hacked until they find out that he got hacked?
00:26:24.460 --> 00:26:25.819
No, it's a curious one.
00:26:26.299 --> 00:26:26.779
Take a guess.
00:26:27.980 --> 00:26:28.379
Exactly.
00:26:28.539 --> 00:26:30.059
190 days.
00:26:30.460 --> 00:26:30.859
Yeah.
00:26:31.180 --> 00:26:34.059
In 2025, that's the survey data that came back.
00:26:34.299 --> 00:26:37.420
Now, you give me 190 days, Christ, I'll own you.
00:26:37.500 --> 00:26:42.059
All I need is a day or two, and I I've sucked everything out of your system that I could.
00:26:43.420 --> 00:26:43.900
Right?
00:26:44.220 --> 00:26:45.259
Potentially, yeah.
00:26:45.500 --> 00:26:47.339
Yeah, set up all the things.
00:26:49.180 --> 00:26:51.819
It's uh it's really it's really weird.
00:26:51.980 --> 00:27:04.460
And then I I I I think you and I talked about a canvas, which is pretty commonly used across the school systems, high schools and universities, went down for three to five days because it got hacked.
00:27:05.180 --> 00:27:13.180
So every education system in the country, which is dependent on canvas, that's probably 60-70% of the schools, was out of luck.
00:27:14.619 --> 00:27:15.099
Yeah.
00:27:17.500 --> 00:27:21.900
This is exposing a whole bunch of stuff that we didn't really need to be exposed.
00:27:26.220 --> 00:27:31.579
Yeah, I I I think that's um that's ever really gonna change.
00:27:31.740 --> 00:27:49.500
You you always it's one of the arguments I make why it like why hosting in a in a truly cloud type environment instead of like hosted servers um that are still running older legacy software.
00:27:49.980 --> 00:28:14.619
Why that's the big difference is that you know, Microsoft has 10,000 security engineers um working on their cloud environment to keep it secure, you know, and they don't have that many working on you know the server rack that happens to run your software on that server rack over there that feels like it's cloud-based, but it's not it's not really, right?
00:28:14.700 --> 00:28:16.539
It's not it's basically on-prem.
00:28:17.180 --> 00:28:28.940
And at some point, that that security model, um you have to stay sort of in where where the people are fighting it and and out of the space, right?
00:28:29.019 --> 00:28:39.019
It's it's like the WordPress conundrum where like every hacker has access to every release of WordPress that you're using to build your website, they have access to all the code.
00:28:39.259 --> 00:28:42.539
So they can find that's how they find all the vulnerabilities all the time.
00:28:42.779 --> 00:28:43.579
Yeah, yeah.
00:28:43.819 --> 00:28:45.660
So and and the other thing.
00:28:46.619 --> 00:28:48.940
Yeah, the other thing that is is very common.
00:28:49.099 --> 00:28:53.980
Here comes T Mobile and I get Mombi three updates a week.
00:28:54.460 --> 00:29:00.779
Here comes Windows, Microsoft, I'll get two updates a week.
00:29:01.339 --> 00:29:17.339
Here comes PCMatic, which is my security system, and I run a full scan once a week to identify vulnerabilities, and they they find them, they park them.
00:29:18.940 --> 00:29:28.940
But all this stuff going on, and quite frankly, I don't know how PCMatic does their what the algorithm they look at, nor do I know what WordPress or you know any of those things.
00:29:29.019 --> 00:29:43.339
The only thing I do know is that Google says of the money that they spend on investments every year, seven percent goes to improving the processes that they use now that touch customers.
00:29:43.980 --> 00:29:49.579
Two percent is the new products that they're tuning to make better.
00:29:49.900 --> 00:29:56.700
One percent are I don't remember what they call them, but you know, while just something out there.
00:29:57.579 --> 00:30:05.099
So they take one percent of their investment, and every year they throw it against something they have no idea if it's going to be of any value or not.
00:30:05.259 --> 00:30:14.460
I'd like to have that be a higher number, but the 721 seems to be, or the 9721 seems to be what what Google follows.
00:30:14.619 --> 00:30:16.700
Most dealers don't do any of that.
00:30:17.099 --> 00:30:20.379
We used to do, I used to do that with my teams on Friday afternoons.
00:30:20.779 --> 00:30:21.099
Perfect.
00:30:21.579 --> 00:30:24.299
With the beer afterwards, even better.
00:30:25.019 --> 00:30:39.900
Uh yeah, we would we would just like stop work and start thinking about like what do you want to what idea do we want to play with and you know the to structure actual time on the time, especially Friday afternoons, people are just dogging it anyway.
00:30:40.059 --> 00:30:51.740
Um, and so stop the regular work and play uh with ideas, build something, you know that that idea that you don't know if it'll work yet, you you put the time into it.
00:30:52.220 --> 00:31:07.579
I think that's a place where uh AI tools, if you structured the time and the team to make time to do it, where you can then develop an idea much faster and actually get more value out of the exercise.
00:31:08.220 --> 00:31:14.619
You know, it takes a while to get going in a team activity or you know two people working together on an idea.
00:31:14.940 --> 00:31:19.019
So I think that's that would be a really good upside.
00:31:19.500 --> 00:31:26.700
I used to take a department on the consulting world, and I had a little exercise called Five Things.
00:31:27.660 --> 00:31:39.819
So I got everybody in the department, 20 people, five people, fifty people, saying, Okay, so make make a list of five things that you see that we could do that would make more money for the company.
00:31:40.379 --> 00:31:41.660
And just let them go.
00:31:43.099 --> 00:31:47.259
Then the second one is give me five things that are a real pain in the ass to do.
00:31:50.139 --> 00:31:51.099
And then that's right.
00:31:51.259 --> 00:31:54.859
And then the last one is things would make your job easier.
00:31:55.259 --> 00:31:55.900
Yeah.
00:31:56.460 --> 00:31:58.539
And then we compare the lists.
00:31:59.819 --> 00:32:03.579
And anything that's a pain in the ass to do, how come we still do it?
00:32:03.660 --> 00:32:04.379
Number one.
00:32:04.619 --> 00:32:06.539
And why is it a pain in the ass to do?
00:32:06.619 --> 00:32:07.980
And different people are doing it differently.
00:32:08.220 --> 00:32:13.900
I mean, it's a very powerful exercise, and it's outside the normal job.
00:32:14.700 --> 00:32:15.180
Mm-hmm.
00:32:15.420 --> 00:32:35.019
And and also a great application for a tool like Claude, where you can do the exercise, you can sit around, everyone can fill out the papers, but like if you have to read through all of them and try and collate them all and kind of group ideas and then turn them into something, you you know, whoever's doing that has got two or three hours of work ahead of them.
00:32:35.180 --> 00:32:42.619
You take all of that and send it to Claude, and it'll come back with a great little summary and like make sense of it.
00:32:42.700 --> 00:32:59.259
And because I did it, I did like a set of interviews at a dealership where I interviewed all of the service uh supervisor and staff, and and it was all just like all my notes and notes and notes and notes, and like crushed it all together and came up with like air list.
00:32:59.500 --> 00:33:02.059
So like, oh see, and that's what it does really well.
00:33:02.220 --> 00:33:02.700
Yeah.
00:33:02.940 --> 00:33:05.819
Well, it collates data or information like that really well.
00:33:06.379 --> 00:33:11.339
It does that extremely well, and that that's like you say, that's exactly what I used to do for 20, 30 years.
00:33:11.420 --> 00:33:13.660
I go into a dealership, talk to everybody.
00:33:13.819 --> 00:33:15.259
Yeah, what's a pain in the ass?
00:33:15.420 --> 00:33:15.819
What do you like?
00:33:15.900 --> 00:33:16.379
What don't you like?
00:33:16.539 --> 00:33:17.660
What do you like to change?
00:33:18.460 --> 00:33:20.139
You and and take their input.
00:33:20.299 --> 00:33:23.259
Now I like you say, give it to Claude or any of them.
00:33:23.740 --> 00:33:30.139
And you also you also expose the guy who wants, he doesn't, he's not good at making presentations.
00:33:30.220 --> 00:33:33.180
So if you had to make a PowerPoint, it'd take him half a day.
00:33:33.500 --> 00:33:34.539
Oh at least, yeah.
00:33:34.700 --> 00:33:35.339
Yeah.
00:33:35.900 --> 00:33:36.539
Yeah.
00:33:36.940 --> 00:33:43.420
So but I've been we've been doing it with training people how to do um like a standard report builder.
00:33:43.660 --> 00:33:53.900
And you get that horror, you get that old school editor that you can design the report and make a grid, and this field takes that, and that field takes this, and you gotta do all that.
00:33:54.220 --> 00:33:58.700
Um that's what I've been teaching customers to do, just be like, tell it what you want.
00:33:58.779 --> 00:34:04.700
And like here, I give them like a set of database schemas for the tables for the information they might be interested in.
00:34:05.259 --> 00:34:11.660
And it just tell it what to make as a report, and it'll build you the file, and you open it up in the report editor, and it'll be done.
00:34:11.740 --> 00:34:13.019
And it'll pull data.
00:34:13.180 --> 00:34:20.700
And like you don't have to know how to design a report setting more, or send it to someone who's like it because it writes reports like native language.
00:34:21.019 --> 00:34:25.500
Yeah, yeah, no, it's it's it it's it's it's amazing.
00:34:25.900 --> 00:34:33.579
The um Claude is Claude Gemini, co-pilot, chat GPT.
00:34:35.019 --> 00:34:43.340
There's and and those names are pretty common, and I bet you five years from now, those names will not be in existence.
00:34:45.900 --> 00:34:52.619
Nvidia, I think has got about 80% of the high-tech chips in the world.
00:34:53.179 --> 00:34:57.500
And all of them are in South in uh Taiwan.
00:34:58.380 --> 00:35:00.619
Yeah, though it's someone will find a way around it.
00:35:00.860 --> 00:35:01.259
Of course.
00:35:01.500 --> 00:35:03.179
There's too much, there's too much pressure on it.
00:35:03.420 --> 00:35:04.219
No question about it.
00:35:04.940 --> 00:35:12.219
There's a team out there that's almost cracked it, and they're gonna come out and be like, look, we make them for half the price, and they have more power.
00:35:12.539 --> 00:35:29.259
So imagine a salesman going out with his laptop and sitting beside the customer, and GPS is connected, and we have lifecycle management, and he can go through, you know, one, two, five, seven, twenty-one, how many ever they are, and say, Okay, here's how much you've spent on that machine since you bought it.
00:35:29.900 --> 00:35:34.300
Here's the graph by month at the rate of change of the spending.
00:35:34.699 --> 00:35:37.980
And this is where we say you should change it.
00:35:38.619 --> 00:35:42.460
We'll give you another machine, we'll leave the price exactly the same.
00:35:43.099 --> 00:35:43.980
Just keep on going.
00:35:44.139 --> 00:35:46.059
You got a new machine, we got the used machine.
00:35:46.219 --> 00:35:53.500
The other factor that comes into that is now I can pre-plan what my used equipment prices are going to be.
00:35:54.619 --> 00:35:54.780
Right.
00:35:55.019 --> 00:35:56.860
And I do make margin on used equipment.
00:35:56.940 --> 00:35:59.900
I don't make it on new equipment anymore, but I make it on used.
00:36:00.780 --> 00:36:04.380
So it you know, it changes the whole business, doesn't it?
00:36:04.860 --> 00:36:08.619
It it has the potential to, let's put it that way.
00:36:09.019 --> 00:36:14.300
Yeah, so one of the things that's sort of like classically challenging, as you know, in the service side, right?
00:36:14.460 --> 00:36:19.980
Um, is like simple example, maintenance planning, right?
00:36:20.139 --> 00:36:26.460
You get off schedule, you skip one, you did the wrong level of service at some point.
00:36:26.619 --> 00:36:34.460
And like, but that's chronically difficult for to manually write software to plan around or to manage and figure out what has next.
00:36:34.940 --> 00:36:37.099
AI does that flawlessly.
00:36:37.340 --> 00:36:39.900
It can look at it and be like, oh, I understand how it's supposed to run.
00:36:39.980 --> 00:36:47.340
This is the schedule it's supposed to be running on, and then it can just read through bad service history and be like, eh, that's the one you should be doing.
00:36:47.579 --> 00:36:58.699
So like it can it can rectify, it can improve, it can read through service history in a in a completely different way than we have tried to do it in with like pure data models.
00:36:59.019 --> 00:37:07.900
So, yeah, you you can then say, you know, like review this customer's tweet of machines and all the service history we have on it.
00:37:07.980 --> 00:37:09.420
And it's gonna be sporadic.
00:37:09.659 --> 00:37:12.460
It's gonna have you know notes over here and notes over there.
00:37:12.619 --> 00:37:19.099
It would take forever to program a model, but it'll do it in a very different approach.
00:37:19.420 --> 00:37:23.099
And can tell you, and then come up with its with its answer, right?
00:37:23.500 --> 00:37:24.860
Yeah, yep.
00:37:25.179 --> 00:37:34.059
I used to I used to give 30, 40, 50 machines to a a a maintenance technician and say you set the schedule of the customer.
00:37:35.579 --> 00:37:47.739
And I don't care what you do or how you do it, I want him happy because I'm gonna give you a bonus of you know 10 grand if you get all your dealer's maintenance machines signed up for next year.
00:37:50.380 --> 00:37:51.739
It changes the whole context.
00:37:51.820 --> 00:37:53.980
But now here comes AI, and you're right.
00:37:54.300 --> 00:38:04.460
The machine can calculate all this stuff a hell of a lot better than people, but now I've got people that were doing that who will look at this as holy macros, that ever saved me a lot of time.
00:38:05.179 --> 00:38:32.619
Yeah, and and so I think what you're what we're kind of honing in on is that the there's a lot of potential in these kind of tools, but more than ever, once again, training people on how to actually use them rather than just do the tasks that they're used to doing the same way but faster, is to train people and show them what else they can get out of them.
00:38:32.860 --> 00:38:37.900
So you have to sort of like it's sort of like training people on Excel.
00:38:38.139 --> 00:38:44.380
If you if they can just add up some cells and you never train them how to do more, then they never get more power out of Excel.
00:38:44.860 --> 00:38:45.980
These tools are the same.
00:38:46.139 --> 00:39:00.940
And I and my worry would be that, you know, yeah, everyone gets a copy of a license for Claude, but um they're just using it to do the same tasks faster or do more of them in the same day, and you know, not changing how they think.
00:39:01.500 --> 00:39:12.940
So I believe that this is a job function that has to be done within the quote, in in your world, in the systems and IT world.
00:39:13.179 --> 00:39:16.860
You sell hardware, you sell software, you train people.
00:39:17.259 --> 00:39:25.579
Now you're gonna get into you know process improvement, continuous improvement, whatever CIO, whatever the hell we're gonna call this.
00:39:26.619 --> 00:39:29.019
Because you're you're a logical one to do it.
00:39:29.099 --> 00:39:37.659
You're already in the technology, and you know more about good ones, know more about how the dealer operates than the dealer does.
00:39:38.699 --> 00:39:40.139
And away you go.
00:39:40.780 --> 00:39:49.659
Yeah, but then we still have to fix the problem, which is that there's like a disinterest and a lack of value placed on these tools.
00:39:49.820 --> 00:39:53.420
They're like, you know, what do you call that?
00:39:54.059 --> 00:39:56.860
Necessary evils for some people.
00:39:57.340 --> 00:39:59.820
They they don't see the value in it.
00:39:59.980 --> 00:40:11.340
You know, I I always argue that or say you tell the dealer that runs a fleet of rental equipment that it blew an engine and he needs to spend$25,000 on an engine, he'll be like, Yeah, of course, spend it.
00:40:11.579 --> 00:40:16.139
You tell him you want to spend$25,000 on a piece of software to do some stuff, he won't get it.
00:40:16.300 --> 00:40:17.179
He won't do it.
00:40:18.059 --> 00:40:28.300
So there's like there's a lack of interest in our industry in in trying to do these things and trying to apply it and seeing the value in giving it.
00:40:28.460 --> 00:40:28.860
Yeah.
00:40:29.340 --> 00:40:41.900
We we had in 1970, I think it was 70, it might have been 71, we created the company created two committees, an executive committee and a working committee.
00:40:42.539 --> 00:40:45.980
And there were six of us on the working committee.
00:40:46.219 --> 00:40:52.780
And the very first thing the company did was send us to IBM to train us on what we were trying to do.
00:40:54.219 --> 00:41:04.219
And then we came back as a group, it was the service management, the parts, the engineering, the usual thing, representatives of just about everything.
00:41:04.699 --> 00:41:11.420
And we would prioritize them and we would write up a report and send it to the executive committee for their consideration to implement.
00:41:12.619 --> 00:41:15.259
This is our opinion of what you need to do first.
00:41:15.579 --> 00:41:19.099
If you agree, here's how you do it, and give us the money and go away.
00:41:19.259 --> 00:41:19.820
Let us do it.
00:41:21.820 --> 00:41:25.900
That doesn't happen in today's business as dealers at all.
00:41:27.340 --> 00:41:33.500
No It's it's really an an interesting time.
00:41:33.980 --> 00:41:39.340
And so I'd I'd say that next year when you sign everybody up, you got your annual maintenance fee.
00:41:39.500 --> 00:41:42.940
I'd have an annual whatever the hell we're calling this fee.
00:41:44.380 --> 00:41:48.860
You know, future thinking, or artificial intelligence fee.
00:41:49.179 --> 00:41:54.460
You know, call it what it is, you know, or Claude or whatever the hell Mets, you know.
00:41:55.420 --> 00:41:56.860
Mets mean, you know.
00:41:57.579 --> 00:41:58.699
Or mean mets.
00:42:00.380 --> 00:42:08.539
Yeah, it's um I I guess the punchline when we started this was like, how is it gonna change dealers?
00:42:08.780 --> 00:42:19.500
And I think if you don't want to invest the time in understanding it and applying it properly, I don't see it having a ton of impact.
00:42:19.739 --> 00:42:20.139
Yeah, yeah.
00:42:20.380 --> 00:42:24.059
Um except that some tasks get done faster.
00:42:24.460 --> 00:42:30.699
Um, but likely that'll have the result that we've always seen, which is like less people will do the same number of tasks.
00:42:31.179 --> 00:42:34.780
Um, and so, you know, cut cut the overhead.
00:42:35.019 --> 00:42:37.900
What get the revenue per employee up?
00:42:38.780 --> 00:42:38.940
Yeah.
00:42:39.500 --> 00:42:44.699
Maybe AI can teach people how to read metrics and while metrics actually work on human psychology.
00:42:45.099 --> 00:42:45.739
Yeah, yeah.
00:42:45.980 --> 00:42:46.539
Imagine.
00:42:46.699 --> 00:42:56.940
Well, near EL, he used to teach at uh MIT or Sanford or somebody, behavioral scientist from Israel, he doesn't teach anymore, he writes books.
00:42:57.579 --> 00:43:04.619
And how they how people work, we're to-do driven.
00:43:04.780 --> 00:43:08.940
But every time the phone rings, you get you you're accepting an interruption.
00:43:09.179 --> 00:43:09.500
Yeah.
00:43:09.820 --> 00:43:12.780
Interruptions kill efficiency.
00:43:13.739 --> 00:43:14.219
Yeah.
00:43:14.539 --> 00:43:16.539
So even more so now.
00:43:17.019 --> 00:43:18.219
Oh, much more so now.
00:43:18.300 --> 00:43:21.099
And and the other thing, so I I changed the thing around a little bit.
00:43:21.259 --> 00:43:25.019
I said, okay, tell me what the difference between effectiveness and efficiency is.
00:43:25.900 --> 00:43:28.619
So every I start with efficiency, well, doing things well.
00:43:28.780 --> 00:43:30.699
I said, fine, what's effectiveness?
00:43:30.940 --> 00:43:34.219
And then everybody draws a back, a blank.
00:43:34.619 --> 00:43:37.900
And I say, well, efficiency, I agree with you, it's doing things faster.
00:43:38.139 --> 00:43:39.980
Fewer errors, that's more efficient.
00:43:40.059 --> 00:43:40.699
That's great.
00:43:40.940 --> 00:43:43.579
Effectiveness is doing the right thing.
00:43:44.539 --> 00:43:49.980
And we don't very often do the right thing because we don't think about what the right thing is.
00:43:50.300 --> 00:43:53.259
And that's again what you and I are talking about right now.
00:43:53.980 --> 00:43:54.619
Yep.
00:43:55.019 --> 00:44:06.139
But I think that's like, you know, the reason I said that about the interruption is kind of interesting, is remember earlier I was saying how how much more intense it is mentally to work at this kind of pace.
00:44:06.380 --> 00:44:06.780
Oh, yeah.
00:44:07.019 --> 00:44:11.579
Like it, you know, um, yeah, you produce a lot more, but you're it is more work.
00:44:11.820 --> 00:45:17.739
Now that interruption is even more damaging because um, you know, it it takes more to get in and and I think like you'll I think you'll see a lot of things get started where people spend a lot of time starting to try and use these tools this AI to do things but finishing them you're you're gonna have like a ton of 90% done stuff or 80% done stuff that never hit gets finished because you know it's too easy to abandon something um it's too easy to forget where you were it's tied it's it's lost in some one of your chat streams with Claude the you know we can't remember where it was how far you got and to pick it up so like the remember that the idea of multitasking at baloney and that it's important to set apart time and actually work for those hours like I've worked for two hours and I won't do email I won't do phone calls I'm gonna work on this task becomes even more important I think because I think otherwise people won't finish things.
00:45:18.059 --> 00:45:39.500
The other thing that's happening that's regarding to the intensity I think there's going to be a lot more four-day weeks 32 hour weeks because people are going to need more time to refresh they're already you know I neither you or I worked eight hour days they were always longer than that.
00:45:39.739 --> 00:46:11.579
And some days you'd leave and you go on your drive home you're thinking holy Christ I'm tired today and it's becoming more common I thought it was my age but it ain't it's it's the fact we can do a hell of a lot more things with the tools that we got today than we ever dreamt of isn't that the classic issue with productivity tools and computers that was supposed to give us more time back because we can do our tasks faster and we just keep doing more tasks.
00:46:11.900 --> 00:46:14.300
That's correct that's ex and that's human nature.
00:46:14.539 --> 00:46:57.099
Oh I got that done let me get give me another one give me another one yeah yeah and and maybe that'll be the reason why like it'll struggle to get applied um yes across dealerships is because why why why would I do more than I don't I don't really have to you know like I'm a sales rep I will sell pretty much the same amount no matter what there's no real incentive um well the incense yeah the incentive is perverse though there's people that are hiring 20 year olds for a million bucks a year because they're because they're kind of brilliant in the in the in the engineering space.
00:46:57.420 --> 00:47:01.099
In the in the very small piece of expertise.
00:47:07.739 --> 00:47:15.739
Oh yeah I hope this discussion that you and I are having is is getting attention from people in the audience listening.
00:47:16.059 --> 00:47:23.099
Because this is stuff they should be seriously thinking about and how they're going to go forward with their business and make it better.
00:47:23.420 --> 00:47:29.820
Because the tools are there for us to help us but we got to figure out how to use those tools.
00:47:30.219 --> 00:47:32.460
Okay people can't be left to figure out for themselves.
00:47:32.699 --> 00:47:49.019
No as an as an organization you you can see this wall coming and um I think you really need to invest in bringing in people who understand it who can train it who can you know actually get something out of it and support your people to do it.
00:47:49.259 --> 00:48:05.259
Otherwise I don't know I think that's a wonderful way to end this mess unless you've got something you want to add as a better close but I thought that was an excellent summary of what the dealer needs to deal with today.
00:48:06.380 --> 00:48:28.699
It's monumental it's it's a lot bigger than people think it is and so I think they do need to have a real sit down and think about what it is they want to do rather than just letting it permeate through and turn into what some you won't know for like six months the amount of mess being created by people if they're left to do it on their own.
00:48:29.099 --> 00:48:30.539
That's exactly right.
00:48:30.940 --> 00:48:39.900
Thank you Mesh thank you for your insight in the discussion and thank all of you who are listening and we look forward to having everybody with us at the next candid conversation.
00:48:40.219 --> 00:48:40.940
Mahalo