What Will AI Mean for Your Career—and Your Company? LIVE BROADCAST

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Well, Jonathan, welcome to the show.
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Thanks for having me, Matt.
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Thank you.
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Glad to be here.
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So uh thank you, uh audience members, as well.
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Today, joining us is Jonathan Tobb, president and CEO of Fieldboss, a field service software company built entirely on the Microsoft stack for elevator contractors.
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Jonathan has spent more than 25 years in the Microsoft business applications industry and has become one of the sharper voices on what AI actually means for contractors, not the hype version, the operational one.
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He spoke on this exact topic at the NAEC Educational Conference back in March.
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And today we're continuing the themes with updated information from his March session.
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Jonathan tells us uh, sorry, jumped ahead there.
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Jonathan, tell us about your background and how you found your way into the elevator industry.
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Um 25 years of sobering to hear out loud, um, gone by in a flash.
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Um I graduated business school and I I got a job at KPMG, um, pretty much the only firm that would hire me.
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Um and um I got it got hired late in the process, which was humbling because it was usually pretty easy to get an audit job, but I ended up in the uh real estate and insurance um practice, which was um completely new for me.
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Um I ended up um getting my certification, my chartered accounting designation then, which is now a CPA designation.
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And I went into accounting because my father owned an accounting firm and he was he did very well.
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He had some very strong clients uh, you know, he uh you know propelled him, you know, small company, but you're only successful as your clients.
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So I thought I would just get my designation take over.
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Boy, was I wrong because it was all personal services and he was really good at it, and I was not nearly as good.
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But I I liked playing video games and flight simulator, so I knew how to build computers and clones.
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I started selling them to the clients and on the side.
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Yeah, because I was just an auditor and it wasn't like fun.
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But um it was when computer accounting was coming online, so I ended up I ended up uh becoming a micros a great plane software reseller because I went to Vegas to the computer electronics show and there was a guy there uh promoting a 32-bit Windows accounting system.
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I thought it was the greatest thing since Slight Spread.
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Although they didn't have purchasing and sales over processing at the time, I still thought it was the future of accounting, so I signed up.
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That product, Great Plane Software, ended up becoming Microsoft Dynamics when they got acquired by Microsoft, which became Microsoft Dynamics 365.
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Um, and you know, sort of grew a business.
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I left the accounting firm that my father was, I think there's no way I was gonna get anywhere with it.
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And I went into the computer accounting business with his clients.
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And 25 years later, you had staff, you had clients, slowly.
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Here we are.
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That's what happened.
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Yeah.
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How how does your experience in software strategy implementation and operation shape the way that you view AI?
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Um well, you know, there's been uh in the course of all those years, like big events.
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So, you know, I think back about when um they went from prem to cloud and then mobile became a thing, which is when we really got into the elevator space.
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And now we have we have AI.
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So in all cases, they you know, the hype was how transformational these things are gonna be.
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But like it's kind of the step back, it's all sort of the same theme where you know there's a shift, there's gonna be productivity gains, life will get easier, opens up lots of opportunity for this and that.
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So I I'm you know, and I'm not immune to some of the hype either.
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It's easy to get sucked in, but I have uh, you know, my moments where I step back and I get clarity.
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And I to me it's just another innovation for we can you know exploit for fun and profit.
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Yes, it's listen, it's big, but we don't have to lose our heads about it.
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Yeah.
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Well, and it sounds like you've been at it you know through a few different iterations, right?
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Whether whether it was the the initial, you know, Windows-based accounting, and then you know, you you've seen a different way, a few different waves of this type of thing.
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But but yeah, to your point, it's maybe not it sounds like we don't need to to lose our heads about it.
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And at the same time, it is it's probably the in my opinion, the the biggest of the waves, right?
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There's a lot more that it can do this time.
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So uh absolutely.
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I mean, I don't want to, I didn't want to like um discount it my comments.
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Yeah, it's both.
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It it you know, it's big, it's it's it's really, really, really big.
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But you know what?
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All our clients make their money installing, maintaining, repairing elevators.
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You know, a lot of themes still hold true.
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Um, we just have more to work with now.
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Yeah, yeah, yeah.
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So um I'm just gonna insert one quick question.
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How did you you mentioned getting into elevators when when kind of the mobile thing, what was it that led you to to choose elevators?
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Well, uh this is a for me, it's an interesting story.
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Glad you asked that.
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So we had a diverse practice group, many different industries were selling accounting systems, and a generator guy called and he needed a maintenance scheduling system, and he happened to have the accounting product we were supporting, and Microsoft came out with dynamic CRM.
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And I had a perfect storm of a guy at Microsoft that had had it up with meetings and stuff, and he was looking to do something new.
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I was close with him.
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Um, and he's uh mechanically minded, and he um we were talking, you know, Microsoft was also pushing their partners to not be in like horizontal software space, you have to verticalize.
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No pun intended.
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And this guy who I really liked and trusted said, look, look at all the trucks running around the streets with ladders on the roof and vans.
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It's like field service is huge and it's so gross.
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Anyone that would go into the software business for that, it's got to be crazy.
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It's really hard.
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So we should do it.
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And he had a whole, he was really good at the CRM stuff.
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Right.
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And I also had a close friend that was um left Microsoft who actually had the patent for TAD browsing.
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So I had a perfect storm of like an accounting um consultancy.
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I had a guy that knew the CRM side of it, and I had a developer.
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And I was like, there's no way I could live with myself if I didn't jump right into this thing.
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So he kind of talked me into the market.
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A generator guy called looking for a scheduling and maintenance solution.
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And then an elevator guy called right after that.
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And we did a little research and we realized that the elevator market was pretty antiquated in terms of business systems.
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And we went to like one of the one of the NAC shows at the time, and we were swarmed.
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There was just nothing.
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And I thought, oh, this is gonna be pretty easy.
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Turned out to be like really hard to make a sale, but you know how the elevator industry can be once it gets in your blood, like it's kind of does something to your wiring of your brain and there's no getting out.
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Yeah, yeah.
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So um, I was just gonna share a uh an audience comment.
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AI is good if used properly, they say, when people rely on it to do their jobs, then is when you have problems.
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So we'll get it.
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Oh yeah.
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Yeah.
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We'll get into that uh even more.
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And and I know that there's uh uh appreciate that comment.
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And and yeah, there's a there's a lot to come.
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We'll be talking a lot about that in the next little bit here for sure.
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Absolutely.
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Absolutely.
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Um so in last last March you spoke at the NAEC Educational Conference and you um you know you talked about this exact theme.
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And I know when we talked before, you said, oh, so much has changed since then.
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Talk to me about that.
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What has changed?
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And I think that's one of the scary things about AI is it it does seem to change so fast that you may you may learn something and a few weeks later it's no longer relevant.
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I wouldn't say that you learn something and it's no longer relevant because the this stuff builds on itself.
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So that I actually don't agree with.
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Um I mean, there's all the hype that you read in the headlines that's like coming at us like endlessly.
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It's infinite, it feels like.
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And especially even like this morning, I was looking on a browser between meetings, and the Chinese Communist Party is worried about the impact on AI on their ability to keep power.
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So, like, you know, it never stops.
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And it gets seems like it's getting bigger and bigger and bigger.
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I think since the show, um, I like to present at these shows because I it forces you to learn something you don't know.
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And I really, really did a deep dive into like the sort of the mechanics of it all.
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And I spent a lot of time at the show talking, you know.
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I remember asking the room, like, you know, who here's budgeting for tokens?
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Nobody even knew what a token was in that room, which was not unreasonable.
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And so, you know, and and then since the show though, which was I think in March, for us, like if I really want to focus on elevators and elevator contractors, Microsoft released a lot of stuff that um really puts it in the hands of the users and the company.
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So they they they put um they they put AI right into the platform.
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So it's right embedded in the sales app, the a lot in the accounting app.
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So it's no longer like you just ask it a question, you get an answer.
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Now we could actually do things for real.
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So more of the agents.
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I think that's is that right?
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Yeah, engineering.
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It's more like agents out of the box that you can leverage.
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Also, I think what's changed since I spoke was just governance, security, all the all of the guardrails you need, I think for me is something I would highlight a lot more now.
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And also, like, you know, I don't I don't know what the equivalent Moore's Law is in AI, but also like models are getting a lot cheaper.
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You can do a lot more.
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So things have just got it's just gotten like bigger, stronger, faster, you know, but the sort of basic tenants of the elevator industry haven't changed.
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Yeah, yeah.
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Well, and and back to your comment, right?
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I may have misspoke when I said everything changes, but I think it just it just grows.
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It becomes bigger, more complex, and uh builds on what it was, but it but it is growing fast.
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You know how I like to explain it to people or to myself.
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Um, think of it as a fully loaded plane full of fuel, full of luggage or full of people rolling down the runway.
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Okay.
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And it started lumbering down and you're going slow, it's not so bad.
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But it feels like it's now we we're getting airborne and we're starting to climb.
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And as like you get higher, like there's less resistance in air, and the thing starts going faster and faster.
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Feels like we're or like a rocket leaving, you know, gravity and it accelerates.
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I feel like we're we're starting to like really get rolling.
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Yeah, yeah, yeah.
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Well, and the question that comes to mind, you know, where's it going?
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Right?
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That's always the question.
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Well, uh again, it's easy to get sucked into the headlines and all the hype and everything.
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It's like, where's where's an elevator contractor going?
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And that's how you can ground yourself.
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Good point.
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Yeah, yeah.
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Thank you.
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Well, that's how I I keep myself saying back to first principles.
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No, it's that's a good, that's a good way of thinking it, right?
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It's it's just a tool.
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So where wherever you're going, it can help you get there.
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Um why should people in the elevator industry, owners, managers, office, employees, uh why should they pay attention to AI now?
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Um because it's it's gonna impact everybody.
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It's gonna impact you like all the workers in the industry, field, back office.
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It's gonna impact the owners.
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Like, you know, we've been at this a long time.
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These businesses are personal, and you know, a lot people's and identities and family wealth is wrapped up in these things.
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And AI is gonna have an impact on valuations, gonna have an impact on jobs, it's gonna have an impact on safety for sure, it's gonna have an impact on the traveling public, the building owners.
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So it's everywhere, and it is really, really, really big.
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So, you know, um I I think to to sort of sort of sit back and say, well, you know, we'll see what happens, nothing's gonna change.
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I actually think it's huge.
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And it doesn't matter what your role is in the industry, there's a there's an existential impact to your job or your or your assets.
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That's how I feel.
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Yeah, yeah.
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Yeah.
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What do you hope people will understand differently by the end of this conversation?
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Um, I guess not to be afraid of this, but to take it seriously and get on it.
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You know, I think um also it would be nice if people left this thing questioning like some of the stuff they're hearing, getting second opinions.
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There's a you know, I I I think though at the end of the day, if they if they if I did one good thing under this podcast, it would get somebody to like get a subscription to one of the AI, you know, hyperscalers and just like start using it, learning it, start moving your life along.
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Yeah, yeah, no, that's great.
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So when we hear the the term AI, what are we talking about from you know from your research and and knowledge?
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Yeah, well, um AI's been around a long time.
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It doesn't just come out like you know, when Athropics ship the legal tools and the SAS apocalypse hit us all.
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It was it's way before that.
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AI is like an umbrella term like biology or chemistry.
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There's a lot of like subsets to it.
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Um, you know, it's it's technologies that like help predict, understand, create, and act.
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So, you know, sort of like five kind of hierarchy of five core technologies that like spell check is AI in a way.
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Right.
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So, you know, and if you sort of dial it down to like our business solution, again, I'm not trying to make this podcast about our product, but like, you know, automation, you know, billing automation or getting like an automated procedure done, like a macro in a in a software product, that's AI, in my opinion.
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And then with the uh when ChatGPT came out with you know, uh OpenAI came out with all the stuff and Claude, then you're getting into like AI assistants that could like, you know, you can query them and ask questions, and they have these models, data sets that they trained on.
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So you went from like sort of macros and automation to something you could ask questions of, but it's basically looking at a data set.
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And then co-pilots came along, which is what Microsoft branded theirs, but that would like give you a more of an agent-like experience.
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It would take not only the model that was trained on, but data that you give it access to would do something.
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Okay.
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Now, you know, there's like um now then we're into agents where they you know you can sort of set a goal and they'll actually just do it.
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So there's a hierarchy of of capability in AI.
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Um but it's generally just a family of tools to achieve a certain kind of outcome.
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Gotcha.
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Okay.
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So um you mentioned briefly uh AI governance just a minute ago.
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What did that mean for our listeners?
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How should they think about that?
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Well, you hear about the term governance and guardrails.
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Um think of it like with AI.
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Um you you've got your colleagues who wear polo shirts and logo uh logo shirts in the elevator industry.
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We all do that.
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Sure.
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Think of having a bunch of colleagues with no heads and no polo shirts, right?
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So you've got people you work with that are running around doing stuff.
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So, you know, like who manages them?
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What data can they look at?
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Right.
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You know, what decisions can they do on their own where would somebody need to intervene?
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You know, who approves their work?
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How do we monitor their performance?
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That's governance.
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For sure.
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Well, and and you've talked about, you know, AI kind of uh embedded in the in the systems you use.
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But if you if you had like, you know, Chat GPT and you upload your PL and say, hey, you know, give me an analysis, well, as I understand it, that now lives on the internet and anybody can or out in cyberspace.
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Depends on the account you have.
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Okay.
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I do searches to see if that happens in the elevator industry.
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I do that for fun when my wife's watching some show I don't want to watch.
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Um so yeah, like um, I'm not gonna say if I found anything or not, but you know, the day will come where you can make an agent to go look for me.
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I suppose I should be doing that, paying more attention to what's going on in the room.
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Um so it includes that though, the the risks of okay, not only does Bob the Headless uh you know employee not need to see this, but it doesn't need to be sneaking out on the internet.
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It's more than that.
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You don't want like, you know, like these are systems of records.
00:15:57.919 --> 00:16:04.559
You don't want the agent writing to like a database, you know, over over like bypassing the uh business rules and things.
00:16:04.799 --> 00:16:05.279
For sure.
00:16:05.519 --> 00:16:11.519
So like it's amazing what like it's absolutely incredible, like you know, even what we do internally to run our own business.
00:16:11.759 --> 00:16:20.399
And you gotta like really you really gotta think through who can do what because they can start doing things at scale.
00:16:20.480 --> 00:16:23.360
And then like it's one thing, okay, a client blows something up.
00:16:23.440 --> 00:16:28.480
We could call Microsoft and roll back the database at a certain point in time and keep going, which is ugly unto itself.
00:16:28.720 --> 00:16:35.440
These things, doing things at scale because something you know got over overlooked is uh terrifying to me.
00:16:35.840 --> 00:16:40.720
When you say to scale, do you mean over the whole enterprise system is that we're talking about?
00:16:40.799 --> 00:16:48.720
Or it just I just think about like some kind of recurring process that just writes uh thousands of transactions that it shouldn't do.
00:16:49.360 --> 00:16:52.399
You know, just when I say scale, I'm talking about volume of data.
00:16:52.799 --> 00:16:53.120
Okay.
00:16:54.159 --> 00:16:54.480
Okay.
00:16:54.960 --> 00:16:58.879
That's you know, these are the black places I go to in my industry.
00:16:59.120 --> 00:17:02.559
Now, like so we have a lake of, you know, we got over 125 clients.
00:17:02.879 --> 00:17:08.240
You know, we've got and it's our it's our responsibility to uh make sure they're okay.
00:17:08.319 --> 00:17:10.799
And and you know, we are very, very careful about this stuff.
00:17:10.880 --> 00:17:27.839
But God forbid we get a phone call where some system's gone, you know, we missed something and like tons of data got overwritten, or somebody, a client goes hires some like you know, contractor to do something, know something about AI and does something we don't know about, you know, how do we how do we fix that?
00:17:28.079 --> 00:17:28.319
Right.
00:17:28.559 --> 00:17:35.039
So so to boil it back to to governance, it's really keeping a lid on it in the way that it needs to operate.
00:17:35.119 --> 00:17:45.039
Because if it gets outside that lid, um, and we've got one of our listeners saying, you know, what are the resources out there to help a company establish AI policies and and training principles?
00:17:45.200 --> 00:17:47.839
Um, because it it does seem, at least in my mind, right?
00:17:47.920 --> 00:17:51.119
It's a little bit what what are the fence, what what are the rules, right?
00:17:51.200 --> 00:17:53.279
What are the brother?
00:17:53.519 --> 00:17:56.480
Yeah, well, that's why we're on the Microsoft platform for a reason.
00:17:56.559 --> 00:17:58.640
You log in with your username and password.
00:17:58.880 --> 00:18:03.200
If your tenants set up properly, like you get all that governance as part of the platform.
00:18:03.519 --> 00:18:03.759
Sure.
00:18:04.480 --> 00:18:13.440
If you're on Amazon with something that somebody wrote and using Google and you're using different platforms and there's different handoffs and stuff, that's where the vulnerabilities happen.
00:18:13.759 --> 00:18:20.480
So, you know, for me personally, like I sleep at night because we don't have to pay, we don't have to fund this, we don't have to design it.
00:18:20.559 --> 00:18:21.920
It's all part of the platform.
00:18:22.000 --> 00:18:24.880
You when you license a piece of software, you inherit that.
00:18:25.119 --> 00:18:31.200
But if you're you, you know, and and and this is big for the elevator industry, there's a lot of different ways to run an elevator contractor.
00:18:31.519 --> 00:18:41.039
And if you're using multiple platforms, multiple pieces of software, you have different people giving you advice or doing things, that's where you start creating like airspace between the things and things happen.
00:18:41.279 --> 00:18:50.319
You know, so I think if you want to um inherit governance, just use a proven product that's that does that for you.
00:18:50.559 --> 00:18:50.720
Okay.
00:18:50.880 --> 00:19:03.359
So so in other words, it's a risk to yeah, use use, you know, just some some bolt on or maybe uh maybe you're pulling it into your system, but you don't have it well covered.
00:19:03.519 --> 00:19:03.920
Listen out.
00:19:04.160 --> 00:19:05.519
It's all situational specific.
00:19:05.680 --> 00:19:06.799
It depends on the use case.
00:19:06.880 --> 00:19:11.119
Like, you know, a lot of the really interesting AI solutions are very, very, very specific.
00:19:11.200 --> 00:19:12.799
So maybe the risk isn't as big.
00:19:12.960 --> 00:19:13.200
Okay.
00:19:13.359 --> 00:19:36.960
But you know, what happens is like say, you know, you're working away at a certain process you've done you know for years, and then you suddenly, I don't know, you meet somebody in a bar who knows AI, and then you bring them back to your office and they say, hey, let me do this for you, and I'll save you like, you know, 15 minutes an hour for the next like three months, and you sort of say, yeah, let's do it, and they miss something, that's where that's when you have risk.
00:19:37.039 --> 00:19:44.319
I'm sort of making a joke about just bringing someone off, you know, and test give, you know, but everybody seems to know somebody that knows a lot about AI.
00:19:44.400 --> 00:19:49.119
There's a lot of people that have really embraced it, a lot of really smart people that have talent.
00:19:49.279 --> 00:19:54.640
But it's one thing to know, like to do something in isolation on a business system is risky.
00:19:55.039 --> 00:19:55.599
Yeah, yeah.
00:19:55.759 --> 00:19:56.559
I I appreciate that.
00:19:56.799 --> 00:20:02.319
Well, and and one of our one of our listeners is saying it feels overwhelming to figure this out for my company, to be honest, right?
00:20:02.400 --> 00:20:03.680
There's there's so many.
00:20:04.319 --> 00:20:05.839
Absolutely real and legitimate.
00:20:05.920 --> 00:20:08.480
And you if you don't feel that way, there's something wrong.
00:20:08.720 --> 00:20:17.759
Like, listen, we I have access to such like both internally and you know in our network, and it is really hard for all of us.
00:20:18.640 --> 00:20:19.920
So that's legit.
00:20:20.160 --> 00:20:20.559
Yeah, yeah.
00:20:20.640 --> 00:20:20.880
Yeah.
00:20:21.039 --> 00:20:25.200
What what are some things that the AI can genuinely do well today?
00:20:26.240 --> 00:20:33.279
Well, I mean, it's all about pattern recognition at its core, like, you know, all these sort of responses it does or all these agents.
00:20:33.440 --> 00:20:38.720
It's ultimately just running math and looking at probabilities and figuring out the most likely result.
00:20:39.119 --> 00:20:49.359
Um, so, you know, how how does that like capability or recognizing power patterns translate into like like uh you know the elevator industry?
00:20:49.599 --> 00:20:52.720
Well, an example could be like sensor data coming off an elevator.
00:20:52.960 --> 00:20:54.079
There's an anomaly.
00:20:54.240 --> 00:20:54.559
Sure.
00:20:54.720 --> 00:21:00.799
Um, you know, that you know, you can get an alert, you can turn that into a service ticket that gets dispatched.
00:21:00.960 --> 00:21:07.200
You know, so it's just you know, that's an example of a pattern, you know, um sort of anomaly that could trigger an event.
00:21:07.359 --> 00:21:15.839
Another thing could be like um in the in our accounting system, Business Central, Microsoft ships a payment predictor, a late payment predictor.
00:21:15.920 --> 00:21:22.640
So it's just looking at patterns of behavior of a customer cash flow-wise, the next thing you know, you get a notice that you might want to look at this thing.
00:21:22.880 --> 00:21:28.240
I think um, you know, so there's alerts about things that don't smell right, like rustling in the leaves.
00:21:28.400 --> 00:21:32.880
If someone better get on this, that's where you want a human on it, but you can actually use agents too.
00:21:32.960 --> 00:21:34.960
I think it's particular.
00:21:35.200 --> 00:21:35.680
Yeah.
00:21:36.160 --> 00:21:40.319
It's all different, you know, based on the companies, even in the within the elevator industry.
00:21:40.400 --> 00:21:50.319
But the other thing that, you know, AI can do really well is just some of the administrative grunt work, the repetitive stuff, data entry matching, reconciliations, and stuff.
00:21:50.400 --> 00:21:51.440
It does really well.
00:21:51.599 --> 00:21:59.759
But anything that needs like a human judgment, like that's where you still want to have a person with that real head and that logo t shirt involved.
00:22:00.799 --> 00:22:01.119
Right.
00:22:01.279 --> 00:22:01.680
Right.
00:22:01.839 --> 00:22:08.559
Well, I was thinking about some some comments you'd made before, just talking about, you know, the uh where where it's being oversold.
00:22:08.640 --> 00:22:19.519
And I think maybe even the comment earlier from our audience, right, that if you're trying to delegate your job to AI and and just want to turn your brain off, that that's a major red flag.
00:22:20.240 --> 00:22:22.559
Well, I mean, that's like anything in life.
00:22:22.799 --> 00:22:23.119
Okay.
00:22:23.440 --> 00:22:36.640
You know, and you know, I mean, if my kids are in their late 20s, early 30s, but if I had kids getting into school, like I want them learning things the old-fashioned way because you need to have the fundamentals to get through life.
00:22:36.799 --> 00:22:38.480
And it's the same in the elevator industry.
00:22:38.559 --> 00:22:41.759
Like, there's no shutting off your brain and letting the I AI do it.
00:22:41.920 --> 00:22:47.519
It just frees you up to do the work that you are better, you know, that that's a better use of your time.
00:22:47.680 --> 00:22:48.079
Yeah.
00:22:48.319 --> 00:22:59.839
I so no, like there's like I I uh I almost take offense to that kind of sentiment, you know, because that there's so much upside to this stuff.
00:23:00.799 --> 00:23:03.039
I think what offense is the wrong, I'm sorry to interrupt you.
00:23:03.119 --> 00:23:08.400
It just it just tells me that those kinds of like fears are coming from an uneducated place.
00:23:09.039 --> 00:23:12.559
And it's time to roll up your sleeves and understand really what's going on here.
00:23:12.880 --> 00:23:13.119
Good.
00:23:13.279 --> 00:23:18.640
Well, and and speaking of what what are some AI develop developments that you're really excited about?
00:23:20.079 --> 00:23:30.720
Um Well, I guess it's you know, I'm I'm I'm secretly really intrigued by the Chinese models that are available that you can get for way cheaper than the frontier.
00:23:30.960 --> 00:23:36.640
So by frontier model, we're talking about like open AI, anthropic, like the stuff that costs real money.
00:23:36.960 --> 00:23:42.160
But there's there's much cheaper models that you can get to do certain tasks that are low risk.
00:23:42.319 --> 00:23:44.640
So it kind of like, how do you budget for all this stuff?
00:23:44.720 --> 00:23:46.640
And you know, like what do you use where?
00:23:46.960 --> 00:23:55.359
Um, you know, competition is good for everybody, and you know, America's got like a huge investment in data centers and all these, you know, major, major tech companies.
00:23:55.440 --> 00:24:06.640
And then you've got like, you know, just pick on China for a second, they've got like a huge labor force dedicated to like, you know, I guess replicating and coming at it cheaper, which is good for a lot of businesses.
00:24:06.720 --> 00:24:16.079
So I think what I'm excited about is sort of the potential to apply compute at, you know, for our for a normal company at a much more cost-effective way.
00:24:16.240 --> 00:24:24.640
And there's still a place for these frontier models to be used for certain types of processes, but we just have more options, more competition is more just gives us more options.
00:24:24.720 --> 00:24:27.440
The other thing that I'm really into is like the IoT stuff.
00:24:27.599 --> 00:24:27.839
Okay.
00:24:28.000 --> 00:24:32.720
Um I don't feel that it's really been uh like taken in as well as it will be.
00:24:32.880 --> 00:24:47.440
Um there's no really I don't I can't point a finger as to why, but I think that there's a huge opportunity to like be able to um execute in the elevator business using the telemetry, combine it with um agentic workflows.
00:24:47.519 --> 00:25:03.680
Um the other thing that um I'm like really, really excited about, mostly it's because I'm seeing what we're working on internally, is um just the the reporting and the so we're taking the data and putting it into like a lake house and then being able to do stuff much differently uh with AI.
00:25:03.839 --> 00:25:10.640
So we've got like data in the elevators, we've got data in the field, we've got data in the accounting system, we've got data in sales, we've got data everywhere.
00:25:10.720 --> 00:25:10.960
Right.
00:25:11.200 --> 00:25:19.119
And right now, like what it the way it used to be is like you would use like a viewing tool, you could use Excel or Power BI or whatever, and like sort of query the stuff and get an answer back.
00:25:19.359 --> 00:25:26.799
But now with having all the stuff dumped into like uh like a lake house, like a like a like a big data lake, they call it.
00:25:26.880 --> 00:25:30.799
Okay, we can query and do stuff with that way, way, way, way faster.
00:25:30.960 --> 00:25:31.200
Sure.
00:25:31.440 --> 00:25:41.039
So the potential for um insights in real time and having agents work off that is like it opens up a whole whack of possibilities how to run these businesses.
00:25:41.599 --> 00:25:45.599
That's really big and no one's talking about it, but that's probably the future of these systems.
00:25:45.839 --> 00:25:46.559
Awesome, awesome.
00:25:46.720 --> 00:25:51.759
So I want to talk uh for just a minute about what AI could mean for elevator companies specifically.
00:25:51.839 --> 00:25:54.480
And I just want to give a quick shout out to the audience.
00:25:54.559 --> 00:25:59.359
You know, if your company's already committed to or using AI on a regular basis, go ahead and put a note in the chat.
00:25:59.440 --> 00:26:06.720
I I would just be curious how many people are already kind of marching down this path, whether they've, you know, got a plan or not.
00:26:06.799 --> 00:26:08.000
I'd be I'd be interested.
00:26:08.160 --> 00:26:16.480
Um, but the first question for you in this regard is when you look across the elevator industry, where do you see the greatest opportunities for AI?
00:26:19.039 --> 00:26:23.119
Um, well, you know, there's I don't there's two ways to answer this question.
00:26:23.200 --> 00:26:30.480
Like you can generalize, and then you know, what I find is my experience has been, you know, all these companies are snowflakes.
00:26:30.559 --> 00:26:32.240
So they all have their unique challenges.
00:26:32.319 --> 00:26:34.799
And it's often based on the people that work there.
00:26:34.960 --> 00:26:43.759
So they may have a complement of staff that are good at one thing, and they're they may not have, you know, be it IT or process people, or you know, they're always missing somebody.
00:26:43.920 --> 00:26:44.400
We always are.
00:26:44.720 --> 00:26:48.559
It doesn't matter how big you are, you always have a hole you feel like you need to fill.
00:26:48.960 --> 00:27:01.599
So um, you know, so the opportunities are gonna be site specific, but if you sort of take a step back and look at the industry, you know, like these are businesses that are valued based on EBITDA.
00:27:01.759 --> 00:27:04.079
So you just gotta follow the money, you know.
00:27:04.240 --> 00:27:13.599
So I think, you know, in terms of like what's what what does the industry struggle with, um, you know, it's getting the time in and the bills out the door.
00:27:14.000 --> 00:27:26.880
So I think the biggest opportunities, like if we can solve whatever we can do, whatever incremental gains we can do around um, you know, there's a whole like sales and proposal turnaround time and dispatch stuff.
00:27:26.960 --> 00:27:28.480
Let's park that for a second.
00:27:28.720 --> 00:27:41.839
But once the guy gets dispatched and he's out there like about to do a job, anything we can do to make sort of what happens after that frictionless is money in people's pockets and time back in their life.
00:27:42.079 --> 00:27:52.240
So I think the real uptime or the real upside in the field has to do with um, you know, uh just getting these guys to do a better job.
00:27:52.319 --> 00:28:10.480
You know, it's such a cliche first-time fix rates, but doing it right the first time and documenting everything so that we can get a bill out the door that they're gonna pay with the right evidence and um accounts for all the nuances around the contract, that problem can be solved today.
00:28:10.559 --> 00:28:16.960
And that's kind of where we're focusing because we, you know, so we want to give a much better experience to the guy in the field so he can be part of the team.
00:28:17.119 --> 00:28:22.240
And then in the back office, when all stuff comes in, it's like, how do we clean this up and get it invoice ready?
00:28:22.480 --> 00:28:27.519
It doesn't matter if it's like an AIA bill or a maintenance run or just a quoted repair.
00:28:27.759 --> 00:28:35.119
Um, just what we can do in there's so many, because when you think about the minutiae that go into like this industry, that's what kills you.
00:28:35.279 --> 00:28:37.759
So I think I'm really excited about all of that.
00:28:37.839 --> 00:28:40.720
And then, you know, I'm sort of you know responsible for sales around here.
00:28:40.880 --> 00:28:46.640
So anything you can do to turn around a proposal that's gonna sell and win, I'm always like close to my heart.
00:28:46.720 --> 00:28:52.960
But if I go down the hall and talk to the people that pay the bills around here, they've got different things that they feel should be close to my heart as well.
00:28:53.039 --> 00:28:53.599
So for sure.
00:28:53.759 --> 00:28:55.519
It's all go ahead.
00:28:55.839 --> 00:29:05.359
So so one of the things you you kind of talked about just a minute ago was um the importance of uh you know, like sensor-driven, uh at least that's what I I understood.
00:29:05.440 --> 00:29:15.119
You said IoT, but I was thinking specifically of of remote monitoring software and and connecting that with AI so that AI says, hey, you need to go check on such and such.
00:29:15.279 --> 00:29:27.039
In other words, you could you could be proactive and before it even shuts down, possibly, have your guy go out there and and fix it and bill for it without waiting for uh an angry customer.
00:29:27.440 --> 00:29:28.960
I'm an I'm a CPA.
00:29:29.039 --> 00:29:31.920
Remember the first question you asked me, like, how did I get into this industry?
00:29:32.160 --> 00:29:34.559
I'm an accountant and I sell a billing machine.
00:29:34.799 --> 00:29:39.440
I don't know if predictive maintenance is something that can truly be achieved.
00:29:39.680 --> 00:29:39.920
Okay.
00:29:40.160 --> 00:29:46.880
I would sort of step aside and let other people in the industry answer that because you know it seems to sound like a holy grail.
00:29:47.039 --> 00:29:56.079
I think in my limited ability, it sounds plausible, but you hear so many people that are elevator whisperers that say this will never happen.
00:29:56.160 --> 00:29:57.279
So I don't know.
00:29:57.519 --> 00:29:58.079
Fair enough.
00:29:58.160 --> 00:29:58.480
Fair enough.
00:29:58.559 --> 00:29:58.720
Thank you.
00:30:05.759 --> 00:30:07.039
It's it was worth asking.
00:30:07.200 --> 00:30:18.799
And I do I do appreciate you you saying, hey, there's enough blocking tackling in the back office to do without without getting speculative about what we could fix premat, you know, preemptively.
00:30:19.039 --> 00:30:29.200
Um so uh no, that's I believe in predictive maintenance, but I think there's so many little things that derail that, especially for the independence that we service.
00:30:29.440 --> 00:30:43.759
I I'm not sure that's what I if I was a business owner or if I were not like an elevator technical person, there's other places I'd be putting my time and money to like um you know increase cash flow and mitigate risk.
00:30:44.559 --> 00:30:59.680
And you've kind of yeah, I was just gonna say you expound on that, but you've you really already said that the billing and the you know just that whole the cash flow process of getting somebody out in the field, getting the the the work order back, and and anything else you'd like to add to that?
00:31:00.160 --> 00:31:08.319
Well, the ROI is around getting sales up, getting costs down, improving cash flow, managing compliance, right?
00:31:08.480 --> 00:31:10.559
Because a regulated industry and risk.
00:31:10.880 --> 00:31:18.079
So depending on your company, where you're at, and what's going on, different things are going to be flashing in terms of a payoff for you.
00:31:18.400 --> 00:31:25.599
Um and so, you know, like AI can go after all of these things, and some things are out of the box, turnkey, boom, done.
00:31:25.839 --> 00:31:29.519
Other things just require a reimagining of how you do things internally.
00:31:29.759 --> 00:31:36.400
Like I keep, I I I've done sales, I think a lot about the sales I didn't make and what would be different now.
00:31:36.640 --> 00:31:56.240
And I remember, I'm not gonna say who the company is, that's for sure, but it happens to be in New York, and you know, and this could be one of many, so I'm safe to say, like all these violation departments, I think they could be completely overhauled, you know, between like what we can get off the Department of Buildings and what we can do to like manage compliance.
00:31:56.400 --> 00:32:01.759
My God, there's such a huge opportunity there around getting costs down, being more ahead of that stuff.
00:32:01.920 --> 00:32:10.559
So I just think about like the kind of stuff that maybe when I was making sales five years ago as to how we would approach it, now it's completely different.
00:32:10.880 --> 00:32:11.759
Right, right.
00:32:12.079 --> 00:32:13.920
No, I I I appreciate that.
00:32:14.160 --> 00:32:20.640
Um what um do you think you know AI could help independent contractors?
00:32:20.799 --> 00:32:24.079
Um you you mentioned them a minute ago.
00:32:24.160 --> 00:32:29.119
I'm just curious that the difference maybe between them versus larger companies, in your opinion.
00:32:29.599 --> 00:32:31.359
That's a tough one for me.
00:32:31.599 --> 00:32:40.079
Um, you know, like um I'm you know, I know what it's like to be the only employee in the company with your dreams of you know getting somewhere in life.
00:32:40.160 --> 00:32:43.599
So I'm a sucker for a startup or you know, story.
00:32:43.920 --> 00:32:47.599
Um I'm worried, I I actually think it's both.
00:32:47.759 --> 00:32:57.119
Again, I'm sorry to say it that that, but you know, like in terms of um the smaller independence that want to get somewhere in life is absolutely yes, for sure, this is a force multiplier.
00:32:57.359 --> 00:33:02.240
They can do a lot more than they ever could, but you need to have the right people in the back office.
00:33:02.480 --> 00:33:09.920
Noah Ugone are the days where you can bootstrap it and get like, you know, somebody that knows QuickBooks and like can get them cheap or whatever.
00:33:10.079 --> 00:33:10.640
Like, no way.
00:33:10.720 --> 00:33:19.599
You if you're gonna use AI to like try and take on um who you're gonna be taking on, you're gonna need some pretty sophisticated people that can leverage these tools, in my opinion.
00:33:19.839 --> 00:33:25.519
Um, I actually think that and and and where they have an like I listen to me.
00:33:25.599 --> 00:33:28.319
Like, like I don't know everything, this is just my my opinion.
00:33:28.400 --> 00:33:28.640
Okay.
00:33:29.119 --> 00:33:32.000
Um, it's worth like, you know, take it for what it's worth.
00:33:32.160 --> 00:33:39.440
I think that you know, if you're well run and well managed, you have a shot at really taking the stuff and making it work for you.
00:33:39.519 --> 00:33:39.759
Okay.
00:33:39.920 --> 00:33:45.759
And if you are in a market where the bigger guys are dysfunctional, you absolutely can like smoke them.
00:33:45.839 --> 00:33:46.799
You can outflank them.
00:33:47.119 --> 00:33:48.240
It's really exciting.
00:33:48.480 --> 00:34:00.880
However, if a larger company that works at scale figures out how to actually deploy the stuff properly, they will be killers.
00:34:01.119 --> 00:34:02.079
Absolute killers.
00:34:02.240 --> 00:34:06.799
They'll be able to like, you know, they'll they'll be able to compete with you on margin and price.
00:34:06.960 --> 00:34:15.119
I think like, I think it's gonna be a world of whoever like sort of makes it work for them best, really for the customers and for the field guys, they're gonna win.
00:34:15.280 --> 00:34:17.440
So I'm not trying to be evasive about it.
00:34:17.599 --> 00:34:26.480
I think, you know, I think like a big, large private equity roll-up or a very large company, if they have the right leadership management and execute, they will win absolutely.
00:34:26.559 --> 00:34:26.960
That's right.
00:34:27.119 --> 00:34:31.039
If I had to invest$1,000 in like shares of one of these companies, right?
00:34:31.199 --> 00:34:32.880
I'm betting on management.
00:34:33.199 --> 00:34:33.679
Sure.
00:34:33.920 --> 00:34:34.559
Sure.
00:34:35.119 --> 00:34:41.840
Well, and and you um, you know, in in previous conversations, you'd kind of just just really, you know, the the operations, right?
00:34:42.000 --> 00:34:50.159
Um, because you know, the question being, does does becoming AI capable begin with buying an AI tool or with improving the company's operations?
00:34:50.239 --> 00:34:53.119
And and I'm hearing you say operations, hands down, right?
00:34:53.199 --> 00:34:55.199
You've got to you've got to solve that nut.
00:34:55.679 --> 00:35:00.559
You've got to you've got to redesign the processes um to align with the tools.
00:35:00.719 --> 00:35:05.440
It used to be like, you know, you don't want to buy software that you have to adjust your processes to the software.
00:35:05.599 --> 00:35:06.719
Nobody wants to do that.
00:35:06.880 --> 00:35:09.920
And then, you know, everybody wants it to work the way they like to work.
00:35:10.079 --> 00:35:11.840
That's all got to go out the window now.
00:35:12.000 --> 00:35:15.360
I think you've got to be open to like new ways of working.
00:35:15.519 --> 00:35:17.920
And you have to, and it's really hard, you know.
00:35:18.000 --> 00:35:21.920
Like you think, you know, people get it down to a science of their day.
00:35:22.079 --> 00:35:28.719
Um, I think it's both, but also so you've got to you've got to find a software provider that can deliver to you.
00:35:28.880 --> 00:35:34.000
You know, and I don't and and there's products out there right now that I are just like obsolete.
00:35:34.079 --> 00:35:42.239
Like they just aren't they just can't sort of accommodate what's possible just because of their lineage and legacy.
00:35:42.320 --> 00:35:48.880
It's not it's not that anyone did anything wrong, but um it's we'd have to rewrite the whole thing, is what I'm hearing you say, right?
00:35:48.960 --> 00:35:55.119
It's like Yeah, there's there's so many things around the governance, around the data structure, and then the fact that they don't have everything.
00:35:55.199 --> 00:35:57.519
So they're using other products and you got to account for that.
00:35:57.599 --> 00:35:58.400
Like it's ugly.
00:35:58.559 --> 00:36:02.960
You know, I'm mostly worried about like, you know, Oracle, Salesforce, you know, and we're on Microsoft.
00:36:03.119 --> 00:36:05.360
So we have like the benefits of those platforms.
00:36:05.519 --> 00:36:11.199
So, you know, that's who I think we're gonna be competing against going forward, unless there's things I don't know about, what's out there, I don't know everything.
00:36:11.360 --> 00:36:13.760
So I don't want to shoot off my mouth, you know, improperly.
00:36:13.920 --> 00:36:14.159
Sure.
00:36:14.320 --> 00:36:25.199
But I think like if you were starting an elevator company from scratch with like 25 people or 50 people, you are starting with raw clay, blank slate, and the world is your oyster, like for sure.
00:36:25.440 --> 00:36:29.199
Um, that's hard to do when you've, you know, you're tearing your hair out day every day.
00:36:29.360 --> 00:36:32.079
You've got like you know, phones ringing and life goes on.
00:36:32.159 --> 00:36:33.280
Like, how do you do both?
00:36:33.440 --> 00:36:36.079
It's like the old joke I've been saying for 15 years.
00:36:36.159 --> 00:36:38.159
You're changing the engines on a plane while it's flying.
00:36:38.239 --> 00:36:41.039
Well, you're actually redesigning the aircraft while you're flying.
00:36:41.679 --> 00:36:43.039
But you don't have a choice.
00:36:43.199 --> 00:36:51.519
You have to do it and you have to embrace the challenge because you'll come out of it like amazing, and it can be done, but it's not easy.
00:36:51.679 --> 00:36:58.400
And that's why back to your original question like the small guys, you know, is this gonna benefit the small guys to compete or the larger ones?
00:36:58.480 --> 00:37:01.199
It all boils down to ability to execute.
00:37:01.440 --> 00:37:01.679
For sure.
00:37:01.920 --> 00:37:04.880
It's leadership and focus and intent, in my opinion.
00:37:05.199 --> 00:37:05.519
Right.
00:37:05.679 --> 00:37:17.039
So, so obviously you gotta execute, but you've also got to, like you said, design efficient systems and then be willing to change the way you like to do things, and and that's the that's the awkward part.
00:37:17.119 --> 00:37:18.639
And then you gotta execute on those.
00:37:19.599 --> 00:37:20.960
You might have to let people go.
00:37:21.119 --> 00:37:33.199
If you have people that can't like adjust, like it's really this is big, you know, like you know, like you've got a process now that you know, and you've got a person in there who knows everything, knows everybody's relationships.
00:37:33.440 --> 00:37:38.880
If they can't like like change, they will hold you back and you really won't have a choice.
00:37:39.039 --> 00:37:41.840
The economics will get you because your competitors will get you.
00:37:42.000 --> 00:37:43.039
That's how I I see it.
00:37:43.760 --> 00:37:55.280
Let's talk just a minute, because we we are talking about you know the the benefits, risks of of changing, upgrading, not just software, but you're talking about process procedures and and and teaching old dogs new tricks and all that.
00:37:55.440 --> 00:38:01.039
Um, what are the the benefits and risks of actually making some of these changes we're talking about?
00:38:01.440 --> 00:38:04.639
Well, again, I think the payoff is varies by company.
00:38:04.800 --> 00:38:10.239
It's gonna be like an increase in sales, reduce cost, better compliance, you know, mitigate risks.
00:38:10.559 --> 00:38:19.519
So, you know, these these sort of platform solutions that are available now, um, it just changes the economics of the business.
00:38:19.679 --> 00:38:31.119
You know, my favorite movie is Moneyball, you know, so you can completely re-you're talking about yield on payroll, because like the on the margins on on parts and stuff, like let's assume they're sort of like they are what they are.
00:38:31.360 --> 00:38:38.079
But it, you know, depending on what your payroll is, you're talking about like are we can we get like a 5, 10, 15, 20, 35% lift?
00:38:38.800 --> 00:38:42.960
So, you know, like you are, and these are you know, it's not like a 2% gain here.
00:38:43.039 --> 00:38:44.480
We're talking about real money.
00:38:45.039 --> 00:38:57.119
You know, like if we can get like invoices out the door like 25% faster, and then we could add like another, you know, 25% to your field staff and number of elevators we're maintaining and not have to add headcount.
00:38:57.360 --> 00:39:00.320
I mean, that's you can't ignore that.
00:39:00.639 --> 00:39:01.280
Yeah.
00:39:01.519 --> 00:39:02.559
No, that's big.
00:39:02.880 --> 00:39:08.079
So what typically makes a technology transformation succeed or fail?
00:39:08.320 --> 00:39:10.400
And yeah, I'm sure you've seen a few of those in your day.
00:39:10.639 --> 00:39:11.119
Oh my God.
00:39:11.199 --> 00:39:11.280
Yeah.
00:39:11.360 --> 00:39:14.000
And you know, everybody has good jobs and bad jobs.
00:39:14.239 --> 00:39:25.280
Um, it one, I think if I had to pick one thing that like um was a predictor of success, it's who's running the project on both sides, our side and the client's side.
00:39:25.519 --> 00:39:27.039
You know, and it's really the client's side.
00:39:27.119 --> 00:39:34.400
And so, so, so much so that I think, you know, we're developing job descriptions for what we need on the client side to execute.
00:39:34.639 --> 00:39:47.840
And it includes like, you know, technical skills, communication skills, you know, sort of like elevator industry awareness, you know, like they need to know what happens on a phone, what the impact is like, you know, on a you know, in the office.
00:39:48.000 --> 00:39:51.679
So who you assign to own this is critical.
00:39:52.079 --> 00:39:57.840
And, you know, I d and you, you know, ideally, you know, there's so many like major um pillars to this stuff, right?
00:39:57.920 --> 00:40:05.760
Because there's a sales aspect, the operation stuff, there's the materials stuff, there's the accounting stuff, there's the IT stuff, and there's the leadership stuff.
00:40:06.000 --> 00:40:16.639
So, you know, we've had some really amazing stories where one person could handle elements of that really well and could orchestrate the change at our at our clients.
00:40:16.719 --> 00:40:19.440
And it doesn't, I'm not talking about like big giant jobs either.
00:40:19.920 --> 00:40:23.679
You know, it just so happens like some of those roles are, you know, three hats are worn by one person.
00:40:23.840 --> 00:40:24.480
That's okay.
00:40:24.639 --> 00:40:27.360
It's kind of nice because they know everything and the impact.
00:40:27.599 --> 00:40:29.920
So, you know, it's it's back to the same old thing.
00:40:30.000 --> 00:40:36.239
It's leadership, it's it's people, it's organizational skills because the technology works.
00:40:36.400 --> 00:40:40.480
You know, like the data comes in, it does a report, everything works.
00:40:41.280 --> 00:40:47.599
It's it's it's it's um it's an exercise in it's a project.
00:40:47.679 --> 00:40:49.519
It it's not a technical problem.
00:40:49.840 --> 00:41:02.800
So I think you're saying that that humans uh uh introduce the the biggest obstacles, whether they're capable or incapable or willing or or not, or yeah, like the the tech, the plat all the software products in the industry work.
00:41:03.039 --> 00:41:04.639
You can run an elevator company in everything.
00:41:04.719 --> 00:41:05.519
So it's not about that.
00:41:05.760 --> 00:41:09.119
And yes, some of them are missing things or not as sophisticated, like who cares?
00:41:09.280 --> 00:41:17.760
You know, you get like the right people with the right attitude and the right, you they got to do their homework, they got to make themselves available, and they have to want to do it, right?
00:41:18.000 --> 00:41:19.280
Then you can achieve greatness.
00:41:19.360 --> 00:41:27.199
And then if you couple that with like a really strong platform like what we've got and what AI can do, like it's the the, you know, how high is up.
00:41:27.360 --> 00:41:29.760
Like the potential is that's what's so exciting.
00:41:29.920 --> 00:41:35.920
Like you get there, you put this thing in the right hands of the right kind of organization, and it's really I have no idea.
00:41:36.000 --> 00:41:39.840
You know, like sometimes I hear things that our clients are doing, I can't believe it.
00:41:40.079 --> 00:41:40.320
Cool.
00:41:40.480 --> 00:41:42.719
Like I can't, like, you know, they did what?
00:41:42.960 --> 00:41:45.440
And we're supposed to how are we supposed to support that?
00:41:45.679 --> 00:41:51.519
And we hate that so much, but like to me, I'm kind of secretly like, yes, you know, they get it.
00:41:51.840 --> 00:41:52.480
Right, right.
00:41:52.559 --> 00:41:53.760
I mean, they're they're yeah.
00:41:54.000 --> 00:42:10.000
Sounds like they're adding or maybe showing you new capabilities that you weren't didn't know it was there, but now they're well, we get a lot of people buying us because they want to get access to the platform because they think they could do things themselves, and then they have to fight with our product team that don't allow stuff because it's we need guardrails on our own stuff.
00:42:10.079 --> 00:42:11.679
We need to be able to upgrade, support it.
00:42:11.920 --> 00:42:15.360
You can't have them down, and it's easy to have it's like raising kids.
00:42:15.440 --> 00:42:20.800
Like you want them to make mistakes, but not so much that you're gonna get sued or there's gonna be like physical harm, right?
00:42:21.039 --> 00:42:23.599
So it's tough because you never want to say no to a client.
00:42:23.679 --> 00:42:25.760
Like you're on their side, it's their system.
00:42:26.079 --> 00:42:29.840
They uh, you know, and and you know, you don't want to burst their bubble, you know.
00:42:30.000 --> 00:42:36.480
At the same time, like, you know, we we can't bail you out of jail on a weekend.
00:42:38.159 --> 00:42:39.440
Yeah, yeah, yeah.
00:42:40.079 --> 00:42:41.199
I I can see that.
00:42:41.360 --> 00:42:46.159
So let's let's talk for just a minute about, you know, obviously there's a lot of private equity activity.
00:42:46.559 --> 00:42:56.320
Um are you seeing or or have you seen the you know, the I guess valuations affected by technology versus operational maturity?
00:42:56.480 --> 00:42:58.639
What does that look like from your perspective?
00:42:59.199 --> 00:43:02.559
I have no idea what goes on in all these acquisitions.
00:43:02.639 --> 00:43:05.760
We have a lot of private equity companies that use us and are buying companies all the time.
00:43:05.920 --> 00:43:10.480
And it feels like every elevator company of substance is always for sale in the elevator industry.
00:43:10.639 --> 00:43:12.800
I deliberately don't pay attention to stuff.
00:43:12.960 --> 00:43:17.679
You know, I know that it used to be like, oh, you get 30 times monthly maintenance, that's evaluation.
00:43:17.840 --> 00:43:25.360
And I always thought, like, you know, that sounds like dot-com bubble because if you're like losing money, but you've got a big maintenance book, like how, you know, why is that good?
00:43:25.519 --> 00:43:30.000
So for sure, I'm under the impression, you know, I know for a fact eBIT is still number one.
00:43:30.159 --> 00:43:33.199
And then the multiplier you get off of that is gonna really vary.
00:43:33.280 --> 00:43:38.320
You know, in the past, like earnings quality, you know, repeatability, all that, the standard stuff applied.
00:43:38.559 --> 00:43:47.039
But there's no doubt in my mind, and this is where I feel a lot of pressure personally, like we have to deliver a platform that impacts that valuation, and it's definitely changing.
00:43:47.119 --> 00:43:56.079
So, like you're gonna have your EBITDA number and your multiple, but I think you know, even ourselves, you're gonna be measured by revenue per employee, agents per employee.
00:43:56.239 --> 00:43:59.599
Like there's other there's other KPIs that are gonna factor.
00:44:00.159 --> 00:44:02.960
To your attractiveness to a buyer if you want to get out.
00:44:03.119 --> 00:44:12.719
And you will get heavily like if I was um knowing what I know, and if I was on the PE side buying up companies, I'd be looking at companies that will never be able to execute properly in this new world.
00:44:12.800 --> 00:44:15.119
And you should be able to pick them up at a bit of a discount.
00:44:15.280 --> 00:44:19.039
And I'd be really looking at their field people and their salespeople, and that's it.
00:44:19.280 --> 00:44:26.239
So I think it like just to summarize, I believe that the standard like sort of, you know, numbers and formulas applied.
00:44:26.320 --> 00:44:28.639
I don't think they do as much anymore.
00:44:28.880 --> 00:44:36.400
Because we, you know, when you think about the product we sell, you're moving away from licenses per user per month to like some kind of measurable outcome.
00:44:36.559 --> 00:44:41.039
So it's got to be this is such a cliche, it's got to be a win-win for us and the customer.
00:44:41.119 --> 00:44:41.280
Okay.
00:44:41.519 --> 00:44:41.679
Right?
00:44:41.840 --> 00:44:44.480
We have to deliver something that's of value to them.
00:44:44.639 --> 00:44:45.519
And it's not even us.
00:44:45.679 --> 00:44:50.079
Like Microsoft is going to be moving away from like selling a license per user per month.
00:44:50.239 --> 00:44:55.519
Now it's going to be like, okay, if we saved you 50% of that payroll, we're not charging you$100 a month.
00:44:55.760 --> 00:44:57.199
I don't know what that number's going to be.
00:44:57.360 --> 00:44:58.079
Oh my gosh.
00:44:58.320 --> 00:44:59.920
But that's what, but think about it.
00:45:00.000 --> 00:45:03.840
Like if you deliver software that cuts you know the processing cost down.
00:45:04.079 --> 00:45:09.840
Say you're paying someone$100,000 a year and you save 35% of their job, you don't license that out for 10 bucks.
00:45:10.000 --> 00:45:13.760
Not to mention the fact that we got to pay off the data centers, all the energy costs.
00:45:13.840 --> 00:45:16.719
Like there's a huge infrastructure cost to recover on, right?
00:45:16.880 --> 00:45:20.239
You know, and you know, and people need to compensate for all the risk that's been put out there.
00:45:20.480 --> 00:45:24.400
So where all where the economics are going is going to really evolve.
00:45:24.559 --> 00:45:26.719
I think it's happening already now for sure.
00:45:26.960 --> 00:45:27.440
Sure.
00:45:27.920 --> 00:45:29.119
This is a big topic.
00:45:29.280 --> 00:45:34.880
Another one, a podcast I'll tune into when you decide to rope someone in who can talk about this better than I can.
00:45:35.039 --> 00:45:36.800
Because it's there's a lot of meat on this one.
00:45:36.960 --> 00:45:37.360
Yeah, yeah.
00:45:37.440 --> 00:45:37.599
Yeah.
00:45:37.760 --> 00:45:41.679
Well, let's let's shift gears for just a minute about um, you know, AI and careers.
00:45:41.760 --> 00:45:49.519
We and we've touched on this a little bit, but um how do you think people should in the elevator industry should feel about AI?
00:45:49.840 --> 00:45:52.159
Excited, concerned, combination of both.
00:45:53.119 --> 00:45:55.280
You should be concerned and curious.
00:45:55.519 --> 00:45:56.000
I love that.
00:45:56.159 --> 00:46:00.960
And you should you you should be concerned for sure because it'll creep up on you.
00:46:01.119 --> 00:46:07.360
I think that and I don't mean to be doomer here, but I really I you've got risk.
00:46:07.599 --> 00:46:10.800
I think it's not gonna happen next month or next quarter.
00:46:11.039 --> 00:46:15.840
Um, you know, the Jonathan prediction, which has, you know, never been right.
00:46:16.159 --> 00:46:22.159
I would say by the end of next year, you're gonna see elevator companies in the space really pulling away from the pack in terms of how they work.
00:46:22.480 --> 00:46:22.559
Okay.
00:46:22.800 --> 00:46:25.119
So I think market value of salaries are gonna evolve.
00:46:25.199 --> 00:46:27.920
I mean, you're gonna know better than anybody given what you do for a living.
00:46:28.159 --> 00:46:38.480
I think like certain jobs are gonna devalue based on like you'll have institutional knowledge where that has value, but your ability to like do things that an agent can do will discount.
00:46:38.719 --> 00:46:39.440
I'll give you an example.
00:46:39.519 --> 00:46:49.920
So like we're looking for a salesperson, and I interviewed somebody, and you know, um it was like I, you know, I they're in the industry, I get a paycheck, I get a bonus.
00:46:50.079 --> 00:46:51.119
What's a bonus based on?
00:46:51.199 --> 00:46:52.079
I don't know, it's just a bonus.
00:46:52.320 --> 00:46:53.440
You know, it's industry standard.
00:46:53.519 --> 00:47:01.199
Like, okay, I got a I got a car allowance and I get a cell phone allowance and I'm worth X.
00:47:01.360 --> 00:47:02.000
And that's true.
00:47:02.079 --> 00:47:04.320
That's what that person is worth in the in the market.
00:47:04.480 --> 00:47:12.159
But to me, person has no capability to use like co-work and learn how to do an agent themselves and identify their own little day-to-day life.
00:47:12.559 --> 00:47:14.960
So I like I don't want to hire someone like that.
00:47:15.119 --> 00:47:17.519
I don't care how much you know about elevators and who you know.
00:47:17.679 --> 00:47:20.239
If you're gonna be a burden internally, we've got to carry you.
00:47:20.400 --> 00:47:21.280
I'm not interested.
00:47:21.440 --> 00:47:24.320
And I think that's gonna be the case in many, many different roles.
00:47:24.480 --> 00:47:34.480
And I don't care if you're a bank clerk or uh like a purchasing agent or whatever, you need to know how to use these tools to justify your paycheck.
00:47:34.719 --> 00:47:46.159
Because when you and you mentioned that in a, you know, just that just that getting involved in the process and and how it's implemented rather than waiting or sitting back is, you know, it could be a new career path for you, right?
00:47:46.239 --> 00:47:51.679
If if you're willing to learn it and and teach others or you know, whatever the case may be.
00:47:52.000 --> 00:47:59.840
If someone came to me and said, you know, I worked on junk, but I was able to take ChatGPT and program it to do X, Y, and Z, I'm getting my attention.
00:47:59.920 --> 00:48:03.840
Like, what have you, what did you do on your own that showed innovation and resourcefulness?
00:48:04.000 --> 00:48:04.159
Right.
00:48:04.400 --> 00:48:05.360
That I love.
00:48:05.679 --> 00:48:07.920
You know, um, it's table stakes too.
00:48:08.000 --> 00:48:08.639
I really think it is.
00:48:08.800 --> 00:48:10.559
You have to know how to use these tools.
00:48:10.719 --> 00:48:17.039
When like, you know, like I, you know, I think about like my kids and kids that are coming out of school.
00:48:17.360 --> 00:48:26.960
You have to have not superpowers, but you have to have something that I don't have to invest in to get you there because there are other people that have it.
00:48:27.119 --> 00:48:30.480
So, you know, I you really, really, really do need to be concerned.
00:48:30.719 --> 00:48:42.480
And I think if you're working at a place that isn't sort of willing to invest in that in your in in in HR for your people, um, I'd be I'd be looking for other places that are more open to like making that change.
00:48:42.639 --> 00:48:46.239
I think employers have a responsibility to help people retrain for this stuff.
00:48:46.719 --> 00:48:51.280
But you know, I can't emphasize enough, like I think you really need to be concerned.
00:48:51.360 --> 00:48:52.960
And I also think it's a solvable problem.
00:48:53.039 --> 00:48:56.880
Like, even if your employer does nothing for you, just get a license, get online.
00:48:57.039 --> 00:49:04.800
There's a billion YouTube videos, a billion podcasts, start learning how to do something that makes your life faster, and you're off to the races.
00:49:05.119 --> 00:49:05.760
Cool, cool.
00:49:05.840 --> 00:49:16.400
Well, and that uh along with that question, what what human abilities do you think will become more valuable as AI takes on some of the routine work or the you know repeatable tasks?
00:49:17.599 --> 00:49:23.679
Um the ability to communicate will always be really, really important, even if you're just writing up a prompt.
00:49:23.760 --> 00:49:26.159
Like some of these systems work and they need context and stuff.
00:49:26.320 --> 00:49:30.960
So listen, you still need to be able to, I think, yeah, I think you need you need to know the industry.
00:49:31.119 --> 00:49:34.880
I think more than ever, you need to know the elevators and the elevator industry.
00:49:35.039 --> 00:49:35.199
Right.
00:49:35.360 --> 00:49:39.360
Because that's because the systems are gonna do stuff for you and the and the knowledge is free.
00:49:39.440 --> 00:49:41.840
You can ask the thing a question, it's gonna come back to you.
00:49:42.159 --> 00:49:51.519
So you need to be able to have, I think you need more institutional knowledge, like that you can apply to what these systems can do is the most important thing.
00:49:51.679 --> 00:49:56.320
But also, like, you know, you know, as a business owner and I gotta hire people.
00:49:56.480 --> 00:50:04.320
I want people that can communicate, that can, you know, verbal, written, and know how to have relationships, you know.
00:50:04.719 --> 00:50:05.039
Yep.
00:50:05.119 --> 00:50:08.639
And I'm and I'm hearing, you know, you mentioned curiosity a minute ago, communication.
00:50:08.719 --> 00:50:18.000
There's a lot of it kind of comes down to what are you willing to to tackle, to take on, to take some initiative, and you know, you'll become more valuable.
00:50:18.079 --> 00:50:22.000
But if you sit back and you kind of wait to be displaced, that's not gonna be.
00:50:22.320 --> 00:50:23.280
You will get that, will happen.
00:50:23.440 --> 00:50:25.119
I think you need to be passionate about something.
00:50:25.199 --> 00:50:26.000
I don't care what it is.
00:50:26.079 --> 00:50:30.239
If you're passionate about doing a bank wreck in five seconds, I got respect for that.
00:50:30.400 --> 00:50:30.639
Sure.
00:50:30.800 --> 00:50:33.360
You know, it does there's nothing that's too obscure.
00:50:33.519 --> 00:50:37.519
You just gotta have purpose and and a real passion for something.
00:50:37.679 --> 00:50:38.000
Yeah.
00:50:39.440 --> 00:50:39.760
Yeah.
00:50:40.000 --> 00:50:46.960
Let's let's um invite some, you know, if there's uh members of our audience that wanna ask some questions, go ahead and do that now.
00:50:47.039 --> 00:50:52.079
We'll we'll take a few minutes and and uh see if they have something to say.
00:50:52.239 --> 00:50:57.519
Um I will I will share one of the the comments that came in earlier, but we we'd kind of moved on by the time I noticed it.
00:50:57.599 --> 00:51:02.079
But the question was whether there are resources out there to help companies establish AI policies.
00:51:02.159 --> 00:51:04.000
And somebody said, yes, there are.
00:51:04.159 --> 00:51:06.559
There's you know, you don't have to implement it all yourself.
00:51:06.719 --> 00:51:10.400
There's of course there's software out there, um, other tools.
00:51:10.559 --> 00:51:13.920
So again, about being curious, I don't know if you can find it.
00:51:14.079 --> 00:51:24.480
I don't I I I don't know about, you know, like I don't know what tool is referenced or what it does, but the problem is like again, if you're on multiple pieces of software, maybe a tool like that will help.
00:51:25.039 --> 00:51:29.280
But the the the the software products that people in the industry use come with it.
00:51:29.440 --> 00:51:31.360
So you don't have to build this stuff yourself.
00:51:31.440 --> 00:51:36.559
You you you have to worry about it if you're you're bringing in consultants and they're doing this and they're doing that, then yeah, you need it.
00:51:36.639 --> 00:51:41.440
But you should be able to use a platform or a tool that it comes, it's part of the package.
00:51:41.920 --> 00:51:42.400
Gotcha.
00:51:42.559 --> 00:51:43.679
Yep, yep.
00:51:43.920 --> 00:51:51.599
Um so I'm gonna still invite guests if they're if they're out there to go ahead and throw something in the chat, we'll we'll respond to it.
00:51:51.840 --> 00:51:56.079
Um before we wrap up, we've got just a a few minutes here.
00:51:56.480 --> 00:52:04.480
And um maybe I'll move on and s and see if any hop in there.
00:52:04.639 --> 00:52:12.239
But um uh Jonathan, question if you owned an elevator company that had done very little with AI, where would you begin?
00:52:12.559 --> 00:52:13.840
Where would you even start?
00:52:15.119 --> 00:52:21.039
Um if they're on like Microsoft Office, I'd get everybody a subscription to Copilot.
00:52:21.199 --> 00:52:24.159
So you have like an Outlook and Word and Excel.
00:52:24.639 --> 00:52:24.880
That.
00:52:25.440 --> 00:52:32.480
So at least, you know, and then you know, and start using it for little things like, you know, if you if you're not good with pivot tables, this thing can help you.
00:52:32.719 --> 00:52:36.079
If you want to use more sophisticated formulas, like try that.
00:52:36.159 --> 00:52:38.000
So there's very low risk things.
00:52:38.239 --> 00:52:39.119
It's scary.
00:52:39.280 --> 00:52:45.519
Take baby steps, and it doesn't even have to be like elevator stuff or accounting stuff.
00:52:45.679 --> 00:52:54.880
Like, I don't know, like you know, like if you if you want to renovate your living room, take some pictures, build, do a project, learn how these things do stuff.
00:52:55.039 --> 00:52:55.280
Sure.
00:52:55.440 --> 00:53:02.400
And before you know it, your social feed's gonna start sending you like all kinds of prompts you can buy online to help do your living room rent out.
00:53:02.480 --> 00:53:04.159
So just use it to do something.
00:53:04.320 --> 00:53:16.480
I don't like, you know, they're they were like, yeah, they were making fun of me today at lunch that I sort of said that I was kind of using ChatGPT to help me pack for like a two-week trip I'm doing, you know, and I'm like, you know, and they were like making fun of me, but you know what?
00:53:16.559 --> 00:53:25.039
It saved me a ton of time, and I'm not gonna look stupid because I have the right like I took pictures of my clothes and I just uploaded it and it like saved me some time.
00:53:25.199 --> 00:53:31.840
So I think you know, if you're not at work, there's all kinds of examples around like exercise or shopping, nutrition.
00:53:31.920 --> 00:53:33.280
It's endless, it's endless.
00:53:33.440 --> 00:53:39.280
And then you'll become one of these people that just starts dictating to your phone your new friend Chat GPT or whatever you're using.
00:53:39.440 --> 00:53:41.280
You know, grok is pretty good too, actually.
00:53:41.360 --> 00:53:42.159
I like them all.
00:53:42.480 --> 00:53:46.960
And the next thing you know, you're turning into like some, you know, like like who are you talking to, getting it?
00:53:47.039 --> 00:53:48.400
But the stuff is amazing.
00:53:48.559 --> 00:53:53.360
I think at work, though, like just pick the most mundane, stupid thing that you hate doing.
00:53:53.440 --> 00:53:56.480
And there are ways to like sort of streamline those things.
00:53:56.559 --> 00:53:58.719
So just start small and build on that.
00:53:58.800 --> 00:54:01.519
Is like I taught, like I'm begging everyone to do that.
00:54:01.599 --> 00:54:03.199
Yeah, don't be afraid of this stuff.
00:54:03.440 --> 00:54:13.280
I've got a friend that's a business consultant and he'll he'll say something, you know, if you if you do a task more than five times a day, if it takes you more than you know 15 minutes, he's got kind of this rules that he goes through.
00:54:13.440 --> 00:54:21.039
But if you know, if if certain tasks fall under these, you know, requirements, then then it's time to try to automate it.
00:54:21.199 --> 00:54:24.719
And yeah, but you don't want to be a slave to these things, these rules either.
00:54:24.880 --> 00:54:28.239
Like maybe the ritual of doing those five things is what centers you in life.
00:54:28.400 --> 00:54:31.760
So you you gotta be you gotta be money smart about.
00:54:32.000 --> 00:54:36.480
I mean, I'm joking, you know, like it's like the people that wear these sleep trackers and are slave to like their sleep score.
00:54:36.559 --> 00:54:38.639
Like you can also let it go and just sleep.
00:54:38.880 --> 00:54:47.199
Um I think that I think that um the but there there are, you know, there's definitely if you like sort of take a step and say, what do I do all day long?
00:54:47.599 --> 00:54:49.199
You know, it it's it's really amazing.
00:54:49.280 --> 00:54:50.800
One of the things I'll give everybody a tip.
00:54:50.880 --> 00:54:57.039
Like I record meetings, I take the transcripts, and then I have like summary sheets or or Excel files that I do.
00:54:57.119 --> 00:55:03.920
I take the transcripts and I give the thing rules and it it populates like my notes for certain for like the next handoff internally.
00:55:04.079 --> 00:55:05.679
You don't need like I did that myself.
00:55:05.840 --> 00:55:07.119
I didn't need any help to do that.
00:55:07.199 --> 00:55:09.440
You can't believe how much time that's saved me.
00:55:09.519 --> 00:55:11.840
And it did a better job than me taking notes in a meeting.
00:55:11.920 --> 00:55:22.960
So there's lots of little wins if you don't have it in your software that will get you closer to like then, you know, it like it's like I said, it's that plane that's weighed down and is taking off slowly.
00:55:23.119 --> 00:55:25.039
Start lumbering down the runway.
00:55:25.199 --> 00:55:26.719
You will get off the ground.
00:55:26.960 --> 00:55:27.360
Sure.
00:55:27.760 --> 00:55:31.920
Well, and and you you kind of talked about this before, but you know, just using it as a thought partner, right?
00:55:32.000 --> 00:55:36.559
Like, what should I be thinking of if I want to pack my clothes the most efficient way, right?
00:55:36.639 --> 00:55:42.960
It can start to teach you some just one of the things I learned is you got to give it context.
00:55:43.039 --> 00:55:46.639
My suitcase is, you know, this inches wide, deep, and long.
00:55:46.880 --> 00:55:54.320
And it, you know, I get like stains on my shirts when I, you know, eat street food, and you know, I'm not having access to a washing machine.
00:55:54.400 --> 00:56:00.480
And I've got, you know, like you give it, and the point is you give it really specific context, you get a way better result.
00:56:00.800 --> 00:56:02.480
And that's a skill.
00:56:02.880 --> 00:56:13.599
So, you know, you can make a joke about it, but like you can it can get, and then you learn, okay, so I'm I'm good at like developing these like one-off things, but all these systems have project capabilities, right?
00:56:13.679 --> 00:56:21.199
So you can start uploading files and make and having it like run a whole major thing and it's got resources to look back on.
00:56:21.280 --> 00:56:23.599
And that's how you start learning how these things work.
00:56:23.840 --> 00:56:24.400
Right.
00:56:24.639 --> 00:56:24.960
Right.
00:56:25.199 --> 00:56:26.400
No, it's uh that's a good point.
00:56:26.559 --> 00:56:28.559
So a couple, a couple questions before we wrap up.
00:56:28.639 --> 00:56:36.000
Um, how can leaders include employees in the process instead of making them feel like AI is being imposed upon them?
00:56:37.039 --> 00:56:48.400
Um I think bring them in to the bring them in as early as possible, um, just so they know what's going on and they don't have this like sort of like they're not telling me I don't know if my because you worry about is my job at risk?
00:56:48.559 --> 00:56:49.840
Is he doing this to get rid of me?
00:56:50.000 --> 00:56:52.719
I think you know, just getting that fear off the table is helpful.
00:56:52.960 --> 00:56:58.400
I think also like the staff members know that like the people doing the work know what's going on.
00:56:58.559 --> 00:57:02.320
They're a great source of inspiration and ideas.
00:57:02.559 --> 00:57:08.480
And I I really, really believe you want to you want to be pretty transparent, you know, what will change for their role, what won't.
00:57:08.639 --> 00:57:15.119
And if you're planning to make a major overhaul, like give them a chance to like help with that and reposition themselves.
00:57:15.360 --> 00:57:25.599
Um, they also you also need to get a feedback loop from them because like people might be afraid to say, like, this is a waste of time, or the people you're using, like they don't really know what they're doing, or I don't trust them.
00:57:25.760 --> 00:57:25.920
Right.
00:57:26.159 --> 00:57:28.559
So I think it's about being like supportive.
00:57:28.639 --> 00:57:33.280
It's it's almost like I I do like the analogy of dieting or exercise.
00:57:33.440 --> 00:57:40.159
Like you have to educate them, you got to go slow and you got to go at their own pace, but the changes come with persistence.
00:57:40.239 --> 00:57:43.599
And if you exclude people, it just makes it that much harder.
00:57:43.840 --> 00:57:44.719
Yeah, yeah.
00:57:44.960 --> 00:57:53.679
So, last question for you what should a company have in place before allowing employees to use AI with customer or company information?
00:57:53.840 --> 00:58:01.760
I mean, talked a little bit about some of the You need to be you need someone who knows security to find out what they're doing with the stuff.
00:58:01.840 --> 00:58:04.400
Like you said, like are they just putting stuff into a public model?
00:58:04.880 --> 00:58:07.119
You know, like you need it's IT.
00:58:07.199 --> 00:58:09.599
It's this is an IT um security thing.
00:58:09.760 --> 00:58:11.920
We for our clients, we don't get involved with that stuff.
00:58:12.000 --> 00:58:14.880
We make sure they have an IT company that knows what they're doing.
00:58:14.960 --> 00:58:21.039
And so all the like licensing around the infrastructure, all the security, all the device management is an area for experts.
00:58:21.119 --> 00:58:22.159
So start with that.
00:58:22.320 --> 00:58:32.800
So you want to have like, you know, your customer records, wherever they are, someone needs to know like who can get at what and like you know, what happens where and what doesn't happen, you know.
00:58:32.960 --> 00:58:34.880
Like like someone needs to own security.
00:58:34.960 --> 00:58:39.519
And it's I believe it's typically the IT department and also the accounting department.
00:58:39.599 --> 00:58:41.199
Yeah, because that's financial stuff.
00:58:41.280 --> 00:58:42.880
You know, that's kind of where I would start.
00:58:42.960 --> 00:58:46.000
And then also it depends on what software you're running to run the business.
00:58:46.159 --> 00:58:49.440
They can be an asset or they can be a huge liability depending on what's going on.
00:58:49.599 --> 00:58:50.480
Yeah, absolutely.
00:58:50.719 --> 00:58:53.519
Well, Jonathan, we are out of time, but I appreciate you being with me.
00:58:55.840 --> 00:58:56.960
It's been a lot of fun.
00:58:57.119 --> 00:58:57.840
I appreciate it.
00:58:57.920 --> 00:59:02.159
And and there's a lot we didn't get to cover, so maybe we'll do this again sometime.
00:59:02.480 --> 00:59:05.440
I'm all you got to do is send me an email, dude.
00:59:06.159 --> 00:59:06.800
Sounds great.
00:59:06.960 --> 00:59:07.840
Well, thank you again.
00:59:08.000 --> 00:59:12.159
And uh until next time, we'll uh yeah, we'll sign off.
00:59:12.400 --> 00:59:12.639
Okay.
00:59:12.800 --> 00:59:13.360
Thanks, Matt.
00:59:13.519 --> 00:59:15.599
Appreciate you you give us this opportunity.
00:59:15.920 --> 00:59:16.559
You bet.