Bill Canady: Hello, everyone. It's great to be with you today. Today, we are going to be talking about how companies treat technology, especially AI, as a shortcut to growth, efficiency, and higher valuation. We've got Chris, Mariska. He's gonna explain why technology cannot repair a weak operating model, what private equity investors routinely miss during diligence, and how leaders can separate genuine capabilities from expensive technology stories. He's a Silicon Valley veteran, entrepreneur, technology strategist, and advisor to executive team and investors. He started and guided businesses across several generations of technology and has helped more than 80 organizations, from venture-backed startups to some of the world's largest companies. He's going to help us navigate growth, transformation, and turnaround situations. Today, he is often brought in into private equity environments where the technology story begins to fall apart under scrutiny. Chris, welcome to the show.
Chris Maresca: thanks for having me on.
Bill Canady: I tell you, it's exciting times we had a long time in the in the green room, if you will, talking about what we want to talk about. Man, this is gonna be an exciting one today. One of the things that stuck out to me was you describe yourself as a technology pathfinder. What does that mean in practice?
Chris Maresca: Yeah, that's right. I mean, I think a technology is interesting because it's always changing and the needs of organizations are often changing at the same time. The problem is that in most organizations, the leadership is not necessarily you know, deep technology experts. And so there's trouble understanding what what the right incantation of technology, how it's going to help with companies, you know, what what vendors, how to be choosing vendors, and so on and so forth. And on top of that, you're getting a lot of right now, a lot of propaganda about AI and everything else, and a lot of vendors coming along and saying we can make everything perfect. And that doesn't help anything because at the end of the day, nothing will fix a company that fundamentally has broken processes or incomplete anything and and this is part of the problem. I mean if you look at most companies, you know, a lot of companies can have a pretty well defined process for how they're doing things. But fundamentally companies all up companies operate in the same way. At one end things are coming in, whether it's information or resources or raw materials, and they process that. And then out the other end comes thing something of higher value that they sell. Whether it's a service, it's software, it's a piece of consumer goods, it's machinery, whatever it is. and those processes that transform the raw materials into something of value is effectively the valuation of the company. And you get companies that have pretty well-defined processes, but what you often find is between the well-defined processes there are undefined areas. And those undefined areas are filled by skilled humans. And skilled humans by definition, have all of the things that they need to do in their head. And so when you start introducing technology, getting that knowledge out of the skilled humans and into the technology is quite difficult to do. It's especially visible in the trades where you know getting, you know, decades of knowledge out of a skilled tradesman into some automated piece of equipment has proven to be nearly impossible. And so That problem replicates itself across organizations, you know, top to bottom, you know, from one side of the organization to the other. And and then people start throwing in tech, whether it's AI or more traditional forms of technology. sometimes you can increase throughput at a point in time, but it doesn't help if the other bits are not keeping up. So
Bill Canady: You know, I at this point in your career, you've seen so much and you've lived through several technology revolutions. What has that experience taught you about distinguishing between a genuine transformation, which you were just talking about, from more of a fashionable narrative? Like, I think AI is here to stay, but what do you think?
Chris Maresca: Well, I think AI's here to stay, but one of the problems with AI is that in order to really make it effective, you need to be an AI first organization. And for most organizations that actually requires ripping apart the way they work right now and basically starting over again. and that's you know, you can't It's hard to build an airplane when you're flying it. And that's kind of what you're what you're trying to do. And it becomes very, very difficult. And when people talk about disruption, they talk about this sort of inflection point where, you know, the the companies that are able to do this are going to be the ones that survive, and the other ones are just going to get replaced. I think, you know, I was having a discussion the other day about CRM systems, and I pointed out that really CRM systems are something from you know, last decade. And modern in the modern world, if you were starting from scratch, you'd never buy a CRM system because a CRM system casts your sales process or your customer service process into a a set workflow that's been set up by the vendor or that is sort of the industry standard or whatever it is, right? You have your sales funnel and you have, you know, you're you're trying to figure out what stage a customer is in and so on and so forth. That's not the way you know AI-driven systems work. They're basically individual intelligent things that are feeding data to another thing. And so if you think about that, another thing that's going to do something with it. And if you think about that, like how does that change your view of this sort of customer funnel? You know, all of a sudden you can you can use signaling data, like has somebody changed a job recently? And but you but you can do this on a 24-7 basis. So you could have AI agents that go and and get data about a prospect or or an inbound or whatever it is, or even set up entire marketing p campaigns to target specific market segments and then manage the entire process of sales up until you know you get a human in the loop who comes in to finally close the final details of the deal. And so when you think about that, like what is your CRM system now? Actually, what you do is you're building a set of custom tools. To meet your specific process, not some preordained way of doing things that's been set up by a vendor. And so that kind of trend, and that's just one segment of an organization. If you look across the entire organization, then you you have thousands of inflection points where you where you you need to be thinking about this. And that's that's honestly is extremely hard to do because you're you're flipping the idea of how you do things upside down, right? It it's more analogous to hiring a w a very specialized workforce than it is to using a technology. And and I think that that's the fundamental difference. I joked with somebody the other day. I have a friend and she's building some software and she has 10 AI agents that are doing various tasks for her. And they are always fighting with each other about you know, who's responsible for what bit of tasks. I mean, this is the AI agents are fighting with each other. And so I said, I said, you're gonna need HR for AI, which was pretty funny because, you know, and and it's kinda like that. And if you think about that, it sounds nuts. But of course, these things are are pretty intelligent. I mean, they're very stupid at the same time, but they're pretty intelligent. And so and so th they're very capable of doing things. I think And this is the revolution, if you will. It's it's kind of like I always joke that computers have been promising us, you know, more ease of life for you know 50 years. And they've only made our lives more complicated, as far as I can tell. And I think AI has finally reached the point where the computers can kind of sort of understand us, and that is going to make our life easier finally. That's finally the promise is being achieved. You know, after fifty
Bill Canady: Yeah.
Chris Maresca: years, we've finally gotten to the point where these things are doing what they claim they're gonna do, kind of. as long as they don't hallucinate. But yeah, I think I think I think you know there is a revolution here. But again, if you if your if your internal processes are undefined, if there are holes in your processes that are being filled by humans, if you don't understand where the value is in your company, I I I have to tell you, I'm always shocked. I go to a company. And this doesn't matter the size of company. It could be two people, it could be a hundred thousand people. And and you go to a company and you say, What do you do? I mean, what do you do that makes a difference for your customers? And you know, there's some trite marketing answer. But I'm like, no, no, what do you actually do? And the number of people that can't answer that, where you ask people, you'll get you'll get a different answer every single time, right? So a lot of times companies don't even know what they're doing. And so how are they gonna define a process that takes that raw input and outputs value if the people in the company don't even understand what the company is doing? It's not it's not rocket science to know that if you can't define it, then you can't possibly automate this thing. So yeah, and that that's the way I feel. And so we talked about, you know, when when you look at private equity and you look at people buying or you know, organizations of various kinds. These problems are all over the place. They're
Bill Canady: Mm-hmm.
Chris Maresca: all over the place because companies are built by humans, and humans are imperfect. They they have they like to build fiefdoms, they want to claim credit for everything or whatever. All these human traits that are kind of corrosive exist in companies, and every single one of those things creates ancillary problems for trying to understand how value is delivered. And so if you take that and you say, we're just gonna apply some technology magic. IBM back in the day used to have these commercials about the magic pixie dust that could be sprinkled on things to make a company work better. And of course, everybody knows that doesn't work. And so and that exists all over the place. Of course, with AI, it's even easier to claim these kinds of things. And the question is, how do you prove out that this is actually working? Was it just a cynical exercise to get rid of the people in the company who actually know how things are working and reduce your costs before sale? Who knows? Right.
Bill Canady: bring up you bring up a lot of great points. You know, we're both in private equity, and the ultimate outcome for private equity is the company is going to be sold, right? People,
Chris Maresca: Mm-hmm.
Bill Canady: you know, it's just people pool their resources, make a bet on a company, and looking to get back two, three, four dollars for every dollar they put in.
Chris Maresca: Mm-hmm.
Bill Canady: And when you're getting ready to sell that, an investor comes in, and I've been on both sides of that table. And I'll hear just what you just said. Our technology is proprietary and highly scalable. So we're going to use the CRM. I don't need HubSpot or Salesforce. Man, that stuff's a thousand bucks a seat per month in some cases, right? Depending
Chris Maresca: Mm-hmm.
Bill Canady: on what you get to it. what questions should we be asking? So it's just not so someone's got 10 AI agents that won't actually talk to each other, fighting with each other and circling. What should we be asking to know that this is scalable? How do you know? what does that look and feel like?
Chris Maresca: Yeah, I mean the long and the short of it is that there's no single question you can ask that's gonna answer that question. I mean, unfortunately, there's no magic bullet here. I think there there's some key things, you know. Is there is is the data all centralized and normalized in a single database? Because AI works on data. It doesn't work without data. It can't work in a vacuum. Right. Yeah.
Bill Canady: yeah, garbage in, garbage out. The old gooey, right? G yeah.
Chris Maresca: But most companies have, you know. 10 different systems in the company, each one of which is responsible for a different area, functional area of the business. And oftentimes that data is spread across all these different systems. So it's all fine and good to say, we have AI and blah, blah, blah. But you know, is all that data centralized in a single place? Is it normalized? So is the data that it it's pulling from the accounting system the same as the data that marketing is using to drive marketing campaigns? Is the are the are the the you know, the the the metrics measured in the same fashion. Because if they aren't, then you have to make sense of this. The AI has to make sense of this, right?
Bill Canady: Mm-hmm.
Chris Maresca: And so you can't just say we're gonna throw some AI. It works AI works pretty well at sort of, you know, a po as a point solution, for example. You know, you know, it can it can clean up a lot of stuff. It can pull a lot of conclusions or or or Or some kind of meaning out of abstract data. it's quite good at these things, but if it if it if it's gonna work together, it needs a common data store, which is normalized. It needs references to standard operating procedures, it needs an orchestration layer so that you orchestrate all of the bits of AI that you have working. You know, it needs all these things. You can think of it as literally the AI needs its own company organization. Right? It it it it is, if you think about it, a friend of mine describes it as a bunch of brains. If you think about it as a bunch of brains, they need to work together. For that, they need to be organized, they need to know who gets what data, they need to understand what the standard operating preceders are, they need to understand what your brand identity is. You know, these things all coexist together. There's no easy way around this. If you don't have all of this, what you have is a kinda AI. You know, and and that's what I was saying earlier, when you talk about sort of AI native organizations, they are org organized around the idea that you have this all these agents that are acting as a workforce. And
Bill Canady: Mm-hmm.
Chris Maresca: most companies are implementing it as if it's technology, which is kind of a critical difference. You wouldn't you wouldn't tell a human that describe a human as technology, as a technology you've implemented. Right. And a human is a technology, effectively. I mean, you know, biological biological. Yeah. Well, but also there's
Bill Canady: Yeah, I mean we talk about their assets for the company. They just happen to walk out the door every day, right? Versus
Chris Maresca: a different way that you the you engage with a a human employee versus a a desktop computer, right? And and the problem with AI is people treat it as if it's another kind of Microsoft Windows or whatever, or Excel spreadsheet, or whatever. And that's not really a great way to think about it because you're actually reducing the capabilities to a one-dimensionalness. and I think AI is much more like your coworker than it is you know, like like another piece of software. And you have to engage with it like that. I mean, not just as at you know, interactively. We've all done that with the chat bots of various kinds. We've all we've all done that. but also in the way you implement it. that said, you know, AI is not a magic bullet. I mean, it's it's it's another tool in the toolbox you can use to solve problems. and I think as as as we've seen in a lot of areas, you know, There are issues with people finding enough qualified people to work in in various industries and maybe AI is one of the solutions to that worker shortage.
Bill Canady: good. You know, you bring up a really good point. What are they, what are you going to be looking at to say that this is either, hey, this is real and this is a game changer, how they're deploying it and it's defensible, or it's just theater. They're just dressing themselves up. How do you tell the difference? And how does a business owner get real value? Because this stuff is not cheap.
Chris Maresca: You have a limited amount of time to judge what's going on in a company and a limited amount of data. I think I think there are key things you can do as a business owner. Like, do you do you understand con key concepts like orchestration? And do you have you know a can you express how orchestration is changing the way your company is working with AI, right? Like you're orchestrating a bunch of AI workers in the same way that you'd have a management structure to manage people. Orchestration is that management structure of your AI tools. And do you have centralized data, which is all normalized so all of the AI can judge that? Do you have standard operating procedures that are documented? Do you have these things ready to go? When you put together your VDR, can you put all that stuff in your VDR? and and show that, you know, this is real. This isn't just. vaporware. you know, I think that that's important. You can also show metrics like, you know, you've been able to process N number of whatever it is faster, you know, quadruple your production without increasing your headcount, for example. There's metrics you can use to show that this is really optimal, or, you know, higher close rates for sales or whatever it is, right? There's a lot of metrics that show the you know the the time. you know, from when you implemented this thing to now, like how has it changed the fundamental KPIs in your organization? but those only tell part of the story. I mean, the other question is, you know, talk to the people on the ground. When I did a lot of consulting, I used to go, you know, several hours before my meetings, I would go and stand with all the smokers. And I would ask, I would ex introduce myself, I would tell them who I was and what I was doing there and who I was meeting with. And I would I would ask them questions about what they were doing. Like, you know, these are the people at the coal face. Do do they what do they say? I mean, that's always been my trick because I don't want to talk to the executives because nine times out of ten, they actually don't know anything. they just have, you know, a line to tell you about what they're doing. But if you talk to the people, you know, in the factory, you know, at the loading dock or whatever, you know, in the cafeteria, then those people can tell you what's actually going. And I find that much more useful. If so, if you're in PE, that's what you need to do. You need to actually go and talk to the employees, either, you know, through a conference call or whatever, or or in person, and understand like where are they? What where is the company? They will tell you if if if AI is being implemented, they will tell you how they feel about it. I guarantee you. It's inevitable and whether it's good or bad. If it's good.
Bill Canady: Yeah, it's for sure the object of conversations a lot. Yeah,
Chris Maresca: Yeah.
Bill Canady: you're you're you're I mean you're really nailing it there, right? Like get to the people on the floor who are who are being directly affected and implementing it, have your KPIs you're looking at. When you look and you're going through your your technology diligence on these companies, what what are some of the more conventional processes that people are constantly overlooking that says this is real for this company and it's making a difference?
Chris Maresca: Hiring. Sounds crazy.
Bill Canady: The higher the people.
Chris Maresca: Sounds crazy. But you know, even in today, companies are built on people, right? And and finding the absolute best people is very, very non trivial. I mean, in in my last company, that was one of the first things I did when I joined, is I set up a hiring pipeline. And I set up a a methodology for hiring employees. This was even before I needed to. I set this all up. I set up interview templates, I set up a you know, a a specific pipeline for finding people, I set up tools so that we could go out and we could identify people that we were interested in hiring, even before we contacted them. Right. And and I think you need to do that. There's, you know. Unfortunately, there's there's no loyalty in companies to employees, but it goes the other way around as well. So because because of that, people leave their jobs constantly. Every couple of years, it seems people seem to move around. That's pretty common. And so you have to anticipate that. And when you lose somebody, it's you're losing all the the knowledge that they have is walking out the door. No matter how hard you've tried to document that it it it is leaving. And it doesn't matter if they're a junior or then the most senior person in the company. In fact, a junior walking out the door is probably worse than an executive. And so
Bill Canady: Mm-hmm.
Chris Maresca: because they have a tendency to be doing all kinds of things that you don't even know and that are undocumented. And so I think that having a really strong hiring pipeline is really important, having standards and and sort of a a process for doing that. And for me it Really came to the fore when our lead software developer left. He left because somebody offered him his dream job, which you can't stop somebody from leaving in that circumstance. He wasn't unhappy with us, it was just he somebody offered him his dream job. And one of my staff said to me, I would be really worried, but I saw what happened the last time somebody left. And because I'd set up a hiring pipeline, they knew it was no problem. We went from the day he left to hiring somebody in three weeks.
Bill Canady: Wow, that's unheard of. That
Chris Maresca: Right.
Bill Canady: is amazing.
Chris Maresca: she was she was she was brilliant, still one of the one of the best software engineers I've ever worked with. And it was completely random because we hired she did not have the skills we were looking for, by the way. But she had the personality that we needed. Like so we hired
Bill Canady: think I've heard other people make the statement, I can teach you the skills, it's the fit I'm looking for, the capability, right? You can every company does
Chris Maresca: Yeah. She was and the
Bill Canady: it differently.
Chris Maresca: Yeah. And the fundamental thing was she was curious. She was curious and she was engaged. And so she was she was willing to to learn and do whatever it took to make things happen. It was the kind of person you can throw in at the deep end and said say swim and they would be just fine. You know? And and and you know, and she was twenty five.
Bill Canady: Amazing.
Chris Maresca: Yeah, and so and our senior developer was, you know, sh that's who she was replacing. And she was no way a
Bill Canady: Mm-hmm. Interesting.
Chris Maresca: senior, right? I mean, I mean, she doesn't have didn't have the experience. But you know, again, that your hiring process is so important. It gets even worse with AI, right? Because now you've got other tools that can come into the mix, but also you've got a smaller number of people, presumably. So each person counts even more. so so yeah, I look at that because I think that tells me a lot about an organization. Like how much do you care? People pay lip service. people are a most important asset. You know, you could hear that a lot. I mean, come on, what a line. It's just I mean Yeah,
Bill Canady: We're all family. you fire your family? I'd like to fire sometimes, but
Chris Maresca: right. but but I think I think, you know, it it translates through that sort of thing. I heard something ridiculous the other day, which was actually this morning, which was a company that couldn't find any candidates for a particular position, so the hiring manager submitted his own resume underneath a a false name and then got rejected within five minutes.
Bill Canady: That's great line.
Chris Maresca: And and and the what they found was that the HR team had put in there as a requirement that you needed to know this particular software development stack called React. except that they had specif specified React dot JS, which is the formal name, and on his resume it just said React. And for whatever reason their hiring system was too stupid to understand that they were one and the same thing. And so they were losing a lot of candidates due to stupidity.
Bill Canady: Amazing. Switching gears a little bit, you've argued
Chris Maresca: So yeah.
Bill Canady: AI cannot take an unproductive company and make it productive. Why not? If it's this powerful, why can't it fix it all?
Chris Maresca: Well, because AI's like a toddler. It needs to be taught everything that needs to be done. So, you know, you need a couple of different things. It needs it needs training, typically, on the workflow and the process that's going to or, you know, a a defined list of standard operating procedure procedures. It needs a set of data to work on. AI works on data. And typically If your company is unproductive, then chances are you don't have either one of those two things, right? You don't have a standard set of operating procedures. Or they're incomplete, or they're wrong, right? Or you know, or they're just lip service. yeah, we have SOPs, you know, whatever. Or, you know, however you want to define, it doesn't necessarily be up standard operating procedures, but whatever, however you define it, whatever terminology you use. and so You can imagine you you you point an AI at something which is incomplete with a lack of data or you know with no training, what is it gonna do? Well, it's just gonna make things up. That's not gonna help. It's never gonna the thing about AI is it will not it will not say, I can't do this. It's like a human. A human won't say, I can't do well, they may, but you know, a lot of humans will just try to do it and they'll do it wrong. And then you wonder why they're doing it wrong. Well, AI is no different. Same deal, you know? same thing, right? So you you don't you can't train them, you you don't have operating procedures, you don't have data for them to work on. What do you think the result is going to be?
Bill Canady: Yeah, it'll make it up, yeah. You know, tr
Chris Maresca: It'll make it up.
Bill Canady: training is so important and getting a getting the data eventually. So the data silos are frequently blamed as we're just doing for AI failures. Are they
Chris Maresca: Mm-hmm. Mm-hmm.
Bill Canady: primarily a technical problem or are they evidence more of a deeper organizational problem?
Chris Maresca: yes and no. So the data silos exist because organizations are functional silos. So if you think about most organizations, they have these sort of vertical organizational areas, you know, usually under some kind of executive leadership, a VP or whatever. You know, marketing, finance, sales, IT, HR, whatever, product, you know. production, if they're doing production, whatever it is. they're their own little fiefdoms, right? With their own sets of software, which don't exist in in cohesion with anything else often. You know, that the cohesion exists at the executive level, right? That's where all the aggregation happens, which is incredibly stupid because of course marketing and sales need to talk to each other, but so does finance, because sales and finance are tied at the hip, right? And so you you have this sort of disjoint because humans being humans, you have this disjointed, you know, tribal nature of of corporate organization. It's a problem. My view is I I I like to take a holistic view of things because each one of these things is pushing and pulling in a certain direction, and everything needs to pull in the same direction for a company to be successful. And oftentimes they're at odds with each other, not on purpose, but just over time. You know, yeah, the the accounting system doesn't talk to the e-commerce system and we have to buy this expensive translator to import all this stuff or whatever it is, you know? And we've all heard those stories. And of course, the result of that is you get those data silos, right? Is it solvable? Sure. And it winds up costing a lot of money because now you have to extract the data out of all these systems. put it into a single data store, and then you have to make that normalized. In other words, all the data's got to be relatable to all the other data. And then and then you have to, then you can point your AI at it and hopefully you can make heads or tails of it. But of course now, training, standard operating procedures, all that stuff, how do you apply it now to this big data store? Well, you you you need to process well, so the first thing is you need to process the data in situ if you can, which is very difficult to do because we just talk about all the data silos with different data formats and all kinds of mess there. you need to process the data in situ, but you need to also process the data in streaming in a streaming basis. So instead of saying, you know, I'm I'm trying to think of a really good example, but effectively, instead of building a report at the end of the week, you need the report to be constructed throughout the week as things are happening. Right. And so
Bill Canady: Mm-hmm.
Chris Maresca: now you don't need this giant data store of stuff. You can just have the report be generated as the data that's that it's based on is moving through the system. but that re again, this is a different mindset. This is not the mindset we all grew up with, effectively.
Bill Canady: Yeah.
Chris Maresca: And and that's that's a hard shift, right? I mean, in your own companies you probably see this. Like trying to shift from one mindset to another is a nightmare.
Bill Canady: For all the reasons that we know, people don't like change, there's comfort, they gotta go learn new stuff, they're already busy, they're fully occupied. They may be occupied doing the wrong things. It's just they, you know, it's now I gotta do two jobs. And there's also it creates a sense of of fear or they don't feel safe because maybe this robot's gonna replace me When you look at middle market companies today, that you're through the lens of what you're doing. What do you see companies currently spending money on with AI that that really has no defensible return? There's a lot being wasted today.
Chris Maresca: I think my biggest worry with most companies these days is that they're sending all of their data to the open AIs and the clouds of the world.
Bill Canady: Notebook LM, which is a Gemini product, because you
Chris Maresca: Yeah. Yeah.
Bill Canady: can ring fence it. So if I've got something and they can take massive amounts of data. So if I'm looking at something, I can dump it in and with a second they can go through a spreadsheet. and because you can keep it off the internet and everything else, I actually think it's a great product for for safety.
Chris Maresca: Yes. Yeah, I do too. And but there's a lot of people that are using it, especially in sort of non tech industries. there's a lot of people that are just using all this stuff, even you know, at the middle management level that, you know, they're they're kind of unaware and they've they've got they're overloaded with work and they're turning to these tools to make their lives easier, which is completely sensible, but it's kind of an uncontrolled leak, if you will, often. and so I think I think that the I think that that's an existential risk to these middle market companies because especially if you're a kind of a SaaS software company, the AI tools are learning about how you do stuff, maybe not explicitly but implicitly. And then you will get your customers replicating your software using AI tools internally. I've already seen this happen once, you know, where a healthcare company replaced a revenue cycle management tool that they were that was costing them a hundred and twenty thousand dollars a month. And in three months they replaced that specific tool. You know, and and so and I think that's gonna be more and more common. and so that's a risk. If you're a middle market software company of some sort, then you have that risk. You know, and and then and that that that leakage problem exists, you know, writ large. I think the big trend is going to be more local AI. And by the way, I think Apple's gonna be the big winner there. They already have been. So if you if you if you want to run local AI, you typically will buy an NVIDIA graphics processing unit, a GPU. And and and the card, the AI can use all of that memory. Now it can't really use any of the memory on in your computer if you're using an Intel machine, because they're separate. The memory is separate, right? The Macs, Apple has engineered their chips and their hardware so that the system memory and the GPU memory are one and the same. And so you can buy a Mac Mini with like you know 64 gigs of RAM and all of that memory can be used for AI.
Bill Canady: Mm.
Chris Maresca: And and the performance is like 90% of an NVIDIA GPU.
Bill Canady: For a fraction of the cost.
Chris Maresca: Apple's created an architecture which is absolutely tailor-fit for AI. And they did this before AI. I don't think they even realized it, but people have realized that and they're like, my God, I can run this thing on my desktop, you know, literally in but you know, the point is you can use a system like that for local AI, which will rival whatever you're getting from Claude and so on and so forth, right? And and then you won't leak any data. view. I I and so I think if you look at the middle market, you know i there are companies that are gonna be disrupted by AI. They're gonna come be companies that are not gonna be disrupted by AI. I I'm a partner in a PE firm and we buy lower middle market companies. most of the companies we're buying have nothing to do with tech. We have a We have twelve in the pipeline right now. healthcare, food distribution, metalworking. You know, nothing to do with with AI.
Bill Canady: That's the same type we buy. We have a good one of our other groups does technology, but man, that's hard. We're industrial.
Chris Maresca: That's hard.
Bill Canady: I I'm all my stuff's industrial. I buy widgets, things made out of hunks of metal, you know, and all that. So yeah, yeah. That's what, but that's
Chris Maresca: Well that's your that's your business, so yeah, I'm not surprised. Yeah.
Bill Canady: you know, look, I want to own businesses that other people need to run their business with. So it like, like
Chris Maresca: Right. Yes.
Bill Canady: it, like, and I want it to be like like you're making a car. I want to help you paint that. Right. And if my stuff goes down, you can't make your car. And and guess what? There's a huge market for cars. So so imagine you acquired a company tomorrow in your PE firm. What should happen during the first thirty days that you're going to go through with your technology assessment, amongst other things?
Chris Maresca: Well, the I mean the the th the thing is that and I'm sure you've seen this, most of the companies that we acquire are small enough that they're technology light. And so the first thing to do is to look at what technology we can easily and immediately implement in order to bring them up to speed, so to speak. I think the the first benchmark for us is because you know, our goal is to buy somewhere between three and five thousand companies.
Bill Canady: You talked about that. That's the most I've ever heard of anyone doing it. It's got a really novel model you've got. I you're gonna ri we're off just remember us little
Chris Maresca: I I know. Yeah. Yeah, it's exact there's a company in
Bill Canady: people.
Chris Maresca: there's a company in Canada called Constellation Software, which you might have heard of, which has bought fifteen hundred software companies. And so they're they're I I I know some people there and we talk a lot about it, but and one of the things that they do, which we've replicated is they the the key output from their from those companies for for the mothership is a standardized PL. With standardized KPIs. And so that for us in the first 30 days, that's really the goal is to get that in place. Right. So then we can understand what's going on with the organization and and make whatever changes we need. But we have a little bit of a different model because we have 50 operating partners that work in 15 countries and they are responsible for their pool of local companies. we don't at the at the level at the partner level we don't really look at that too much. We look at the roll-up of what's going on in each regio. so yeah it's an interesting challenge.
Bill Canady: Okay,
Chris Maresca: Fire away.
Bill Canady: what's the most overrated technology trend today?
Chris Maresca: I mean probably AI, honestly. And the reason is because and it's a very simple reason. Silicon Valley, and I you know, I gotta remember I I spent thirteen years and I spent thirty years in Silicon Valley and did fourteen startups, including seven exits and an IPO. Silicon Valley has an operating model that most of the rest of the world doesn't really understand. And the operating model is to invest enough money to grow explosively. And then have a lot of propaganda explaining how great all this is. And then to try and get out from that before it explodes. And and and that's kind of what's going on with AI right now. AI works writ large. It does work and it does do a lot of cool things, and it is pretty smart and can consume a lot of data quickly. I mean, I use it for 300 page CDDs and trying to figure out what the hell they say. but There are like every tech, there's limitations and and you have to be aware of that. And like every tech, it's not free. It's not free both in dollars, but it's also not free in time. And so, and time's usually the bigger component than dollars. So yeah.
Bill Canady: What's a metric you distrust?
Chris Maresca: I kind of distrust all metrics because I think that they're they're they're easily manipulable. I I don't I understand why people do it because it's very reductionist. but there's a guy named Rory Sutherland who who used to be the chairman of Ogilvy who who has this thing he talks about called the doorman fallacy. And doorman fallacy is basically like you have a doorman in a hotel and you get rid of him in favor of an automatic. Door opener and it immediately saves you 80 grand a year or whatever. And and then, you know, what happens is that nobody's unloading people's luggage, the the recurring guests are not greeted by anybody, homeless people start sleeping in your doorway, nobody's hailing cabs, and pretty soon your your booking rates are down 30%, and you're wondering why. And I think a lot of metrics are the doorman fallacy, right? They're measuring something. that is ostensibly tangible, but there's a lot of humanist bits that get lost in the KPIs or the metrics or whatever. I'll tell you what I do believe in. I do believe in financials. I always talk about how the secrets are buried in the financials. If you want to know what's going on with the company, go look at the financials. Whatever.
Bill Canady: True.
Chris Maresca: It it it doesn't matter whatever else, it's gonna be in there. You know, hopefully in a PL or something like that that you can actually read cogently rather than You know, some crazy chart of accounts you can never understand. But
Bill Canady: Just gotta figure out all the ad backs and the what really is the
Chris Maresca: Right. I mean
Bill Canady: S D E, so
Chris Maresca: well the and the thing is that AI is really good at unraveling all that stuff nowadays.
Bill Canady: Yeah, then.
Chris Maresca: but and and and that's the real data, right? Assuming people aren't cooking the books, that's really where the bug stops, right? So Well, yeah.
Bill Canady: Yeah. Well you have ways of dealing with that, doing Q of E so.
Chris Maresca: But you know, you know what I mean. I mean it it is Yeah, so
Bill Canady: I know, I know people, people, they do a lot of theater. I don't want to say they lie, cheat, and steal. I just think they they try to put a spin on it sometimes. All right, final question. All
Chris Maresca: Well and go ahead.
Bill Canady: right. What failure did you have that taught you something indispensable?
Chris Maresca: I got a good one actually. I have a a a good spectacular one. So and I could talk about this now because it's no longer an NDA, but many, many eons ago I ran a consulting firm and we were hired by Adobe to figure out what to do with Flash on mobile. And and this was before the iPhone was released, so you can imagine how long ago that was. And And I I had a pretty decent idea of what they should be doing based on some experience I experiences I had with Flash running CNC machines, believe it or not. Because you I know you're an industrial guy, you're gonna go, what? Yeah. and and so I I had I had come up with a strategy, and this is me and my team, and you know, I had talked to all the C level people and everything else. And I'd all got we'd all gotten agreement and everybody was aligned and everything and they all thought it was a great idea and so on. And then I went to an executive meeting, which was mostly all the C level people, not the CEO, but pretty much everybody else. And it it was a spectacular disaster. Like, you know, like it was a complete failure, like they hated it. And everyone was like, how did this happen, you know? And what I realized was there was a a performative aspect to this that I didn't understand because not only were all the C level people in the room, but all their assistants were there too. And I'm I'm not entirely sure of the dynamic, but there was a performance that needed to be done about this. that I kind of was unaware of, a political dimension that I didn't understand. And even though every every one of those people had agreed in advance that this was a great strategy, when it came to sort of agreeing in public, it was a different story. And
Bill Canady: Mm-hmm.
Chris Maresca: and that was that was a really valuable lesson. And now I'm much more careful about that sort of thing.
Bill Canady: thank you for sharing that with us. So, Chris,
Chris Maresca: Yeah.
Bill Canady: thank you today for helping us look beyond the technology narrative and focus on operating reality underneath it. You really brought some great insights to, I think, you know, people who run businesses, whether you're buying them or selling them or just, you know, getting them going. I I think this this brings a lot. AI and technology in general is is for the future. It allows us to multiply ourselves and and you're right in the heart of it. Though, but the central lesson, I think, is clear. It can amplify a strong business, but it cannot rescue an organization that has not confronted, you know, its priorities, processes, and data. If they're just doing it for for performative, if they're just making it seed or it shows once you dig in on it. So where should listeners go to learn more about you and your work?
Chris Maresca: so they can visit my consulting company's website, c thirty two dot co. That's c three two dot co. And they can reach me at ckm at c thirty two dot co. and yeah, if you if you need some help trying to figure this out, just reach out. Happy to help.
Bill Canady: All right. Well, thank you so much for joining us. This has been the Thousand Day CEO. I'm Bill Canady See you all soon.
Chris Maresca: thank you. Thanks for having me.
Chris Maresca: thanks for having me on.
Bill Canady: I tell you, it's exciting times we had a long time in the in the green room, if you will, talking about what we want to talk about. Man, this is gonna be an exciting one today. One of the things that stuck out to me was you describe yourself as a technology pathfinder. What does that mean in practice?
Chris Maresca: Yeah, that's right. I mean, I think a technology is interesting because it's always changing and the needs of organizations are often changing at the same time. The problem is that in most organizations, the leadership is not necessarily you know, deep technology experts. And so there's trouble understanding what what the right incantation of technology, how it's going to help with companies, you know, what what vendors, how to be choosing vendors, and so on and so forth. And on top of that, you're getting a lot of right now, a lot of propaganda about AI and everything else, and a lot of vendors coming along and saying we can make everything perfect. And that doesn't help anything because at the end of the day, nothing will fix a company that fundamentally has broken processes or incomplete anything and and this is part of the problem. I mean if you look at most companies, you know, a lot of companies can have a pretty well defined process for how they're doing things. But fundamentally companies all up companies operate in the same way. At one end things are coming in, whether it's information or resources or raw materials, and they process that. And then out the other end comes thing something of higher value that they sell. Whether it's a service, it's software, it's a piece of consumer goods, it's machinery, whatever it is. and those processes that transform the raw materials into something of value is effectively the valuation of the company. And you get companies that have pretty well-defined processes, but what you often find is between the well-defined processes there are undefined areas. And those undefined areas are filled by skilled humans. And skilled humans by definition, have all of the things that they need to do in their head. And so when you start introducing technology, getting that knowledge out of the skilled humans and into the technology is quite difficult to do. It's especially visible in the trades where you know getting, you know, decades of knowledge out of a skilled tradesman into some automated piece of equipment has proven to be nearly impossible. And so That problem replicates itself across organizations, you know, top to bottom, you know, from one side of the organization to the other. And and then people start throwing in tech, whether it's AI or more traditional forms of technology. sometimes you can increase throughput at a point in time, but it doesn't help if the other bits are not keeping up. So
Bill Canady: You know, I at this point in your career, you've seen so much and you've lived through several technology revolutions. What has that experience taught you about distinguishing between a genuine transformation, which you were just talking about, from more of a fashionable narrative? Like, I think AI is here to stay, but what do you think?
Chris Maresca: Well, I think AI's here to stay, but one of the problems with AI is that in order to really make it effective, you need to be an AI first organization. And for most organizations that actually requires ripping apart the way they work right now and basically starting over again. and that's you know, you can't It's hard to build an airplane when you're flying it. And that's kind of what you're what you're trying to do. And it becomes very, very difficult. And when people talk about disruption, they talk about this sort of inflection point where, you know, the the companies that are able to do this are going to be the ones that survive, and the other ones are just going to get replaced. I think, you know, I was having a discussion the other day about CRM systems, and I pointed out that really CRM systems are something from you know, last decade. And modern in the modern world, if you were starting from scratch, you'd never buy a CRM system because a CRM system casts your sales process or your customer service process into a a set workflow that's been set up by the vendor or that is sort of the industry standard or whatever it is, right? You have your sales funnel and you have, you know, you're you're trying to figure out what stage a customer is in and so on and so forth. That's not the way you know AI-driven systems work. They're basically individual intelligent things that are feeding data to another thing. And so if you think about that, another thing that's going to do something with it. And if you think about that, like how does that change your view of this sort of customer funnel? You know, all of a sudden you can you can use signaling data, like has somebody changed a job recently? And but you but you can do this on a 24-7 basis. So you could have AI agents that go and and get data about a prospect or or an inbound or whatever it is, or even set up entire marketing p campaigns to target specific market segments and then manage the entire process of sales up until you know you get a human in the loop who comes in to finally close the final details of the deal. And so when you think about that, like what is your CRM system now? Actually, what you do is you're building a set of custom tools. To meet your specific process, not some preordained way of doing things that's been set up by a vendor. And so that kind of trend, and that's just one segment of an organization. If you look across the entire organization, then you you have thousands of inflection points where you where you you need to be thinking about this. And that's that's honestly is extremely hard to do because you're you're flipping the idea of how you do things upside down, right? It it's more analogous to hiring a w a very specialized workforce than it is to using a technology. And and I think that that's the fundamental difference. I joked with somebody the other day. I have a friend and she's building some software and she has 10 AI agents that are doing various tasks for her. And they are always fighting with each other about you know, who's responsible for what bit of tasks. I mean, this is the AI agents are fighting with each other. And so I said, I said, you're gonna need HR for AI, which was pretty funny because, you know, and and it's kinda like that. And if you think about that, it sounds nuts. But of course, these things are are pretty intelligent. I mean, they're very stupid at the same time, but they're pretty intelligent. And so and so th they're very capable of doing things. I think And this is the revolution, if you will. It's it's kind of like I always joke that computers have been promising us, you know, more ease of life for you know 50 years. And they've only made our lives more complicated, as far as I can tell. And I think AI has finally reached the point where the computers can kind of sort of understand us, and that is going to make our life easier finally. That's finally the promise is being achieved. You know, after fifty
Bill Canady: Yeah.
Chris Maresca: years, we've finally gotten to the point where these things are doing what they claim they're gonna do, kind of. as long as they don't hallucinate. But yeah, I think I think I think you know there is a revolution here. But again, if you if your if your internal processes are undefined, if there are holes in your processes that are being filled by humans, if you don't understand where the value is in your company, I I I have to tell you, I'm always shocked. I go to a company. And this doesn't matter the size of company. It could be two people, it could be a hundred thousand people. And and you go to a company and you say, What do you do? I mean, what do you do that makes a difference for your customers? And you know, there's some trite marketing answer. But I'm like, no, no, what do you actually do? And the number of people that can't answer that, where you ask people, you'll get you'll get a different answer every single time, right? So a lot of times companies don't even know what they're doing. And so how are they gonna define a process that takes that raw input and outputs value if the people in the company don't even understand what the company is doing? It's not it's not rocket science to know that if you can't define it, then you can't possibly automate this thing. So yeah, and that that's the way I feel. And so we talked about, you know, when when you look at private equity and you look at people buying or you know, organizations of various kinds. These problems are all over the place. They're
Bill Canady: Mm-hmm.
Chris Maresca: all over the place because companies are built by humans, and humans are imperfect. They they have they like to build fiefdoms, they want to claim credit for everything or whatever. All these human traits that are kind of corrosive exist in companies, and every single one of those things creates ancillary problems for trying to understand how value is delivered. And so if you take that and you say, we're just gonna apply some technology magic. IBM back in the day used to have these commercials about the magic pixie dust that could be sprinkled on things to make a company work better. And of course, everybody knows that doesn't work. And so and that exists all over the place. Of course, with AI, it's even easier to claim these kinds of things. And the question is, how do you prove out that this is actually working? Was it just a cynical exercise to get rid of the people in the company who actually know how things are working and reduce your costs before sale? Who knows? Right.
Bill Canady: bring up you bring up a lot of great points. You know, we're both in private equity, and the ultimate outcome for private equity is the company is going to be sold, right? People,
Chris Maresca: Mm-hmm.
Bill Canady: you know, it's just people pool their resources, make a bet on a company, and looking to get back two, three, four dollars for every dollar they put in.
Chris Maresca: Mm-hmm.
Bill Canady: And when you're getting ready to sell that, an investor comes in, and I've been on both sides of that table. And I'll hear just what you just said. Our technology is proprietary and highly scalable. So we're going to use the CRM. I don't need HubSpot or Salesforce. Man, that stuff's a thousand bucks a seat per month in some cases, right? Depending
Chris Maresca: Mm-hmm.
Bill Canady: on what you get to it. what questions should we be asking? So it's just not so someone's got 10 AI agents that won't actually talk to each other, fighting with each other and circling. What should we be asking to know that this is scalable? How do you know? what does that look and feel like?
Chris Maresca: Yeah, I mean the long and the short of it is that there's no single question you can ask that's gonna answer that question. I mean, unfortunately, there's no magic bullet here. I think there there's some key things, you know. Is there is is the data all centralized and normalized in a single database? Because AI works on data. It doesn't work without data. It can't work in a vacuum. Right. Yeah.
Bill Canady: yeah, garbage in, garbage out. The old gooey, right? G yeah.
Chris Maresca: But most companies have, you know. 10 different systems in the company, each one of which is responsible for a different area, functional area of the business. And oftentimes that data is spread across all these different systems. So it's all fine and good to say, we have AI and blah, blah, blah. But you know, is all that data centralized in a single place? Is it normalized? So is the data that it it's pulling from the accounting system the same as the data that marketing is using to drive marketing campaigns? Is the are the are the the you know, the the the metrics measured in the same fashion. Because if they aren't, then you have to make sense of this. The AI has to make sense of this, right?
Bill Canady: Mm-hmm.
Chris Maresca: And so you can't just say we're gonna throw some AI. It works AI works pretty well at sort of, you know, a po as a point solution, for example. You know, you know, it can it can clean up a lot of stuff. It can pull a lot of conclusions or or or Or some kind of meaning out of abstract data. it's quite good at these things, but if it if it if it's gonna work together, it needs a common data store, which is normalized. It needs references to standard operating procedures, it needs an orchestration layer so that you orchestrate all of the bits of AI that you have working. You know, it needs all these things. You can think of it as literally the AI needs its own company organization. Right? It it it it is, if you think about it, a friend of mine describes it as a bunch of brains. If you think about it as a bunch of brains, they need to work together. For that, they need to be organized, they need to know who gets what data, they need to understand what the standard operating preceders are, they need to understand what your brand identity is. You know, these things all coexist together. There's no easy way around this. If you don't have all of this, what you have is a kinda AI. You know, and and that's what I was saying earlier, when you talk about sort of AI native organizations, they are org organized around the idea that you have this all these agents that are acting as a workforce. And
Bill Canady: Mm-hmm.
Chris Maresca: most companies are implementing it as if it's technology, which is kind of a critical difference. You wouldn't you wouldn't tell a human that describe a human as technology, as a technology you've implemented. Right. And a human is a technology, effectively. I mean, you know, biological biological. Yeah. Well, but also there's
Bill Canady: Yeah, I mean we talk about their assets for the company. They just happen to walk out the door every day, right? Versus
Chris Maresca: a different way that you the you engage with a a human employee versus a a desktop computer, right? And and the problem with AI is people treat it as if it's another kind of Microsoft Windows or whatever, or Excel spreadsheet, or whatever. And that's not really a great way to think about it because you're actually reducing the capabilities to a one-dimensionalness. and I think AI is much more like your coworker than it is you know, like like another piece of software. And you have to engage with it like that. I mean, not just as at you know, interactively. We've all done that with the chat bots of various kinds. We've all we've all done that. but also in the way you implement it. that said, you know, AI is not a magic bullet. I mean, it's it's it's another tool in the toolbox you can use to solve problems. and I think as as as we've seen in a lot of areas, you know, There are issues with people finding enough qualified people to work in in various industries and maybe AI is one of the solutions to that worker shortage.
Bill Canady: good. You know, you bring up a really good point. What are they, what are you going to be looking at to say that this is either, hey, this is real and this is a game changer, how they're deploying it and it's defensible, or it's just theater. They're just dressing themselves up. How do you tell the difference? And how does a business owner get real value? Because this stuff is not cheap.
Chris Maresca: You have a limited amount of time to judge what's going on in a company and a limited amount of data. I think I think there are key things you can do as a business owner. Like, do you do you understand con key concepts like orchestration? And do you have you know a can you express how orchestration is changing the way your company is working with AI, right? Like you're orchestrating a bunch of AI workers in the same way that you'd have a management structure to manage people. Orchestration is that management structure of your AI tools. And do you have centralized data, which is all normalized so all of the AI can judge that? Do you have standard operating procedures that are documented? Do you have these things ready to go? When you put together your VDR, can you put all that stuff in your VDR? and and show that, you know, this is real. This isn't just. vaporware. you know, I think that that's important. You can also show metrics like, you know, you've been able to process N number of whatever it is faster, you know, quadruple your production without increasing your headcount, for example. There's metrics you can use to show that this is really optimal, or, you know, higher close rates for sales or whatever it is, right? There's a lot of metrics that show the you know the the time. you know, from when you implemented this thing to now, like how has it changed the fundamental KPIs in your organization? but those only tell part of the story. I mean, the other question is, you know, talk to the people on the ground. When I did a lot of consulting, I used to go, you know, several hours before my meetings, I would go and stand with all the smokers. And I would ask, I would ex introduce myself, I would tell them who I was and what I was doing there and who I was meeting with. And I would I would ask them questions about what they were doing. Like, you know, these are the people at the coal face. Do do they what do they say? I mean, that's always been my trick because I don't want to talk to the executives because nine times out of ten, they actually don't know anything. they just have, you know, a line to tell you about what they're doing. But if you talk to the people, you know, in the factory, you know, at the loading dock or whatever, you know, in the cafeteria, then those people can tell you what's actually going. And I find that much more useful. If so, if you're in PE, that's what you need to do. You need to actually go and talk to the employees, either, you know, through a conference call or whatever, or or in person, and understand like where are they? What where is the company? They will tell you if if if AI is being implemented, they will tell you how they feel about it. I guarantee you. It's inevitable and whether it's good or bad. If it's good.
Bill Canady: Yeah, it's for sure the object of conversations a lot. Yeah,
Chris Maresca: Yeah.
Bill Canady: you're you're you're I mean you're really nailing it there, right? Like get to the people on the floor who are who are being directly affected and implementing it, have your KPIs you're looking at. When you look and you're going through your your technology diligence on these companies, what what are some of the more conventional processes that people are constantly overlooking that says this is real for this company and it's making a difference?
Chris Maresca: Hiring. Sounds crazy.
Bill Canady: The higher the people.
Chris Maresca: Sounds crazy. But you know, even in today, companies are built on people, right? And and finding the absolute best people is very, very non trivial. I mean, in in my last company, that was one of the first things I did when I joined, is I set up a hiring pipeline. And I set up a a methodology for hiring employees. This was even before I needed to. I set this all up. I set up interview templates, I set up a you know, a a specific pipeline for finding people, I set up tools so that we could go out and we could identify people that we were interested in hiring, even before we contacted them. Right. And and I think you need to do that. There's, you know. Unfortunately, there's there's no loyalty in companies to employees, but it goes the other way around as well. So because because of that, people leave their jobs constantly. Every couple of years, it seems people seem to move around. That's pretty common. And so you have to anticipate that. And when you lose somebody, it's you're losing all the the knowledge that they have is walking out the door. No matter how hard you've tried to document that it it it is leaving. And it doesn't matter if they're a junior or then the most senior person in the company. In fact, a junior walking out the door is probably worse than an executive. And so
Bill Canady: Mm-hmm.
Chris Maresca: because they have a tendency to be doing all kinds of things that you don't even know and that are undocumented. And so I think that having a really strong hiring pipeline is really important, having standards and and sort of a a process for doing that. And for me it Really came to the fore when our lead software developer left. He left because somebody offered him his dream job, which you can't stop somebody from leaving in that circumstance. He wasn't unhappy with us, it was just he somebody offered him his dream job. And one of my staff said to me, I would be really worried, but I saw what happened the last time somebody left. And because I'd set up a hiring pipeline, they knew it was no problem. We went from the day he left to hiring somebody in three weeks.
Bill Canady: Wow, that's unheard of. That
Chris Maresca: Right.
Bill Canady: is amazing.
Chris Maresca: she was she was she was brilliant, still one of the one of the best software engineers I've ever worked with. And it was completely random because we hired she did not have the skills we were looking for, by the way. But she had the personality that we needed. Like so we hired
Bill Canady: think I've heard other people make the statement, I can teach you the skills, it's the fit I'm looking for, the capability, right? You can every company does
Chris Maresca: Yeah. She was and the
Bill Canady: it differently.
Chris Maresca: Yeah. And the fundamental thing was she was curious. She was curious and she was engaged. And so she was she was willing to to learn and do whatever it took to make things happen. It was the kind of person you can throw in at the deep end and said say swim and they would be just fine. You know? And and and you know, and she was twenty five.
Bill Canady: Amazing.
Chris Maresca: Yeah, and so and our senior developer was, you know, sh that's who she was replacing. And she was no way a
Bill Canady: Mm-hmm. Interesting.
Chris Maresca: senior, right? I mean, I mean, she doesn't have didn't have the experience. But you know, again, that your hiring process is so important. It gets even worse with AI, right? Because now you've got other tools that can come into the mix, but also you've got a smaller number of people, presumably. So each person counts even more. so so yeah, I look at that because I think that tells me a lot about an organization. Like how much do you care? People pay lip service. people are a most important asset. You know, you could hear that a lot. I mean, come on, what a line. It's just I mean Yeah,
Bill Canady: We're all family. you fire your family? I'd like to fire sometimes, but
Chris Maresca: right. but but I think I think, you know, it it translates through that sort of thing. I heard something ridiculous the other day, which was actually this morning, which was a company that couldn't find any candidates for a particular position, so the hiring manager submitted his own resume underneath a a false name and then got rejected within five minutes.
Bill Canady: That's great line.
Chris Maresca: And and and the what they found was that the HR team had put in there as a requirement that you needed to know this particular software development stack called React. except that they had specif specified React dot JS, which is the formal name, and on his resume it just said React. And for whatever reason their hiring system was too stupid to understand that they were one and the same thing. And so they were losing a lot of candidates due to stupidity.
Bill Canady: Amazing. Switching gears a little bit, you've argued
Chris Maresca: So yeah.
Bill Canady: AI cannot take an unproductive company and make it productive. Why not? If it's this powerful, why can't it fix it all?
Chris Maresca: Well, because AI's like a toddler. It needs to be taught everything that needs to be done. So, you know, you need a couple of different things. It needs it needs training, typically, on the workflow and the process that's going to or, you know, a a defined list of standard operating procedure procedures. It needs a set of data to work on. AI works on data. And typically If your company is unproductive, then chances are you don't have either one of those two things, right? You don't have a standard set of operating procedures. Or they're incomplete, or they're wrong, right? Or you know, or they're just lip service. yeah, we have SOPs, you know, whatever. Or, you know, however you want to define, it doesn't necessarily be up standard operating procedures, but whatever, however you define it, whatever terminology you use. and so You can imagine you you you point an AI at something which is incomplete with a lack of data or you know with no training, what is it gonna do? Well, it's just gonna make things up. That's not gonna help. It's never gonna the thing about AI is it will not it will not say, I can't do this. It's like a human. A human won't say, I can't do well, they may, but you know, a lot of humans will just try to do it and they'll do it wrong. And then you wonder why they're doing it wrong. Well, AI is no different. Same deal, you know? same thing, right? So you you don't you can't train them, you you don't have operating procedures, you don't have data for them to work on. What do you think the result is going to be?
Bill Canady: Yeah, it'll make it up, yeah. You know, tr
Chris Maresca: It'll make it up.
Bill Canady: training is so important and getting a getting the data eventually. So the data silos are frequently blamed as we're just doing for AI failures. Are they
Chris Maresca: Mm-hmm. Mm-hmm.
Bill Canady: primarily a technical problem or are they evidence more of a deeper organizational problem?
Chris Maresca: yes and no. So the data silos exist because organizations are functional silos. So if you think about most organizations, they have these sort of vertical organizational areas, you know, usually under some kind of executive leadership, a VP or whatever. You know, marketing, finance, sales, IT, HR, whatever, product, you know. production, if they're doing production, whatever it is. they're their own little fiefdoms, right? With their own sets of software, which don't exist in in cohesion with anything else often. You know, that the cohesion exists at the executive level, right? That's where all the aggregation happens, which is incredibly stupid because of course marketing and sales need to talk to each other, but so does finance, because sales and finance are tied at the hip, right? And so you you have this sort of disjoint because humans being humans, you have this disjointed, you know, tribal nature of of corporate organization. It's a problem. My view is I I I like to take a holistic view of things because each one of these things is pushing and pulling in a certain direction, and everything needs to pull in the same direction for a company to be successful. And oftentimes they're at odds with each other, not on purpose, but just over time. You know, yeah, the the accounting system doesn't talk to the e-commerce system and we have to buy this expensive translator to import all this stuff or whatever it is, you know? And we've all heard those stories. And of course, the result of that is you get those data silos, right? Is it solvable? Sure. And it winds up costing a lot of money because now you have to extract the data out of all these systems. put it into a single data store, and then you have to make that normalized. In other words, all the data's got to be relatable to all the other data. And then and then you have to, then you can point your AI at it and hopefully you can make heads or tails of it. But of course now, training, standard operating procedures, all that stuff, how do you apply it now to this big data store? Well, you you you need to process well, so the first thing is you need to process the data in situ if you can, which is very difficult to do because we just talk about all the data silos with different data formats and all kinds of mess there. you need to process the data in situ, but you need to also process the data in streaming in a streaming basis. So instead of saying, you know, I'm I'm trying to think of a really good example, but effectively, instead of building a report at the end of the week, you need the report to be constructed throughout the week as things are happening. Right. And so
Bill Canady: Mm-hmm.
Chris Maresca: now you don't need this giant data store of stuff. You can just have the report be generated as the data that's that it's based on is moving through the system. but that re again, this is a different mindset. This is not the mindset we all grew up with, effectively.
Bill Canady: Yeah.
Chris Maresca: And and that's that's a hard shift, right? I mean, in your own companies you probably see this. Like trying to shift from one mindset to another is a nightmare.
Bill Canady: For all the reasons that we know, people don't like change, there's comfort, they gotta go learn new stuff, they're already busy, they're fully occupied. They may be occupied doing the wrong things. It's just they, you know, it's now I gotta do two jobs. And there's also it creates a sense of of fear or they don't feel safe because maybe this robot's gonna replace me When you look at middle market companies today, that you're through the lens of what you're doing. What do you see companies currently spending money on with AI that that really has no defensible return? There's a lot being wasted today.
Chris Maresca: I think my biggest worry with most companies these days is that they're sending all of their data to the open AIs and the clouds of the world.
Bill Canady: Notebook LM, which is a Gemini product, because you
Chris Maresca: Yeah. Yeah.
Bill Canady: can ring fence it. So if I've got something and they can take massive amounts of data. So if I'm looking at something, I can dump it in and with a second they can go through a spreadsheet. and because you can keep it off the internet and everything else, I actually think it's a great product for for safety.
Chris Maresca: Yes. Yeah, I do too. And but there's a lot of people that are using it, especially in sort of non tech industries. there's a lot of people that are just using all this stuff, even you know, at the middle management level that, you know, they're they're kind of unaware and they've they've got they're overloaded with work and they're turning to these tools to make their lives easier, which is completely sensible, but it's kind of an uncontrolled leak, if you will, often. and so I think I think that the I think that that's an existential risk to these middle market companies because especially if you're a kind of a SaaS software company, the AI tools are learning about how you do stuff, maybe not explicitly but implicitly. And then you will get your customers replicating your software using AI tools internally. I've already seen this happen once, you know, where a healthcare company replaced a revenue cycle management tool that they were that was costing them a hundred and twenty thousand dollars a month. And in three months they replaced that specific tool. You know, and and so and I think that's gonna be more and more common. and so that's a risk. If you're a middle market software company of some sort, then you have that risk. You know, and and then and that that that leakage problem exists, you know, writ large. I think the big trend is going to be more local AI. And by the way, I think Apple's gonna be the big winner there. They already have been. So if you if you if you want to run local AI, you typically will buy an NVIDIA graphics processing unit, a GPU. And and and the card, the AI can use all of that memory. Now it can't really use any of the memory on in your computer if you're using an Intel machine, because they're separate. The memory is separate, right? The Macs, Apple has engineered their chips and their hardware so that the system memory and the GPU memory are one and the same. And so you can buy a Mac Mini with like you know 64 gigs of RAM and all of that memory can be used for AI.
Bill Canady: Mm.
Chris Maresca: And and the performance is like 90% of an NVIDIA GPU.
Bill Canady: For a fraction of the cost.
Chris Maresca: Apple's created an architecture which is absolutely tailor-fit for AI. And they did this before AI. I don't think they even realized it, but people have realized that and they're like, my God, I can run this thing on my desktop, you know, literally in but you know, the point is you can use a system like that for local AI, which will rival whatever you're getting from Claude and so on and so forth, right? And and then you won't leak any data. view. I I and so I think if you look at the middle market, you know i there are companies that are gonna be disrupted by AI. They're gonna come be companies that are not gonna be disrupted by AI. I I'm a partner in a PE firm and we buy lower middle market companies. most of the companies we're buying have nothing to do with tech. We have a We have twelve in the pipeline right now. healthcare, food distribution, metalworking. You know, nothing to do with with AI.
Bill Canady: That's the same type we buy. We have a good one of our other groups does technology, but man, that's hard. We're industrial.
Chris Maresca: That's hard.
Bill Canady: I I'm all my stuff's industrial. I buy widgets, things made out of hunks of metal, you know, and all that. So yeah, yeah. That's what, but that's
Chris Maresca: Well that's your that's your business, so yeah, I'm not surprised. Yeah.
Bill Canady: you know, look, I want to own businesses that other people need to run their business with. So it like, like
Chris Maresca: Right. Yes.
Bill Canady: it, like, and I want it to be like like you're making a car. I want to help you paint that. Right. And if my stuff goes down, you can't make your car. And and guess what? There's a huge market for cars. So so imagine you acquired a company tomorrow in your PE firm. What should happen during the first thirty days that you're going to go through with your technology assessment, amongst other things?
Chris Maresca: Well, the I mean the the th the thing is that and I'm sure you've seen this, most of the companies that we acquire are small enough that they're technology light. And so the first thing to do is to look at what technology we can easily and immediately implement in order to bring them up to speed, so to speak. I think the the first benchmark for us is because you know, our goal is to buy somewhere between three and five thousand companies.
Bill Canady: You talked about that. That's the most I've ever heard of anyone doing it. It's got a really novel model you've got. I you're gonna ri we're off just remember us little
Chris Maresca: I I know. Yeah. Yeah, it's exact there's a company in
Bill Canady: people.
Chris Maresca: there's a company in Canada called Constellation Software, which you might have heard of, which has bought fifteen hundred software companies. And so they're they're I I I know some people there and we talk a lot about it, but and one of the things that they do, which we've replicated is they the the key output from their from those companies for for the mothership is a standardized PL. With standardized KPIs. And so that for us in the first 30 days, that's really the goal is to get that in place. Right. So then we can understand what's going on with the organization and and make whatever changes we need. But we have a little bit of a different model because we have 50 operating partners that work in 15 countries and they are responsible for their pool of local companies. we don't at the at the level at the partner level we don't really look at that too much. We look at the roll-up of what's going on in each regio. so yeah it's an interesting challenge.
Bill Canady: Okay,
Chris Maresca: Fire away.
Bill Canady: what's the most overrated technology trend today?
Chris Maresca: I mean probably AI, honestly. And the reason is because and it's a very simple reason. Silicon Valley, and I you know, I gotta remember I I spent thirteen years and I spent thirty years in Silicon Valley and did fourteen startups, including seven exits and an IPO. Silicon Valley has an operating model that most of the rest of the world doesn't really understand. And the operating model is to invest enough money to grow explosively. And then have a lot of propaganda explaining how great all this is. And then to try and get out from that before it explodes. And and and that's kind of what's going on with AI right now. AI works writ large. It does work and it does do a lot of cool things, and it is pretty smart and can consume a lot of data quickly. I mean, I use it for 300 page CDDs and trying to figure out what the hell they say. but There are like every tech, there's limitations and and you have to be aware of that. And like every tech, it's not free. It's not free both in dollars, but it's also not free in time. And so, and time's usually the bigger component than dollars. So yeah.
Bill Canady: What's a metric you distrust?
Chris Maresca: I kind of distrust all metrics because I think that they're they're they're easily manipulable. I I don't I understand why people do it because it's very reductionist. but there's a guy named Rory Sutherland who who used to be the chairman of Ogilvy who who has this thing he talks about called the doorman fallacy. And doorman fallacy is basically like you have a doorman in a hotel and you get rid of him in favor of an automatic. Door opener and it immediately saves you 80 grand a year or whatever. And and then, you know, what happens is that nobody's unloading people's luggage, the the recurring guests are not greeted by anybody, homeless people start sleeping in your doorway, nobody's hailing cabs, and pretty soon your your booking rates are down 30%, and you're wondering why. And I think a lot of metrics are the doorman fallacy, right? They're measuring something. that is ostensibly tangible, but there's a lot of humanist bits that get lost in the KPIs or the metrics or whatever. I'll tell you what I do believe in. I do believe in financials. I always talk about how the secrets are buried in the financials. If you want to know what's going on with the company, go look at the financials. Whatever.
Bill Canady: True.
Chris Maresca: It it it doesn't matter whatever else, it's gonna be in there. You know, hopefully in a PL or something like that that you can actually read cogently rather than You know, some crazy chart of accounts you can never understand. But
Bill Canady: Just gotta figure out all the ad backs and the what really is the
Chris Maresca: Right. I mean
Bill Canady: S D E, so
Chris Maresca: well the and the thing is that AI is really good at unraveling all that stuff nowadays.
Bill Canady: Yeah, then.
Chris Maresca: but and and and that's the real data, right? Assuming people aren't cooking the books, that's really where the bug stops, right? So Well, yeah.
Bill Canady: Yeah. Well you have ways of dealing with that, doing Q of E so.
Chris Maresca: But you know, you know what I mean. I mean it it is Yeah, so
Bill Canady: I know, I know people, people, they do a lot of theater. I don't want to say they lie, cheat, and steal. I just think they they try to put a spin on it sometimes. All right, final question. All
Chris Maresca: Well and go ahead.
Bill Canady: right. What failure did you have that taught you something indispensable?
Chris Maresca: I got a good one actually. I have a a a good spectacular one. So and I could talk about this now because it's no longer an NDA, but many, many eons ago I ran a consulting firm and we were hired by Adobe to figure out what to do with Flash on mobile. And and this was before the iPhone was released, so you can imagine how long ago that was. And And I I had a pretty decent idea of what they should be doing based on some experience I experiences I had with Flash running CNC machines, believe it or not. Because you I know you're an industrial guy, you're gonna go, what? Yeah. and and so I I had I had come up with a strategy, and this is me and my team, and you know, I had talked to all the C level people and everything else. And I'd all got we'd all gotten agreement and everybody was aligned and everything and they all thought it was a great idea and so on. And then I went to an executive meeting, which was mostly all the C level people, not the CEO, but pretty much everybody else. And it it was a spectacular disaster. Like, you know, like it was a complete failure, like they hated it. And everyone was like, how did this happen, you know? And what I realized was there was a a performative aspect to this that I didn't understand because not only were all the C level people in the room, but all their assistants were there too. And I'm I'm not entirely sure of the dynamic, but there was a performance that needed to be done about this. that I kind of was unaware of, a political dimension that I didn't understand. And even though every every one of those people had agreed in advance that this was a great strategy, when it came to sort of agreeing in public, it was a different story. And
Bill Canady: Mm-hmm.
Chris Maresca: and that was that was a really valuable lesson. And now I'm much more careful about that sort of thing.
Bill Canady: thank you for sharing that with us. So, Chris,
Chris Maresca: Yeah.
Bill Canady: thank you today for helping us look beyond the technology narrative and focus on operating reality underneath it. You really brought some great insights to, I think, you know, people who run businesses, whether you're buying them or selling them or just, you know, getting them going. I I think this this brings a lot. AI and technology in general is is for the future. It allows us to multiply ourselves and and you're right in the heart of it. Though, but the central lesson, I think, is clear. It can amplify a strong business, but it cannot rescue an organization that has not confronted, you know, its priorities, processes, and data. If they're just doing it for for performative, if they're just making it seed or it shows once you dig in on it. So where should listeners go to learn more about you and your work?
Chris Maresca: so they can visit my consulting company's website, c thirty two dot co. That's c three two dot co. And they can reach me at ckm at c thirty two dot co. and yeah, if you if you need some help trying to figure this out, just reach out. Happy to help.
Bill Canady: All right. Well, thank you so much for joining us. This has been the Thousand Day CEO. I'm Bill Canady See you all soon.
Chris Maresca: thank you. Thanks for having me.
