Scale Like a CEO

Scaling Startups: Challenges, Management & The Role of AI | Scale Like a CEO

• Justin Reinert • Season 1 • Episode 41

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“Cutting costs with AI” sounds sharp until the bots get confused and your help desk catches fire. We sat down with Matt Peters of Fixify to unpack a smarter path: design for the mix—pair automation and agentic AI with skilled people who step in at the right moments—and invest in the management muscle that keeps quality rising as complexity grows.

Matt explains how Fixify handles roughly 75% of break‑fix tickets by integrating with existing stacks, not ripping and replacing. We dig into the false choice of quality versus cost, why seven direct reports remains a meaningful manager span, and how AI changes capital allocation more than it eliminates headcount. You’ll hear a practical hiring framework—experience, skills, traits—that helps teams screen for resilience, systems thinking, collaboration, and trust, the traits that actually scale when problems evolve and tools shift underfoot.

We also explore the idea that “everyone is a manager of one.” Communicating intent, setting context, decomposing work, and giving feedback aren’t soft extras—they’re now the core skills for directing AI agents and shipping reliable outcomes. Matt shares real examples of multi‑agent problem solving, the onboarding playbooks that prevent new‑hire limbo, and the performance conversations that name superpowers with concrete evidence so people grow where they add the most value.

If you’re leading a growing team or modernizing IT support, this conversation offers a grounded blueprint: integrate with what works, automate the obvious, reserve humans for the weird and the vital, and train managers to orchestrate it all. Subscribe, share this episode with a founder or IT leader who needs it, and leave a review with your biggest question about scaling people and AI together.

Matt:

We can produce a lot more solutions quicker, right? But and you actually alluded to this earlier, it shifts the capital allocation strategy. It's not that we're going to spend less on people. And this is a I I gotta say, if I were to double underline something, there's actually an article today that I read and the Schadenfreud was real. The number of CEOs I hear who are like, we're gonna cut costs by getting AI. And I'm like, okay, cool. But I don't know that we're far enough along the AI route that we can really be like, oh, we're totally cutting costs. What I am doing is reallocating them, right? And so we're spending a lot of time thinking about, okay, where do I need to put in quality assurance or where do I put in people to and think about them more as managers of a group of agents, that sort of stuff.

Speaker 00:

Welcome to today's episode where we dive deep into the world of startups and scaling. In this conversation, Justin and Matt explore the critical challenges of growing a company from managing rapid expansion to building effective teams. They'll discuss the evolving role of management in the age of AI and share insights on what it really takes to scale successfully in today's fast-paced business environment.

Justin:

Matt, thank you so much for joining me on Scale Like a CEO. Just to get us started, if you wouldn't mind, give us a 90-second intro to you and your business.

Matt:

Because once you get to 25, you could stop wanting to add years to that. The current company that I'm that I'm at, I started with two co-founders. And what we're trying to do is change fundamentally the way that folks deliver breakfix IT help desk support. We built a platform with AI and automation and people come into a company and we can take on usually about 75% of the break fix. And that lets customers' teams focus on things like project work or you know escalated tickets and really helps drive quality of service in the organization, improves employee morale. And what do you see as the biggest problem in your industry and how you're solving it? Yeah, I think that one of the largest problems that we ran into was people having to make real trade-offs, right? So there was the fundamentally a tension between achieving scale in your company. As your company grew, you know, through it usually hits you around 300, 400, 500 people, and you want to be delivering a high quality IT experience. At the same time, you're being pulled in 50 different directions. And so one of the things that leaders were being asked to do was effectively make a decision between like, okay, well, do we achieve cost control or do we achieve quality? And we thought that that was a little bit of a distinction that we shouldn't have to make. And so fundamentally what we said was if we build the right platform and we have the right offering with the right people involved and to have that mixture be correct, we can end up making it so IT leaders can go ahead and say, look, I'm gonna continue to deliver that same bespoke feeling, white glove feeling, quality of service to end users, even as my organization scales, and I'm gonna do it without having the cost just to absolutely get uncorked. Nice. And what makes you unique in the way that you're helping others? I think it comes down to sort of three key things that we do, right? So the first one is we believe fundamentally that people are a feature, not a bug. So in this world of AI and automation and things like this, everyone's like, oh, okay, we're just gonna automate all the work away. And we discovered that fundamentally, like, you can't do that. That's just not a thing. Like you can do some of it, right? But the actual interesting part is that you know you can automate 85% of any job, but you can't automate 85% of the jobs. And that is to say every job will have, oh, the first 10% you can do automated, and then you need a human here, and then you need 50% that's automated, and then you need another human moment. And if you understand that mixture, you can end up achieving a tremendous amount of quality, but you still have to make room for the humans to be in that process. And we've all dealt with this. And if you're in IT or you're in any sort of technology, you you realize this. Every now and then the robots just get confused, right? And when they do, if you've anticipated that, then you provide room for humans to step in and go, okay, let me help out, let me figure this thing out. So that's the first piece. The second piece is we know that change is difficult. There's kind of a saying that in in IT, the last mile is the bumpiest mile. And when you're talking about big complex environments, big complex organizations, they don't want to rip and replace anything. So what we did was we came in and we we integrated with the existing technology. So I'm gonna integrate with your ticketing system, I'm gonna integrate with your back office systems, and we're using APIs and a real platform technology layer to help do that. And what that does is it basically allows us to achieve attention that the existing uh solutions weren't. MSPs tend to be focused on kind of people power, right? They are fundamentally the existing providers were primarily, hey, you can outsource to my people. And then you have these automation vendors who are selling, like, hey, we can automate everything, right? Um, and what we're saying is actually there's a mixture, there's an ad mixture that is more correct or more profound, and that's what I think the key differentiator is getting that mixture right.

Justin:

Nice. So pivoting a little bit, I was looking at Fixify on LinkedIn, and if if your employee count on LinkedIn is accurate, it looks like you are growing really fast, which is amazing. Tell me about that. As you've gone from, you know, a founding team of two to where you are today, what are some of the challenges that you faced in scaling?

Matt:

Sure. So I think one of the things that happens when you scale an organization, I've done this, I've been in an early stage startup a couple of other times as well. And when you start scaling, what you've got to realize is the fact that when you start with an organization and you start doing this, the inner volume actually increases pretty dramatically, right? And it increases at a rate that is pretty rapid. And so very quickly you have to start thinking about the idea that management in your organization actually becomes a thing, right? It's very easy to say, okay, well, we'll hire some smart people and they'll know what to do. But very quickly, you wake up one day and you realize everybody's doing every one of these smart people is kind of off doing their own thing, and that gets really janky really fast. So actually thinking about the org structure and okay, so in a well-run organization, every manager is going to be loaded at about seven to one, eight to one, something like that. Any bigger than that, and you're risking, hey, I have no idea what my people are doing. Now, in a startup that's growing really quickly, that might change. You might end up with, well, we got one team that's a 10 to one, they're on a the big hiring sprint, but that's not maintainable for long term, right? And so that's one of the things is you have to understand you're trading off. Like when you do that, you're trading off that's like management level burnout. So that investment in management early and management is an honest to God job. It's an actual factual craft. As technological founders, we always want to think like everyone's hands on keyboard slinging code. Like if you want a well-run company, if you want a company where people don't hate to come to work, you're gonna end up having some people who aren't like that. So that's one thing. Second thing is like there are some things that you need to think about in terms of how you hire and how you scale hiring, because we as the founders very quickly realize like, I'm not able to interview everybody, just not enough hours in the day to do it. And so what we end up doing is we've sort of set up a pretty rigorous way that we define and and structure interviewing and job descriptions so that we can ensure a certain quality in the new hires. And then we spend a lot of time. This is a lesson hard learned at previous gigs. We spend a lot of time trying to onboard people. When you come to work at the company, you get this document that's sort of like, this is your job. I'm gonna go over your job description with you. Here are links to a bunch of stuff. Here's some videos you need to watch and stuff like this. The goal is to try to make sure that you know everybody's had that experience. You go to your new job, you sit down your first day, and you're like, So after that lunch with the team, what do I do? And so we try to make sure that nobody has that experience here at Fixify.

Justin:

Yeah. Okay, so all of that's music to my ears. I I I want to dig in a little bit on the first thing you were talking about, which is um having managers. I also love hearing you say this, like this seven to one ratio, because that is, I mean, you know, based on the you know, any recent research that I've read, that is kind of as long as you're an experienced manager, seven direct reports is kind of optimal. But I'm hearing so much, and you know, I don't think that there's going to be any magical equation on this for a while. I'm hearing so much that, you know, with AI, you know, AI enabled employees, they can do so much more that then that ratio is is getting blown out. So we're looking at, you know, 10 to 15 to one. Um, and you know, I'm not I I don't know yet. It's I think it's too early to really determine that. But I'm curious what led you to that? Because that's not something that typical founders want a management heavy organization.

Matt:

Sure. So two things. One, I've worked in bigger companies before, right? And I've worked at growing startups before. And so I've seen sort of what big and operational organizations look like. And they usually tend to, if they're not completely janky, they usually tend to that seven to one ratio. I've also been at startups where we violated that, where we bent closer to 10 to 15 to one because we were scaling up quickly. And so I've seen the problems that yields down the road, right? And then I'm in a position to kind of make those trade-offs and be like, oh, okay, there will be times we will push the envelope on that. There, there absolutely will be. Well, times when we're like hiring so fast that we're like, sorry, hey, buddy, you're gonna have to kind of take a hit for the team and like you're gonna have, you know, 12 direct reports for the next quarter. But I also understand the cost of that both to that person and to all their direct reports, and honestly to the effectiveness of the team. So having seen all that viscerally, like I'm not, I didn't read in the book, like I'm experienced. And when you've experienced that, like, you know, you're at your startup and you're like, why does everything suck? And then you're like, oh, oh, that's right, because nobody actually knows what they're doing, and everybody's got like nine complaints and there's no one to really complain to. So they complain to each other, and then you've got this like toxic soup. Let's not do that. Let's let's have good management, let's have that structure. Now, you did mention, and I'm interested, I don't know what the impact of AI longer term is gonna be. I don't know that we've necessarily seen a big expansion or compression in loading factors in that class of work. And I'll tell you why. The at least as I think about it, is management is fundamentally a human endeavor, right? And it isn't to say that I couldn't use AI. So, like we're doing some, we get some AI help when we write job descriptions when we do certain types of reviewing of materials. But at the end of the day, so much of if you look at manager tools, for instance, or any of the manager structured things, one of the first things they say is, okay, one of your biggest tools with managing your team is the one or ones you have with each one of your employees. Boom. Okay, cool. So if you're using a recorder and you can summarize notes from it, that may help make you significantly more effective. But there's no AI avatar going to that meeting. Similarly, your team meeting, right? You can use AI to like take summary notes. You can do do-outs, stuff like this. Maybe you have an agent reminder, but at the end of the day, like presence is important, right? So I think that that we'll get better with it, and I think it will end up helping. But I think the amount of focus, the amount of human focus that you need to have, I don't know that it ends up being like one to 15, 1 to 50. Like, I just I'm not I'm not sure we get there, but it's early day. So who who can tell? Who can tell?

Justin:

Yeah, I think I'm with you. Just because you know, if I have seven director ports, and just because they might be more productive because they're using AI, that doesn't make them less human. You know, like they're not they're not then 0.5 person, and I can have 14 now. And we'll see where it goes. The other interesting shift that someone told me recently is that now with AI, everyone is a manager of one. Yeah, yeah, which was an interesting thing to wrap your head around.

Matt:

It so this is where I think it gets really interesting because I myself use a lot of AI in my day-to-day life. And what became interesting as I fumbled my way through it, right, was that so many of the skills that you pick up as an early manager ended up being the skills that were necessary to drive chat-based interactions with large language model-based agentic AI, right? So, like things like being very clear in what you want, setting up the context, providing instructions and feedback. This it was amazing to me. And I was like, wow, so these are all skills that honestly, if you think about skill development in techno, particularly we'll talk technical roles for right now, but I think this is across the board, is that you come into early career and we teach you a lot of technical skills. This is how you slang some code, this is how you work with these tools, this is how you you do this programming language or whatever. And then later on, we start teaching you about this is how you do project management, this is how you interact with teams, this is how you do like, and you pick up those as later career things. And then management is a later career thing. And it's like we may see an inversion, right? So as I'm talking to college students now, I continue to harp on like actually communication skills are a critical thing. The ability to think in language is important. I don't care if you think the ChatGPT can do it for you because you have to at some point talk to the robot, right? Um, but these management skills might be a thing that we want to front load as we're having people go through is like, how do you coordinate and collaborate and think abstractly about the pieces of a problem, right? So my CTO recently just solved a relatively complicated problem and he basically built a multi-agent system that, you know, each each one of them was given subparts of the problem, right? Divided it up, you know, away we go. Well, there's a a skill there of like how do I conceptualize the end state and then break that down into a chunk series of chunks? And that's not a thing in general. If you have a software engineering background, you went to software engineering engineering school, like you may have done a lot of that in decomposition, but a lot of other technical or non-technical groups, they just don't treat teach you just to think that way. So I think there is a lot to that. I think there is a lot to that management team of love.

Justin:

Yes, and those skills that make us human are becoming so much more necessary. I've actually I've written an article and spoken several times on you know the urgent need for critical thinking in this age of AI, that, you know, sure, we can plug in a prompt, and even if we're good at the prompt, we'll get a decent response, but we still need humans to make sure that what we're getting is is going to work. And also, is it is it even valuable or is it differentiated? Uh, because a lot of what you get from, you know, I'd think of in kind of content generation or things like that is very generic. So those things that make us human are so much more important, which makes me think about then as you're hiring your team. So you had mentioned the the interview process and bringing people on board. I'm curious, as you've been growing the team, what are some of the things that you're looking for in the skills that you're hiring for?

Matt:

Sure. And I'll just operationalize just to start with, in terms of how we do this, because we have a pretty structured way. And like if anybody's interested in the in the book, I think it's called the first break all the rules or something that like elaborates on this way of thinking about things. We break things into three buckets, right? So you have experience, skills, and traits when we're evaluating somebody for a job. So we take your job description and we extract out from that experience we need you to have. And experience really just sounds like you've been doing X for 10 years, you have done a thing, right? You have like working experience doing this thing, right? And then you have skills, which are things like you can program in Rust or you can make a budget, or right? These are things where like you have the knowledge to do the thing at some level. The key thing is I can teach a skill, right? Like if you didn't know how to program in Rust, we could teach you how to do it if we really had to, right? So there's an evaluative criteria. I cannot teach experience, you either have it or you don't. I can teach skills. And then the last piece is traits. And this is what I think you were alluding to. Traits are like the inbuilt things that make you you, right? And that you bring to problem solving, right? So for instance, a good trait would be something like resilience, right? Systems thinking, right? Collaboration, the ability to engender trust. There's a whole bunch of things that we can unconsciously bring to problem solving. And this is why when you get a group of people together, if you've correctly assembled your team, you end up with these amazing superpowers because you have one person is just always the out-of-the-box thinker. They just will not be constrained, their muse is not fettered by the problem space, right? And they always come up with these interesting things. And you have somebody else who's really a much more of a linear thinker and they're able to break things down into small chunks and help with the delivery. And you have somebody else who's gonna crack the joke at exactly the right time, and you have assembled the Avengers. Those are all traits. You can't teach them, but you can screen for them. So when we're looking, we're looking for people, and and the other piece of those three is they operate at different timescales. Experience, I can count on your experience day one. If you have done a thing before, I'm gonna show up, hand you a laptop, and be like, you've done this, go get it. But very quickly, that the utility of that to my company atrophies because I'm not trying to do something people have done before. We're doing something. So if you've done the thing, cool, but now you're gonna have to learn how to do it in my company. Skills, they're good for about the first six months, but I can teach almost anything to almost anybody in about six months. So if you think about it and you go, okay, well, I need these skills, but they're a lot more negotiable than you might imagine because of that ability. A decent computer programmer is gonna come in, and if you're they're not used to programming language that you have used, they're gonna spend a couple of weeks and they will get facility and they will over time become very, very good at it. It's not a thing you have to worry about. But it's really the traits. And when we think about things like flexibility, when we think about things like resilience, think about things like systems thinking, these are some of the things that we tend to screen for in our roles because they tend to be when you see those things in people, then you end up seeing people who are going to adapt well to change in the environment. And that's fundamentally what we're talking about. When we're talking about AI, when we're talking about any massive new technology shift, is people who are able to say, oh, okay, cool. What worked yesterday doesn't work today. The models just got a lot better. Hey, let me change my working style, that sort of thing. And so people who are very flexible in the face of new things, they tend to adapt really well. And so we'll ask questions to sort of suss out, like, hey, give me examples of other times you've done that. Like, tell me about that. So that's kind of how we approach it here at Shakespeare.

Justin:

Yeah, I like that approach to thinking about the skills, competencies, the traits. There's I've been teaching interview skills for nearly 20 years, and there's this quadrant that I use that's similar where on the horizontal axis it's either easy to teach or tough to teach. And on the vertical axis, it's nice to have or must-have. And too often we focus on things that are must-haves but easy to teach. And the things that we need to be focusing on are the things that are hard to teach and must-haves. Because that's where you know people are really differentiated. And it is in those things of systems thinking, problem solving, critical thinking, those things that they are dealt developable, developable, teachable. Go with it, yeah. Teachable in people, but it's not easy and it takes a lot of time. And sometimes there's kind of attitudinal things that get in the way. So yeah, I really like that approach that that you're using.

Matt:

And I would spend a lot of time honing those skills and a lot of times defending those skills. So, like I was a developer, like I was a software engineer, I built software, I knew how the metal worked. That was that. And it took a while for me to realize that those actually weren't the skills that got me the job or kept me in the job or made me useful, right? The skills that made me useful was my ability to learn quickly and my ability to do systems thinking and my ability to collaborate with other people. And I think too often we give short shrift to the sort of they're they're slightly more ephemeral. But if you go through as a manager and you actually have a structured approach to performance management in your company, you can sit down with people and be like, hey, let me talk to you about your superpowers. And this is actually the structure we tend to use. It's like, let me just tell you about your superpowers and let me tell you about your super weaknesses, right? And those superpowers aren't going to show up like you're the best gosh darn Golang programmer I've ever seen. Like, that's sure. Great. They're gonna show up like, hey, you're a really amazing systems thinker. Let me give you a couple examples of that so that you can take a moment to think about valuing that in yourself so that when you're looking for your next job, you can go ahead and say, Yeah, actually the things that I'm good at and the things that give me energy look like this. Let me go find that, right? I the best example I've worked in cybersecurity for years and ended up working with the incredibly talented person who was who did threat intel and her ability to match attackers to to to threat groups and like techniques and things like this was just unparalleled. And they were, they actually thought that was their job scale. And I was like, no, no, no, your job scale is in like connecting information. Like you can do that with anything, right? Like you, you like you could sit down with anything and almost immediately see the mappings between A and B and C and D. It's the way your brain is wired, right? You do that like breathing, right? That's your superpower, not yes, you're an amazing at threat on Intel, so maybe stay in that industry, that's fine. But the actual thing that you do, that's a thing that I think a lot of people fight because um, again, like what's valued on any given Thursday is my ability to slang some software.

Justin:

Yeah. I like two things. One is I really like the that when you call out someone's superpower, you give them examples, concrete examples of why that is having that illustration is so important. And I also appreciate your your vulnerability in sharing, you know, kind of what held you back earlier around those technical skills. Because I see it so often. And it's it's in in particular for people who invest a lot of time and energy into developing specific skills, they tend to overvalue those skills over the things that like you've got to do that job, right? If that's your job, it can be new software programming, you've got to be able to do that, but you it it never happens in a vacuum. And so you also need to be able to do that with people and collaborate and problem solve and all of those other things. So I really appreciate that vulnerability in sharing that. I'm curious, as you look forward, what does the future look like at Fixify?

Matt:

Well, so we're obviously a relatively early stage startup, so we're continuing to hire and build a company. We have got uh paying customers now, which is always an exciting milestone. We closed some really big ones recently. As we continue to grow the investments that we're actually making, and this is where I think it really gets interesting, is as you think about when you're building out a large-scale organization, and as the CEO or as the leadership team, your job, like one of your jobs, I guess, is capital allocation. So you start thinking about like, okay, where are we gonna put resources to solve certain classes of problems? One of the things that's interesting to me is when we think about injecting a lot of AI, making people much more effective with using tooling and things like this, we could produce a lot more software, we can produce a lot more solutions quicker, right? But, and you actually alluded to this earlier, it shifts the capital allocation strategy. It's not that we're gonna spend less on people. And this is a I gotta say, if I were to double underline something, there's actually an article today that I read and the Schadenfreud was real. The number of CEOs I hear who are like, we're gonna cut costs by getting AI. And I'm like, okay, cool. But I don't know that we're far enough along the AI route that we can really be like, oh, we're totally cutting costs. What I am doing is reallocating them, right? And so we're spending a lot of time thinking about, okay, where do I need to put in quality assurance or where do I put in people to and think about them more as managers of a group of agents, that sort of stuff. But longer term, you know, building that out, establishing the larger market presence and building out teams around now that we have uh a growing customer base, thinking about customer success, customer support, and then continuing to build the internal team, both in engineering and our operations, the people actually do help desk work. And the thing there is as we do that, we're investing, we're starting to invest in management as a real craft. Okay. So think management training courses, think a performance management structure. So all of the internal gorp that it takes to actually run a well-functioning organization, because we could get to be 300 people and be totally dysfunctional, don't want to get there. So we're we're trying to get there on the glide path by making those investments.

Justin:

Yeah, that's great. Yeah, to make a little commentary on the recent article. What is disturbing to me is that a couple of years ago, when companies were doing layoffs, it was like shameful. And now there's this sense of pride. Like I won't just wrote read an article, I won't name the CEO by name, but like this kind of prideful announcement of how many people he was gonna lay off because of AI. I'm like, wow, okay, and is that gonna be the most effective way to manage your humans? Like because I know I know case studies where where CEOs have have hired someone to come in and automate like crazy with a promise of we're not gonna let go of people, and then they let go of the people, but then three months later, all of that automation broke because there were no humans tending to it. And then not sure how they're doing today, probably not too well, because I don't think many of those people wanted to go back to that company.

Matt:

Um, so it's I mean, just to call out, ever since Frederick Taylor did like the first work study, okay, we've been trying to take people and optimize them to the point where we don't need them anymore. And there are places where that works, and that's the thing I think we all need to embrace is there's places where honestly people don't need to do this labor, but there are so many places where they still do, and I think to your point, losing that view or like saying, Oh, well, it's like this this expansion of the robots is infinite. If it is okay, but it's definitely not today, right?

Justin:

Yeah, for sure. Well, Matt, thank you so much for joining me. If folks want to get in touch with you, what's the best way to do so?

Matt:

I'm on LinkedIn and active there, so just look me up, Matt Peters from Fix Fy and on LinkedIn. And also you can hit up our corporate website as well, fixfy.com.

Justin:

Great, thank you so much, Matt.

Matt:

Thanks so much.