ERP129 - Less Admin, More Impact: AI for MSPs

Matt, welcome to the Evolved Radio podcast. Thanks for having me, Todd. Happy to be here today. All right, so we'll get started as we usually do. Kind of give me a bit of a background on yourself, kind of where you've come to in the MSP industry, bit of your background and we'll jump into it from there. Yeah, definitely. So my background, I wasn't as an MSP operator at first. I've been a software engineer, worked in the sphere in different roles, mostly in operations in the end. And then I joined an MSP four years ago now and just as a CEO operation director, helping them scale and really going into nitty gritty and learning the trade I guess by doing lots of it. And that's how I came into the MSP sphere. So really by doing everything from tickets to trying to manage and scale operations in the, I would say lower, smaller to mid size MSP if we were growing. Okay. And current role kind of being a bit different as well. Yeah, definitely. So this MSP adventure led us to realize that we were well positioned to develop an AI agent that could help solve some of the issue we were trying to solve at first through human labor, through processes and in the end we ended up developing Mizo and that's now I am CEO of Mizo. So we're really deploying AI agents for our customers, helping them scale their operations with digital labor. All right, so, and this touches on one of the things I find really interesting about the MSP software industry is I feel like most of the really sort of darling success stories of the industry and probably 50 to 70% of the software out there really sort of develops out of some MSPs need. Right. Like I had front row seat to the development of IT glue and this was sort of a product internally that we needed as an msp. And every time we had a strategic session we talked about our, our strengths as an organization. Our documentation platform was always one of those. It's like there's an opportunity here, like this is good for us. There's, there's definitely an opportunity for others to kind of leverage the same strength. So you know, the, the software in the MSP industries is so much driven out of MSPs solving their own needs. I'd love to hear you kind of talk about how you guys identified that and how I guess sort of three stage process. A very long question, but I'll let you work through it and ask me if you need me to re prompt you on this as well. So how did you identify that this was something you guys needed to do, then determining you should do it yourself. And then when you sort of realize like, hey, there's a market opportunity, we should actually start selling our own solution, how did you guys sort of work through that process? Yeah, definitely. So our goal at first wasn't to get into the AI software or the MSP software company. Our goal was to fix our problem internally. So we were scaling, we had good traction in MSP. We were growing 50 to 100% annually for two, three years. So that meant revenue growth was great. Operation growth has its challenge. So we sort of tried everything and not everything. I mean there's a lot of things we could have tried too, but just trying to always hit the same nail the same way doesn't fix the same issue. And we were seeing that as we scaled. Adding more bodies to the problem, adding more people internally didn't get us the efficiency we were aiming for. So the realization was really we were at the conjuncture of AI agents or it wasn't even agents back then, it was AI or chatbots or the hyperscale of this world were getting more, I would say commoditized. ChatGPT had been around for a year or two and people were starting to see the value in that. We saw that technology as something that could help us. And that was really the, the moment where we thought internally let's try to do something out of it and let's try to fix some of the problems we have. And the first one we started with was, I guess the obvious one was triage and dispatch where we saw we spent a lot of time, we were one of those MSPs which probably you wouldn't recommend, but having one of the level 2, level 2.5 tech doing the triage and dispatch because we thought we needed that high level person to understand issues and be able to know who can work on that, how should they solve that issue. So, and this was obviously very costly. That person, we aimed her, that person to be sort of a service delivery manager, have service delivery manager, have dispatcher. It was just too much they couldn't do was more than a full time role. And in the end all that person ended up doing was tickets like the so, so the, the idea came from that if we had that problem we, we tried with processes to fix it. And we thought maybe the way to fix this is just to externalize that function and instead of hiring an admin to do it, we can do it through an AI agent. And that was really the start. It was, I guess more than two years ago. And we worked on the product for about six to seven months internally as an MSP and just iterating on it, starting with really basic categorization, renaming, just very basic stuff and seeing can we have a value of that. And it was like very, I wouldn't say hacky, but very low level integration with the PSA we were using back then, which was Halo and getting those features on and getting those, those replacement on or delegation on. And what we saw is that our back then dispatcher, level two dispatcher was able to delegate those tasks and we had even better results than humans sometimes. So I say humans are flawed in many ways. And what we saw is that the operations we did were often better from an ITIL perspective, from a standardization perspective and what our dispatcher would have done in that case. So after that six months we sort of stopped and said what did that project cost us and what did it give us? And our conclusion was that even though there was a lot of R and D investment, it's obviously expected and we knew about that, there was definitely a lot of value we got out of it. And we, I think we saw that it was just getting started. We as a company, as a software were just getting started. But also the hyperscaler, the AI capabilities on the model side and the product were also very early. I think we still are in many ways. So that was that point, that was December 2024 when we decided, yeah, let's try to make a product out of this. We had great results for ourselves. Let's try to see if others in the ecosystem could benefit from it. So we did that and we went into the market, went to other MSPs in our peer groups, other MSPs we knew and asked them do you guys have that problem? And the conclusion was a lot of people ended up having the same issues and we tried it out, developed some more integration with new PSAs, new softwares, just adapting what we had and we saw some good results there. So this brought us all the way here I guess more than a year later, a year and a half later. But I think I'm missing one part to your question. So we had the, I think you got it is like why, how did you do it? And then the pivot basically. I am curious though, how much of this was people being attracted to the solution based on what you had versus like I think we have something that we could market, right? Because like I've seen both of those where like a lot of people are going to peer groups or they're just talking to sort of like peer MSPs in the industry and you know, you're like, oh, you're doing this. Like, how are you doing that? Oh, like, can you show me this? Oh, this is really neat. Like, can I have this? Like before it's a product, was there an element of that or was it sort of a conscious decision? Before it was really sort of public to peers and other people in that you guys had in peer groups and stuff? I think it was a bit of both. So coming from having other businesses before, I've always liked branding. So we sort of thought of a name for the product and logos and I mean we started ironing on that of creating that company. But the first few MSPs we talked to, they were just seeing us as like, yeah, their friend developing something nice and sort of their in house development team just helping them do more. So I think, and it's a great relationship to have with your customers too, that proximity. But it was a bit of both. We thought about it as a product, about having that company, creating company out of it. But I think the first customers were really like, yeah, I want to use this too and show me how you're doing it and get it in my environment. And this is the problem I'm having too. And then you get a list of problems that you're having to which you can help solving. So that was really great. Cool. Okay, great. That also lends to. The other thing I wanted to explore here is like, I am continuously shocked. I've talked about this on other podcasts and all over the place because as I was traveling last year and late this year as well, about how we, the people that are leading edge on sort of AI and automation are pretty far ahead of, I think the industry average. So you guys were, I think, way ahead of the curve in the fact that you were developing this in house and with enough time to be able to then turn around and start or as a separate product. But I'm still shocked by the number of MSPs that are not on AI in a meaningful way. Right. And I see a couple of points of pushback about this. And you guys, I think like having a lot of touch points with potential client prospects for the solution, probably see a lot of this. But you know, the couple of points of pushback that I see are like, people just don't have the time to know that the solutions are available. Like I put out this, this is the one I've sort of banted on a Lot about is I put out this call out to a group of MSPs and said, if you had an AI army to build a solution for you, what would it be? And invariably, nine times out of 10 people described exactly what you guys do and for that matter, several other solutions in the market. I'm like, come on you guys, what you're asking for exists. Go get it. So I'm partly flabbergasted by the fact that these solutions are not as prevalent as they probably can and certainly should be in the, in the wider ecosystem. And the other part that I see is, you know, people feel that it will somehow change something tangible about the human connection. And I see this maybe as an extension of a lot of owners are really sort of precious about live answer to a point where it becomes actually problematic for the MSP where like we have to have a live answer, you know, we don't want, you know, it has to go straight to the tech, you know, like we can't have anything go to voicemail, blah blah, blah, blah, blah. And that becomes problematic over, over time. And I don't think it's as important as people think it is. And maybe it's sort of related. Like you said, you actually had better results utilizing the AI than you did with human labor in a lot of those cases. Right. So maybe if, again, kind of a long, long question, but just exploring that idea of like, why isn't this more prevalent yet, considering how we should be more technically a bleeding edge in this industry and AI has been around long enough that it sort of blows me away that it's not more prevalent in a lot more msps right now. Yeah, it's, it's actually a great question. And I mean it's, it's something we, we often ask ourselves. I think one of the, one of the issue or one of probably something that's slowing down this AI adoption is the hyper customization of most MSPs. I feel what we see is that no processes are the same. We often hear like that's my secret sauce and that's how I'm doing it. And it's also my, I want to keep it for myself. I don't want to share that secret sauce necessarily, but I think a lot of MSPs have been built on hyper customization or hyper specification of processes where everything is very specific to their operations and their company and their context and build around that. Most people probably feel that AI wouldn't be able to understand that or to adapt to those situations. While it's completely the Inverse. If there's something that's good at getting context and getting an answer out of it, it's no, it's good at being adaptable to adapt to any context. So I think that's one of the things that's slowing down adoption. The other one is probably maybe, and this is really speculation my part, but MSP is being really at the leading edge of technology. I think they've been the first to see the problems with AI. It was probably two years ago, like there was some hallucination you would ask ChatGPT whatever question it would probably give you something wrong and for, for many different reasons. This happened back then. And I think a lot of the hyperscalers have worked on that. The AI labs have worked on that for, from their perspective. Also the prompting side on the agent side, there's a lot of improvement that were done but I feel like sometimes people are still stuck there. Yeah, I'm giving it something and I get AI slot out of it. This is maybe something we're scared about and it ties into the service quality. I feel like most MSPs want to keep their service quality at the highest level possible and feel like having a conversation with a badly trained AI agent with their customers would lower their service quality. But while it's inverse, if you have something really good that is fine tuned to how you work as an msp, you get those results much better. And people would, I think would rather chat with an AI agent, have a conversation that's tickets all very fast and they have a human in the loop where it needs to be and still be able to talk to a human if they need to while instead of waiting for a human to be able to solve it and then having that really inconsistent service quality. So these are my takes. I mean there's probably more to it and I'm curious to get maybe your impression how you feel about that. I think you're right, the quality does matter. I'm not saying that people are sort of misguided in wanting that obviously better quality service, especially at the front line is important. It's a service based industry and if your first interaction with the help desk is terrible, then that sets a really bad tone for that relationship as a whole. But I think to your point, like I had Megan Giholy on I guess a couple of years ago now, like it was a long time ago and we were talking about AI agents, right, like customer service agents and she said, you know the problem, like AI agents are actually really good. The problem is most of them are terrible. So like our expectations are so low because like the implementation of them are bad, but when they're done well, they're amazing. And that's sort of like it's the quality of the service that you provide, both in a human interaction but also in an agentic interaction I think matters. Right. And to some extent, I think a lot of the MSP industries kind of run off their feet and they look for cool solutions around automation, but they don't really have the time or the bandwidth to capitalize on those things. And I think it's maybe just sort of the same thing. Right? So yeah, those are some of the big ones that I tend to see as sort of the sticking points for this. But the other point that you make I think is actually really important for how you guys are a bit different than a lot of the other solutions in the industry is that so from a philosophy standpoint, like you guys are much more facing the technician than it is facing the client. So you guys don't really have those problems. I mean right now, depending on what your roadmap looks like. But right now a lot of the agentic interaction is with the tech, like trying to make them more expedient, providing them context and information rather than trying to front end the conversation with an AI and then sort of creating that risk of a bad interaction between an AI and an end user, I guess, right? Yeah, definitely. And we really see the value, and I mean the value of technicians are into solving hard problems to solve and also creating that customer relationship. You have an MSP as an SMB. You do business with an MSP because they are your IT department, you know them probably by name or you have a relationship with them. And that's the value for technicians in the msp. And we see ourselves as just helping those technicians do more. And for us the future is about having technicians manage fleets of agents and mostly delegating most of their tasks, if not all their tasks to AI agents and just being able to have them in the loop where they need to be, if there needs to be human contact for some reason, if there needs to be an approval for some risky operation. But our goal and our philosophy is to have those technicians perform better and reduce the amount of low value tasks and non rewarding tasks they do. And if you ask a technician what they do on a daily basis and what they like doing on a daily basis, I mean it's that Venn diagram isn't that big. And they like to solve problems, issues and they don't like to create reports and document resolutions and send customer requests for a meeting or whatever. So all those lower value tasks that are not, first of all they have low value for customers, they have low value for technicians. It's something we can easily help them with and also elevate those technicians to higher tests that devalue more. So this is really philosophy we have and all the interactions we're having are always transparent. So even though we have interactions through the PSA with some of our, with end users, everything is done on behalf of the technicians. So we're just helping those technicians do more and reduce the amount of work they do by themselves for those interactions. Okay, great. I guess the other thing I wanted to touch on here is as people may have, may have picked up with your, your fantastic accent is. You're a Canadian. French Canadian. And this is another aspect of the MSP industry that I find really fascinating. And like there's a long history to this. Like I obviously, you know, it Glue and Scalepad, fairly recognizable Canadian brands. Also Passportal. But even all the way back to like Enable, you know, being out of Ottawa, like a Canadian company. There's a really long history of MSP software in Canada which is not a hu, especially relative to the us. I mean you guys must have thought about this. But like why do you think that is that there's, you know, as Canadians we tend to punch above our weight in the MSP industry when it comes to software. It's a great question. And there's, there's probably two parts to it. I think the, the first part is the, the SMB rich ecosystem in Canada. So a large part of the Canadian economy is based on SMBs. Like it's probably higher than most Western countries. So this means more SMBs, more MSPs to serve them. So this is definitely something that we see that there's a lot of MSP and this is probably a lot, there's even much more in the United States, but there's many MSPs in Canada. So as a, as a Canadian company or a Canadian msp, you have much, you have a lot of ground to test your product on different customers. And so that's, I think that's one of the first one. And what we see is that In Canada most MSPs will have smaller clients than what we see in the United States. Less co managed. So they have larger client bases, meaning that they have well, larger amount of clients or count of clients which means that they have more diversity to test their ideas. I think that might be one of the first one just being like ASMB fertile ground. The other one might be just related to innovation savviness in Canada. I think as Canadian we're much less risk prone than United States. And you see this in VC world, you see this in any startup world and that's why we have such a deficit probably in productivity and in innovation. But launching a product in the Canadian market is probably much higher bar to launch because people are very, very demanding. I think since they are less prone to innovation, Kinean MSPS will want to have a product that's much higher quality. So this might be something just in having the philosophy in how canium companies build product they want to build. And it's always a challenge when you're building a startup but you can never launch too early probably and if you feel like you've launched at the right time, it's because it's too late. But I feel like this is something that Kenyan companies might be more inclined to having that higher standards, higher bar for product quality and that might lead them to creating better products. But this is honestly just our take on it and speculation. I mean you guys are a Canadian software development company, so your opinion matters in this matter. Just curious, did you guys leverage like shred for those not familiar and especially those not in Canada, this is scientific research and experimental development. It's like a, like a tax incentive program that the Canadian government has for, you know, anything scientific, but it also lends to software development and process innovation. Was that something you guys were able to leverage? Yeah, definitely. So as you mentioned, there's shred, but there's many other governmental programs to accelerate. Yeah, Iraps one of them too that you can. So yeah, we leverage all of those actually. So as Canadian also we have that advantage of being able to subsidize most of our development R and D, I wouldn't say subsidized, but get those tax returns for it helps, right? Yeah, definitely. It does make a big difference. That paired with having great talent pool is probably something that that helps a lot. For those again, not familiar with Canadian sort of markets and you guys based in Quebec. There's a lot of software development like Ubisoft and a lot of the Canadian software development houses. EA is Canadian west coast. But Ubisoft being huge in your region, that's the one that sort of comes to mind for me. But I'm sure there's a ton of other sort of companies that tend to create a bit of a gravitational force for good talent in the region, right? Yeah, definitely. And I think us being based in Montreal Montreal has the longest survey with AI being one of the first cities to have AI labs from Meta that have been here for a while now. Google also has an AI practice in Montreal. This AI I think there was a good movement around AI five years ago generating a talent pool in Montreal and generating those various deep research on AI development in Montreal. And this obviously shifted a lot to applied AI now as the fundamental research is mostly taken care of by the big labs. But I think we have that talent pool that stayed there that has been doing AI before AI if you want. I mean even Geoffrey Hinton, the godfather of modern AI from Toronto, right? Yeah, exactly. So we have a lot of. So we have a lot of those talent in Canada that have been at the forefront of the research before it got before it scaled as it has. So I think that talent is really leverageable to apply those principles and start from first principle and not just learn about how ChatGPT works from reading the API doc, but really understanding the fundamentals, fundamentals of models and being able to really fine tune the usage to how it should be used. Yeah, cool. The other piece of this kind of lends to small upstart David and Goliath type approach. I'm curious sort of how you think about your opportunity as a small development company relative to a lot of the PE led companies, you know, the big giants in the industry. I personally feel like there's a strong and movers advantage for smaller companies to be able to iterate and innovate and improve upon those platforms certainly at a faster cycle and than some of the larger companies. I assume you think that that's true as well. But I'm be curious kind of how you feel like you guys have a competitive advantage as a smaller entity being able to capitalize on on sort of market opportunities and be a bit more nimble. Yeah, I think we try to do what we preach really internally and I feel one of our main advantages being an AI native company and what we define as an AI native company, and there's probably a lot of buzz around it, is that even though we're a small team, we have agents working for us doing all sorts of tasks and on the marketing side, on the development side, obviously on the account payable size on the accounting side. So we deploy our own AI agents for many tasks internally so we can leverage that technology and we can also create processes around using those AI agents and not having to rethink already scaled processes to how we should use AI internally so we have the opportunity of leveraging AI everywhere. We need, and this is really, I think one of the main advantage and it leads to, as you mentioned, faster iteration cycles. And I think we have iteration cycles that you can match in any industry. And I mean we try to stay on top of that, but we ship multiple times a day. And this is something that with such a small team that we can do as an AI native company that a lot of the incumbents wouldn't be able to do. And I think even starting five years ago, you still have a lot of that gap to bridge because you've built your processes and you hired people and you build your company processes into working how they should work around people. And we are building our processes into how we should work around agents and how humans can intervene in the right place in the loop through agents. So this is, I think it's definitely a fundamental shift that just creates more faster iteration cycles and in the end better product quality for the customers. That sort of reminds me of reminiscing back in the good old days of Connectwise when Connectwise again they were building their own software for themselves as an msp. So it sort of speaks to this kind of building to scratch your own itch. But I think part of what made them successful in the very early days was they were building it for themselves and being able to test the things that they needed, solving for their own problems. And I think because you guys were born out of an MSP and still have sort of that, that high touch point of like what do we need, what do we see internally, what's working and then sort of iterate from there. It gives you that, that higher fidelity understanding of what's required to make this work in an msp, right? Yeah, definitely. And I think one of those advantage also is that we're not afraid to change things. We built something that didn't work, we'll just scratch it and start again. And that's something we can do because we have that proximity and we can create that iteration cycles, but we also have that velocity of developing it. So where I think as a larger company you have to plan ahead and create that big roadmap and then you launch and hopefully it works and you do all that product led development all around. But I think the processes are so much larger that you end up probably launching a product a little too late where it doesn't exactly fit the need where it's at now. So being in an MSP and also having that evolution speed allows us to continuously adapt and our customers are changing on a day to day basis. And we talked earlier about how they are starting to adopt AI. But AI will obviously create a lot of process change for our customers. So we need to be at the forefront of that and being able to adapt to what they are currently doing and will be doing in a month or in a year. Yeah, but lends well to a question. Before we started recording I was like is this worth asking? But I think you had a good answer to this. So we can kind of go down this road and it sort of lends from that is, you know, the future of AI. Right. And totally recognizing to your point. I always joke that the MSP industry and IT in general has a six month shelf life. Right. And now like I would say with sort of the cycles of AI and how quickly things change, it's like maybe three to six week shelf life. Like things are moving so fast. Right. And I'm curious sort of how you think about, you know, planning for the future for in an industry that is subject to such radical change within a six month period. Yeah, it's. I think it's a great question. I feel like the, when you look at the fundamentals of the industry, they won't be changing like and the MSP industries are all about service quality. It's all about providing service, first of all service quality. And there's a lot of probably uncertainty about how the business will evolve. And I think PAX8 and a lot of players are pushing for that MIP which is obviously really great into how the MSPS can evolve into being strategic advisors to their customers, into uptake technology which obviously includes a lot of AI. But I feel like this is a direction where we're going in and this means that all the tasks to be done will remain the same. It's just that our customers will want to focus on tasks that have a higher value for them and this will be all the strategic advisorship for their customers. They will want to have their best tech players and their best technicians into advising and upskilling them into advising their own customers into how to take that AI turn and leverage AI as much as they can internally. So we have that opportunity of having them delegate all their lower value tasks to us and all their service desk. And this comes into all those agentic level one, just doing the full level one agentically and doing a level two in the same ways and really removing all those lower value tasks so that the msps can focus and where do they deliver the most values and change that part from their P and L of the support team or the help desk from being cost center to being just a cost of doing business. And they will generate a lot of good revenue out of that and increase their capacity to generate even more revenue through the advisorship. So yeah, I certainly think that is the direction of the industry is sort of a joke that we're kind of going back to the 90s where there's a lot more consultative work that's happening where you know the agents can take care of the a lot of the rote work that needs to happen still in a lot of environments. I know you and I have talked about back in the day when working on compact computers and we had like a, like a self healing floppy disk that we put into it and this has always been the dream of like telemetry tells us the things that we need to do. Computers take care of it by themselves. Right. But, but we're almost there so it's exciting times for sure. This has been great. Matt, anything we haven't touched on or any other sort of last minute topics you want to hit on before we wrap? No, I think that sums it up. Thanks for that conversation. Thanks for having me today and I'll. Link to you and Meso in the show notes as well. Just a quick call out if people want to hear more about what you do or connect with you. What's the best place to connect with you and do that? Yeah, of course you can reach out on LinkedIn or email me at Mattizl Tech and I'll answer. So happy to chat with anybody, happy to have conversations around AI, around how to deploy it in your msp. So looking forward to that. Thanks Matt. Thanks to you Todd.