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.