I'm Adam Larson and welcome back to Count Me In,
the podcast by and for management accountants.
Today's guest comes to us from the forefront of the data automation revolution.
Anthony Nitsos, a proud CMA, a consulting CFO,
and the founder of SAS gurus shares the unique story of how he
transitioned from pursuing a career in medicine to how he discovered the power
and beauty of accounting.
From explaining how accounting forms the spinal column of any manufacturing
business to practical advice for writing the coming tidal wave of financial
automation,
Anthony's insight and expertise is important listening for management
accountants everywhere. Enjoy the show.
Anthony, thank you so much for coming on the podcast today.
We're really excited to have you on and today we're gonna be focusing in on
automation and what that means for the management accountant. But to start off,
I wanted you to kind of tell us a little bit about your story.
So thank you, Adam for that.
And I really appreciate being on your program today. You know,
I've had an association with the IMA for a long time,
which we uncovered in our kind of a preliminary,
so that'll be part of the story.
But I actually started off in medicine of all things.
I was accepted into an accelerated program at the university of Michigan at the
age of 18, but several years into it,
I realized I really did not want to be a doctor.
So it was one of those kind of all right,
well you're most of the way to a doctor and you've kind of got a bachelor's
degree to show up for,
but what are you gonna do with your life now if you've decided not to go in
medicine. So I think by, you know,
stint to the fact that they both started with am,
I went into manufacturing right after medicine.
I don't know if it was anything more than that, just like, okay, I need a job.
I need to, you know, make money. But the strange thing is,
is what they had me do was really, you know,
kind of the beginnings of process reengineering analysis and trying to figure
out why in this particular case, you know,
logistics were breaking down material, wasn't ending up where it went.
So I got kind of a baptism in fire.
What it showed me was that corporations are very similar to bodies. You know,
they even, you know, means the same thing.
So this training that I got in medicine actually translated pretty well into
manufacturing. And so there, I, you know, from there I took off. After
that, I did a stint where I was doing a lot of ERP implementations.
If folks recall back around the year, 1997,
everybody started panicking that their code would blow up when the year 2000
showed up. So there was this huge, you know, Y2K, doomsday disasters,
et cetera, et cetera.
And at that time I was picking up accounting skills.
It was one of those things where it was pretty clear that the impact of
manufacturing was absolutely a financial one in that, you know,
when you got right down to it,
you're making decisions on the shop floor that impact profitability.
So it was kind of a natural progression for me to just kind of move over into
more of an accounting type of world. And ERP really brought that together,
cuz that's where you really unify back then still in today, you know,
the operations of the company with finance.
And so that's where the accounting management accounting piece came into it.
And it was right around 1996 where I actually got my first certification in
accounting and it was the CMA.
And I remember that being really useful to me and still to this day,
I'm not gonna say it really stopped being useful because whereas the CPA exam
and I've taken that, and you know,
I've also passed that and I'm also a CPA, but I became a CPA later.
I was a CMA first because CMA was very broad based. And from my training,
you can't look at one part of a body anymore than you can look at one part of a
corporation it's an integrated systemic whole.
And so how those pieces work together and how they work most
efficiently together, the principles are very similar between medicine and,
you know, process reengineering. They really are, you know,
you go after the root cause the idea of medicine is not to treat symptoms.
I know there's a big debate about that,
but really what we are trained to do is find the root cause and fix it.
And manufacturing is no different and neither is IT.
So moving from the physical body to the physical manufacturing now to a
more, you know, electronic realm,
bringing ERP and all the systems and how they touch everybody together and
unifying that ultimately in a framework,
which was based in accounting in my mind, because in my mind,
the accounting pieces, like the spinal column of the body,
you really build everything off of that. All of your reporting,
all of your metrics comes off of that.
And so focusing the attention to get to the numbers most accurately,
most efficiently really became kind of the focus of my next position,
which was a controller for a Japanese company.
It was a company that was a manufacturing company that had been purchased by,
was an English company that had been purchased by the Japanese,
excuse me and the president at the time really wanted to have his own
money guy rather than have somebody from Japan come in and do the numbers.
And so he quickly moved to hire a new controller and that was me.
And so at that point we knew we were going to scale the company
10 times within the next three years. And so my experience in accounting,
the fact that I also spoke Japanese, cuz I had actually studied there,
helped out, I understood the cultural kind of the, you know,
became kind of like the cultural liaison with the Japanese people when they
showed up and then going over there. But that was kind of a side issue.
The big issue was they gave me opportunity in a Greenfield implementation
to design the entire data collection, information, reporting,
financial reporting and whatnot for what was going to be a $50 million company
that I had inherited at 5 million using Peachtree.
So here I am,
freshly minted CMA got his first controller job,
applying all these skills and saying, okay,
we now get to design a data collection piece for all the production data,
all the manufacturing data, all the material data, all the labor, blah, blah,
blah, in a way where we really kept the cost down.
So this was my first and in some ways,
best experience scaling a company because I was actually given that power.
I was given the authority to basically design the system.
And so we had at the time when we started that 5 million,
there was myself,
a full-time accountant and kind of a half-time payroll person running, you know,
the entire back office. When we reached 50 million,
I had the same three people, only the payroll person was now full-time.
And so we were able to,
by applying Japanese manufacturing principles and techniques
of totally total quality management, plus Six Sigma black belt process,
reengineering analysis,
plus my training in ERP systems and what could be done.
And at that point,
the state of automation was you drove everything off of barcode scanners.
And so once everything was set up easily, so each person had their own badge,
their own employee badge.
That was what they used to swipe in and out of the clocks and also what they
were used to swipe in and out of jobs. And we made it easy for them to do that.
We had readers everywhere and the jobs themselves had their own codes.
And so all you had to do is just match the two up in a system and boom outfalls
the data. So it was the principle of a single point of data entry,
which was the scanners. Poka-yoke,
which in Japanese basically means idiot proof, makes it full proof.
So you can't make a mistake. So there's no typing in of job numbers.
There's no typing in of labor numbers, it's all being scanned, right?
And so by being able to do that,
you could then control literally every piece of data coming into the system in a
high quality manner,
that easily attained Six Sigma and beyond in terms of accuracy. And,
you know,
basically made lives easier so that data was producible and allowed the
organization to scale because you can, now,
all you're doing is just turning up the volume to 11 on a system,
that's able to handle it.
And so kind of in a journey that that's where I ended up in the early 2000's
is at that point in my life is having achieved, you know, this level of,
you know, automation and sophistication in a very real world sample.
That's, that's quite a story. And you know,
the fact that 20 years ago that level of automation you were able to accomplish
and now fast forward to now, you know, automation is everything.
People are talking about it. They're talking about RPA,
they're talking about AI coming and doing all these things and helping out the
finance function has anything really changed from automation in,
you know, 20 years ago to now the fact that, you know, since, you know,
you've been doing it for so long, what,
what has changed and has it changed really? Or what is the same?
Well, okay, so it has changed and it is somewhat, and is also the same.
It's the same, I think from basic principles, right?
The basic principle of a single point of data entry data creation
is the point of greatest impact of quality. So you get the data in,
right the first time that principle hasn't changed. You know, going to,
you know, applying, you know,
the hierarchy of controls where prevention comes before detection comes before
correction and keeping that firmly in mind when you're designing that you can
take basically a system in manufacturing,
Six Sigma at the time was considered to be state of the art. You had quality at,
you know, 99.99966%, whatever it was,
that was the three sigmas off the mean, you know,
so you're basically three parts per million. Well, in a computer system,
there's no such thing as three parts per million failure.
If we had a three parts per million failure in a computer system, you know,
there would be havoc, all right. It would be chaos.
In a computer system it's effectively a hundred percent.
So you go from Six Sigma to infinite Sigma, if you will.
And that's a level of quality that you simply can't attain in a manufacturing in
a real world setting, not yet with our current state of technology,
there's science fiction about how that, you know,
nanotechnology might change all that, but we're not there yet,
but in a computer system you can make it pretty well,
a hundred percent accurate. And from a quality control standpoint,
you're not applying the principles differently.
You're going from single point of data entry, make it correct upfront.
So preventive type of mechanism. So you can't, so you idiot proof it,
you poka-yoke it. And voila,
you end up with systems that are now much faster at taking in
vast amounts more of data,
but what you do with it is now the different part, right?
And the methods that the data are delivered are different, you know,
20 years ago,
having an on-premise solution of your own servers with your own installed ERP
system that you paid a license for one time, and then maybe you had a, you know,
an ongoing maintenance agreement, but you know,
the software was yours effectively. We are now in a different world. Well,
different only in probably I think methodology,
but not in what I call paradigm basics. Okay.
So the server is now called Amazon web server and,
or Azure or whatever you happen to be using,
but there's still a box sitting somewhere that's crunching numbers.
It's just not yours anymore. All right. So, and then the application,
in this case, you're not buying an ER, you know, you're not buying a license to,
you know, JD Edwards or Bon or whatever it is.
You're now subscribing to NetSuite and intact, and you're paying an ongoing,
you know, fee for that. Well,
that's still software only in where it's really paid off, of course,
is that you,
your own organization doesn't have a huge IT bloated infrastructure to support
all that on premise type of activity. So in terms of, again,
that just makes things more productive. I now, you know,
just how it's impacted me as a, you know, a fractional CFO, if you will,
you know,
finance expert and a process re-engineer is I can serve 30 or 40 clients from
my desktop, directly. Right.
And as I multiply that with the other people in my organization that reaches
many more, but in terms of, let's say my direct clients that I deal with,
I can easily handle 30.
Well that's only because I can access every one of their accounting systems
online at will easily.
Data's immediately available that everything is structured in a way to make it
efficient. So, you know, the way you put the tools together now is the same way,
right back then we were using barcode scanners, using barcodes, collecting over,
you know, network, hard wire into a server that was then, you know, you know,
step all the way through it. Those steps are effectively the same,
but it's now you have more flexibility in which you can plug and play
together. So that's changed.
So the breadth of things that are available to us as, you know,
financial professionals, you know,
and accounting professionals is much broader and richer than, you know,
we had Peachtree and QuickBooks. That was pretty much it.
Or you spent $5 million to go to an ERP system because you had to buy this huge,
you know, IT, so there was like this tiny little piece that you could,
everybody kind of fought and made things work in.
And then there was this huge leap up. Well,
there's nothing in that middle real estate. Well,
there's a lot of stuff in that middle real estate now.
So it makes available to, you know,
another thing that's changed is the size of the companies now that can access
these kinds of automation tools, it's much smaller than it was in the past.
You know, we were a large-ish manufacturing company, a conglomerate,
we were one plant of like 20 in North America and then worldwide, you know,
1500, right? So we had capital available to us so we could buy these systems,
even though we were too small for it.
But back then a company that was $5 million in revenue on its own
good luck trying to get the money to, you know,
invest in something like that. Today, I can go subscribe to QuickBooks.
I can subscribe to HubSpot or nutshell if I want a really cheaper version,
I can go out and there's so much available in what we call the SMB,
the small and medium business market available to us that now it's kind of
taking the knowhow of putting those together in a way that's
most efficient for, let's say an industry. And that's,
that's where we focus our attention. We're, you know,
very focused on SaaS companies and setting up their infrastructure,
using all these tools and all this past practice to come up with a really,
even today, the most efficient way of delivering numbers.
So the goal's not any different. Turning raw data into useful information,
right? That's what we do as management accounts.
That is our primary function in life is to translate, you know,
what's happening in the real world into something that makes sense for
executives on a financial basis. Let's say it's all, you know,
it is about the money, right, but mixed dashboards too.
And that's where the breadth of the IMA education, you know, comes into it.
The CMA education is that you're across multiple areas of the organization,
looking at the whole theme systemically.
And that's what it takes to put data systems together.
Now to the point of AI and where this all fits in, in machine learning.
Even now, QuickBooks is, you know, first we started off with rules, right?
Where you could classify transactions as they came in using rules.
Now it suggests rules. And if you turn on their more advanced feature,
it will actually just do it for you, create the rules. And then, you know,
you can go in and check later. So they're,
they're involving their machine learning capabilities inside of the accounting
system.
Accounting in particular is a very rules based,
you know, practice, right? We have GAAP, we have, you know, practices,
ways of doing things. And it's so it's, and it's also, you know,
very structured, right?
Because it's structured around this chart of accounts thing,
which goes back five centuries.
So it's a robust system if it's lasted that long. Right. Well,
that also means it's subject to machine learning because anything that can be
really systematized that's structured can then be replaced by faster what are
called heuristic self-learning systems. And that's what these things are.
So whether you call 'em machine learning, ML or AI,
what it is is as it has been all along computers,
doing things faster than people,
and the faster is now changing into not just the repetitive tests,
but the non-repetitive stuff. Oxford University, I think it was,
or one was one of the major universities in England and I can't remember,
but Oxford is the one that sticks in my mind did a study back and this was in
2005, 2006, if I recall,
and they were taking trends of automation at the time and applying, you know,
various analyses to different industries and said, you know, by the year 2050,
this was the headline,
by the year 2050 half of all jobs in the UA would be automated out of existence.
But if you read the paper, they were actually, you know,
they weren't being alarmist about, they're just saying, look,
that's not to say they won't be replaced by something else. But right now,
based on automation trends,
these jobs are likely to be automated out of existence and humans won't do them.
When you go back to my example of automating the Greenfield manufacturing,
we were doing everything by barcode scanner. Well, in the past,
there would be people writing numbers on things, right?
So you're just replacing people. And that's what the power of SaaS is nowadays.
And all of this is that ultimately what you're doing is you're replacing people
with tools that can do the jobs that they did for lower
cost. And so you're trading off people in their jobs. You know,
this is the human element for automation.
So the drive to, you know,
increase profits to gain market share to gain efficiency is all driven
ultimately by in many ways lowering cost and increasing sales.
What we do on the IMA side,
the CMA side of things is we control that cost piece. We're not really,
you know, we're out there selling that. We didn't, go into sales.
That's why we're in accounting and finance, right?
And so the long and short of it is, you know, you can use these tools, but,
you know,
keep in mind that what you're doing is you are automating out of existence,
you know, somebody's job. And in the top 10 jobs in this study,
accounting was like number seven at threat for automation.
And it makes total sense. It's a very structured, yes,
I know GAAP opens up, you know, alternatives, but you know,
if an AI can be taught how to beat the world's champions at chess,
I think it probably can be taught to figure out what the, you know,
the best GAAP solution is for a particular circumstance.
How close are we to that? I don't know. I mean,
it's probably not as far away as we'd like to think, to be comfortable.
Yeah. I think it's coming in that as accountants,
we should be aware of the fact that we've got kind of a choice.
We can ride that tidal wave or we can be, you know, drowned underneath it,
but the tidal wave is gonna come.
Yeah, it definitely is with, I mean, the rise of,
and I'll say software as a service or SaaS as you've been referring to it's
definitely taken over.
And I know that there's been lots of papers written and then lots of articles
and the things you've mentioned about how that it is taking away jobs,
but you know, there,
so there are big issues coming for the accounting and finance team as we do go
to things like SaaS, softwares and programs to help automate our systems,
it makes us become more efficient.
And as long as the data going into that is accurate,
all the rest of the stuff should be accurate.
So what can the accounting and finance do?
What are the issues that they are facing as they go into this world of trying to
ride the wave instead of being drowned under it?
Well, it's, it's, first of all, you need to learn how to swim, right. You know,
to extend the analogy.
And in this case means you need to get up to speed and up to date on what is out
there, you know, and there are a lot of resources out there to tell you,
you know, what's the best. So that's kind of number one. Number two,
and this has been something that I liked about the messaging of the IMA
and still do to this day is that there isn't really a decision anywhere in the
organization that doesn't have a financial impact. Therefore, you know,
we can legitimately look outside of our areas for opportunities to save in the
bottom line, and that could be finding a tool.
So I would rather be the one out there finding the tools than somebody finding
them and replacing me with a tool they found.
So number one is to learn how to swim. Number two is to swim.
You've gotta be moving forward.
If you think that your let's say you're in an organization and been
doing the same things over and over again, and it's resistant to change, well,
it could be that your organization itself is at threat because any organization
that doesn't adapt to changing environmental circumstances is, you know,
a Darwinian evolution. You know, if you don't adapt,
you basically don't survive. And the world is changing.
So to me, it's, you know, learn what's out there, they're available tools.
You can just start with a Google search, like automation tools for accounting,
and just start reading, you know, it'll come at you. It's not difficult.
It's scary is what it is. I mean,
it's frightening to think about the fact that the, you know, the livelihood
that you've been relying on for years might be a threat of being automated.
The reality is that that step isn't too far off of reality,
or that view isn't too far off of reality.
And so you should be reacting accordingly.
And with anything that involves, say a threat to your financial security, or,
you know, whatnot, or a risk to your financial security, you know,
we gotta mitigate it. And that means learning.
That means opening yourself up to new ways of doing things,
even if it seems really strange and radical. Here's an example.
This one I went through was a great, you know, great experience for me.
So I came from the world of Microsoft, right? Everybody in accounting, you know,
all of it in accounting is on windows machines. It's like,
that's just the way it is. It's Excel. It's QuickBooks on, you know,
for windows, it's, you know, Outlook it's, you know, all that stuff,
word cetera.
Then there's this whole other universe out there that's called G suite,
which does all the same things. And so when I went,
so what happens, I'd been doing fractional accounting, financial controller,
CFO work since about 2006 that's when I went independent,
actually partnered up with somebody and been doing that mostly since,
except there was a period of four years in there where I was actually an
employee.
And I went back to being employed because it was my second scaling opportunity.
And this time was now how to scale an organization,
10 times revenue in three years. But instead of being manufacturing,
it was software.
And there was no way I was in charge of everything because I was coming into a
system that was already set up. It was basically, how do you,
how do you fix the mess that's already there.
But in that particular period of time, you know,
that's where the learning how to adapt existing systems.
And suddenly what's out, there was this G thing. And they said, oh,
you don't have a Windows machine. Here's your MacBook. Oh. And by the way,
what's out, we don't do any Microsoft products here at all.
We're a security company, their security is too weak. Right.
I went to work for a cybersecurity company, right?
So the CFO hired me to build his entire back office,
which was what I had done before. Only instead of doing it fractionally,
I basically needed to become, you know, salaried again,
cuz this was a full time gig. And we did it in three and a half years.
We scaled the company from 13 to 130 million in revenue.
And we did it just basically by having a great
product, but it was security. So they, they gave me this MacBook and said,
you don't get Outlook. You don't get Office anymore. That doesn't exist here.
Here's your G suite account. And I was like, what? I mean,
I was like, oh my God. But
after three months I had so come totally around.
So here's where, you know, I'm using an example.
I was afraid to learn a new system, even though it was coming down,
the job that I came in. So it's all right, well,
you're gonna have to learn this.
You can hate it or you can learn it and learn it I did.
And I learned to love it and not because it,
I had to learn to love it because it was better.
And it was better because where Microsoft had created all these silo products
before and was trying to knit them together in a, you know,
kind of an internet based, cloud-based environment,
G suite from the beginning was designed that way.
And so as a collaboration tool,
it is far superior to anything that teams or one SharePoint
or whatever can offer.
And so that's why most of the tech companies I work with now are now G suite
houses.
I have one or two that are Outlook and because it knits together so easily,
that's why I can serve those 30 clients. They're all on G.
And so the ability to collaborate multiplicatively has greatly increased.
Why? Because I learned how to swim. I was thrown in the water. I had no choice,
but now I really embrace it. And you know, we're on,
we're on the lookout constantly. So, you know, I've heard,
maybe you've heard of bill.com.
You have folks using bill.com or Expensify or Concur.
There's a system out there now that knits all that together, that's free,
and comes with a corporate credit card.
And suddenly your automation increases dramatically and your cost go
down, but you're gonna replace anybody who's managing the Expensify instance.
And you're going to cut down on the people who are modifying, you know,
in using the bill.com instance,
you have to be willing to face those kinds of choices.
Again, the human element, accounting is not numbers.
It's also people and there are people behind it.
And that goes all the way back to my medical training. These are human beings.
And first and foremost,
all along every organization is composed of us and having a good,
fun job to do and not worrying about the, you know,
headsman's axe is something that, you know, we want,
but you have to be aware that you are in an industry right now that is subject
to automation and you should be learning how to embrace and use those tools,
not resist it.
This has been Count Me In,
IMA's podcast providing you with the latest perspectives of thought leaders from
the accounting and finance profession. If you like what you heard,
and you'd like to be counted in for more relevant accounting and finance
education, visit IMA's website at www.imanet.org.