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.