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I'm Adam Larson and welcome
back to Count Me In,

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the podcast by and for
management accountants.

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Today's guest comes to us
from the forefront of the
data automation revolution.

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Anthony Nitsos, a proud
CMA, a consulting CFO,

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and the founder of SAS gurus
shares the unique story of how he

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transitioned from pursuing a career in
medicine to how he discovered the power

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and beauty of accounting.

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From explaining how accounting forms
the spinal column of any manufacturing

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business to practical advice for writing
the coming tidal wave of financial

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automation,

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Anthony's insight and expertise is
important listening for management

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accountants everywhere. Enjoy the show.

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Anthony, thank you so much for
coming on the podcast today.

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We're really excited to have you on
and today we're gonna be focusing in on

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automation and what that means for the
management accountant. But to start off,

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I wanted you to kind of tell us
a little bit about your story.

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So thank you, Adam for that.

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And I really appreciate being
on your program today. You know,

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I've had an association with
the IMA for a long time,

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which we uncovered in our
kind of a preliminary,

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so that'll be part of the story.

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But I actually started off
in medicine of all things.

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I was accepted into an accelerated program
at the university of Michigan at the

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age of 18, but several years into it,

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I realized I really did
not want to be a doctor.

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So it was one of those kind of all right,

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well you're most of the way to a doctor
and you've kind of got a bachelor's

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degree to show up for,

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but what are you gonna do with your
life now if you've decided not to go in

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medicine. So I think by, you know,

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stint to the fact that
they both started with am,

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I went into manufacturing
right after medicine.

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I don't know if it was anything more
than that, just like, okay, I need a job.

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I need to, you know, make
money. But the strange thing is,

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is what they had me do
was really, you know,

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kind of the beginnings
of process reengineering
analysis and trying to figure

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out why in this particular case, you know,

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logistics were breaking down material,
wasn't ending up where it went.

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So I got kind of a baptism in fire.

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What it showed me was that corporations
are very similar to bodies. You know,

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they even, you know, means the same thing.

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So this training that I got in medicine
actually translated pretty well into

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manufacturing. And so there, I, you
know, from there I took off. After

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that, I did a stint where I was
doing a lot of ERP implementations.

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If folks recall back
around the year, 1997,

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everybody started panicking that their
code would blow up when the year 2000

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showed up. So there was this huge,
you know, Y2K, doomsday disasters,

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et cetera, et cetera.

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And at that time I was
picking up accounting skills.

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It was one of those things where it
was pretty clear that the impact of

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manufacturing was absolutely a
financial one in that, you know,

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when you got right down to it,

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you're making decisions on the shop
floor that impact profitability.

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So it was kind of a natural progression
for me to just kind of move over into

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more of an accounting type of world.
And ERP really brought that together,

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cuz that's where you really unify
back then still in today, you know,

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the operations of the
company with finance.

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And so that's where the accounting
management accounting piece came into it.

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And it was right around 1996 where I
actually got my first certification in

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accounting and it was the CMA.

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And I remember that being really
useful to me and still to this day,

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I'm not gonna say it really stopped
being useful because whereas the CPA exam

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and I've taken that, and you know,

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I've also passed that and I'm also
a CPA, but I became a CPA later.

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I was a CMA first because CMA was very
broad based. And from my training,

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you can't look at one part of a body
anymore than you can look at one part of a

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corporation it's an
integrated systemic whole.

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And so how those pieces work
together and how they work most

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efficiently together, the principles
are very similar between medicine and,

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you know, process reengineering.
They really are, you know,

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you go after the root cause the idea
of medicine is not to treat symptoms.

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I know there's a big debate about that,

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but really what we are trained to do
is find the root cause and fix it.

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And manufacturing is no
different and neither is IT.

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So moving from the physical body to
the physical manufacturing now to a

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more, you know, electronic realm,

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bringing ERP and all the systems and
how they touch everybody together and

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unifying that ultimately in a framework,

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which was based in accounting
in my mind, because in my mind,

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the accounting pieces, like
the spinal column of the body,

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you really build everything off
of that. All of your reporting,

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all of your metrics comes off of that.

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And so focusing the attention to
get to the numbers most accurately,

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most efficiently really became kind
of the focus of my next position,

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which was a controller
for a Japanese company.

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It was a company that was a manufacturing
company that had been purchased by,

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was an English company that had
been purchased by the Japanese,

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excuse me and the president at the
time really wanted to have his own

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money guy rather than have somebody
from Japan come in and do the numbers.

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And so he quickly moved to hire
a new controller and that was me.

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And so at that point we knew we
were going to scale the company

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10 times within the next three years.
And so my experience in accounting,

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the fact that I also spoke Japanese,
cuz I had actually studied there,

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helped out, I understood the
cultural kind of the, you know,

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became kind of like the cultural liaison
with the Japanese people when they

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showed up and then going over there.
But that was kind of a side issue.

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The big issue was they gave me
opportunity in a Greenfield implementation

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to design the entire data
collection, information, reporting,

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financial reporting and whatnot for what
was going to be a $50 million company

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that I had inherited at 5
million using Peachtree.

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So here I am,

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freshly minted CMA got
his first controller job,

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applying all these
skills and saying, okay,

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we now get to design a data collection
piece for all the production data,

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all the manufacturing data, all the
material data, all the labor, blah, blah,

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blah, in a way where we
really kept the cost down.

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So this was my first and in some ways,

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best experience scaling a company
because I was actually given that power.

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I was given the authority to
basically design the system.

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And so we had at the time when
we started that 5 million,

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there was myself,

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a full-time accountant and
kind of a half-time payroll
person running, you know,

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the entire back office.
When we reached 50 million,

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I had the same three people, only
the payroll person was now full-time.

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And so we were able to,

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by applying Japanese manufacturing
principles and techniques

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of totally total quality management,
plus Six Sigma black belt process,

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reengineering analysis,

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plus my training in ERP
systems and what could be done.

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And at that point,

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the state of automation was you drove
everything off of barcode scanners.

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And so once everything was set up easily,
so each person had their own badge,

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their own employee badge.

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That was what they used to swipe in and
out of the clocks and also what they

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were used to swipe in and out of jobs.
And we made it easy for them to do that.

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We had readers everywhere and the
jobs themselves had their own codes.

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And so all you had to do is just match
the two up in a system and boom outfalls

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the data. So it was the principle
of a single point of data entry,

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which was the scanners. Poka-yoke,

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which in Japanese basically means
idiot proof, makes it full proof.

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So you can't make a mistake. So
there's no typing in of job numbers.

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There's no typing in of labor numbers,
it's all being scanned, right?

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And so by being able to do that,

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you could then control literally every
piece of data coming into the system in a

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high quality manner,

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that easily attained Six Sigma and
beyond in terms of accuracy. And,

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you know,

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basically made lives easier so that
data was producible and allowed the

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organization to scale
because you can, now,

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all you're doing is just turning
up the volume to 11 on a system,

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that's able to handle it.

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And so kind of in a journey that that's
where I ended up in the early 2000's

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is at that point in my life is having
achieved, you know, this level of,

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you know, automation and sophistication
in a very real world sample.

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That's, that's quite
a story. And you know,

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the fact that 20 years ago that level of
automation you were able to accomplish

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and now fast forward to now, you
know, automation is everything.

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People are talking about it.
They're talking about RPA,

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they're talking about AI coming and doing
all these things and helping out the

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finance function has anything
really changed from automation in,

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you know, 20 years ago to now the
fact that, you know, since, you know,

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you've been doing it for so long, what,

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what has changed and has it changed
really? Or what is the same?

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Well, okay, so it has changed and it
is somewhat, and is also the same.

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It's the same, I think from
basic principles, right?

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The basic principle of a single
point of data entry data creation

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is the point of greatest impact of
quality. So you get the data in,

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right the first time that principle
hasn't changed. You know, going to,

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you know, applying, you know,

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the hierarchy of controls where prevention
comes before detection comes before

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correction and keeping that firmly in
mind when you're designing that you can

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take basically a system in manufacturing,

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Six Sigma at the time was considered to
be state of the art. You had quality at,

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you know, 99.99966%, whatever it was,

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that was the three sigmas
off the mean, you know,

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so you're basically three parts per
million. Well, in a computer system,

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there's no such thing as three
parts per million failure.

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If we had a three parts per million
failure in a computer system, you know,

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there would be havoc, all
right. It would be chaos.

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In a computer system it's
effectively a hundred percent.

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So you go from Six Sigma to
infinite Sigma, if you will.

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And that's a level of quality that you
simply can't attain in a manufacturing in

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a real world setting, not yet with
our current state of technology,

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there's science fiction
about how that, you know,

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nanotechnology might change all
that, but we're not there yet,

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but in a computer system
you can make it pretty well,

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a hundred percent accurate. And
from a quality control standpoint,

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you're not applying the
principles differently.

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You're going from single point of
data entry, make it correct upfront.

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So preventive type of mechanism. So
you can't, so you idiot proof it,

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you poka-yoke it. And voila,

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you end up with systems that
are now much faster at taking in

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vast amounts more of data,

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but what you do with it is
now the different part, right?

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And the methods that the data are
delivered are different, you know,

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20 years ago,

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having an on-premise solution of your
own servers with your own installed ERP

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system that you paid a license for one
time, and then maybe you had a, you know,

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an ongoing maintenance
agreement, but you know,

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the software was yours effectively.
We are now in a different world. Well,

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different only in probably
I think methodology,

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but not in what I call
paradigm basics. Okay.

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So the server is now called
Amazon web server and,

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or Azure or whatever
you happen to be using,

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but there's still a box sitting
somewhere that's crunching numbers.

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It's just not yours anymore. All
right. So, and then the application,

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in this case, you're not buying an ER,
you know, you're not buying a license to,

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you know, JD Edwards or
Bon or whatever it is.

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You're now subscribing to NetSuite and
intact, and you're paying an ongoing,

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you know, fee for that. Well,

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that's still software only in where
it's really paid off, of course,

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is that you,

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your own organization doesn't have a
huge IT bloated infrastructure to support

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all that on premise type of
activity. So in terms of, again,

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that just makes things more
productive. I now, you know,

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just how it's impacted me as a, you
know, a fractional CFO, if you will,

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you know,

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finance expert and a process re-engineer
is I can serve 30 or 40 clients from

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my desktop, directly. Right.

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And as I multiply that with the other
people in my organization that reaches

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many more, but in terms of, let's say
my direct clients that I deal with,

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I can easily handle 30.

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Well that's only because I can access
every one of their accounting systems

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online at will easily.

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Data's immediately available
that everything is structured
in a way to make it

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efficient. So, you know, the way you put
the tools together now is the same way,

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right back then we were using barcode
scanners, using barcodes, collecting over,

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you know, network, hard wire into a
server that was then, you know, you know,

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step all the way through it. Those
steps are effectively the same,

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but it's now you have more flexibility
in which you can plug and play

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together. So that's changed.

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So the breadth of things that
are available to us as, you know,

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financial professionals, you know,

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and accounting professionals is much
broader and richer than, you know,

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we had Peachtree and QuickBooks.
That was pretty much it.

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Or you spent $5 million to go to an ERP
system because you had to buy this huge,

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you know, IT, so there was like this
tiny little piece that you could,

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everybody kind of fought
and made things work in.

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And then there was this
huge leap up. Well,

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there's nothing in that
middle real estate. Well,

231
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there's a lot of stuff in
that middle real estate now.

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So it makes available to, you know,

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another thing that's changed is the size
of the companies now that can access

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these kinds of automation tools, it's
much smaller than it was in the past.

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You know, we were a large-ish
manufacturing company, a conglomerate,

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we were one plant of like 20 in North
America and then worldwide, you know,

237
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1500, right? So we had capital available
to us so we could buy these systems,

238
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even though we were too small for it.

239
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But back then a company that was
$5 million in revenue on its own

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good luck trying to get
the money to, you know,

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invest in something like that. Today,
I can go subscribe to QuickBooks.

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00:14:16,021 --> 00:14:20,380
I can subscribe to HubSpot or nutshell
if I want a really cheaper version,

243
00:14:20,381 --> 00:14:24,140
I can go out and there's so much
available in what we call the SMB,

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00:14:24,141 --> 00:14:29,100
the small and medium business market
available to us that now it's kind of

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taking the knowhow of putting
those together in a way that's

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most efficient for, let's
say an industry. And that's,

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that's where we focus our
attention. We're, you know,

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very focused on SaaS companies and
setting up their infrastructure,

249
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using all these tools and all this
past practice to come up with a really,

250
00:14:47,750 --> 00:14:50,400
even today, the most efficient
way of delivering numbers.

251
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So the goal's not any different. Turning
raw data into useful information,

252
00:14:56,160 --> 00:14:58,960
right? That's what we do
as management accounts.

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That is our primary function in
life is to translate, you know,

254
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what's happening in the real world
into something that makes sense for

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executives on a financial basis.
Let's say it's all, you know,

256
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it is about the money, right,
but mixed dashboards too.

257
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And that's where the breadth of the
IMA education, you know, comes into it.

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The CMA education is that you're across
multiple areas of the organization,

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looking at the whole theme systemically.

260
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And that's what it takes to
put data systems together.

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Now to the point of AI and where this
all fits in, in machine learning.

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Even now, QuickBooks is, you know,
first we started off with rules, right?

263
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Where you could classify transactions
as they came in using rules.

264
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Now it suggests rules. And if you
turn on their more advanced feature,

265
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it will actually just do it for you,
create the rules. And then, you know,

266
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you can go in and check later. So they're,

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they're involving their machine learning
capabilities inside of the accounting

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system.

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Accounting in particular
is a very rules based,

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you know, practice, right? We have
GAAP, we have, you know, practices,

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ways of doing things. And it's
so it's, and it's also, you know,

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very structured, right?

273
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Because it's structured around
this chart of accounts thing,

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which goes back five centuries.

275
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So it's a robust system if it's
lasted that long. Right. Well,

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that also means it's subject to machine
learning because anything that can be

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really systematized that's structured
can then be replaced by faster what are

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00:16:26,841 --> 00:16:30,960
called heuristic self-learning systems.
And that's what these things are.

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So whether you call 'em
machine learning, ML or AI,

280
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what it is is as it has
been all along computers,

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00:16:37,641 --> 00:16:39,240
doing things faster than people,

282
00:16:40,020 --> 00:16:45,000
and the faster is now changing
into not just the repetitive tests,

283
00:16:45,360 --> 00:16:50,160
but the non-repetitive stuff.
Oxford University, I think it was,

284
00:16:50,161 --> 00:16:53,400
or one was one of the major universities
in England and I can't remember,

285
00:16:53,401 --> 00:16:56,960
but Oxford is the one that sticks in my
mind did a study back and this was in

286
00:16:56,961 --> 00:16:59,840
2005, 2006, if I recall,

287
00:17:00,420 --> 00:17:04,240
and they were taking trends of automation
at the time and applying, you know,

288
00:17:04,241 --> 00:17:07,600
various analyses to different industries
and said, you know, by the year 2050,

289
00:17:07,601 --> 00:17:08,434
this was the headline,

290
00:17:08,530 --> 00:17:13,080
by the year 2050 half of all jobs in the
UA would be automated out of existence.

291
00:17:13,980 --> 00:17:16,400
But if you read the paper,
they were actually, you know,

292
00:17:16,430 --> 00:17:18,840
they weren't being alarmist
about, they're just saying, look,

293
00:17:19,030 --> 00:17:23,280
that's not to say they won't be replaced
by something else. But right now,

294
00:17:23,281 --> 00:17:24,680
based on automation trends,

295
00:17:24,681 --> 00:17:29,119
these jobs are likely to be automated out
of existence and humans won't do them.

296
00:17:29,120 --> 00:17:32,960
When you go back to my example of
automating the Greenfield manufacturing,

297
00:17:33,330 --> 00:17:35,880
we were doing everything by
barcode scanner. Well, in the past,

298
00:17:36,080 --> 00:17:37,960
there would be people writing
numbers on things, right?

299
00:17:37,961 --> 00:17:42,600
So you're just replacing people. And
that's what the power of SaaS is nowadays.

300
00:17:42,601 --> 00:17:45,920
And all of this is that ultimately what
you're doing is you're replacing people

301
00:17:46,350 --> 00:17:51,000
with tools that can do the
jobs that they did for lower

302
00:17:51,440 --> 00:17:55,290
cost. And so you're trading off
people in their jobs. You know,

303
00:17:55,291 --> 00:17:57,970
this is the human element for automation.

304
00:17:59,060 --> 00:18:00,770
So the drive to, you know,

305
00:18:01,010 --> 00:18:04,450
increase profits to gain market share
to gain efficiency is all driven

306
00:18:04,451 --> 00:18:07,970
ultimately by in many ways
lowering cost and increasing sales.

307
00:18:08,400 --> 00:18:10,090
What we do on the IMA side,

308
00:18:10,091 --> 00:18:13,570
the CMA side of things is we control
that cost piece. We're not really,

309
00:18:13,571 --> 00:18:17,570
you know, we're out there selling
that. We didn't, go into sales.

310
00:18:17,571 --> 00:18:18,970
That's why we're in
accounting and finance, right?

311
00:18:20,750 --> 00:18:24,970
And so the long and short of it is,
you know, you can use these tools, but,

312
00:18:24,971 --> 00:18:25,331
you know,

313
00:18:25,331 --> 00:18:29,290
keep in mind that what you're doing is
you are automating out of existence,

314
00:18:29,750 --> 00:18:34,290
you know, somebody's job. And in
the top 10 jobs in this study,

315
00:18:34,720 --> 00:18:38,690
accounting was like number
seven at threat for automation.

316
00:18:39,270 --> 00:18:43,369
And it makes total sense.
It's a very structured, yes,

317
00:18:43,370 --> 00:18:46,290
I know GAAP opens up, you know,
alternatives, but you know,

318
00:18:47,691 --> 00:18:50,810
if an AI can be taught how to beat
the world's champions at chess,

319
00:18:50,811 --> 00:18:54,050
I think it probably can be taught
to figure out what the, you know,

320
00:18:54,051 --> 00:18:57,010
the best GAAP solution is for
a particular circumstance.

321
00:18:57,470 --> 00:19:00,130
How close are we to that?
I don't know. I mean,

322
00:19:00,131 --> 00:19:04,530
it's probably not as far away as we'd
like to think, to be comfortable.

323
00:19:06,980 --> 00:19:09,910
Yeah. I think it's coming
in that as accountants,

324
00:19:09,920 --> 00:19:12,990
we should be aware of the fact
that we've got kind of a choice.

325
00:19:12,991 --> 00:19:16,710
We can ride that tidal wave or we can
be, you know, drowned underneath it,

326
00:19:16,711 --> 00:19:18,470
but the tidal wave is gonna come.

327
00:19:19,060 --> 00:19:21,670
Yeah, it definitely is
with, I mean, the rise of,

328
00:19:21,671 --> 00:19:25,390
and I'll say software as a service or
SaaS as you've been referring to it's

329
00:19:25,391 --> 00:19:26,270
definitely taken over.

330
00:19:26,271 --> 00:19:30,150
And I know that there's been lots of
papers written and then lots of articles

331
00:19:30,151 --> 00:19:33,390
and the things you've mentioned about
how that it is taking away jobs,

332
00:19:33,609 --> 00:19:35,550
but you know, there,

333
00:19:35,551 --> 00:19:39,910
so there are big issues coming for the
accounting and finance team as we do go

334
00:19:39,911 --> 00:19:43,510
to things like SaaS, softwares and
programs to help automate our systems,

335
00:19:43,720 --> 00:19:45,190
it makes us become more efficient.

336
00:19:45,191 --> 00:19:48,350
And as long as the data
going into that is accurate,

337
00:19:48,869 --> 00:19:50,390
all the rest of the
stuff should be accurate.

338
00:19:50,840 --> 00:19:52,910
So what can the accounting and finance do?

339
00:19:52,940 --> 00:19:57,830
What are the issues that they are facing
as they go into this world of trying to

340
00:19:57,831 --> 00:20:00,150
ride the wave instead of
being drowned under it?

341
00:20:00,700 --> 00:20:04,630
Well, it's, it's, first of all, you need
to learn how to swim, right. You know,

342
00:20:05,311 --> 00:20:06,310
to extend the analogy.

343
00:20:06,311 --> 00:20:10,830
And in this case means you need to get
up to speed and up to date on what is out

344
00:20:10,831 --> 00:20:14,230
there, you know, and there are a lot
of resources out there to tell you,

345
00:20:14,231 --> 00:20:17,630
you know, what's the best. So that's
kind of number one. Number two,

346
00:20:17,631 --> 00:20:22,109
and this has been something that I
liked about the messaging of the IMA

347
00:20:22,359 --> 00:20:26,670
and still do to this day is that there
isn't really a decision anywhere in the

348
00:20:26,671 --> 00:20:30,869
organization that doesn't have a
financial impact. Therefore, you know,

349
00:20:31,119 --> 00:20:35,910
we can legitimately look outside of our
areas for opportunities to save in the

350
00:20:35,911 --> 00:20:38,830
bottom line, and that
could be finding a tool.

351
00:20:38,840 --> 00:20:43,190
So I would rather be the one out there
finding the tools than somebody finding

352
00:20:43,191 --> 00:20:45,430
them and replacing me
with a tool they found.

353
00:20:46,200 --> 00:20:49,359
So number one is to learn how
to swim. Number two is to swim.

354
00:20:49,970 --> 00:20:51,880
You've gotta be moving forward.

355
00:20:51,880 --> 00:20:56,280
If you think that your let's say
you're in an organization and been

356
00:20:56,281 --> 00:21:00,160
doing the same things over and over
again, and it's resistant to change, well,

357
00:21:00,161 --> 00:21:04,320
it could be that your organization itself
is at threat because any organization

358
00:21:04,321 --> 00:21:08,720
that doesn't adapt to changing
environmental circumstances is, you know,

359
00:21:08,990 --> 00:21:12,600
a Darwinian evolution. You
know, if you don't adapt,

360
00:21:12,601 --> 00:21:16,080
you basically don't survive.
And the world is changing.

361
00:21:17,140 --> 00:21:21,810
So to me, it's, you know, learn what's
out there, they're available tools.

362
00:21:21,811 --> 00:21:24,890
You can just start with a Google search,
like automation tools for accounting,

363
00:21:24,891 --> 00:21:28,810
and just start reading, you know,
it'll come at you. It's not difficult.

364
00:21:29,240 --> 00:21:31,530
It's scary is what it is. I mean,

365
00:21:31,531 --> 00:21:34,250
it's frightening to think about the
fact that the, you know, the livelihood

366
00:21:36,091 --> 00:21:39,850
that you've been relying on for years
might be a threat of being automated.

367
00:21:40,350 --> 00:21:44,130
The reality is that that step
isn't too far off of reality,

368
00:21:44,131 --> 00:21:46,090
or that view isn't too far off of reality.

369
00:21:46,091 --> 00:21:48,450
And so you should be reacting accordingly.

370
00:21:49,020 --> 00:21:53,450
And with anything that involves, say a
threat to your financial security, or,

371
00:21:53,451 --> 00:21:56,690
you know, whatnot, or a risk to
your financial security, you know,

372
00:21:56,691 --> 00:21:59,930
we gotta mitigate it.
And that means learning.

373
00:21:59,931 --> 00:22:02,890
That means opening yourself up
to new ways of doing things,

374
00:22:02,891 --> 00:22:05,810
even if it seems really strange
and radical. Here's an example.

375
00:22:05,811 --> 00:22:09,330
This one I went through was a great,
you know, great experience for me.

376
00:22:09,619 --> 00:22:13,410
So I came from the world of Microsoft,
right? Everybody in accounting, you know,

377
00:22:13,411 --> 00:22:15,890
all of it in accounting is on
windows machines. It's like,

378
00:22:15,891 --> 00:22:18,730
that's just the way it is. It's
Excel. It's QuickBooks on, you know,

379
00:22:19,030 --> 00:22:22,650
for windows, it's, you know, Outlook
it's, you know, all that stuff,

380
00:22:22,651 --> 00:22:23,484
word cetera.

381
00:22:23,920 --> 00:22:28,090
Then there's this whole other universe
out there that's called G suite,

382
00:22:28,770 --> 00:22:32,970
which does all the same
things. And so when I went,

383
00:22:33,180 --> 00:22:37,050
so what happens, I'd been doing fractional
accounting, financial controller,

384
00:22:37,051 --> 00:22:41,250
CFO work since about 2006
that's when I went independent,

385
00:22:41,410 --> 00:22:44,330
actually partnered up with somebody
and been doing that mostly since,

386
00:22:44,331 --> 00:22:47,650
except there was a period of four
years in there where I was actually an

387
00:22:47,930 --> 00:22:48,470
employee.

388
00:22:48,470 --> 00:22:52,530
And I went back to being employed because
it was my second scaling opportunity.

389
00:22:52,531 --> 00:22:55,250
And this time was now how
to scale an organization,

390
00:22:55,251 --> 00:22:59,090
10 times revenue in three years.
But instead of being manufacturing,

391
00:22:59,091 --> 00:22:59,930
it was software.

392
00:23:00,510 --> 00:23:03,930
And there was no way I was in charge of
everything because I was coming into a

393
00:23:03,931 --> 00:23:07,130
system that was already set up.
It was basically, how do you,

394
00:23:07,310 --> 00:23:09,609
how do you fix the mess
that's already there.

395
00:23:10,980 --> 00:23:14,490
But in that particular
period of time, you know,

396
00:23:14,520 --> 00:23:19,130
that's where the learning how
to adapt existing systems.

397
00:23:19,130 --> 00:23:23,170
And suddenly what's out, there was
this G thing. And they said, oh,

398
00:23:23,171 --> 00:23:27,530
you don't have a Windows machine.
Here's your MacBook. Oh. And by the way,

399
00:23:27,531 --> 00:23:30,609
what's out, we don't do any
Microsoft products here at all.

400
00:23:30,610 --> 00:23:34,410
We're a security company, their
security is too weak. Right.

401
00:23:34,840 --> 00:23:37,090
I went to work for a
cybersecurity company, right?

402
00:23:37,091 --> 00:23:39,850
So the CFO hired me to build
his entire back office,

403
00:23:39,851 --> 00:23:43,570
which was what I had done before.
Only instead of doing it fractionally,

404
00:23:43,640 --> 00:23:46,320
I basically needed to become,
you know, salaried again,

405
00:23:46,520 --> 00:23:49,600
cuz this was a full time gig. And
we did it in three and a half years.

406
00:23:49,601 --> 00:23:52,480
We scaled the company from
13 to 130 million in revenue.

407
00:23:53,410 --> 00:23:58,119
And we did it just
basically by having a great

408
00:23:58,120 --> 00:24:02,760
product, but it was security. So they,
they gave me this MacBook and said,

409
00:24:02,761 --> 00:24:06,440
you don't get Outlook. You don't get
Office anymore. That doesn't exist here.

410
00:24:06,441 --> 00:24:11,040
Here's your G suite account.
And I was like, what? I mean,

411
00:24:11,041 --> 00:24:12,160
I was like, oh my God. But

412
00:24:14,041 --> 00:24:18,580
after three months I had
so come totally around.

413
00:24:19,270 --> 00:24:21,340
So here's where, you know,
I'm using an example.

414
00:24:21,369 --> 00:24:24,460
I was afraid to learn a new system,
even though it was coming down,

415
00:24:24,461 --> 00:24:26,260
the job that I came in.
So it's all right, well,

416
00:24:26,261 --> 00:24:27,100
you're gonna have to learn this.

417
00:24:27,101 --> 00:24:30,020
You can hate it or you can
learn it and learn it I did.

418
00:24:30,119 --> 00:24:32,260
And I learned to love
it and not because it,

419
00:24:32,261 --> 00:24:34,060
I had to learn to love
it because it was better.

420
00:24:34,800 --> 00:24:39,540
And it was better because where Microsoft
had created all these silo products

421
00:24:39,541 --> 00:24:42,619
before and was trying to knit
them together in a, you know,

422
00:24:42,620 --> 00:24:45,340
kind of an internet based,
cloud-based environment,

423
00:24:45,650 --> 00:24:47,940
G suite from the beginning
was designed that way.

424
00:24:47,941 --> 00:24:49,740
And so as a collaboration tool,

425
00:24:49,741 --> 00:24:54,740
it is far superior to anything
that teams or one SharePoint

426
00:24:54,741 --> 00:24:55,700
or whatever can offer.

427
00:24:56,000 --> 00:25:00,820
And so that's why most of the tech
companies I work with now are now G suite

428
00:25:00,821 --> 00:25:01,654
houses.

429
00:25:01,850 --> 00:25:06,619
I have one or two that are Outlook and
because it knits together so easily,

430
00:25:06,620 --> 00:25:09,540
that's why I can serve those
30 clients. They're all on G.

431
00:25:10,530 --> 00:25:15,510
And so the ability to collaborate
multiplicatively has greatly increased.

432
00:25:15,730 --> 00:25:20,100
Why? Because I learned how to swim. I
was thrown in the water. I had no choice,

433
00:25:20,790 --> 00:25:24,090
but now I really embrace
it. And you know, we're on,

434
00:25:24,091 --> 00:25:26,770
we're on the lookout constantly.
So, you know, I've heard,

435
00:25:26,771 --> 00:25:28,210
maybe you've heard of bill.com.

436
00:25:28,211 --> 00:25:31,290
You have folks using bill.com
or Expensify or Concur.

437
00:25:31,740 --> 00:25:35,210
There's a system out there now that
knits all that together, that's free,

438
00:25:36,040 --> 00:25:37,740
and comes with a corporate credit card.

439
00:25:37,741 --> 00:25:42,619
And suddenly your automation increases
dramatically and your cost go

440
00:25:42,620 --> 00:25:46,540
down, but you're gonna replace anybody
who's managing the Expensify instance.

441
00:25:46,840 --> 00:25:50,260
And you're going to cut down on the
people who are modifying, you know,

442
00:25:50,460 --> 00:25:51,859
in using the bill.com instance,

443
00:25:52,119 --> 00:25:54,780
you have to be willing to
face those kinds of choices.

444
00:25:56,859 --> 00:25:59,820
Again, the human element,
accounting is not numbers.

445
00:25:59,850 --> 00:26:02,300
It's also people and there
are people behind it.

446
00:26:02,420 --> 00:26:05,460
And that goes all the way back to my
medical training. These are human beings.

447
00:26:05,660 --> 00:26:06,461
And first and foremost,

448
00:26:06,461 --> 00:26:11,140
all along every organization is
composed of us and having a good,

449
00:26:11,141 --> 00:26:14,180
fun job to do and not
worrying about the, you know,

450
00:26:14,181 --> 00:26:17,220
headsman's axe is something
that, you know, we want,

451
00:26:17,720 --> 00:26:21,700
but you have to be aware that you are
in an industry right now that is subject

452
00:26:21,701 --> 00:26:26,100
to automation and you should be learning
how to embrace and use those tools,

453
00:26:26,101 --> 00:26:27,740
not resist it.

454
00:26:30,810 --> 00:26:32,300
This has been Count Me In,

455
00:26:32,730 --> 00:26:36,900
IMA's podcast providing you
with the latest perspectives
of thought leaders from

456
00:26:36,901 --> 00:26:39,660
the accounting and finance profession.
If you like what you heard,

457
00:26:39,661 --> 00:26:42,700
and you'd like to be counted in for
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458
00:26:42,701 --> 00:26:46,460
education, visit IMA's
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