Intro
A massive 63% drop in direct labor costs
for just that one department.
I mean, when you see a number like that,
the decision to jump on the AI train seems
like a total no-brainer, right?
It’s a clear-cut massive win.
Or is it?
The Real Cost of AI No One Budgets For
Every business out there is looking at
AI and seeing one big thing, right?
Dollar signs.
A super powerful way to slash costs
and crank up efficiency.
And you know the promise?
It’s really tempting.
It seems simple, almost too good to be true.
But here’s the thing.
Just like with any big business shift,
the costs that can really sink you,
they’re not the ones you see on the surface.
So in this video, we’re going to take
a look below the waterline.
All right, let’s just dive right in
with a kind of math that makes every
CFO’s ears perk up.
So on the left you’ve got your standard
customer support team.
We’re talking five people costing you
about 282 grand a year
when you factor in all the benefits.
Now look at the right.
This is the new model.
One AI chatbot plus two human agents
to jump in on the tricky stuff.
And the total cost
a little over 105 grand.
Yeah.
And that right there is the big flashy number
everyone sees.
A massive 63% drop in direct labor costs
for just that one department.
I mean, when you see a number like that,
the decision to jump on the AI train
seems like a total no-brainer, right?
It’s a clear-cut massive win.
Or is it?
Because, you know, if the savings
were really that simple,
you’ve got to ask yourself this tough question.
30% of AI projects fail
Gardner found that almost a third,
30% of all generative AI projects
just get dumped after the pilot phase.
So there’s this huge gap between
what looks good on paper
and what actually happens
when the rubber hits the road.
And the answer is actually pretty simple,
but it’s a big one.
The hidden iceberg beneath AI costs
That software license, you know,
the number you see on the invoice
from the vendor,
that’s just the tip of the iceberg.
It’s a huge expensive iceberg,
and the real investment,
the part that can totally wreck your budget
and your timeline,
is all hiding just below the surface.
So to get through this
without hitting that iceberg,
leaders have to change how they think.
It’s not about the purchase price anymore.
It’s about the total cost of ownership,
or TCO.
And as KPMG points out,
this isn’t just about the check
you write for the software.
No.
It’s about every single cost,
direct and indirect,
that you’re going to face
over the entire life
of that AI investment.
All right, so let’s break this whole thing down.
We can basically group all these hidden costs
into five big categories.
And honestly, every leader needs to get
their head around these and budget for them.
Because these are the areas
that decide if your project
is going to be a huge success
or if it’s just going to quietly disappear.
Saving time and opportunity costs with AI for business
Okay, first up, time and opportunity cost.
Let’s start with the one thing
AI is supposed to save you
in the first place, time.
And here’s the funny thing,
the big irony.
This whole transition to AI,
it eats up a massive amount of time.
And if you don’t manage that carefully,
all that time spent
doesn’t actually turn into
any real value for the business.
This brings us to a really powerful idea
from Gardener.
AI productivity leak
They call it the productivity leak.
It’s a great way to think about this problem.
So imagine your shiny new AI tool
saves an employee an hour every day.
Great.
But what if that hour just gets soaked up
by longer coffee breaks
or gets spent on other busy work
that doesn’t actually move the needle?
Well, that productivity
has just leaked away.
You paid for the tool.
You got the efficiency gain.
But you didn’t actually bank
the financial benefit.
So where does this leak spring from?
Well, it’s coming from all over.
It’s the hundreds, maybe thousands of hours
your team spends just trying
to learn the new tools.
It’s all the projects that suddenly
start moving at a snail’s pace
while everyone gets used
to the new way of doing things.
And maybe the biggest one of all
is the opportunity cost.
Think about it.
What other big ideas are you not working on
because all your best people
are tied up in an AI pilot
that’s dragging on and on?
Process debt and change management
And that brings us right
to our second hidden cost,
change management and the human factor.
Look, rolling out AI
is not like installing a new printer
in the office.
Not even close.
This is a massive disruption
to how your teams work,
to their daily habits,
and let’s be real,
even to their sense of job security.
If you ignore this stuff,
you are basically planning to fail.
You know, if you just take
a super powerful AI tool
and plug it into a clunky, old,
inefficient process,
you don’t magically fix the process.
No.
You just create a much faster way
to do the wrong things.
And this has a name.
It’s called process debt.
It’s like you’re taking out a loan
on your future efficiency.
And the interest payments on that loan,
they come in the form of constant rework,
frustrated employees,
and just a ton of wasted potential.
So the big takeaway here
is that your real investment
isn’t in the software.
It’s in your people
and your processes.
You’re paying for those long
workflow redesign meetings.
You’re paying for real hands-on training
and upskilling,
not just some boring one-hour webinar.
And trust me,
you are absolutely going to be paying
to deal with the resistance and the rework
that always happens
when you ask people to change
how they’ve done their jobs for years.
AI learning and iteration: The price of innovation
All right, number three, learning and iteration.
Adopting AI isn’t some one-and-done installation.
It’s more like a constant cycle
of experimentation.
You’re basically a scientist
testing out different ideas.
And let’s be honest,
not all of those experiments
are going to work.
Innovation always comes
with a price tag.
So when everyone’s in a rush
to get a pilot project launched,
what happens?
Teams take shortcuts.
And this creates something
we call technical debt.
Think of it like using duct tape
to fix a leaky pipe
when you really need a proper weld.
Sure, it might hold
for a little while.
But that quick fix
is basically taking out a mortgage
on your company’s future.
And the interest you pay on that debt,
it comes in the form of expensive rework,
total maintenance nightmares,
and systems that just break
the second you try to scale them up.
So what does this mean
for your budget?
It means your total cost of ownership model
has to include the cost
of those first pilots.
And yes, that includes
the ones that don’t work out.
It has to account for the fact
that you’re going to have to tweak
and fine-tune your models
over and over again
because the first set of data was off.
This isn’t a sign of failure.
It’s just the built-in unavoidable cost
of learning something new.
The foundational cost of AI
Okay, number four is a big one.
Data and integration.
Let’s talk about the foundation
that this whole thing is built on.
I don’t care how fancy your AI model is.
It is only as good as the data you feed it.
Period.
And getting all of your company’s messy data
AI ready,
that is easily one of the biggest
and most consistently underestimated expenses
you will face.
And this really gets to the core risk.
There’s a great quote
from a machine learning practitioner
in an archive study
who said, basically,
doing data work the wrong way
is way more dangerous
than not doing it at all.
And why is that?
Because bad data doesn’t just give you
a bad answer one time.
It actually embeds those errors
deep inside your systems,
creating these little time bombs
that might not go off for months.
So what does this actually look like
in your budget?
Well, it’s the long, tedious work
of cleaning up and labeling your data.
It’s the nightmare engineering project
of trying to connect your new AI tool
to that ancient CRM system
you’ve had for a decade.
It’s setting up all the rules for governance.
And this is a big one.
It’s the huge salaries you have to pay
for those specialized MLOps engineers.
One source calls them
the hidden anchor of AI budgets.
This foundational work,
you just can’t skip it.
AI risk and error mitigation
And finally, number five,
risk and error mitigation.
Let’s think of this one
as your insurance policy.
Because here’s the deal.
When an AI makes a mistake,
it doesn’t make a tiny one.
It makes mistakes
at a massive, massive scale.
So the money you spend
preventing and managing those errors,
that’s your insurance.
The math on this is actually pretty simple.
On one hand, you have the potential cost
of a complete disaster.
Think a huge data breach
or a customer service meltdown
that goes viral.
And on the other hand,
you have the very real day-in day-out cost
of making sure that never happens.
It’s a balancing act.
So what’s in this insurance budget?
A few key things.
First, human oversight.
You’re literally paying people
to be a human in the loop
to double-check the AI’s work.
Then you have the constant technical costs,
like monitoring the model’s performance
and paying for those pricey security audits.
And last, you’ve got to deal
with these tricky long-term risks,
like something called a hidden feedback loop,
which is where a model can slowly poison
its own performance over time
without you even noticing.
Okay, so I know that looking
at all these hidden costs
can feel a little overwhelming.
But the point isn’t to get scared
and run away from them.
It’s about managing them smartly
with a strategy.
From AI cost to AI strategy
The real key here
is to stop thinking about this
as just cutting costs
and start thinking about it
as a strategic investment
where you line up your spending
with what you actually want
to achieve as a business.
And Gartner actually has
a great framework for this.
It helps you align your spending
with your level of ambition.
So level one is defend.
This is all about using AI
for basic productivity stuff
like automating reports
or summarizing your meetings.
It’s lower cost
and you see the ROI pretty fast.
The next level up is extend.
Here you’re using AI
and your own special data
to get a real edge
on the competition.
Maybe you build a recommendation engine
that nobody else has.
The cost is higher,
but so is the reward.
And finally, the top level, upend.
This is where you use AI
to totally disrupt your entire industry,
creating new products
or even a whole new business model.
This is the big transformational bet.
It’s the highest cost,
the highest risk,
but the potential payoff is huge.
Knowing which of these three games
you’re playing
is absolutely essential
to getting your budget right.
Because at the end of the day,
the true cost of AI
is really the cost of transformation itself.
And that brings us
to the final most important question
every single leader needs to be asking.
The question isn’t
how much is the software license.
The real question is,
do we have a solid framework in place
to make sure that this huge,
complicated investment
is actually giving us the value
we expected?
Because answering that question,
that’s how you successfully navigate
everything that’s lurking
below the waterline.
Fine.
