Let's just start with the fact that really changes
everything. A staggering 95 % of corporate generative
AI pilots are failing. 95%. That's not a typo.
It's not. And it means businesses are officially
done experimenting. They've burned through those
early budgets. And now, well, they're demanding
answers. And that simple phrase, we do AI automation,
it's already a dead language in the enterprise
market. Welcome to the Deep Dive. That failure
rate you mentioned is basically a clearing function.
It's changing the entire landscape for AI services
as we head into 2026. It really is. Our mission
today is to unpack what that means, whether you're
running an agency or you're on the other side
trying to hire one. We're seeing this rapid transition
away from that. crowded, kind of low value, generic
automation vendor era. The AI automation bro
era. Right. And we're moving very decisively
toward this high ticket transformation partner
model. That's where the real value is. Exactly.
So today we're going to cover that central tension,
the pivot to demanding measurable ROI. We'll
look at the new entry point, the paid audit,
and we'll talk about why fixing messy data is
suddenly like. 80 % of the actual work. And then
we'll wrap up with the strategic end game. This
is where the smart agencies start owning their
own IP, turning all that service work into scalable
software. Okay, let's get into it. Let's do it.
So that MIT report stat, 95 % failure rate. It's
just wild. When you hear that, you have to ask
what really went wrong. It wasn't just bad tech,
was it? No, it rarely was the tech itself. The
failure was almost entirely about poor strategic
execution. Scope creep. Companies got really
seduced by the idea of large language models,
you know, the algorithms that can generate text
or code. But they never actually integrated those
cool projects into their core business. It feels
like they prioritized innovation theater over
just... Real operational efficiency. Precisely.
And that has caused this massive behavioral shift.
It's no longer, hey, let's try some AI. The mindset
is now, prove this saves us X amount of money
or Y amount of time or just don't even bother.
Clients are deeply skeptical now. And, you know,
they should be. And this is why that generic
automation agency label is suddenly so meaningless,
isn't it? Completely. The survival requirement
now is to niche down hard. You have to stop leading
with the tech. Don't say, we use LLMs and vector
databases. That's just feature talk. Instead,
you need to lead with outcomes. Quantified outcomes.
Yeah. Say, we help e -commerce teams cut support
costs by 30 % in 90 days, or something just as
specific. You need those bulletproof case studies
with real numbers. Like, we saved Company X $180
,000 a year by automating their intake process.
It has to be about money saved or revenue gained,
not about features installed. So if the market
is demanding that kind of concrete value immediately,
what is it that specifically lets a smaller agency
command higher fees and become that trusted long
-term partner? By offering comprehensive guidance,
partners eliminate the chaos of managing a bunch
of small vendors. Which brings us to the next
big thing, because the fundamental problem for
clients is just confusion. They know AI matters,
but they don't know where to start. And they
definitely don't trust a random vendor to tell
them. Exactly. The solution to that is the paid
AI audit. It's really becoming the new universal
entry point. It reminds me of the early 2000s,
right? Every business needed a website, even
if they weren't sure why. Now, every business
needs an AI roadmap. And this model works because
you charge, say, $5 ,000 to $15 ,000 just for
the diagnosis, depending on the company's size,
of course. You're getting paid to figure out
the problem. Not to build the solution right
away. Right. You map their existing, usually
messy processes. You find every single friction
point and you identify the key opportunities
that will give them a fast ROI. And then you
present this really clear roadmap. It's a strategic
document. And that audit, it naturally upsells
into the big implementation packages. We're talking
$10 ,000 up to $100 ,000 or more. Yeah, I saw
a stat that one program's average audit deal
was $11 ,400 just for the initial diagnosis.
That money immediately covers your discovery
time and it just builds trust from day one. The
strategic value there is just huge. Once you
diagnose what's broken. you automatically become
the most knowledgeable person in the room. You
stop selling. You just continue the work. So
what's the core reason that initial diagnosis
is more valuable than just trying to build a
pilot project right away? Understanding what's
broken makes you the expert, and that eliminates
the vendor -client friction. Okay, so let's get
into this uncomfortable truth, which I think
explains a lot about that 95 % failure rate.
Most businesses, they're just not ready for advanced
AI. Not even close. There was a Cisco report.
It said 92 % of companies still don't have AI
ready data. Yeah, their systems are fragmented,
scattered. They're held together by like duct
tape and spreadsheets from 2015. That is the
messy reality we're dealing with. So if you're
selling a transformation package, you're not
really selling AI. You're selling digital plumbing.
Exactly. And that means 80 % of the actual work
is going to be general software development.
You know, building internal tools, dashboards,
cleaning up systems before the AI can even do
anything useful. You're stacking Lego blocks
of clean data. Perfect analogy. But here's the
upside we can't ignore. Building that foundational
stuff is so much faster and cheaper than it used
to be. Oh, way faster. AI has basically cut the
cost of general software development by like
five to ten times. Developers using tools like
Copilot are reporting speed ups of... 20, 30,
even 55%. I saw one user who said they built
30 working apps in 30 days, something that would
have taken months to do manually. Whoa. I mean,
just imagine scaling a business that serves a
billion queries based on that kind of build rate.
The speed at which you can fix the mess is just,
it's astonishing. Okay, so that foundational
layer has to be addressed. But we also have to
talk about what you call the adoption tax. I
love that framing. What does that mean exactly?
It means that all the potential time savings
from a new system often get canceled out by user
distrust or just complexity. Think about Apple's
Newton message pad. It was amazing tech for its
time, but people didn't trust the handwriting
recognition, so they just stuck with paper. So
a great system can completely die if the team
doesn't trust it or know how to use it. That's
it. So education stops being optional in 2026.
It has to be built into every single proposal.
Agencies actually need to add a mandatory, say,
$15 ,000 to $20 ,000 education and adoption package
to every project. That covers training, documentation,
all of it. Yeah. And, you know, I still wrestle
with prompt drift myself sometimes. Oh, yeah.
That's when the instructions you give the AI
just kind of lose their effectiveness over time.
So I know from experience that professional training
is absolutely essential for the client's team
to succeed. And then there's the iteration imperative,
the idea that the real money isn't in V1, it's
in what happens after the launch. That post -launch
refinement is everything. There was a case study
where continuous testing on a client's app over
18 months resulted in user retention peaking
at nearly three times what it was at the start.
And they used clear metrics to prove it was working.
So treating V1 as the start, not the end, is
what turns those one -off projects into reliable,
ongoing revenue. That's the key. So if iteration
is how you stabilize revenue, what does the truly
ambitious partner aim for beyond that? And I
think that gets us to the strategic endgame,
owning IP. Yes. Let's shift to that. The ultimate
goal should be owning your own intellectual property.
You have to use your client work as a live R
&D lab. So if multiple clients are paying you
to solve the same problem. That's your signal,
a huge signal. You've got a scalable software
asset on your hands. The classic case study for
this is group talent, right? Exactly. They were
a recruitment agency. They built some internal
software to fix their own sales process. And
that tool eventually spun out to become Outreach
.io. Now a $60 million plus business. Service
work becoming a scalable asset. Yeah. With much,
much higher margins. Which leads to the next
prediction. The partner race. Agencies need to
focus on the mid -market. $50 to $500 million
in revenue. They have budgets. They feel the
pain. And they can actually move fast enough.
They're too big for freelancers, but too small
for McKinsey. It's the absolute sweet spot. And
the race is on to lock down long term relationships
with these companies right now. OK, but if 92
percent of companies have messy data, isn't the
mid market just a minefield of legacy systems?
Yeah. That seems like a contradiction. That's
a fair point. It is messy, but that's why we're
seeing the rise of Prediction 9, the forward
deployed engineer, the FDE, this new hybrid role.
Yes. Half consultant, half developer. This person
goes on site, they interview staff, and then
using what we're calling vibe coding, they build
working prototypes right there in front of the
client. And vibe coding is just using AI tools
to build functional code really fast based on
verbal feedback. Right. And companies will pay
a huge premium for that immediate visible value.
Then there's prediction 10, the AI executive's
assistant offer. This is a high -end service
for leadership. Yeah, building systems that pull
critical insights from across the whole company
for planning and decision -making. This is a
strategic partnership. A $50 ,000 to $150 ,000
annual retainer opportunity. You become a strategic
advisor, not just a chatbot builder. And finally,
prediction 11, the Google effect. We have to
think about the threat of big platforms like
Google or Microsoft launching these sophisticated
business setup wizards. That could vibe build
a basic tech stack for a small business instantly.
When that happens, the agency role shifts. You're
not building from scratch anymore. You're focusing
on integration and facilitation, connecting those
powerful platform tools to the client's messy
legacy systems. Okay, let's recap the big moves
for 2026. Niche down hard. Focus beats broad
every single time. Two, expand your scope. You
have to add consulting, software development,
and that non -negotiable education package. After
the audit. Turn that $5 ,000 diagnosis into a
$50 ,000 project. Invest in your internal infrastructure
so you can iterate fast. And five, go after that
mid -market now before your competition does.
The gap between that cheap automation freelancer
and the true AI transformation partner who guarantees
ROI, it's just widening exponentially. The opportunity
isn't disappearing at all. It's just evolving
into a much more mature professional market where
expertise and results are all that matter. So
a final thought for everyone listening. If most
businesses are still using those messy Excel
sheets from 2015 to run core functions, what
deep foundational layer of our current business
infrastructure will we look back on in five years
and realize was the single biggest obstacle to
true AI transformation? Something to think about.