Think about this for a second. All those hours
you spent learning API docs, webhook structures,
error handling, that really hard -earned technical
skill you have in AI automation, well, it just
became a simple text prompt. Yeah, it's a pretty
stark, that protective moat, the one that shielded
specialized builders and automation experts for
years. It's just rapidly, almost violently evaporating.
It really makes you wonder, doesn't it? If the
technology itself is being commoditized this
fast, what are the non -technical skills, the
high value business skills you absolutely must
master right now? Because that's clearly where
the premium pricing is going to live. Welcome
to the Deep Dive. Today, we're really digging
into this seismic shift. We're calling it the
great commoditization of technical AI automation
skills. Yeah, we've been going through some deep
source material, really analyzing how these new
natural language workflow builders are just collapsing
the barrier to entry into this whole world. So
our mission today is simple. Give you a shortcut.
We want to pull out the three absolutely indispensable
business skills that, you know, still command
a high price, even when the ability to technically
build the automation is becoming, well, practically
free. Okay, let's unpack this. We probably need
to start with the specific event that really
kicked this acceleration into high gear. We saw
M8n launch their natural language workflow builder
recently. And that was the shockwave. I mean,
we sort of knew it was coming, but seeing it
live. For listeners who maybe aren't building
custom workflows daily, tools like N8n, Zapier,
they're the standard engines, right? The heavy
lifters connecting different software. Right.
And the AI automation specialists, the people
we're really talking about here, they spent months,
sometimes maybe a year, mastering that technical
side. You know, connecting APIs, setting up authentication,
handling the detailed logic flow. That was the
barrier. That was the value. But now imagine
a client needs, say, a simple contact form automation.
Before, the specialist had to manually set up
the webhook, map all the fields, authenticate
the CRM API, build the error logic. It was maybe
five, six distinct technical steps needing real
expertise. And now? Now anyone, literally anyone,
can just type. When someone fills out my contact
form, add them to my CRM, send them a personalized
welcome email, and notify my sales team on Slack
with the details. And the platform just gets
it. It understands the intent. Exactly. It recognizes
the tools, Slack, your CRM, email, figures out
the integrations needed, builds the workflow.
And crucially, it handles all that complex logic
without you needing to know what an API or a
webhook even is. So that specialized knowledge,
the stuff that was hard won, the thing you charged
a premium for because it was difficult, it's
now accessible instantly. It's moving from being
a valuable specialty to just a baseline expectation.
It's a genuinely terrifying pivot if your entire
value proposition was built purely on that technical
execution. Okay, so here's the key question then.
If technical proficiency is quickly becoming
the new baseline, what actually becomes scarce?
What commands the high fees now? It shifts completely.
It moves to the business skills that AI, well,
it can't replicate yet. Things like strategic
thinking, interpersonal intelligence, understanding
the business context. That really sets up the
brutal economic reality we need to face here.
For years, technical knowledge was that defensive
barrier, right? The moat giving you pricing power.
And these natural language builders, they just
blew up that moat with a single text input box.
It feels so similar to the Squarespace moment
for web developers or maybe Canva for graphic
designers. The barrier to entry just dissolved
almost overnight. And when that barrier falls,
you see these four predictable economic outcomes
hitting the purely technical operators. It starts
with... Price compression. Oh, absolutely. That
$4 ,000 automation, the one that took a week
of deep technical work, suddenly it's maybe a
$400 job anyone can spin up in an afternoon.
So the builder charging premium last year, they're
seeing competitors undercut them by like 90%.
It's drastic. Which, of course, immediately leads
to margin erosion. Lower prices just squeeze
your profit margins. What used to be a high profit
specialized service turns into a low margin commodity.
And if everyone can deliver the same technical
result, you get differentiation collapse. You're
forced into competing purely on price. And we
all know where that goes. It's a race to the
bottom and nobody really wins that long term.
And the final piece is competitive pressure.
New players flood the market because, well, the
barrier's gone. Minimal technical skill needed.
So the pool of competition instantly expands
from just experienced specialists to pretty much
anyone who can write a clear sentence. It's intense
pressure, yeah. So the new moat, it has to be
something deeper than just technical execution.
It's got to be about understanding the business,
finding those really expensive problems, and
designing complete human -centric systems. I
generally agree that's the direction, but...
Is it possible we're slightly overstating the
speed here? Won't there always be a need for
that true, deep API expert for the really bespoke,
niche, complex cases? Oh, for sure. Absolutely.
For the top 1%, the super complex edge cases,
yes. But the vast majority of the market, you
know, the small and medium businesses who pay
the bills, they often just need the 80 % solution.
And that 80 % is what's being rapidly automated.
That's why focusing on skills that resist automation
strategy, human interaction is so critical now.
Okay, that makes sense. Let's dive into the first
of those essential skills then, which sounds,
frankly, much harder than mastering code, problem
discovery. The idea that businesses pay you a
premium because you diagnose the expensive hidden
problems, not just because you build what they
initially asked for. Exactly. If a client comes
and asks for X, the amateur builder just builds
X. The professional, though, stops, puts the
keyboard down, and asks the deeper questions.
Why do you want X? What's the real maybe $10
,000 problem you're actually trying to solve
here? You know, I have to admit, I still wrestle
with this myself sometimes. Prop drift is one
thing. But just taking the client's stated problem
literally. It's often a mistake. It's genuinely
hard sometimes to look past the immediate technical
request and see the underlying business pain.
That's my vulnerable admission here. It takes
real discipline. Think about that HVAC company
case study we looked at. The client came to an
agency asking for simple scheduling automation.
Just handle the daily volume. Seems like a standard
request, right? Yeah, typical. But the professional,
the one thinking strategically. didn't just build
it. They spent, what, 45 minutes asking probing
non -technical questions like, what's the average
distance between your appointments? Or how often
are you actually turning down jobs on a Tuesday?
Things like that. And what they found was this
huge, expensive operational flaw hidden beneath
the surface. Their techs were driving 30, sometimes
45 minutes between jobs. Why? Because the dispatch
system didn't use any kind of proximity logic.
They weren't just losing potential jobs. They
were bringing fuel, wasting technician time.
effectively losing two or three appointments
per tech per day. And the fix wasn't some super
complex AI system. It was relatively simple,
wasn't it? Like a dashboard and a basic geographical
proximity rule built into their dispatch logic.
Exactly. Simple fix, but the business impact,
staggering. Jobs per tech jumped from maybe four
or five a day to six or seven. That translated
to over a million dollars in new annual revenue.
The value there was like 80 % in the diagnosis,
finding that hidden operational bleed, and only
maybe 20 % in the simple technical implementation.
That diagnosis difference is incredible. But
how do you even start to cultivate that skill,
that kind of listening, that kind of questioning?
It sounds so abstract. Well, it's actually more
of a framework. You can approach it systematically.
Step one. Pick one industry. Just one. Stop trying
to be everything to everyone. Specialization
really is key there, isn't it? Your third dental
client is going to be 10 times easier than your
first retail client because you start learning
the language, the specific regulations, the common
operational patterns of that niche. Precisely.
Step two. Research expensive problems. Really
dig in. Spend time on industry blogs, trade publications,
even Reddit forums where owners in that niche
are complaining about their operations. Find
out what they already pay, say, $5 ,000 a month
to try and fix. And then step three, practice
pattern recognition. After enough of these discovery
calls within that niche, you'll start spotting
the same three or four chronic, expensive pain
points popping up again and again. That consistency
builds incredible confidence when you talk to
new prospects. So just to reiterate, how crucial
is that specialization piece for effectively
diagnosing these hidden, often systemic problems?
It's fundamental. Specialization allows much
faster pattern recognition, and it lets you speak
the client's specific nuanced language right
from the first conversation. Okay, moving to
the second essential skill, demand generation.
This is all about converting attention, converting
ideas into actual paying clients. Because AI
can handle the building now, maybe the basic
web design, even some copy, the human differentiator
becomes the ability to actually sell a valuable
solution. And the strategy here seems critical.
Pick one expensive problem that businesses are
already paying, say, $2 ,000 to $5 ,000 a month
to solve. You're targeting existing budgets,
not trying to convince them to spend on something
totally new. Exactly right. Take something like
social media management. Businesses routinely
pay a human agency maybe $2 ,500 a month for
that. Now, if your AI -powered system can handle,
say, 90 % of the actual work... the posting,
the scheduling, the basic content creation, your
cost might only be $200. You could charge $1
,500, undercut the agency, and still have a massive
70%, 90 % profit margin. That's real leverage.
Okay, but before you even start looking for clients,
you absolutely need a repeatable system in place.
That system seems to have three core components.
First, a clear input process, right, for gathering
their brand guidelines, their voice, their goals.
Second, the AI workflow itself needs to be documented.
The exact prompts you use, the specific tools,
the sequence of steps. It has to be consistent
and reliable every single time. And third, critically,
quality control. A human review step is essential.
Checking for tone, brand alignment. accuracy.
This is what separates a professional AI -leveraged
operator from someone just copying and pasting
raw output from an LLM. Absolutely. Once that
system is solid, there are three pretty fast
methods to get that first yes. Method one, your
existing network. But don't just send a weak
proposal. Lead with a strong, low -risk offer.
Something like, let me run this for you for 30
days for just $500. Focus on proving the outcome
for them. Method two. Local businesses offer
a free, quick, maybe 15 -minute diagnostic audit
focused only on pinpointing one specific bottleneck
related to the expensive problem your system
solves. Pure value up front. And method three,
LinkedIn outreach. But make it personalized and
value -driven, not generic spam. Something like,
hey, I have a system that creates four weeks
of high -quality on -brand content specifically
for their industry in about an hour. Interested
in a quick five -minute demo. And the universal
key across all of these, it seems, is leading
with the business outcome. More appointments,
better qualified leads, reduced churn, time saved,
whatever it is, not the tools. Business owners
just don't care if you use GLOD or chat GPT or
something else. They care about the ROI, period.
So thinking about that approach, how does leading
with those specific quantifiable outcomes inherently
de -risk the sales process for the client? It
makes it tangible. Focusing on clear ROI helps
clients immediately see the profit potential
or the cost savings, translating your service
directly into their bottom line. All right, the
final differentiator. This feels like the highest
leverage skill. Systems thinking. It's about
your ability to engineer complete end -to -end
solutions. Solutions that account for messy reality,
human behavior, organizational politics, not
just isolated technical workflows. Yeah, let's
go back to that law firm lead follow -up example.
Initially, they just wanted an AI to maybe call
new leads instantly when they came in. The amateur
builder would just build that single automation,
task complete. But the real problem, the underlying
system. It was broken. The attorneys were always
in court. They weren't seeing the lead notification
email for maybe 48 hours. And by that time, predictably,
the hot lead had already found and hired a competitor.
The automation itself wouldn't have solved the
core issue. Exactly. So the professional solution,
the system solution, wasn't just automating a
call. It was designing a whole new process. Like
an immediate automated text confirmation goes
to the lead saying, we got your info. An attorney
will call soon. Then an instant Slack notification
hits a channel the attorneys actually monitor
constantly. Plus, and this is crucial, building
in that human accountability loop. If no attorney
actively claims that lead and initiates contact
within, say, 30 minutes, an escalation alert
automatically pings the managing partner. Right.
And you add a dashboard tracking everything,
conversion rates, claim times, bottlenecks. Whoa.
Imagine scaling that kind of system, a system
with built -in accountability, ensuring consistent
follow -up across dozens of law firm branches,
maybe globally. That's real operational transformation,
far beyond just a neat piece of tech. And that
really highlights why AI can't do this yet, right?
AI can build a specific workflow if you prompt
it clearly enough. But it can't design a complete
business system that anticipates organizational
politics, handles messy human edge cases, or
builds in those essential accountability loops.
That requires human insight. And to manage the
complexity and risk of designing these bigger,
high -value systems, the really successful operators
use what they call an exploration phase. This
is a paid, upfront discovery period, typically
maybe $5 ,000 to $7 ,000 for two to four weeks
of focused work before the main build starts.
And that time is specifically used to validate
the technical feasibility, maybe test tricky
integrations with the client's ancient legacy
software, and really confirm the ROI assumptions.
basically de -risks the whole project significantly
for both the client and the builder before committing
to the full potentially expensive build. So thinking
about that exploration phase, what would you
say is the single biggest hidden cost it actively
helps prevent in these larger, more complex projects?
Oh, definitely costly scope creep. And maybe
even worse, it prevents building systems that
are technically impressive, but in the end, functionally
useless for the actual business needs. So stepping
back, this whole paradigm shift feels pretty
undeniable, doesn't it? Technical execution skills
are rapidly being democratized. Strategy and
deep business acumen are becoming the real protective
moat. The future seems to belong to what we might
call AI -leveraged operators. And those three
skills we unpacked are non -negotiable now. Problem
discovery, demand generation, and systems thinking.
These are the skills that truly resist the economic
pressures of commoditization because they require
that uniquely human intelligence. The market's
already moving fast and the trajectory seems
crystal clear. If you're currently spending your
time just learning the nuances of yet another
automation platform, maybe pause, ask yourself,
is this skill I'm building going to be fundamentally
more valuable next year or potentially less?
That perspective shift is everything right now.
So consider this your immediate call to action.
Pick one expensive problem within one industry
you know something about and start practicing
that discovery framework today. Ask those deeper
questions. That really is the quickest path to
ensuring your relevance and your value in this
rapidly evolving AI economy.