If you've been following the AI scene for the
last couple of years, it often feels like success
means building the most technically complicated
thing, or maybe just being fastest on the draw
with every new tool that drops. But here's the
big surprise, the thing that really flips the
script, that the most successful folks in this
space figured out, profiting from AI. It's not
really about your tech skills, not primarily
anyway, it's almost entirely about strategy.
Yeah. Strategic thinking. The kind of insight
that led agencies like True Horizon that our
sources mentioned scale up past two and a half
million dollars a year. They didn't win with
some secret sauce prompt. They won because they
really got these four kind of non -obvious lessons
about how businesses actually use and pay for
AI automation. Right. So welcome to the deep
dive. Today, our mission is basically to cut
through all that noise. You know, the social
media hype, the constant buzz. We're going to
zero in on the strategic secrets, the things
that really separate the generalists, the ones
stuck competing on price, from the true experts
who can command those premium fees. We'll unpack
these four big shifts, moving from thinking about
full replacement to thinking about leverage,
then moving from breadth to depth, from complexity
towards simplicity, and maybe most importantly,
shifting focus from just prompts to the actual
process. Let's dive into that first big one,
the failed promise of 100 % automation. Okay,
yeah, let's rewind a bit, a little tech nostalgia.
Think back to, say, 2022, early 2023. The hype
cycle was intense. Remember tools like AutoGPT?
They were everywhere. And the promise was huge.
End -to -end autonomous agents doing everything.
Booking meetings, researching markets, even writing
code. Zero human needed. It honestly felt like
science fiction becoming real. A lot of us, I
think, really believed that, you know, full AI
replacement was just around the corner. Just
one software update away, beat. But then reality
hit. For businesses actually trying to use these
early systems, it was, well, pretty brutal. It
really was. In the real world, those fully autonomous
systems, they turned out to be super fragile.
fundamentally fragile. They'd break the second
they hit any real world messiness, like a spreadsheet
formatted slightly wrong or, you know, an unexpected
question from a stakeholder. Often they just
created more work, more bottlenecks than the
old manual way they were supposed to replace.
Right. So the big takeaway there, full automation,
at least in that sense, is fragile. The real
insight that came out of those early struggles
wasn't about replacing humans. It was about leverage.
Exactly. Leverage. It's a total mindset shift.
The goal isn't human replacement. It's human
amplification. You use AI to get rid of the boring,
the repetitive, the soul -crushing stuff so your
human experts can focus on the high -value tasks,
the things needing creativity or empathy. And
this idea kind of crystallized into a really
useful strategic framework, the golden AI ratio.
The golden AI ratio. Okay, so this is the mix
that actually delivers business value consistently.
It breaks down like this. 60 % full automation.
30 % AI -assisted, and 10 % fully manual. Right,
and that's 60%. That's your low -hanging fruit.
Target the really boring, repetitive, rule -based
stuff. Data entry, scheduling basic meetings,
status updates. The key is, these tasks don't
need complex judgment. And honestly, often you
don't even need fancy AI here. Basic tools like
Zapier or Make .com, perfectly fine for just
moving data around reliably. Okay, so 60 % is
the simple repetitive stuff. What about the 30
% AI -assisted? That's where AI becomes a real
co -pilot. Tasks needing some context, maybe
a bit of creativity. Think generating first drafts
for emails or summarizing a massive research
report or maybe drafting a marketing message
for a specific type of customer. The AI does
the heavy lifting, but the human provides that
crucial final judgment, the polish, the personalization.
And the last 10 % fully manual? Absolutely secret.
This stuff must stay manual. Complex negotiations,
high -stakes decisions, real relationship building,
anything requiring genuine human empathy. Try
automating this 10%. You're basically asking
for trouble. Legal issues, alienated clients,
you name it. Let's make this concrete with that
lead follow -up example. So the 60 % automated
tasks would be things like logging new lead info
into the CRM, right? Or maybe qualifying leads
based on simple rules, scheduling those first
intro calls. Exactly. Then the 30%. The AI -assisted
part is where AI analyzes the lead data company
size, industry, their role, and drafts a really
personalized outreach message. Then the human
reviews it, maybe adds a quick personal note,
and hits send. Or AI could generate a pre -call
brief, summarizing everything so the salesperson
walks in prepared. And the 10%, that's still
the human closing the deal, handling tricky objections,
building that long -term strategic relationship,
the stuff that builds real trust. And the 603010
idea, it works across the board, right? Finance,
marketing, operations. Totally. And it's also
how you actually show the value to a client or
your boss. If you can automate, say, 70 % of
a process that used to take 10 hours, well, you
just found seven hours of savings every single
time. That system runs. That's leverage. That's
measurable ROI they can understand. Okay, so.
If this golden ratio is so broadly applicable,
what's the number one mistake people make when
they try to figure out their 60 %? Where do they
go wrong? Probably targeting tasks that need
complex judgment way too early. That just makes
the whole system shaky. Instability. Yeah, the
enemy of actually getting things done at scale.
Which leads us right into lesson two, depth over
breadth. Go deep, not wide. Oh, yeah. This is
a big one. When people first get into AI automation,
the natural urge is to go wide, right? You fall
into the tool trap. You feel like you have to
learn every single new platform that pops up,
and lovable, whatever new agent framework went
viral this week. And you try to say yes to every
potential client, no matter their industry, gyms,
e -commerce, dentists, whatever. I definitely
know that feeling. It's that shiny object syndrome
and it's everywhere. Honestly, I still wrestle
with prompt drift myself sometimes when I see
some cool new agents set up. It is genuinely
hard to stay focused. The pace is just wild.
You feel like you'll miss out. But going wide
like that, it makes you a jack of all trades,
master of none. That's the killer. You end up
knowing a tiny bit about five different tools,
but not enough about any single one to solve
the really thorny high value problems reliably.
And then you're just competing on price because
your solution isn't special. So the experts,
they flip this. They use the inch wide mile beep
strategy. Like mining for diamonds, you go deep
in one spot. You pick one core automation tool,
maybe it's NAN, maybe make .com, and you commit.
You really master it. You become the NAN expert
for logistics or the make .com guru for sauce
companies. Exactly. And that focused authority,
it's magnetic. It pulls in those specialized,
high -value clients who desperately need your
specific expertise. Plus, think about the time
you save. Mastering one tool instead of just
dabbling in five, you reinvest that time into
understanding real business problems. side. That
deep application knowledge, knowing one industry's
specific challenges, their regulations, their
KPIs, better than anyone else, that's the competitive
advantage. That's your moat. That's what lets
you charge premium rates. So focus isn't limiting.
It actually clarifies your value. And it's the
same for marketing yourself too, right? Beginners
spray and pray TikTok, LinkedIn, YouTube, everywhere
at once. Specialists pick one main platform,
go super deep, build real authority there first,
and then maybe expand. It compounds. way faster.
So if that focused approach gives such a big
edge, how fast can a generalist really catch
up to a specialist who's gone deep? Pretty slowly,
honestly, because the specialist just solves
the core problems more reliably, more consistently.
Expertise compounds. Reliability again. OK, perfect
segue to lesson three. Complexity kills simplicity
scales. Right. We've all seen those demos, haven't
we? There's incredibly complex systems. They
look amazing on screen, like a Jenga tower, 15
steps, 10 different AI agents passing data back
and forth. Super cutting edge look. But they
lack real world robustness. They're brittle.
Yeah. The second one little thing changes in
API updates. One agent misunderstands a prompt
slightly. The whole tower just collapses. But
businesses, they don't actually care about the
fancy architecture. They don't care about the
nodes, the API calls, the technical wizardry.
Nope. They care about three very simple things.
Does it save time? Does it make money? And crucially,
does it work consistently? every single time.
Consistency is everything. So a really simple
workflow, maybe just three steps, maybe it looks
kind of boring technically. But if it reliably
saves 100 hours a month, that's infinitely more
valuable than some flashy 10 agent system that
breaks every other way and needs constant fixing.
Reliability beats complexity, hands down. Exactly.
The value comes from the outcome. from the usability
and the reliability, not from how clever the
engineering was. And this leads to a really powerful
kind of counterintuitive optimization strategy.
Actively try to remove decision -making AI from
your workflows where possible. Wait, remove the
AI? That sounds almost like heresy to a developer.
Why would you swap out AI reasoning for something
simpler? Because if a basic logic step, just
a standard, if this, then that can achieve the
same outcome as a complex language model. The
simpler logic is usually more stable, way cheaper
to run, and much faster to debug if something
goes wrong. You swap complexity for simplicity
whenever stability is the priority. Whoa. Okay,
imagine that. A simple, stable, maybe three -step
process costs maybe $20 a month to run, but it
consistently supports a business scaling up to
millions, maybe even a billion queries or transactions
a year. boring is beautiful then predictability
is your absolute best friend it's a tough mindset
shift though because those flashy complex systems
they're the ones that get all the attention on
social media right they get the views the likes
but they rarely drive sustainable paid business
results so thinking about that why are content
creators maybe partly to blame for this misconception
around complexity why do we see so much of the
flashy stuff well flashy super agentic systems
get clicks and views but stable simple systems
Those get the paying clients. The hype isn't
where the real money is. Right. That leads us
perfectly into our final strategic lesson, lesson
four, process over prompts. And this feels like
where the real strategic mastery comes in. Yeah,
absolutely. Beginners tend to obsess over the
tool itself. They focus on the prompt, how the
workflow looks visually, which is a specific
AI model they're using. They think the tool is
the solution. But the real challenge, the fundamental
thing, is understanding the underlying business
process that the tool is supposed to improve.
You know that old saying? Automating chaos just
creates faster chaos. If the process is broken,
automating it just makes the mess happen quicker.
We use the doctor -patient analogy for this.
A good doctor can't just prescribe medicine the
tool without first understanding the patient's
biology, right? The underlying system, the process.
What depends on what? Where are the current failure
points? What other systems does it touch? If
you just blindly throw the fanciest AI workflow
at a fundamentally broken process, like, say,
a sales team. That doesn't track their leads
properly. Yeah. It's going to fail. It'll crash
right into all the messy operational details
you didn't take the time to understand first.
And here's a tough one. Sometimes after you really
dig into the process, you realize AI isn't actually
the right answer. Our sources really emphasize
this ethical responsibility. You have to recommend
the best fix, even if it's just, say, setting
up their CRM better or cleaning up a database
or maybe just using a simple off -the -shelf
software that already solves that specific friction
point, even if you don't get to build the shiny
AI thing. Exactly. Your main job, before you
write a single line of code or connect a single
node, is to study. Map out the current process.
Figure out which parts are repetitive, that's
your 60 % automation target, which parts need
judgment, that's your 30 % assistance target,
and which parts are purely human touch. There's
that strategic time allocation rule again, which
really nails this. Spend 80 % of your time understanding.
Understanding the process, the users, the data,
the goals. Only 20 % of your time should actually
be spent building the solution. That 80 -20 split,
it's pretty much non -negotiable for long -term
success. And it frees you from chasing perfection
right out of the gate. You embrace the MVP, the
minimum viable product, get something small,
something valuable deployed quickly, and then
iterate fast. Learn from real -world user feedback.
That's how you actually make progress. Okay,
so if understanding the process is absolutely
fundamental, what's the single fastest way to
completely kill a promising new automation project?
Easy. Plugging it straight into a fundamentally
broken process. Just automating the existing
inefficiency. Faster chaos, like we said. Sponsored.
All right, let's just quickly pull these four
core strategic ideas together. These are the
shifts that really create that competitive edge
in the AI automation world. Okay, first, ditch
the quest for 100 % replacement. Focus instead
on leverage. Use that 6 or 30 -10 ratio. Amplify
your human experts. Automate the boring stuff.
Second, prioritize depths over breadth. Go an
inch wide and a mile deep. Master one core tool,
one client niche, that focused expertise. That's
what attracts the premium clients. Third, remember
simplicity scales. Reliability is absolutely
king. Sometimes boring is beautiful. Actively
simplify. Aim for stability. That's what businesses
value and pay for consistently. And finally,
number four, always focus on process over prompts.
Study before you build. Invest that 80 % of your
time up front understanding the user, the system,
the real problem, before you even think about
the specific tool or prompt. And this strategic
mindset, it has really direct applications for
different people listening. If you're a service
provider, an agency, your real value isn't just
the build. It's in that 80%, the strategic insight,
the diagnosis, the communication. And if you're
a business owner, use that 6R3010 leverage idea
to find opportunities inside your own company.
But start small. Nail a simple two -step problem
before you tackle a 10 -step monster. And for
the developers listening, prioritize stability
and simplicity. Don't just build for technical
impressiveness. Design for real -world usability,
for reliability. That's what matters long -term.
Look, the AI gold rush is definitely happening
now. But the winners aren't going to be the ones
with just the fanciest, flashiest tools. Success.
belongs to the strategic partners, the ones who
understand how to solve real business problems
effectively, reliably, and often quite simply.
So here's something to think about before you
jump into your next automation project. Which
core business process in your work or your company
really needs some serious mapping, some deep
conversations with the people involved before
you even think about plugging in any AI? probably
start right there. That shift in thinking from
just being a tech implementer to becoming a true
strategic partner, that's the real key. That's
how you build a profitable, sustainable path
forward in this incredibly fast -moving era.
Thanks for diving deep with us today. We'll see
you next time.