So everyone is playing with AI right now. Yeah,
but almost no one is actually making real money
from it. Why is that? Because they're selling
the technology itself. They're not selling the
actual outcome. Right. Welcome to the Deep Dive.
I'm glad you're here with us. Today we're unpacking
a really specific framework. We're looking at
how to transition from just from staying busy
with AI tools to actually profiting from them.
Exactly. We've got a lot of ground to cover for
you today. We're going to explain why prompt
packs are a complete dead end. Yeah, they really
are. And then we'll show you how to structure
your inputs using the MAPS framework. Plus, we'll
get into the rule of R for automation. And we
also need to talk about the warning signs. You
know, what happens when you scale up with AI
agents. But I think we have to start with the
biggest trap in the AI space right now. Oh, absolutely.
Which is this... this deep obsession with the
tools themselves. It's everywhere. I mean, people
are obsessed with the shiny new toys, and they
completely miss the business value. It's like
they're hypnotized by the tech. They really are.
You see everyone trying to sell these like massive
prompt packs or basic chat GPT tutorials. Even
automation templates kind of fall into this trap.
I mean, technically there is a market for those
things. Sure. Yeah. But the ceiling is brutally
low. Like competition is just overwhelming because
everyone has access to the exact same information.
The real money always sits one level deeper than
the tool itself. Right. It's about finding a
broken process. Precisely, because business owners
don't care about language models. They have a
costly process, right? Say they're losing warm
leads because their follow -ups are terrible.
They're bleeding money. They just want the bleeding
to stop. Exactly. Whoever fixes that specific
outcome gets paid. And they can get paid well,
regardless of the tools they use. The tool should
be a... Totally invisible. Yeah. I like to think
of it's like stacking Lego blocks of data. The
client doesn't care about the plastic bricks.
They just want to buy the finished castle. That
is a perfect analogy. Let's look at IG Group
from our source material. Massive global financial
services company. And finance means heavy compliance.
Incredible compliance, yeah. They had strict
regulatory requirements and they needed multi
-language content fast. They were paying insanely
high agency costs just to keep up. So what did
they do? They deployed Claude. quietly across
their teams. And the results were wild. The analytics
team saved 70 hours a week. Marketing saw triple
-digit speed -to -market improvements. Wow. Yeah.
Full ROI in under three months. But here's the
kicker. Claude was just the invisible engine.
They didn't sell AI to their customers. They
just delivered better financial services. So
looking at that, what are the three conditions
for spotting a real opportunity? You're looking
for a task taking skilled people a long time.
The final output has to be very high stakes,
and the business must already be spending money
to solve it. Look for manual high stakes tasks
where businesses already spend money. Exactly.
Right. So once you identify the right high stakes
problem, you actually have to get the AI to do
the work. And if your output is generic, you
know, the model isn't broken. Your prompt is.
Yeah, your prompt is definitely broken. Vague
prompts just give you vague answers. Which brings
us to the MAPS framework. Let's break this down
for you. M is for mission. You have to give the
AI the actual business goal, right? Right. Not
just the task. So instead of just saying, find
me leads, which is terrible. Because it has no
context. Exactly. You say, I need 30 customers
to hit my $15 ,000 revenue goal. That's the mission.
It grounds the AI. Beat. Then A is for ask. Make
one specific request. Right. Don't say, help
me with sales. Say, give me a list of 40 U .S.
leads with name, company, email, and LinkedIn.
Precision is key here. It is. P is for parameters.
This is the background context. Your ideal customer,
your constraints. And honestly, a massive time
saver here is using voice input to just dictate
those parameters. You can just talk it out. I
have to admit something here. I still wrestle
with prompt drift myself when I rush and skip
the parameters. It's really easy to get lazy.
Oh, we all do it. But skipping parameters guarantees
garbage output. The context is what makes it
work. Yeah. And the last one is S shape. Tell
the AI exactly what the output should look like,
like format it as a table. Right. And I have
to ask you, why is shape the step everyone seems
to forget? Skipping it means wasting 20 minutes
reformatting a useless wall of text. It really
does. It's such an unforced error. Absolutely.
We're going to take a quick break here. Don't
go anywhere. Insert a clear break here for the
mid -roll sponsor read. All right, we're back.
So MAPS can use you perfect single outputs. But
for tasks that happen every single week, running
manual prompts just, it isn't enough. You need
automation. But only if it passes the test. Right,
the rule of R. We use tools like Zapier or Make
for this, which is basically digital glue that
automatically moves data. between your different
apps. That's exactly what it is. So the first
R is repetitive. Does the task happen regularly?
Like manually checking ConvertKit every Monday.
Yeah, and then pasting those email stats into
Notion, that's highly repetitive. The second
R is rule -based. Meaning the logic is perfectly
predictable. Right. Say a Typeform lead comes
in, it goes straight to your HubSpot CRM. A CRM
is a digital filing cabinet tracking all your
customer relationships. Right, so it hits HubSpot,
then triggers a ConvertKit email. The logic never
changes. And the final R is return. Will it save
you at least one hour a week? Because if yes,
the setup effort pays back in a month. Which
is huge. So what's the danger of ignoring the
return rule? You'll spend days building a machine
nobody needs. It's a trap. Spending 60 hours
to automate something that saves two minutes
is a bad trade. An incredibly bad trade. So what
happens when a problem is too complex for a single
prompt or, you know, rigid automation? That's
when you graduate to AI agents, systems that
can actually make decisions mid -task. They adjust
on the fly. Exactly. Look at Klarna. They built
an OpenAI customer service agent. In its first
month, it handled 2 .3 million conversations.
Whoa. Imagine scaling to 2 .3 million queries
in a single month. It's mind -blowing. It cut
resolution time from 11 minutes to 2 minutes,
and repeat inquiries dropped by 25%. That saved
them a ton of money, right? Service costs per
transaction fell from 32 cents to 19 cents. But
there is a real warning here. Klarna eventually
had to rehire human agents. Prioritizing cost
over quality really backfired on them. Right,
because agents can hallucinate. You need humans
in the loop to review drafts. How do we prevent
these long agent workflows from hallucinating
or going off track? Well, you have to engineer
the process carefully. Build checkpoints where
a human or second agent reviews the work mid
-process. That's exactly it. A second agent checking
the first agent is a game changer. It's a fascinating
architecture. So you have this toolkit now. Claude
for Thinking with MAPS. Zapier for scheduled
tasks with the rule of R, and agents for complex
workflows. The final piece is knowing how to
charge for this superpower. Yeah, and this is
where people mess up. You have to become the
orchestrator. You know which tool fits which
problem. But there's a big pricing mistake happening.
Massive mistake. When AI makes delivery faster,
people instinctively drop their prices. But a
lead gen service worth $5 ,000 before AI is still
worth $5 ,000 after... Because the outcome hasn't
changed. Exactly. Klarna didn't pass all their
40 % savings back to the customer. They kept
the margin. You anchor your price to the client's
outcome, not the hours you spent running, Claude.
Let the technology stay invisible. Totally invisible.
Why is it so hard for creators to keep their
prices high when AI does the heavy lifting? Because
of guilt. Honestly, it feels like cheating. They
wrongly tie their self -worth to hours worked
instead of the final results. Exactly that. You
have to break that mindset. Let's synthesize
this. To actually win with AI, stop selling your
knowledge of the tools. Find expensive problems.
Master your inputs with MAPS. Filter your busy
work through the rule of R. Use agents for big
workflows, but keep humans in the loop. And above
all, price for the outcome. I want to leave you
with a final thought to mull over. If the ultimate
goal is to let the AI technology remain completely
invisible to the client, What happens in five
years when every single business has the exact
same invisible AI capabilities? What becomes
your ultimate differentiator then? That's the
real question. Thank you for joining this Deem
Dive. Look at your own workloads today and just
find one task that passes the rule of R. Start
there. OUTRO music.