Most people think AI only threatens certain jobs,
jobs like tech or customer support. But the real
dividing line isn't your job title. It is whether
you choose to learn AI or ignore it until it's
too late. Yeah, that is the crucial threshold
right there. The people getting caught off guard
are the ones waiting for permission to adapt.
While the ground is... Well, it is shifting right
beneath them. Welcome to today's deep dive. We
are unpacking five essential practical skills
today, skills to help you thrive in the AI economy
without needing a new degree. We have a really
packed roadmap for you. We are going to explore
how to become the AI person on your team. We
will look at why judgment always beats speed,
plus mastering context over prompts, the power
of fast iteration, and finally, how to bulletproof
your career with a second income stream. OK,
let's unpack this. Let's start with this idea
of becoming the AI person. It sounds intimidating,
like you need to be a software engineer, but
the landscape is shifting locally. A report that
used to take two hours now takes 30 minutes.
The change happens right at your desk. And you
really don't need a technical background. Becoming
the AI person just means knowing slightly more
than the person sitting next to you. It's about
finding one repeating task and making it noticeably
faster. Which brings us to the Exxon Enterprise
case study. They built hardware and software
for law enforcement. Yeah. And they didn't roll
out some massive, intimidating AI initiative.
Not at all. They built a tool called DraftOne
using the Azure OpenAI service. The mechanics
were actually... Quite focused, they took a massive
bottleneck, police officers typing up incident
reports. Right, which takes hours. Exactly. And
they aimed the AI squarely at it. Draft 1 basically
drafts the report using the audio transcript
from the officer's body camera. And the result
was an 82 % reduction in police officer report
writing time. Huge. Which completely flips the
ROI of an officer. You aren't paying them to
type. You are paying them to patrol. Exactly.
It shifts their cognitive load from manual data
entry to simple editing and verification. But
the critical lesson here is how it started. This
wasn't a top -down mandate where executives forced
everyone to learn prompt engineering. Someone
tested a highly specific repeatable use case.
They proved it worked on one specific task, and
then the adoption just spread naturally. It makes
me think about finding a new gear on a bicycle.
Yeah. You don't need to understand the physics
of the derailleur to realize you are suddenly
pedaling easier and moving faster. You just show
your coworkers how to shift into that gear. Right,
and suddenly everyone wants to ride that way.
It's the exact same dynamic. Pick one tool, stick
with it. Don't scatter your attention across
50 different applications. Apply it to one task
you repeat every single day and track the delta
in your time. Does being the AI person mean you
suddenly have to fix everyone's tech issues?
No. It just means finding one repeating task
and making it noticeably faster. You become the
proof of concept, not the IT department. That
is a crucial distinction. But... You know, finding
shortcuts is one thing. Trusting them blindly
is where companies are getting into serious trouble.
Which leads us to the second skill, building
judgment before speed. Judgment is where the
real premium lives now. Speed is completely commoditized.
AI can generate a thousand words in three seconds.
But judgment knowing if those thousand words
are actually correct. is rare. I still wrestle
with prompt drift myself. Sometimes I just want
to accept the first draft because it looks so
polished. It is a very human temptation. Oh,
we all feel it. I do it too. But that polish
is precisely the danger. The AI's output almost
always looks perfectly fine on the first try.
It utilizes impeccable grammar, excellent structure,
and a highly authoritative tone. Which brings
us to the Deloitte, Australia disaster. I looked
at this case study. What fascinated me wasn't
the technology failing. It was the human psychology
of the failure. It is a textbook example of trusting
the Polish. Deloitte prepared a 237 -page report
for the Australian Department of Employment.
And recently, they had to refund $440 ,000 to
the government. Wow. Because the report was entirely
compromised by hallucinations. Completely compromised.
The AI generated a flawless looking document.
But when a researcher at the University of Sydney
actually audited the citations, it just unraveled.
The report featured fabricated quotes from a
federal court judgment. It referenced academic
research papers that literally did not exist.
The AI just invented them out of thin air to
satisfy the prompt. Why do incredibly smart people
at top Stop consulting firms. Miss these obvious
AI hallucinations. Because the initial output
usually looks perfectly formatted and highly
confident. It doesn't look like a mistake. Exactly.
It looks like a finished product. Which means
the workflow has to physically change. You cannot
just read the output. You have to actively verify
it against a known standard. And more importantly,
you can't just fix the errors manually and move
on. That is a trap so many people fall into.
They get a bad draft from the AI, they rewrite
it themselves, and they try again the next day.
The machine learns nothing. You have to teach
it your standard. If it uses overly formal language,
you don't just delete the words. You reply and
say, make this shorter, remove the academic tone.
Use conversational language. Tell it what you
changed and why. You are essentially calibrating
the machine's judgment to match your own. But
even with good feedback loops, the best way to
prevent the AI from making those confident mistakes
in the first place is to put guardrails up. You
do this before it even starts generating words.
Which brings us to our third skill, context engineering.
This is fundamentally different from what most
people are doing right now. We should define
that clearly. Context engineering simply means
giving AI your background info before asking
a question. Exactly. Prompt engineering is focusing
on how you ask the question. Context engineering
is controlling what the AI already knows before
you even open your mouth. A generic prompt with
no context will always produce a generic hallucination
prone result. I think of it like briefing. a
highly intelligent, but totally on easy act intern.
You can't just walk up to this intern and say,
write a strategy report. Right. You have to hand
them the company playbook, the style guide. And
three examples of last year's successful reports
before they even touch a keyboard. That is the
perfect analogy. And when you look at how enterprise
companies are doing this, the results are staggering.
Look at Wells Fargo. They ruled out an AI tool
for 35 ,000 bankers across 4 ,000 branches. But
they didn't just give their employees a blank
chat window. Right. Giving a bank teller a blank
chat GPT screen sounds like a compliance nightmare.
Oh, it would be catastrophic. So instead, they
built a Microsoft Teams app and loaded it with
1700 internal procedures. They essentially built
a walled garden of context. The AI can only pull
answers from those specific approved internal
documents. And the operational shift there is
massive. The source notes that response times
plummeted from 10 minutes down to just 30 seconds.
Because the banker doesn't have to search a massive
intranet. They don't have to open three different
PDFs and synthesize an answer while the customer
is waiting. The AI does the synthesis, but it
is constrained by the context. Constraining the
AI is the secret. It removes the guesswork. You
don't need a massive corporate system to do this
yourself. You just need to build a context package
for your own work. What does a personal context
package look like in practice? It's basically
a simple text document where you store your rules,
your brand voice, information about your target
audience, examples of your best past work, past
mistakes or cliches you want the AI to avoid.
You feed that document into the AI first and
then you ask it to do the work. So is context
engineering really just writing an incredibly
long, detailed prompt? It is more like giving
the AI a custom instruction manual before assigning
work. Okay, we are going to pause for a quick
word from our sponsor. We will be right back.
And we are back! So once you've loaded up that
context and you understand the value of judgment,
the temptation is to spend six months planning
the perfect, flawless deployment. But the sources
point in the exact opposite direction. You have
to move to skill four. Mastering fast, iteration.
Speed of testing will always beat speed of planning
when it comes to AI. You cannot predict how these
models will behave in the wild until you actually
release them. It's like teaching someone to ride
a bike. You do not sit them down in a classroom
and explain the physics of gyroscopic balance.
Yeah, right. You don't draw diagrams, you put
them on the seat, you let them pedal, and you
catch them when they wobble. You fix the small
mistakes in real time. And that requires a major
shift in corporate culture. You have to be willing
to ship a rough version. Let's look at Klarna.
They are the gold standard for fast iteration
right now. In February 2024, they launched an
open AI powered assistant to handle customer
service. The scale of this rollout is what caught
my attention. In a single month, it handled 2
.3 million conversations. That was two thirds
of all their customer service interactions globally.
The efficiency gains are hard to comprehend.
Their average resolution time dropped from 11
minutes down to under two minutes. Oh, imagine
scaling to a billion queries seamlessly like
that. It completely changes the economics of
the company. And repeat inquiries on the exact
same issue fell by 25%, meaning the AI wasn't
just faster. It was actually solving the root
problems better than the manual process. But
to your earlier point about chipping a rough
version, They didn't just build this perfect
system in a lab and then turn it on. How did
they actually manage that deployment without
alienating their customers? They built an aggressive
audit loop. They didn't wait for angry customer
reviews. They had human teams sampling hundreds
of chats every single week. They were constantly
hunting for friction points. When they found
a content gap, say, the AI didn't know how to
handle a specific type of return, they updated
the context immediately. They were fixing the
wobbles while the bike was moving. Doesn't shipping
a rough version risk frustrating your actual
customers? Not if you monitor it closely, catch
errors early, and fix them immediately. The friction
of a minor AI error is usually lower than the
friction of waiting on hold for 45 minutes. That
brings us to our final skill. This is where all
the previous pieces lock together. When you combine
fast iteration, deep context engineering, and
human judgment, you suddenly possess the leverage
of an entire team. You could do the work of five
people. You become a highly leveraged individual.
Which raises a structural question. Why restrict
that immense power to just one fragile day job?
Relying entirely on one paycheck is a vulnerable
position. If that company downsizes, your leverage
vanishes. That is skill number five. Building
a second income stream using the domain expertise
you already have. And we have a phenomenal case
study for this with Rick Chorney. Rick's story
is a perfect illustration of how to apply AI
to physical, traditional businesses. He wasn't
building a software startup. He was running a
commercial cleaning company called Echo Janitorial
Services in Vancouver. And he was doing it the
hard way. He spent his first year working seven
days a week with absolutely no days off. He was
out in the field managing cleaning crews by 7
a .m., getting home at 8 t .m., and then opening
his laptop to do administrative work until 1
a .m. Calculating quotes, replying to customer
emails, scheduling, the invisible work that actually
runs the business. He was earning roughly $14
an hour when you calculated the sheer volume
of his time. Burnout was inevitable at that pace,
but instead of just grinding harder, he spent
about four hours researching how AI could carry
that administrative load. He wanted to buy his
time back. Let's talk about the actual mechanics
of what Rick built. The source material highlights
three specific tools. Claude, and A .N. and Obsidian.
But listing tools doesn't explain the workflow.
How did he actually connect these things? This
is where it gets brilliant. He essentially built
an automated nervous system for his business.
Imagine a potential customer fills out a contact
form on his website at 10 p .m. asking for a
quote. In the old days, Rick reads that at 1
a .m. and manually types a reply. Total bottleneck.
Right. So now he uses N8n. N8n is basically a
digital bridge. It connects different applications
together based on triggers. It sees that a new
form was submitted. It acts as a dispatcher.
Exactly. N8n grabs the customer's request and
hands it over to Claude, which is the AI language
model. But Claude needs to know Rick's pricing
structure to write a quote. That is where Obsidian
comes into play. Obsidian is a specialized note
-taking app. It stores files as plain text on
your hard drive. Yes. And Rick uses it as his
context library. It holds his pricing tiers,
his scheduling rules, his brand voice. So Claude
reads the customer's request, pulls the correct
context from Obsidian, and drafts a highly personalized,
accurate quote. Then ends the draft back to N8N,
which automatically sends the email. So a customer
asks for a quote at 10 p .m. and within two minutes,
they have a fully customized proposal sitting
in their inbox. Yeah. And Rick was sleeping.
He completely removed himself from the administrative
bottleneck. He set up an AI receptionist to handle
phone inquiries. He automated his intake forms.
And the entire setup process for that automated
workflow took him about half a day. Half a day
of building to reclaim hours of time every single
day. And the financial result is undeniable.
Fortune magazine actually reviewed his business
records. EcoGenitorial is projected to clear
$1 .3 million in sales this year. Because he
finally had the time to focus on growth, rather
than drowning in paperwork. He used tech to scale
a physical cleaning company. He didn't abandon
his industry to become a prompt engineer. Do
you have to invent a totally new tech -focused
business to pull this off? No. You just apply
AI to automate the administrative parts of your
current skills. Stay close to the domain you
already understand. Exactly. We do need to offer
a practical warning here. If you are building
a second income stream, check your current employment
contract. Look out for non -compete clauses or
policies about using company equipment. And most
importantly, do not quit your main job prematurely.
You wait until that side income is completely
stable and proven. It's about creating a safety
net, not jumping without a parachute. Let's recap
the big ideas we've explored today. These are
the levers you can pull right now. It really
synthesizes down to five executable steps. First,
step up and be the AI person by finding just
one repeatable shortcut for your team. Second,
build a strict feedback loop to enforce human
judgment over AI speed. Third, engineer your
context before you engineer your prompts. Give
the machine the playbook. Fourth, iterate fast.
Ship a rough version and fix the wobbles in real
time. And finally, take all that newly created
leverage and build a secondary income stream
that isn't tied to a single employer's payroll.
You don't need to change your career today. Just
pick one of those skills. Build one context package.
Automate one repeating email. That single action
puts you leagues ahead of the people waiting
to see what happens. You have the roadmap. It's
time to start pedaling the bike. Sure. But before
we wrap up, I want to leave you with a thought.
A slightly deeper question about where all this
technological leverage is actually heading. I
love these. If these AI models are making everyone
10 times faster. faster at producing reports,
faster at writing code, faster at analyzing massive
data sets. What happens to the fundamental value
of speed itself? In a world where being fast
is essentially free, maybe deep, slow, critical
thinking becomes the ultimate premium skill.
Two sec silence. That changes the whole game.
Thank you for joining us on this deep dive. Take
care of yourself. We will see you next time.