So, everybody's telling you the same way

to make money with AI in 2026, which is

to start an AI agency, find clients, and

then sell them AI automations. But, the

opportunity to build a profitable AI

agency is shifting. The real opportunity

for most people right now is to become

the AI person. And right now, this is

the closest thing to a job that can't be

replaced. I actually used to be an AI

person at one of the biggest banks in

the world just a few years ago. Now,

that position has seen a huge increase

in demand over the last couple of years,

and it will keep growing. So, in this

video, I'll break down what the AI

person actually is, why every business

is going to be desperate for one, and

the exact roadmap for you to become one.

So, let's begin. All right. So, what

actually is the AI person? Right now,

there's two types of people using AI at

work. The first type opens up ChatGPT or

Co-pilot, asks a couple questions, gets

an answer, and that's pretty much where

it ends. While the second type knows how

to take AI and actually build something

with it. Whether that's an agent that

runs the support inbox, or a workflow

that writes their weekly report, or a

system that cleans up the data before

anyone touches it. The AI person is the

second type of person. You're the go-to

inside a business for anything AI. The

one who finds the problems worth

automating, and then builds the fix. And

you can do this one of two ways. You can

do it as your own thing, going business

to business and selling it as a service,

or in-house, where you become the AI

person inside of a company. And that

seat is worth going after, because it's

the fastest-growing, best-paid ground in

the entire job market right now. And I

mean that with actual numbers. PwC

tracks over a billion job ads every

single year. And in their 2026 report,

workers with AI skills are getting paid

a 62% premium over people doing the

exact same job without those AI skills.

And a couple of years ago, that premium

was only 25%, but it more than doubled,

and it's still going up. The roles at

the very top pay a lot more than that.

The forward-deployed engineer, which is

basically the AI person who's heavily

focused on building, went from around

640 job postings to over 5,000 in a

single year. And Palantir pays them

about 210 grand at the median, and

OpenAI and Anthropic are also hiring the

same role right now. And Chief AI

Officer, a title that barely existed 3

years ago, is paying a median of $1.6

million at the companies that are

actually disclosing this kind of stuff.

Those postings were up 470% in 1 year.

The number of job titles that even

mentioning AI have tripled since 2022

and almost 2/3 of them are outside of

tech. So, healthcare, marketing,

logistics, management. So, being the AI

person isn't just some niche tech job.

It's turning into a requirement in every

company, in every department, and the

earlier that you can become the AI

person, the faster you can move up. So,

that's the value to you. But, the reason

that every business is about to need

this person comes down to one single

gap. Almost every company already knows

that AI matters. You know, they've got

the budget and they're under real

pressure to use it so that they don't

fall behind their competitors. [music]

But, the problem is almost none of them

have pulled it off successfully. I'm

sure you guys have all heard that MIT

study. They ran a study on enterprise AI

products in 2025 and they found that 95%

of company AI pilots delivered no

measurable return at all. And then

McKinsey found basically the same thing.

88% of companies say that they're using

AI somewhere, but only 7% of companies

have actually scaled it across the

business. So, what we're seeing is the

budget's there, the pressure's there,

the results are not there. Every one of

these companies needs someone who can

walk in and turn the AI spend into an

actual result. And right now that seat

is pretty empty. And real quick, before

we jump into the actual roadmap, I want

to let you know that I have a full guide

on how to price AI workflows in my free

school community, which you can access

for completely free using the link in

the description. I also published a

video alongside that guide where I go

over all these different scenarios and

methods, which I will tag right up here

if you want to check that out. Now,

whether you want to sell your services

to businesses or you want to be the

in-house AI person, understanding what I

talk about in that video is really

important because it's all about proving

the value that these systems will create

and justifying the expense, which is

how, if you're the in-house AI person,

you're able to go ask for, you know,

bigger budgets and more resources for

your projects that you want to take on.

So, anyways, let's get back to the

video. Here is the actual roadmap to

becoming the in-house AI person and I

broke it into three phases. So, phase

one is just to position yourself. You

start as a builder and that just means

you're the one actually making things

with AI, not the person just talking

about AI in the meetings. Then, you're

going to go ahead and pick your niche.

So, don't try to be the AI person for

the whole company on day one. Just start

with one team with a few workflows.

Choose the team you're already on and

make that your [music] patch of ground.

Then, you start to market, right? Inside

a company that just means you make your

work visible. So you help co-workers

with their most annoying task. You ask

your boss what eats up the most time in

the department. And now you're not

guessing what to build, you're having

people on the team actually tell you

what would be valuable to them. Then we

move on to phase two, which is proving

your value. So you start with one task

off that list, you know, the most

expensive, the most repeatable, where if

AI gets it a little bit wrong, nobody

gets hurt. So these could be weekly

reports, meeting notes, sorting the

inbox, cleaning up data, just the stuff

that's boring, but really repetitive.

And one quick rule before you touch

anything, only use the AI tools that

your company actually allows and never

put company or customer data into

something that isn't approved. But

here's the most important part, before

you build, you have to name the number

that you're aiming to move. This is the

whole difference between a builder and a

consultant, because a builder is just

going to, you know, build. They're just

going to make some things that look

cool. But a consultant picks one number

and actually moves it. And that number

almost always falls into one of three

buckets, which are time saved, mistakes

cut, or money made. So for example, a

Friday report, that number that you're

trying to move is maybe four hours that

it eats every week and turning that into

something like 20 minutes. But these

numbers can also get more specific and

honestly, the more specific the better,

like organic form submissions per week

or average response time or refund

percentage. And then once you have the

number, you build a fix to move that

number and then you record it. You give

Claude the real docs behind the task,

the last few reports, the template and

you work with it until it nails the

report every time. And then you record a

quick before and after video or case

study. So hey, you know, this used to

take four hours, but watch me do this in

10 minutes because I built this AI

system. And then you deliver it for real

and you prove that the number moved, not

just saying, hey, you know, this should

save you some time. You actually measure

it. Four hours down to 20 minutes is

three and a half hours back every single

week and now you've got proof, not just

a promise. And then you basically just

run that same loop on the next task and

the one after that until you've built up

a whole stack of wins with real numbers

attached and then you move into phase

three, which is impossible to replace.

Because if you have a big stack of wins,

but they're, you know, you're not doing

anything with them, then it's useless.

Because you could build a bunch of

systems, they're actually helping the

business, but if nobody knows, then

what's the point? So phase two is about

getting the results, Phase three is

about making sure those results actually

get pinned to your name. Because here's

what usually happens. Like I said, the

business starts saving real money, and

everybody just assumes someone upstairs

will notice and connect it back to you,

but usually they won't, and it just kind

of disappears into oh yeah, we had a

great quarter. So, what you have to do

is connect it. Every win that you bring

to the team, you frame it as the

business's win, but you make it

undeniable that it came because of the

systems that you built. And when you're

able to show them all of this proof, all

of these outcomes, that's the math that

gets a role created for you, and then

you raise the altitude. Because saving

people time is useful, but it's not

ultimately like what really, really

grows a business. What really grows a

business is attacking constraints. A

company only grows as fast as its single

biggest bottleneck lets it. And at the

highest level, every business is one of

two things. It's either supply

constrained, where they can't produce or

deliver enough to keep up with demand,

or it's demand constrained, where they

could handle way more customers, they

just don't have enough coming in. And a

quick way to tell which one you're in is

just to ask, "What breaks if the company

doubled its customers tomorrow?" If

everything falls over, then you're

supply constrained. And if you can

handle them just fine, and you're just

not out there, then you're demand

constrained. And this is truly where you

stop being just like, "Hey, the AI guy,

the AI automation guy." And you become

the person with the perspective. You

walk into the room and you say, "This is

our real bottleneck. This is the exact

thing capping our growth, and here's the

AI system that I'd like to build to

attack this constraint." Then you build

it, you prove that that constraint

actually opened up, same loop as phase

two, a much bigger number at a much

larger scale. And the second that

bottleneck clears, guess what happens? A

new one takes its place. So, then you go

attack that one, too. And you just keep

doing that, constraint after constraint.

And that's the person that a business

physically doesn't want to have to

replace. You're not shaving a few hours

off someone's week anymore. You're the

one moving the thing that their entire

growth is stuck behind, over and over.

And at that point, they don't really

have a choice. The budget gets bigger

for your projects, they put more people

under you so you can move faster, and

the resources just start showing up.

You're not an employee who's good with

AI, you're the reason the business is

growing. So, that's the full road map.

Position yourself, prove your value, and

become impossible to replace. But there

is one catch. You can follow every step

in this road map, but if you can't

actually build the solutions with AI,

then none of this works. The good news

is you can learn how to build all of it

for free in my community. Full courses,

all the tools, and every resource that I

use in my videos. I also put together a

resource free guide today that covers

everything that I just talked about in

this video, and it's in my free school

community as well. And if you get stuck,

you've got me and the whole community in

there to help you out. So, anyways,

that's going to do it for this one. And

if you got something out of it, please

give it a like. It helps me out a ton.

And as always, I appreciate you guys

making it to the end of the video. I'll

see you on the next one.

Thanks, everyone.