The argument in one line.
AI tool proficiency is commoditizing faster than anyone expected, so the durable edge belongs to the person who can diagnose a real business constraint before anyone picks up a tool — and 88% of organizations currently have nobody who can do that.
Read if. Skip if.
READ IF YOU ARE…
- You have been learning Claude or AI automation tools and are not sure what to do with those skills professionally.
- You work a corporate job and want to advance, protect your role, or get a raise without starting a business.
- You are an aspiring entrepreneur who wants a market with high demand and almost nobody who can actually deliver.
- You already own a business and want to apply AI to your own internal processes before hiring or becoming a consultant.
SKIP IF…
- You already run an active AI consulting practice with paying clients — this is a roadmap video, not advanced strategy.
- You are looking for a technical walkthrough of how to build agents or automations rather than career positioning.
The full version, fast.
AI tool proficiency is commoditizing fast, but the ability to walk into a business, name the real constraint, tie a KPI to it, and prove results afterward remains genuinely rare. The video argues the career move right now is to become an AI consultant — either working inside one company or advising many — by running a four-step internal playbook: audit your role for real constraints (not just repetitive tasks), build a targeted solution with a measurable KPI, recognize the cross-business patterns that emerge, and formalize the role before leadership invents it without you. The AI consultant label has an expiration date, but the underlying skill window is wide open right now.
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00:00 – 01:50
01 · The AI phases timeline
Automation to agency to agent builder to agentic; early movers catch each wave, late movers fight to survive in a crowded race-to-the-bottom
01:50 – 03:05
02 · The gap is the opportunity
88% of orgs use AI, only 6% are good at it; the value shifts to whoever decides what to build, not who builds it fastest
03:05 – 04:13
03 · Doctor vs. pharmacist
Builders give you what you asked for; consultants figure out what you need; the doctor gets the real money because clients only know what hurts
04:13 – 05:11
04 · Two roads
Independent consultant (solve problems for many businesses) vs. in-house consultant (become the AI expert at one company, possibly your current one)
05:11 – 07:00
05 · Credibility and context
Goldman Sachs to True Horizon AI agency ($100K/month) to 400K-member AI community; non-technical background in marketing and analytics
07:00 – 09:34
06 · Four types of AI learners
Hobbyist (validates skill and funds tools), aspiring entrepreneur (massive demand, low supply), employee (cheat code for raises and job security), business owner (you are your own first client)
09:34 – 11:10
07 · The expiration date
The AI consultant label is temporary — like internet marketer or Excel accountant; the edge is real but the window will close
11:10 – 13:56
08 · Operating principle: constraint KPI build
Automating non-constraints wastes time; every project needs a real business bottleneck plus a specific metric to move before touching a tool
13:56 – 16:07
09 · The four-step roadmap
Step 1 Audit role for real constraints. Step 2 Take on small projects with KPIs. Step 3 Become pattern recognizer. Step 4 Formalize the role with evidence.
16:07 – 16:53
10 · Case study and recap
Eilin: 15 years as email dev, team laid off, learned Claude Code, built in public, got Head of AI role at a 15-person company by sending links not explanations
Lines worth screenshotting.
- 88% of organizations use AI, but only 6% are actually good at it — that gap is the entire career opportunity for the next three to five years.
- Getting good at Claude on its own means almost nothing long term because the tools will change; what lasts is the skill of understanding what any tool can actually do for a real business.
- Builders are pharmacists — they give you what you asked for. Consultants are doctors — they figure out what you actually need, and the doctor gets paid the real money.
- Clients never actually know what they need. They only know what hurts.
- Automating a task that does not constrain the business is a week of work saving twenty minutes that nobody was waiting on — nobody cares.
- Constraint first, KPI second, build third — in that order, every time, no exceptions.
- The pay premium for AI skills more than doubled in a single year, jumping from 25% to 56% across a billion job postings.
- Nearly two-thirds of employees have been passed over for promotion because their skills were outdated — AI fluency is now the skill that decides who moves up.
- The AI consultant label has an expiration date — in a few years it will just be consultant, the same way internet marketer eventually became just marketer.
- The window is wide open right now, but it will not stay this wide forever.
- You do not have to be the best in the world. You just have to be the best one in the room.
- Most people who build AI skills never become consultants because they automate the wrong things — things that were never constraining the business in the first place.
- The in-house AI consultant path is the newest and least crowded bucket: become your company's AI person and you protect your job while effectively giving yourself a raise.
- You are not going to find the Head of AI role posted on LinkedIn. You are going to create it from the inside out.
- Proof beats credentials — one community member went from no AI background to Head of AI in one year purely by building in public and having links to send.
Prove results before you call yourself a consultant.
WHAT TO LEARN
The gap between organizations that use AI and organizations that are actually good at it is where every career opportunity in this space currently lives.
- 88% of organizations use AI somewhere, but only 6% have turned that into real results — most businesses know they are bad at AI, which is why they will pay someone who can fix that.
- Automating repetitive tasks is not the same as solving a business constraint — automating something nobody was waiting on produces a week of work that nobody notices.
- Defining a specific KPI before starting a build is what separates a case study from a task completed — the number you moved is the proof, not the thing you built.
- The in-house path (becoming the AI person at your current company) is the lowest-risk entry point: it protects your existing role while creating a new one from the inside out.
- You do not need a following, a technical degree, or a side business to make this work — you need links to things you have built and numbers that show what moved.
- The AI consultant label will disappear within a few years as AI becomes assumed in every professional role — the window to build the early-mover edge is open now, not later.
- Building in public is not about follower count; it is about having evidence to send when someone asks what you have built — that evidence is what gets you past HR to the CEO.
Terms worth knowing.
- Agentic AI
- AI systems that do not just answer questions but autonomously execute multi-step tasks on behalf of the user — Gartner projects $202B in spending on this category in 2026 alone.
- Constraint (business)
- A bottleneck that, if removed, directly makes the business faster, more profitable, or stops a measurable bleed — distinct from a merely repetitive task that nobody was waiting on.
- KPI (Key Performance Indicator)
- A specific, pre-defined number you commit to moving before starting a build — the metric that proves the project worked, not just that something was automated.
- In-house AI consultant
- An employee who becomes the designated AI expert inside a single organization, diagnosing and solving internal AI problems rather than selling services to outside clients.
- Chief AI Officer (CAIO)
- A C-suite role responsible for an organization's AI strategy; IBM's 2026 CEO study found 76% of large organizations now have someone in this role, up from 26% two years prior.
Things they pointed at.
01:50linkGartner Agentic AI forecast ($202B, 2026)
02:30linkMcKinsey AI adoption report (88% use, 33% project)
04:13linkIBM 2026 CEO Study (76% CAIO)
09:34linkWorld Economic Forum Future of Jobs Report 2025
09:34linkPwC AI pay premium study (25% to 56%)
Lines you could clip.
02:30
“Almost everyone is using AI, but almost nobody is good at AI. And that gap right there is the entire opportunity.”
03:12
“Clients never actually know what they need. They just know what hurts.”
08:20
“The people who win in this market aren't the ones who learn the most. They're the ones who can actually prove they can deliver real business results.”
11:30
“Constraint first, KPI second, build third.”
11:00
“The label is the part that's temporary, but the edge is real. The skills are real, and the window to grab it is wide open right now. It just won't stay open this wide forever.”
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
analogystory
00:00So you've learned everything you can about Claude. You can build agents, automations, and complex systems. But what do you do now?
00:05Do you start an AI agency? Do you sell automations, or do you build software for work companies? There are so many different options out there, but 90% of those options aren't relevant to the average viewer of this channel.
00:15And I understand that most of you guys work normal jobs, you have corporate careers, and much prefer the security of being employed than, you know, the ups and downs of being self employed. But with the AI space changing like every single day, that security that most of you are used to is disappearing. So in this video, I'll show you the best thing that you can do right now to make money with your Claude skills inside of your preferred career and the exact road map to do so.
00:35So let's get into it. Before I give you the actual road map, there is one thing that you have to understand first, which is the AI space never stops moving. It does not sit still for a second.
00:43So getting really good at Claude right now on its own means almost nothing long term because the tools are gonna change. So what actually matters are the skills underneath the tool and learning how to take those skills and apply them to every new phase of AI as it shows up. And the reason that matters so much is because the AI space has never really had one fixed best job or one best business model.
01:01It keeps swapping them out every year or so. And every single time it does, a brand new window opens up for whoever is paying attention. So let me walk you through what I mean real quick.
01:09So if you rewind about a year back when AI first really started blowing up, the first real paid gigs were pretty simple. You could be the person who set up one automation or one chatbot for a small business or a team, and that alone was enough to get you paid pretty well. But, obviously, that shifted pretty quick.
01:22It became the whole AI systems phase or what most people now are calling the AI automation agency phase. Everybody's, you know, trying to productize services, spinning up agencies, and selling done for you systems left and right. But then it sort of shifted again, you know, over to the AI agent builder era, and this is where people stopped building those simple little automations, and they started building agents that can actually think and execute a ton of these repetitive tasks that we all do every single day.
01:45And then we get to the newest phase, the agentic one. Gartner is projecting around $202,000,000,000 in spending on agentic AI in 2026 alone.
01:52So if you just think about that for a second, companies are pouring that kind of money into AI that doesn't just answer your questions, but it actually goes and does the work for you. And that seems to be the phase that we're currently sitting in right now. But the pattern that I really want you to catch there is that every single time one of these phases changed, the people who moved early, you know, were able to catch and ride the wave.
02:11And the people who stayed glued to the old phase ended up fighting just to survive in the new super crowded sort of race to the bottom market. It was never really about learning the specific tool. It was about understanding what the tools could actually do and what the value of that was to actual human people.
02:26And the crazy part is that the building itself is getting easier every single month. The barrier to entry keeps lowering. McKenzie found that around 88% of organizations are now using AI somewhere in their business, but only about a third of them have actually turned that into real projects.
02:38So just gonna repeat that real quick. Almost everyone is using AI, but almost nobody is good at AI. And that gap right there is the entire opportunity.
02:45So the next phase isn't some new flavor of builder. The value is shifting to the person who decides what to build in the first place, why you're even building it, and whether the thing actually worked. And that person is an AI consultant.
02:56Now, a consultant is really just the person who figures out what's actually wrong and then figures out how to fix it. So instead of just sitting there and doing whatever they're told to do, think about it like a doctor versus pharmacist.
03:05A pharmacist will basically just hand you exactly what you're asking for, but a doctor has to figure out what you actually need. So builders are kind of like the pharmacists and consultants are the doctors. And the doctor is the one who gets paid the real money because clients never actually know what they need.
03:19They just know what hurts. So your job isn't being the fastest person at the build. Your job is naming the real problem in the first place.
03:26And, of course, the money backs all of this up. AI consulting market is expected to grow past $64,000,000,000 by 2028, and there is a giant gap to fill here.
03:34Roughly 30% of company AI projects just get abandoned, and only about 6% of companies using AI are actually good at it. So almost every business out there is pretty bad at this right now, and the important part is that they know they're bad at it. And this is exactly where the consultant walks right in to help.
03:47So the best move that you can make right now is to become that consultant. But there are actually two completely different roads into that role, and the one that you take really comes down to the type of person that you are. So road number one is the independent AI consultant.
03:59This is where you go into other businesses, you find their problems, and then you prescribe and build the AI solutions that fixes them. It's really just the AI agency idea, but you're just framing yourself way more as a long term partner rather than a team of scrappy devs who can build AI automations. Because instead of selling those automations, you're selling the actual solution to a specific problem.
04:17And full disclosure, this is pretty much the road that I went down myself, so I know it pretty well. But road number two is more of the in house AI consultant. And this is the door that I wanna start talking about more on my channel as I've realized not everyone wants to start their own consulting practice.
04:29So instead of consulting a bunch of different companies from the outside, you basically just become the go to AI person inside of one single company, and that company could even be the one that you're already working at right now. And, of course, companies are starting to take this very seriously. They're starting to hire in house AI leaders with real titles, things like a chief AI officer or a director of AI.
04:46And a lot of these roles are paying into the low to mid 6 figures. IBM actually just put out their 2026 CEO study, and they found that 76% of organizations now have someone in a chief AI officer type of role, and that's up from just twenty six percent two years ago. So that nearly tripled in a record amount of time for a c suite role.
05:02Now I do wanna be fair about that number for a second. That study only surveyed about 2,000 CEOs, and these were pretty massive companies. We're talking a median revenue of around $5,800,000,000, and almost four out of five of them were publicly traded.
05:14So that 76% number is really just, you know, giant enterprises racing to fill the seat first. First. The small and mid sized businesses, which is where most of you guys will try to be working, consulting for, I'm assuming, that market is still very much wide open.
05:26So if anything, that tells me there is a ton of room left for the rest of us to just walk right in. And the cool part is that both of these roads are basically the same idea. Meaning, you diagnose the problem, you prescribe the AI solution, and then you prove that it actually worked.
05:37The only real difference is whether you're doing that for a bunch of companies or just one. And it doesn't matter what tool you end up using as long as you can bring real results, which is why the skills matter so much more. The independent road is gonna fit you if you want full ownership of your time, you like variety, and you don't mind doing things like sales calls and going out to find your own clients.
05:53The in house road fits you better if you'd rather have stability, you want one place where you can go really deep, and you like the idea of a steady paycheck while you do it and not having to, like, pivot out of your job and try to start your own business. So I'm not sitting here telling you that one path is better than the other.
06:06It really comes down to your goals, your experience, what you want out of life, and, you know, what you want out of this whole AI opportunity in the first place. And look, I get it. There's a million people out there telling you what to do with AI right now.
06:15So before we keep going, I wanna give you a little bit of context on why I'm even the one standing here telling you all this because I've kind of been on both sides of what we just talked about. I actually started full time out of college at Goldman Sachs. And when AI first really started taking off, I was the guy on the team who was researching it on the side and who was obsessed with it, playing around with it in my free time, and even trying to, like, show my team and pitch it to my team.
06:35I genuinely wanted to be, like, the in house AI person. I was kinda hoping I could, like, you know, spearhead a new initiative or work on AI projects at Goldman. But it's obviously a massive firm with a ton of regulation and a ton of change management issues, and the whole thing just felt way too slow for what I wanted to do.
06:48So that's when I ended up making a bet on myself and pivoting out of that role. A little while after that, I was doing a lot freelance work. I started my own AI agency called True Horizon with a couple of business partners, and we scaled that thing past a $100,000 a month in under a year, and I ended up exiting it because I just realized that I was way more passionate about educating and getting in front of people and helping as many people as I could figure out how to use this stuff for themselves, which basically led me to where I am now, building what is the largest AI automation community in the world.
07:13We've got over 400,000 members, and the whole thing happened in just under two years. Now the reason I'm telling you guys this isn't to flex. It's basically just to say that I have a big community, and I'm able to see what's actually happening in the market.
07:23And I still get to see what business owners are trying, what employees are trying, what aspiring entrepreneurs are trying. And I've noticed that there's a ton of content out there about how to start an AI business, but most of you guys aren't trying to do that or wanna do that or you think that's the only option. But most of you might just wanna advance in your career as an employee or maybe get a better job or a promotion or more security.
07:41And almost nobody is putting out the stuff that actually helps you guys do that. Now the other important thing I want you to hear is that I don't have a technical background. Like, I came from marketing and analytics.
07:50I'm not an engineer, and I never have been. And the only reason this matters is because the barrier to entry on actually learning this stuff has basically dropped to zero. Like, anyone can do this now.
07:57So if you're sitting there wondering, can I even learn this? That's just not even a question you should be asking anymore. The answer is yes.
08:02The real question that matters is this. When someone asks you, why should I hire you over the person who watched the exact same amount of YouTube tutorials and knows the exact same terminology and has the same experience as you? You have to think about how you answer that question.
08:14Because the people who win in this market aren't the ones who learn the most. They're the ones who can actually prove they can deliver real business results. So that right there is the core question.
08:22It's also a big part of why we've been building something silently on our end to help you guys out with credibility, but more on that later. For now, just sit with that question for a sec because no matter which of these roads you go down, that question is exactly what your entire path comes down to, and you will get asked that question.
08:36Now the great thing is that no matter which of those two roads sounds more like you, becoming an AI consultant is probably the next natural step in your journey. And I wanna break that down for a second because there are basically, in my mind, four types of people who are probably watching this right now, and this move makes sense for every single one of these four buckets.
08:52The first type is the person who's just building with Claude as a passion project. And hey, if that's you, there's nothing wrong with that. It's totally fine.
08:57But if you can actually monetize what you're doing in some way, it does two big things for you. First, it validates that you're actually good at this because it's probably the cheapest way to find out if your skills are good enough where someone's willing to pay for them. And second, it funds the hobby.
09:08You know, there's so many new AI tools and, you know, tokens are expensive and subscriptions are expensive. So if you can have a little bit of extra cash coming in on the side, then you can just keep playing with this stuff and maybe keep experimenting with even more tools. Now the second type of person is the aspiring entrepreneur.
09:22The person who actually wants to make real money with this and wants to build a business. And if that's you, you've built a skill in a market with massive demand and almost nobody who can deliver on it. So just look at the numbers.
09:31The World Economic Forum projects that AI is going to create around a 170,000,000 new jobs by 2030. And even if you subtract, you know, all the roles that it's going to replace or change, that's still a net gain of about 80,000,000 jobs.
09:41On top of that, roughly half of all tech job postings in The US already asked for AI skills, and that number doubled in just one year. So you've got this giant wave of demand, and you've got barely anybody who can actually ride it. And what that means for you is that you don't have to be the best in the world.
09:54You just have to be the best one in the room. And the third type of person is the employee who just wants to level up at work, and this is the newest bucket of the bunch. And it's where that in house road turns into an absolute cheat code because becoming your company's AI person protects your job, and on top of that, it basically works like a raise.
10:07PwC went through close to a billion job postings, and they found the pay premium for AI skills more than doubled in a single year, jumping from 25% all the way up to 56%. And the pay is only half of it. Another study put out a report where nearly two thirds of employees admitted they had passed over someone for a promotion because their skills were outdated.
10:23So the skills you already have are quietly becoming the thing that decides who moves up and who gets left behind. And the fourth type of person is someone who already owns a business. And for you, this one's almost too easy.
10:31You don't need to go hire some expensive consultant, and you don't need to go become one for hire either. You just consult your own business, you know, one process at a time. You already have the perfect first client, and it is you.
10:40There is one thing I wanna call out that's pretty interesting because it changes how urgent all of this really is, and that's basically the label AI consultant. I think that it has an expiration date on it. Just like every other wave that we've talked about earlier, this one's also temporary.
10:51AI is going to seep into every single industry. It's gonna fill the cracks, and it's going to seep into every single role out there. So in a few years, nobody's probably gonna be calling themselves an AI consultant anymore.
11:00It's just gonna be consultant. Because every consultant is going to be using AI and has to be completely native with it because if they don't speak AI, they're really just not gonna get business. So just think about it like this.
11:09Somebody today, if they walked up to you and called themselves an Excel accountant, wouldn't that sound completely ridiculous? Because it's assumed that every accountant uses Excel. But if you rewind to when Excel maybe first came out, you might have a a bunch of accounts that use Excel, but a bunch of them who still stick to the old way.
11:23So maybe calling yourself, you know, an Excel accountant or listing that as one of your skills was important. It's a similar story with people who call themselves an Internet marketer back when the Internet first showed up. But now, that is just marketing.
11:33It's how everybody does it. So that's exactly where we're sitting with AI right now. The label is the part that's temporary, but the edge is real.
11:38The skills are real, and the window to grab it is wide open right now. It just won't stay open this wide forever. Now I know what some of you are probably thinking.
11:44To go become an AI consultant or the AI in house person at your job, you have to go quit your job tomorrow, go build some massive personal brand, start making YouTube videos, and bet your entire life on it, but you really don't. The whole point of this video is that there is a much smarter, much lower risk way to do this.
11:57So let me set up the operating principle that makes the whole road map work and then walk you guys through the four steps that you can start tomorrow. Because most people are approaching this completely wrong, and the difference is exactly why most builders never become consultants. The wrong move here is just looking at your job, finding the repetitive stuff, and automating it.
12:12And don't get me wrong, that's obviously not a bad thing to do. It just isn't what makes you super valuable. If you automate something that isn't actually constraining the business, you've just spent a week saving maybe, you know, twenty minutes on a task that nobody was even waiting on.
12:23Nobody cares as much. So the real move is two things. First, every single project that you build has to target an actual constraint of the business.
12:30Something that if you fix it, the business gets faster or makes more money or stops bleeding somewhere that it shouldn't be bleeding. And second, every single project needs a clear KPI tied to it before you start building it. A specific number that you're actually trying to move, and that's your North Star for that specific project.
12:45So constraint comes first and KPI comes second. And what's important about that is it's how you actually communicate the value in a way that nobody else in this market is currently communicating it. Because anyone can say, hey.
12:54You know, built this AI agent. Almost nobody can say, I built an AI agent that moved this specific number by this much for this specific business, which resulted in x percent more profit or, you know, x more customers or whatever the metric might be. And that right there is what sets you apart.
13:06The full operating model behind this stuff lives in a book that I'm currently writing. But for the purposes of this road map, the rule is very simple. Constraint first, KPI second, build third.
13:13So step one is to audit your own role. Sit down with that constraint lens that we just talked about and walk through your job. Instead of listing every repetitive thing you do, ask which one is actually slowing the team down or gating revenue or causing real pain somewhere downstream that nobody's currently solving.
13:27And that's your real list. Then for each item, write down the specific number you'd be trying to move if you fixed it, the specific metric. And that right there is your first project list.
13:34Not the easy stuff, but the stuff that actually matters. Step two is to actually take on small projects. So you pick one item off that list and you go scope out and build a solution for it.
13:42Then you go ask if you can implement it in just one area of the company. Just one little corner of the business where you can actually test something, prove that it works, and show real results. Because those results, whatever they end up being, are basically your first case study.
13:53You can show it to your boss. You can show it to your team. You can put it on your portfolio.
13:56You could throw it up on LinkedIn just so people start to notice. Step three is then to become an expert at solving problems, not just building. This is actually the most important step in the road map because once you've done enough of those small builds, you start to see the patterns underneath them.
14:08You start to notice that businesses keep running into the same handful of problems over and over again. And that right there is the last skill that you need to become a real consultant. You stop being the person who just builds whatever they're handed.
14:18You're now the person who can walk into a situation, find the actual problem, design the system, and solve the problem. And the moment that happens, your coworkers start coming to you instead of the other way around. Then step four is to formalize it.
14:28Once you've shipped enough proof that the company actually needs this kind of person on the payroll, that's when you formalize it. You go to your boss or your leadership team with the evidence and you propose the role yourself. Most of you guys aren't gonna find this job posted on LinkedIn.
14:39You're going to maybe create it from the inside out. And the only reason you can pull that off is because you spent steps one through three quietly building the case for it. And even if the organization isn't ready to have that role yet, when they are ready, inevitably, you wanna be the first name that pops into their heads.
14:54So that's the whole roadmap. Audit, build, pattern, recognize, formalize. Four steps.
14:58And remember how I talked about AI seeping into everything? You don't have to go learn a completely new industry. Just think about what do you already do on the day to day, what are you good at, and how can AI help you move faster and be smarter and better outputs at what you already do.
15:09And the cool part here is that it's not theoretical. I actually sat down with someone in my community who ran this exact play in real time. Her name is Eilin.
15:15About a year ago, she had spent fifteen years as an email developer. So not super technical, no AI background, but her whole team was then let go, and she had to figure out something new. So she started learning end to end.
15:24She pivoted into Cloud Code. And while she was learning, she did the one thing that people skip, which is she built in public. She started two YouTube channels just showing off the stuff she was making.
15:31She posted her builds on LinkedIn. She recorded demos of every single thing that she had been working on. And the cool thing is she doesn't have thousands and thousands of subscribers and followers.
15:39She just has proof. So when she applied for head of AI role at a 15 person company called Young, the recruiter sent her an email with one question on it which was, what have you built? And remember that is the exact question I told you decides who wins in this market.
15:50So instead of her having to type up some long vague explanation, she just sent links. She just sent her case study. She sent real proof.
15:55And she was able to skip HR entirely, go straight to the CEO, and she got the job. So in a year from basically no AI to head of AI is really cool. If guys wanna see the full interview I did with her, I will tag that right up here so you guys can check that out.
16:06So let me sum up this entire video in one breath. You don't have to quit your job. You don't have to build some massive personal brand.
16:11You don't even have to be super technical. The real move is to pick an actual constraint in your role or your company, tie it to a KPI, build a solution, and then build a few more so you have a real track record. Then put that track record somewhere that people can actually see it.
16:22Even if that's just a personal website where you're building your own portfolio, just something that you can send to people. So if you want help running this whole playbook, the link in the description goes to my free school community. Everything we talked about in this video, I've broken down into a free resource guide that you can access in there as well as other courses, GitHub repos, skills, frameworks, and hundreds of thousands of people who are looking to stay ahead with AI.
16:41It's completely free. It is the first link in the description. But, anyways, that's gonna do it for today.
16:45If you guys enjoyed the video or you learned something new, 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, and I'll see you on the next one.
16:52Thanks, guys.
The Hook
The bait, then the rug-pull.
You built the thing. You know how to wire agents together, chain automations, get Claude to do what you want. And then you looked up and realized you had no idea what to do with any of it. That is exactly the moment this video was made for — and the answer is not what most of the AI content out there is selling.
Named ideas worth stealing.
12:30model
Constraint KPI Build
- Identify the real business constraint
- Define the KPI to move before building
- Build the targeted solution
Three-step sequencing rule that separates consultants from task-automators. Most builders skip the first two steps.
Steal forany client proposal or internal AI project pitch
04:13model
Two Roads Framework
- Independent AI Consultant — solve problems for many businesses
- In-House AI Consultant — become the AI expert at one organization
Same diagnostic skill, two delivery models; choice depends on whether you want variety and ownership or stability and depth.
Steal forpositioning yourself to leads or deciding which AI career path to pursue
13:56list
Four-Step AI Consultant Roadmap
- Audit your role for real constraints, not just repetitive tasks
- Take on small projects with pre-defined KPIs
- Become an expert at pattern recognition across builds
- Formalize the role with evidence before leadership invents it without you
Progressive playbook for turning Claude skills into a consulting role from inside your current job.
Steal foremployee career plan or internal pitch to leadership
07:00list
Four Types of AI Learners
- Hobbyist — monetize to validate skill and fund the tools
- Aspiring Entrepreneur — massive demand, almost no supply
- Employee leveling up — AI fluency equals raise plus job protection
- Business owner — consult your own company first
Audience segmentation that makes the consulting pitch relevant to four completely different starting points.
Steal forcontent segmentation, community onboarding, lead magnet targeting
How they asked for the click.
VERBAL ASK
16:30link
“the link in the description goes to my free school community — everything we talked about in this video, I've broken down into a free resource guide”
Soft sell woven into the closing summary. Also hints at a credentialing product being built, seeding curiosity without a hard pitch.
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
AFFILIATECommission earned if you click.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
