I want to tell you the story of how I ended up here — co-founding an AI coaching company with zero technical background. Not the polished version, the real one, because I think it matters, especially if you're sitting somewhere right now thinking "I should probably start doing something with AI" but haven't yet.
Where I Started
I was leading a learning team at an education company, and our leadership did something that, in hindsight, was genuinely forward-thinking: they made the call to integrate AI across everything we built. AI tools and techniques woven into all our professional development programs. This was early — most organizations weren't even having the conversation yet. Ours was already making moves.
That was the right call, and I agreed with it completely.
But it surfaced something I couldn't ignore: how was I supposed to guide other people through this when I hadn't figured it out myself? I wasn't opposed to AI — I just hadn't started. As a certified career development coach, I knew enough about coaching to know that you can't coach what you haven't practiced.
My colleague Matt and I put together a proposal to attend an external bootcamp — something that would give us a foundation. We wrote the business case, identified who should attend, mapped out how we'd spread the learning across the organization. The investment was modest, but it wasn't available to us at the time. Fair enough.
Around the same time, we were asked to figure out how to get AI to generate slide decks for us. We dug into the research and realized: the tools on the market literally couldn't do this yet. So there we were with two urgent priorities and no roadmap for how to tackle either of them.
And I want to be clear — this wasn't a failing of our organization. They had the vision and they were genuinely ahead of the curve. The problem was that the playbook for how to actually help people get there didn't exist yet, not just at our company but anywhere. Nobody had cracked this.
Building the Plane While Flying It
So we did what came naturally to us as education professionals. We gave ourselves the structure that didn't exist.
Matt helped me get set up — opening VS Code for the first time in my life, figuring out GitHub, getting my environment configured. I distinctly remember the feeling of pushing and pulling the words "Hello World" to a repository and thinking we'd just accomplished something monumental. (We had, honestly. That's how it starts.)
Then we tackled the slide deck generator together. We blocked six hours a week on Google Meet. We gave each other homework. We met again. We built something — using the Cline extension in VS Code, way before tools like Claude Code existed — and it was truly awful. It did maybe 2 of the 10 things we wanted it to do. So we tore it down and started over.
And I want to be honest about what those early weeks felt like, because if you're starting now, you'll probably hit every one of these:
"I could have done this faster myself." Yep — in that moment, absolutely true. Using AI to do something you already know how to do is slower at first, and that's a completely normal part of the process.
The outputs were garbage sometimes. AI gives you junk, you iterate, and that back-and-forth is the process itself — not a sign that something is broken.
Trusting something that isn't human felt weird. Delegating meaningful work to an AI tool is genuinely uncomfortable, and letting go of control takes more practice than you'd expect.
We hit all of these, repeatedly, but we kept showing up — six hours a week, every week, while also doing our regular jobs. And the shift didn't happen in a single breakthrough; it happened in the accumulation of all those messy, imperfect sessions.
How MVP Club Started
Meanwhile, Matt had started a free community — what would become MVP Club. I was one of the first people in. I didn't join as a co-founder; I joined because I wanted to keep learning and I wanted to help. I started community managing, running meetings, diving into conversations with people who were going through the same thing we were. Ryan, who had been building with Matt on projects alongside all of this, came together with us in the community. It was completely organic — no business plan, no pitch deck. Just people who cared about the same thing, showing up.
As more people joined — from our professional networks, from past roles, some who just found us on the internet — the pattern became impossible to ignore. Everyone was in some version of where we'd been: they knew AI mattered, they wanted to engage with it, but they were stuck. Either their organization hadn't provided support, or the support that existed was a one-time workshop that didn't change anything, or they were just overwhelmed and didn't know where to start.
That's when we understood what we were actually building.
Why Starting Now Matters
Here's what I want you to understand about where things are now, because it matters.
The tools have gotten dramatically, almost unrecognizably better since Matt and I were fumbling through our first "Hello World." What took us weeks of stumbling can now happen in hours. And this creates a compounding effect that works in your favor in two ways.
First: the tools keep getting better, which means starting now puts you ahead of where we were. You're not starting where we started. You're starting with tools that are wildly more capable. That's not a reason to wait for them to get even better — it's a reason to jump in now, because the learning curve is gentler than it's ever been, and it'll only keep getting gentler.
Second: the people who've been practicing recognize new capabilities instantly. When a better tool drops, someone who's been building every day sees the possibilities right away. Someone who's been waiting has to start from zero. Practice doesn't just help you use today's tools — it prepares you for tomorrow's.
I'll give you two examples of what this looks like in practice.
A member of our community had an idea for a personal finance app that had been shelved for years. It was one of those "someday" ideas — the kind you think about but can't act on because you'd need to hire a developer, or learn to code, or somehow find months of free time. After a couple of conversations with us about how to communicate with AI tools — how to prompt, how to steer, how to iterate — he generated his first working MVP of the app. It looked great — an idea that had been sitting dormant for years became a real, functioning thing in a matter of days. But it only happened because he started, used the tools aggressively, and had a clear goal he cared about. All three of those matter.
And then there's my own experience. Recently, I developed the concept for a lead magnet — a guide to building your first website with AI in under 45 minutes — and then I designed it, deployed it to our website, and connected it to our email capture system. All of it, me, a person who opened VS Code for the first time a year and a half ago.
And here's the part I love: the thing I built is literally teaching people to do what I did. The lead magnet says "you can build something real with AI" — and the proof that it's true is that I built the lead magnet itself with AI. That's not a marketing strategy, that's just what happens when you practice.
What We Learned
This is why MVP Club exists — not because we have some secret methodology or because we cracked a code no one else could, but because we lived through the exact thing that every professional is navigating right now (urgency without a roadmap) and we came out the other side understanding something simple:
AI adoption is a practice problem, not a training problem. You don't solve it with a workshop or a certification. You solve it by building with AI every day, ideally alongside other people who are doing the same thing.
The most disruptive career moment of our lives is better navigated together than alone. We know that because we tried it both ways.
Your Move
If you're reading this and you haven't started yet — or you started and stalled — I want to say this as clearly as I can:
You are not behind. The best time to start is now — ask me again next month and I'll give you the same answer.
Not because you're late, but because the practice itself is where the understanding comes from. You can't read your way into knowing what AI means for your career; you can only build your way there.
And you don't have to do it alone.
Written by Jill Ozovek, ICF-certified coach and co-founder of MVP Club. Last updated February 7, 2026.