The role doesn’t get posted on an internal job board. It goes to whoever showed up and started figuring it out.
Back when I was at General Assembly, we got a mandate from senior leadership that we had to use AI to gain efficiencies and ______. I specifically use a blank line here because this was such early days that no one even knew was was remotely POSSIBLE yet. But Matt and I got in a (zoom) room for 6 hours a week and tried a lot of silly stuff that went nowhere, until a glimmer of something went somewhere, and we used it to build the internal case for investing in an agentic system in-house.
There weren’t definitive roles there for too many of us at the time, so I left to enhance my AI skills both by building MVP Club and by consulting with AI startups. But Matt stayed til recently and because of that early work, was seen as the go to AI person internally.
That’s how it happens. Not with a promotion. Not with a credential. Someone starts using the tools, demos and posts stuff and then they’re the one getting forwarded the AI-related questions.
I share that lil ditty to say that, if you’ve even built one workflow with AI (as of the writing of this article), you’re probably already further along than you think.
What “the AI person” actually means
It’s worth separating this from some of the loaded language that floats around right now. You’re not trying to become an AI thought leader. You’re not angling for a Chief AI Officer title, at least not yet. The role I’m talking about is simpler and more useful: you’re the person on your team who has actually used these tools on real work, who people feel comfortable asking questions, and whose judgment gets trusted when AI comes up in a meeting.
That’s it. The bar is lower than most people think. Most teams are still in the “everyone has an account, nobody has a habit” phase. Moving into genuine daily use already puts you ahead.
The career piece is real, though, and worth being honest about. When decisions get made about which AI tools the team adopts, which workflows get standardized, who runs the pilot, who helps onboard the rest of the team, the AI person is already in the room. That positioning compounds fast.
Why non-technical is actually an advantage
I came to this from a career coaching background. No CS degree, no engineering team to lean on, no technical cofounder whispering in my ear in the early days. And what I’ve found, after working with a lot of professionals through this transition, is that the non-technical background is a huge advantage.
Why? The people who struggle to become the AI person on their team are often the ones who get lost in tool comparisons and model specifications and trying to understand the architecture behind what they’re using. The people who break through are the ones who pick one real task, open Claude, and start.
On top of that, non-technical professionals tend to be better at translating what AI can do into language their colleagues understand. I know when I’m in a meeting where the subject matter is over my head, I always want another learning design/program design (ie less technical) person in the room to be able to lean on to translate. (Shout out to my TPMs, technical program managers and more!)
The thing most career advice gets wrong
A lot of content about “becoming an AI leader” front-loads the strategy. Learn all the tools. Get certified. Build a portfolio. Then position yourself.
I got some issues with this.
First of all, the tools are changing all the time and there are too dang many of them to become proficient in all of them. That’s a recipe for burnout and AI brain fog.
Second of all, you don’t have to have this all figured out to be the AI person. You have to be the one who is visibly figuring it out, in front of other people. You need to be visible while you’re getting ready.
The reputation doesn’t come before the practice. It comes from the practice. You don’t need to be ready before you’re visible. You need to be visible while you’re getting ready. People at the beginning of this zany journey can’t picture themselves in the shoes of someone who has already arrived.
What the move actually looks like
The behavior is simple to follow, but may feel scary to folks. Essentially it’s: You figure something out. You share it.
And I don’t mean a formal thing. Something like: “Hey, I’ve been using Claude for X and it’s saved me a couple hours a week. Want to see how it works?” The reputation gets built in those small, visible moments. The career advantage comes from this: Habit + Willingness to show your work.
For example, Riel, one of our members, came to MVP Club with no knowledge of leveraging AI. He attended a few sessions, got some additional support from us and voila, maybe week 3 or 4 had a fully developed app. But he didn’t unveil it at the end. He “built in public” showing demos and asking for input from the community along the way.
Four things to do, in order
1. Build one real workflow before you talk to anyone.
You need something real, something that saved you actual time or expanded how you think about the work.
Pick one recurring task. Something you do at least weekly, with a clear output, that you’ve done enough times to recognize whether the AI version is better or worse than your own. Weekly status reports. Inbox triage. First drafts of a recurring doc. Meeting prep.
Open Claude. Give it real context: who you are, what the document is for, who will read it, what the constraints are. Evaluate what comes back. Iterate until it’s genuinely useful, not just technically done. (Claude Pro is $20 a month and worth it for professional work. The free version is fine to start.)
Keep a note of what worked. Even informally. A saved prompt, a short paragraph in your phone. That note is the beginning of something you can show someone.
2. Share before you feel ready.
Once you have one workflow that works, tell someone. One person. Informally.
The timing part matters. Most people wait until they feel like an expert before mentioning anything. With AI that moment doesn’t come, because the tools shift constantly and there’s always more to learn. Waiting for expertise is how you stay invisible.
Share with caveats. “I’ve been experimenting with Claude for my weekly reports. Not perfect but it’s cut my prep in half. Want me to show you?” That framing is honest and inviting, not a performance.
Their response tells you a lot. Some people will be curious. Some will nod politely. Spend your energy on the curious ones first.
3. Help one person get started.
When someone shows real interest, offer to sit with them for fifteen minutes. Not a whole big to-do re: a glossy workshop or anything like that.
This is where the reputation actually takes root. You’re not explaining what Claude can do in theory. You’re showing them the specific thing you built, in the context of work they recognize, with the real output you got. That’s more persuasive than anything you could say, and it positions you instantly as the person who knows how to do this.
Most people don’t push through the initial friction of a new tool on their own. When someone shows them the step that gets them past it, they remember who showed them. That person is you.
4. Stay findable on the topic.
This part is easy to skip, and it matters.
Inside your team, find small ways to stay present. Forward an article about how AI is being used in your industry. Drop a prompt that worked into a Slack channel. Bring up what you’ve been trying in a meeting, briefly, without making it the main event. Answer an AI question even if it wasn’t directed at you.
None of this is showing off. You’re letting your colleagues build an accurate mental model of you as someone who is actively working with these tools. When AI comes up in a real decision, they’ll think of you.
When someone pushes back
The first time you share something and a skeptical colleague says “I tried that and it gave me garbage output,” you’ll be tempted to backpedal.
Don’t do it! Agree with them.
“Yeah, when I first started I got garbage output too! What were you trying to do? Let me show you how I’ve gotten it to work for that kind of thing.”
This is one of the most valuable moves in the whole playbook, and I’ve coached a lot of people through learning it. Skeptics become allies faster when you meet their resistance than when you argue with it. You’re not defending AI. You’re sharing what you’ve learned about how to get good results. That’s a different conversation, and it’s the one that positions you as someone worth listening to.
What this gets you
Let me be direct about why any of this matters for your career.
AI adoption decisions are being made inside organizations right now. Things like:
- Which tools to buy.
- Which workflows to standardize.
- Which roles to redefine.
- Who to put in charge of onboarding the rest of the team.
- Who to include in the pilot.
The people who get included in those decisions are the ones already doing the work. Not the ones planning to start when things settle down, because things don’t settle down. Not the ones waiting until they feel prepared, because that feeling doesn’t arrive.
Being the AI person isn’t about a title. It’s about being in the right conversations at the right time. That positioning gets built before the conversations happen, through visible practice and a willingness to share what you’re learning.
I’ve worked with people across a lot of different industries who came to this with no technical background, me included, and what I can tell you is that the window for this kind of positioning is still open. It won’t be forever, but right now, it’s available to anyone willing to show up and do the work.
One last thing
The hardest part of becoming the AI person usually isn’t the learning. It’s the isolation. You’re trying to build a role that doesn’t have a job description yet, inside an organization that isn’t sure what it wants yet, without a peer group who is navigating the same thing.
If you want to learn in community, take a look at the MVP Club community and join us! We have a 2 week free trial to check us out.