Six months ago I sat down with a sticky note and wrote three words on it. Not a goal. Not a plan. A question. The three words were “become AI fluent” — and I wrote them because I genuinely did not know if I wanted to answer yes.
That might sound strange coming from someone who has spent thirty years in enterprise technology. I have navigated the internet age, the dot-com era, the shift from on-premise to cloud, Smart Home at Amazon, and seven years building AI enablement programs. I know how to reinvent myself when the ground shifts. I have done it before.
But this one was different. And I was behind in a way I had not expected.
“The gap between using AI and becoming AI fluent is wider than most leaders in technology want to admit.”
This is the first episode of Become AI Fluent — a show built for senior leaders, directors, VPs, and executives who are technically literate but quietly falling behind on AI. Every episode shares what I am actually doing, not what sounds good in theory. And this first one starts where every honest conversation starts: with a confession.
What I Had to Admit to Myself
I was using AI on a daily basis and calling it productivity. Summarizing transcripts. Cleaning up emails. Running research queries. All of it useful. None of it transformative. And at the time, I genuinely believed I was keeping pace.
I was not. The people around me who were building real AI fluency were not using it as a text editor. They were designing workflows with it. Building tools with it. Using it as a thinking partner, not a finishing tool. That gap — between using AI and becoming AI fluent — is wider than most leaders in technology want to admit. And the longer you wait to look at it honestly, the harder it becomes to close.
Nobody in this space is a complete expert. The models are evolving too fast, the use cases moving too quickly. But that is actually the good news — it means the window is still open. The question is whether you are willing to walk through it.
“The people building real AI fluency were not using it as a text editor. They were using it as a thinking partner.”
Chaos to Process to Automation: The AI Readiness Ladder
The biggest mistake I see companies make when implementing AI is skipping the steps. They have broken processes — manual, inconsistent, held together by tribal knowledge — and they try to automate them. What they get is automated chaos. Faster output, no better results. Sometimes worse.
The ladder works like this. First, you take chaos and turn it into a process. You document what actually happens, not what is supposed to happen. You clean the inputs and define the outputs. That alone is harder than most organizations expect.
Once you have a real process, you build mechanisms around it. Repeatable steps. Consistent inputs. Quality checks. A mechanism is a process your team can run without you explaining it every time.
Only after the mechanisms exist do you move toward automation. At that point, AI is not replacing a broken system. It is accelerating a working one. The results are different in kind, not just in speed.
“AI is not replacing a broken system. It is accelerating a working one. The results are different in kind, not just in speed.”
What Building AI Fluency Actually Looks Like
The most concrete example I can give you is BecomeAIFluent.com. I am not a coder. I had never touched GitHub. I had never heard of Vercel. I did not know what deploying a web app meant in practical terms.
What I had was a clear vision, a willingness to iterate, and Claude. I started with a conversation. Then another. Then another. Each one pushed the idea further until I had something real — a platform with API integrations, a live subscriber connection to my Substack, and a fully deployed web application I can point people to.
That is what AI fluency looks like in practice. Not mastering a tool. Not memorizing prompts. Knowing what you are trying to build well enough to describe it, and trusting the process enough to keep going when it gets complicated. The technical barriers that used to stop non-technical leaders are not what they were. What remains is the willingness to start.
Episode Chapters
00:00 — The Confession Every Tech Leader Needs to Hear
01:00 — What AI Fluency Actually Looks Like in Enterprise Tech
02:30 — The Sticky Note That Started Everything
04:30 — How to Reinvent Yourself When You're Already the Expert
06:00 — Chaos to Process to Automation: The AI Readiness Ladder
08:30 — How a Non-Coder Built a Web App Using Claude and Vercel
10:30 — Welcome to Become AI Fluent
If this resonated with you, I would love to hear where you are in your own AI journey. Leave a comment below — that conversation is worth having.
And if you know someone in leadership who is quietly wondering whether they are falling behind on this, share this episode with them. The window is still open. But it does not stay open forever.
Subscribe free at LeaderUnlock.com, or join the community for as little as $7 per month. New episodes drop consistently, and every one is built around what I am actually doing — not what sounds good in theory.
