Six months ago I typed a question into Google.
I do that less and less now. Not because I stopped being curious, but because I discovered what happens when you bring that same curiosity to a real conversation instead of a search engine. That shift changed how I work, how I plan, and how I think about the next decade of my career.
It did not happen overnight. It happened through four steps — a framework that took me from AI curious to genuinely AI fluent. Not fluent in the technical sense. Fluent in the way that actually matters for leaders: knowing how to bring AI into your work as a thinking partner, a research engine, a process consultant, and a builder.
Thirty years in enterprise technology and I still had to build this from scratch. That alone tells you where most people are starting from.
Step 1: Research
Stop using search engines as your primary research tool and start using AI models. Not one model — four. Download ChatGPT from OpenAI, Claude from Anthropic, Copilot from Microsoft, and Grok from X. Put them on your desktop and your phone. Then take the same question and run it through all four.
This matters because each model has a different personality, different training, and different strengths. ChatGPT tends to be comprehensive and affirming. Claude tends to push back and help you think through tradeoffs. Copilot connects into the Microsoft ecosystem. Grok pulls from a different information stream entirely. Using one model is like reading one newspaper and calling yourself informed. Using four is how you start to see the full picture.
Try this with something low-stakes first. Planning a trip. Thinking through a career move. Evaluating a business idea. Type the same detailed prompt into all four and watch what comes back. Then have a real conversation in each one — not just a query, an actual back and forth. That conversation is where the value lives.
Using one AI model is like reading one newspaper and calling yourself informed.
Step 2: Process Reengineering
Once you have started using AI as a research tool, the next step is bringing it into your actual work.
Take the things you do every week — the recurring tasks, the reports, the communications, the decisions — and before you do them, ask an AI how you should think about doing them differently. Not for the sake of change. With a specific goal: identifying which parts of that task are truly irreplaceable and which parts could be automated, accelerated, or redesigned.
This is what process reengineering means in practice. You are not asking AI to do your job. You are asking AI to help you see your job more clearly — to find the manual painful steps that could become repeatable mechanisms, and eventually, automations.
The leaders who do this well are going to separate themselves over the next two to three years. The ones who do not will still be doing manually what the others handed to AI in 2025. That gap will compound faster than most people expect.
The leaders who reverse engineer their work with AI now will separate from those who wait. That gap will compound faster than most people expect.
Step 3: Build
This is the step most people skip. It is also the one that matters most for actual fluency.
At some point you have to stop reading about AI and start building with it. Not building in the engineering sense — building in the sense of taking an idea and using AI to make it real.
I had never opened GitHub. I did not know what Vercel was. I had no traditional development background. But I had an idea — a platform to help leaders like me become more AI fluent — and I started having conversations with Claude about how to build it. On an external account, with no internal information involved.
Claude walked me through everything. Setting up a GitHub account. Deploying on Vercel. Pointing my DNS. Connecting an API to my Substack. Every time I got stuck I came back with a screenshot and said “this isn’t right — do you see what’s wrong?” and it helped me fix it.
That platform is BecomeAIFluent.com. It is real, it is live, and I built it without a developer. That is what AI fluency looks like in practice.
Step 4: The Learn-It-All Mentality
The fourth step is the one that sustains everything else — and you are already doing it by watching this.
Becoming AI fluent is not a destination. It is a posture. A commitment to keep learning, keep asking, keep having conversations — with AI, with colleagues, with people who are further along than you are.
Ask the people around you what they are learning. Find the leaders in your organization who are building AI fluency and have real conversations with them. Seek out content, coaching, community — not passively, actively. The people who grow fastest in this space treat every week as another opportunity to learn something they did not know before.
Becoming AI fluent is not a destination. It is a posture. A commitment to keep learning, keep asking, keep having conversations.
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
You can do this. I am in my mid-fifties with 30 years in tech and I still had to build this from scratch. The dots connected faster for me than they might for you — but they will connect. The question is whether you start.
Research. Reengineering. Build. Learn. Four steps. Ninety days. That is the path.
If this episode connected with you, leave a comment. I read every one and try to respond to all of them. And if you know a leader who is quietly falling behind on AI, share this with them. The window is still open — but it does not stay open forever.
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Much Love,
Chris
