I Spent Six Months Making Sense of AI So You Don’t Have To
The Invisible Work of Becoming an AI-Native Executive
When the AI wave first hit, I felt the same quiet paralysis that many of you are feeling today.
Despite thirty years spent at the front lines of new approaches and technologies, AI felt different. The scope felt vast and amorphous, like I was trying to draw a line around a cloud that shape-shifted every time I looked up.
Economist refer to AI as a General Purpose Technology (GPT). To them, GPT is a technical classification. To a leader, it’s a nightmare.
For frame of reference, the steam engine was a GPT. The internal combustion engine was a GPT. The Internet was a GPT. General purpose technologies create breakthroughs on a dozen fronts simultaneously.
The sheer scale of AI results in friction. If you are a biology researcher, AI is an on-deck analyst for data sets, large and small. If you are an Art Director, it’s an infinite canvas for ideation. When a new technology can do anything, the human brain often reacts by doing nothing. The brain wants to wait for “things to settle” or for someone else to show parse the ever-changing landscape of AI.
Many leaders are paralyzed by the AI noise. Doing nothing seems easier. And boy-oh-boy, do I get it. I spent the first six months after the release of ChatGPT doing nothing.
Then, I entered a state I call AI sense-making.
Why I Dedicated Six Months to Finding AI’s Rhythm
AI sense-making is the deliberate effort understand the general shape of AI in order to constrain the infinite potential of AI into a context that makes sense for your business.
Imagine if someone dropped you in the middle of the ocean. Everything around you feels like…salt water. That’s how AI felt to me at first. It all felt the same. Only after months of following Google alerts set for “AI integration,” “AI automation,” and “AI tools,” did I start to get my bearings and get my bearings. (Just like you’d start to signpost around the sun, the currents, and the weather in the middle of the ocean).
Day after day, I watched the same terms, technologies, and patterns appear. Eventually, the AI noise became a tune. I established a baseline and started to see the norm. I could suddenly tell when a headline was hype and when it was a genuine signal of structural change.
Last week, I realized that ‘AI sense-making’ is an invisible, unnamed struggle that leaders face.
And it’s lonely.
How to Build Your Own Sense-Making System
The truth is that most leaders don’t have the luxury to invest six months of full-time headspace to dedicate to AI sense-making. You have a P&L to protect. But you can hack it by following these three steps:
Identify Your Sense-Makers. Stop reading generic AI News. Find the three people in your specific niche who are doing the deep research on your behalf.
Find Your Peers. AI learning is social learning. We learn from each other. Surface others in your industry, in your role, in your function using AI and connect.
Focus on AI Use Cases that Matter. To manage complexity, constrain. Don’t start with “what can AI do”? Start with a specific pain point that you think AI might be able to solve, and go from there.
Need a Safe Space for AI-Sense Making?
I’ve created this space, this community, so that we can help each other make sense of AI. The posts are my gift to you. To go further - connecting with your peers, accessing sense-making tools, exploring topics that matter - consider joining the paid. It is a safe place to have real conversations about things that matter, like:
Our upcoming roundtable on Rewiring Roles with AI
Top influencers in the AI Transformation space
The results of your Hyperadaptive™ Discovery Survey
Making Sense Together
Sense-making is a social act. We learn faster when we learn together.
I want to hear from you: How did you take AI—this massive, overwhelming thing—and finally make it “click” for your specific context? Was there a specific moment where the “amorphous” became “actionable”?
Let me know in the comments below.