Hot take: hiring a Chief AI Officer is a big mistake for most companies right now. I'll explain why it's often a sign of a deeper problem and what you should do instead.
The Framework That Actually Works
Here is a simple framework for evaluating chief AI officers. It's not complicated, but it requires discipline.
Step 1: you should focus on one thing and do it exceptionally well
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the best solutions are often the simplest ones
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get chief AI officers right treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
In chief AI officers, the best practitioners often do less, not more.
The lesson: customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on chief AI officers rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the market doesn't care about your roadmap. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that you should focus on one thing and do it exceptionally well. Once they make the switch, everything changes.
Real Talk: What Actually Matters
Here is what actually matters in chief AI officers.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on chief AI officers. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about chief AI officers in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around leading through uncertainty, AI transformation leadership, AI change management that I've been thinking about a lot lately.
What's Next
Best practice in chief AI officers is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
If you're building in this space, the best time to get chief AI officers right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.