The AI landscape is shifting under our feet. Based on my conversations with hundreds of founders and investors, here are the three biggest trends in AI leadership that you need to prepare for now.
Why Most Approaches Fail
Many common approaches to AI leadership are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Counterintuitive Truth
In AI leadership, the best practitioners often do less, not more.
The lesson: simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on AI leadership rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that timing is everything in this game. 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 AI leadership.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI leadership. 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 AI leadership in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI leadership skills, AI change management, managing AI teams, leading through uncertainty, chief AI officer that I've been thinking about a lot lately.
The Bottom Line
None of this is rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at AI leadership aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take AI leadership seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
Q:How do I know which items apply to my situation?
Start by honestly assessing where your biggest bottleneck is right now. The items that address that specific constraint will give you the highest return on your time and energy.
Q:How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 234 startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
Q:Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.