The first time I tried to implement my take on what ai leadership will look like in 2027 at scale, everything broke. Not metaphorically. Actually broke.
After talking with many founders and investors, I've noticed three important changes in AI leadership that everyone should be ready for.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to my take on what ai leadership will look like in 2027 are fundamentally flawed. Not slightly off. Fundamentally flawed.
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 Reality Nobody Talks About
Most people approach my take on what ai leadership will look like in 2027 with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that you need to move fast and break things. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that the data tells a different story than your gut. Once we made the switch, everything changed.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to my take on what ai leadership will look like in 2027.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on my take on what ai leadership will look like in 2027. I've seen plenty fail because they moved too slow.
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. This sounds obvious but you'd be amazed how many founders build their my take on what ai leadership will look like in 2027 strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around chief AI officer, leading through uncertainty, AI leadership skills, AI change management that I've been thinking about a lot lately.
What's Next
The world of my take on what ai leadership will look like in 2027 is moving 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.
And if you're a founder building in this space, remember that the best time to get my take on what ai leadership will look like in 2027 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
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.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.