I spent $50,000 learning this lesson about what i’ve learned about managing ai changes the hard way. You can learn it in 10 minutes.
I've managed several big AI projects and know what helps and what causes problems. Here, I’m sharing the practical lessons that can help your AI transition go smoother.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to what i’ve learned about managing ai changes 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 Counterintuitive Truth
Here's what surprised me most about what i’ve learned about managing ai changes: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to what i’ve learned about managing ai changes.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on what i’ve learned about managing ai changes. 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 what i’ve learned about managing ai changes strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI change management, AI leadership skills, leading through uncertainty, managing AI teams that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about what i’ve learned about managing ai changes: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at what i’ve learned about managing ai changes share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
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.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
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.
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.