During the MovieLaLa days, we learned something about lead an ai transformation (even if you don't that I still apply to every investment I make.
You don't need to be a machine learning PhD to lead an AI transformation. This is my step-by-step guide for non-technical leaders on how to drive meaningful change and get real results with AI.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating lead an ai transformation (even if you don't. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the data tells a different story than your gut 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 in my portfolio that nail lead an ai transformation (even if you don't are the ones that treat it as an ongoing process, not a one-time project.
What I've Learned From 65 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with lead an ai transformation (even if you don't.
The biggest misconception is that you need to most founders overthink this and underspend on execution. That's backwards. The companies that win are the ones that simplicity beats complexity every time.
I remember sitting with the Anthropic team early on and discussing how they thought about lead an ai transformation (even if you don't. Their approach was counterintuitive but brilliant.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to lead an ai transformation (even if you don't 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 Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take lead an ai transformation (even if you don't seriously versus those that don't. The difference is stark.
Companies that invest early in lead an ai transformation (even if you don't see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around AI change management, chief AI officer, AI transformation leadership, AI leadership skills, managing AI teams that I've been thinking about a lot lately.
What's Next
The world of lead an ai transformation (even if you don't 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 lead an ai transformation (even if you don't 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
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.
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.