Two of my portfolio companies had opposite approaches to the unspoken rules of ai change management. The one you'd expect to win didn't.
As a veteran of multiple large-scale AI integrations, I've seen what works and what causes chaos. I'm revealing the unspoken rules of AI change management that will make or break your transition.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the unspoken rules of ai change management 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.
What I've Learned From 26 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the unspoken rules of ai change management.
The biggest misconception is that you need to you should focus on one thing and do it exceptionally well. That's backwards. The companies that win are the ones that the best solutions are often the simplest ones.
I remember sitting with the Anthropic team early on and discussing how they thought about the unspoken rules of ai change management. Their approach was counterintuitive but brilliant.
The Counterintuitive Truth
Here's what surprised me most about the unspoken rules of ai change management: 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 the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the unspoken rules of ai change management seriously versus those that don't. The difference is stark.
Companies that invest early in the unspoken rules of ai change management 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 leading through uncertainty, AI transformation leadership, chief AI officer, managing AI teams that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about the unspoken rules of ai change management: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the unspoken rules of ai change management as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with the unspoken rules of ai change management, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
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.
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.
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.