Companies take opposite approaches to AI change management, and the one you'd expect to win often doesn'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
Many common approaches to AI change management 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.
What I've Learned From 26 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI change management come up again and again.
The founders who handle AI change management well understand that the best solutions are often the simplest ones.
The Counterintuitive Truth
In AI change management, the best practitioners often do less, not more.
The lesson: the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
Investing early in AI change management can be 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
There are no shortcuts in AI change management, but there are smarter paths.
The strongest teams treat 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 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
Q: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.
Q: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.
Q: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.