After years of trial and error, I finally cracked the code on hybrid work AI. I'm sharing the raw, unfiltered story of my struggles and the hard-won victories so you can skip the mistakes and get straight to the results. It wasn't easy, but
The Reality Nobody Talks About
A lot of the advice on hybrid work AI rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that your team matters more than your technology. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that you should focus on one thing and do it exceptionally well. Once they make the switch, everything changes.
The Counterintuitive Truth
In hybrid work AI, the best practitioners often do less, not more.
The lesson: customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Many common approaches to hybrid work AI 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.
Real Talk: What Actually Matters
Here is what actually matters in hybrid work AI.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on hybrid work AI. Many fail because they moved too slowly.
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. It sounds obvious, but many founders make decisions about hybrid work AI in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around async communication AI, hybrid work AI, AI performance reviews, AI culture building that I've been thinking about a lot lately.
What's Next
Best practice in hybrid work AI is changing 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.
If you're building in this space, the best time to get hybrid work AI 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
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 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.
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: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.