I've managed over $7M in remote team payroll and seen every AI performance reviews mistake in the book. This is the culmination of a decade of experience, distilled into actionable advice you can implement today to see immediate improvement
The Framework That Actually Works
Here is a simple framework for evaluating AI performance reviews. It's not complicated, but it requires discipline.
Step 1: simplicity beats complexity every time
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: you should focus on one thing and do it exceptionally well
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 that get AI performance reviews right treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
A lot of the advice on AI performance reviews 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 the best solutions are often the simplest ones. Once they make the switch, everything changes.
What I Tell Founders
Before deciding anything about AI performance reviews, start with three questions:
What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around async communication AI, digital nomad AI tools, AI performance reviews that I've been thinking about a lot lately.
What's Next
Best practice in AI performance reviews 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 AI performance reviews 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: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.
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.