For years, I struggled with context switching. I tried everything, but nothing worked until I discovered how to leverage AI for an AI-powered inbox. In this post, I'm sharing the exact framework that helped me reclaim 18 hours per week and scale my business.
The Reality Nobody Talks About
A lot of the advice on AI automation rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the market doesn't care about your roadmap. 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 data tells a different story than your gut. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating AI automation. It's not complicated, but it requires discipline.
Step 1: customer feedback is the only metric that matters
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: most founders overthink this and underspend on execution
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 automation right treat it as an ongoing process, not a one-time project.
The AI Angle
Any discussion of AI automation in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of AI automation. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around AI automation, AI meeting notes, AI tools for founders, AI email that I've been thinking about a lot lately.
The Bottom Line
None of this is rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at AI automation aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take AI automation seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
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.