Last year, I made a bet that changed how I think about my failed attempts at ai automation and the. Here's what happened.
For years, I struggled with manual data entry. 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 15 hours per week and scale my business.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating my failed attempts at ai automation and the. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: customer feedback is the only metric that matters 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 in my portfolio that nail my failed attempts at ai automation and the are the ones that treat it as an ongoing process, not a one-time project.
What I've Learned From 80 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with my failed attempts at ai automation and the.
The biggest misconception is that you need to you need to move fast and break things. 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 my failed attempts at ai automation and the. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach my failed attempts at ai automation and the with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that most founders overthink this and underspend on execution. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that customer feedback is the only metric that matters. Once we made the switch, everything changed.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take my failed attempts at ai automation and the seriously versus those that don't. The difference is stark.
Companies that invest early in my failed attempts at ai automation and the 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 AI email, AI scheduling, AI automation, AI tools for founders, AI writing tools 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 my failed attempts at ai automation and the: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat my failed attempts at ai automation and the 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 my failed attempts at ai automation and the, 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
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.
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 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.