When I first tried scaling our sales team, I failed miserably. It wasn't until we implemented revenue intelligence that everything clicked. Here's the exact framework we used to 3x our pipeline without adding headcount.
What I've Learned From 70 Companies
A few patterns in revenue intelligence come up again and again.
The founders who handle revenue intelligence well understand that you should focus on one thing and do it exceptionally well.
The Reality Nobody Talks About
A lot of the advice on revenue intelligence rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that you should focus on one thing and do it exceptionally well. 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 customer feedback is the only metric that matters. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating revenue intelligence. 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: the market doesn't care about your roadmap
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 revenue intelligence right treat it as an ongoing process, not a one-time project.
The AI Angle
Any discussion of revenue intelligence 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 revenue intelligence. 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 deal scoring AI, outbound AI, AI CRM 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 revenue intelligence 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 revenue intelligence 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: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.