I've had this conversation about why most founders get revenue intelligence completely wrong with at least 50 founders. Here's the distilled version.
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.
The Counterintuitive Truth
Here's what surprised me most about why most founders get revenue intelligence completely wrong: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that the best solutions are often the simplest ones. It sounds simple. It's incredibly hard to execute.
What I've Learned From 108 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with why most founders get revenue intelligence completely wrong.
The biggest misconception is that you need to the best solutions are often the simplest ones. That's backwards. The companies that win are the ones that you should focus on one thing and do it exceptionally well.
I remember sitting with the Anthropic team early on and discussing how they thought about why most founders get revenue intelligence completely wrong. Their approach was counterintuitive but brilliant.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to why most founders get revenue intelligence completely wrong are fundamentally flawed. Not slightly off. Fundamentally flawed.
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.
What I Tell Founders
When a founder in my portfolio asks me about why most founders get revenue intelligence completely wrong, I usually 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 revenue intelligence, sales forecasting AI, AI CRM, conversational sales AI, AI sales tools that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about why most founders get revenue intelligence completely wrong: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at why most founders get revenue intelligence completely wrong share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
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.
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.
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.