Learning recommendation engines the hard way is expensive; this is the short version.
People often ask me what went into building our recommendation engine. I want to share the real story—the challenges we faced, how the team worked through them, and the decisions that shaped our journey.
The Counterintuitive Truth
In recommendation engines, the best practitioners often do less, not more.
The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on recommendation engines rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the data tells a different story than your gut. 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 your team matters more than your technology. Once they make the switch, everything changes.
The Numbers Don't Lie
Investing early in recommendation engines can be 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 transformation leadership, AI change management, AI leadership skills, leading through uncertainty that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in recommendation engines, but there are smarter paths.
The strongest teams treat recommendation engines 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 recommendation engines, 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
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.
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.