When we were building RemoteTeam, how ai is used to optimize bank branch networks. nearly killed us before we figured it out.
Bank branch networks are a huge cost center for financial institutions. I’m exploring how banks are using AI to optimize their branch networks, from deciding which branches to close to determining the optimal mix of services to offer at each location. This is a data-driven approach to retail banking.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating how ai is used to optimize bank branch networks.. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things 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 in my portfolio that nail how ai is used to optimize bank branch networks. are the ones that treat it as an ongoing process, not a one-time project.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to how ai is used to optimize bank branch networks. 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.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take how ai is used to optimize bank branch networks. seriously versus those that don't. The difference is stark.
Companies that invest early in how ai is used to optimize bank branch networks. 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 robo-advisors, algorithmic trading, AI fraud detection, AI trading that I've been thinking about a lot lately.
The Bottom Line
Look, how ai is used to optimize bank branch networks. isn't 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 how ai is used to optimize bank branch networks. aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take how ai is used to optimize bank branch networks. seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
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.