Demos Versus Production

The demo looks perfect.

AI analyzes the customer data, surfaces insights, generates recommendations. The CEO nods. The team gets excited. Someone says "let's roll this out."

Then you ask three questions.

What happens when the data is dirty?

What happens when it generates something confidently wrong?

Who's accountable when it makes a decision that costs us a client?

The gap between demos and production is where judgment lives.

Demos show capability under ideal conditions. Production requires reliability under messy reality.

In demos, edge cases are interesting exceptions. In production, they're liability.

In demos, "95% accurate" sounds impressive. In production, it means one in twenty customers gets a broken experience.

In demos, you can restart when something breaks. In production, the system runs while you're asleep.

Good judgment sees the difference.

It asks: What breaks when this scales? What's our fallback when it fails? Who verifies the output? What's the cost of being wrong? How do we know it's wrong before customers do?

Production-ready isn't about capability. It's about resilience.

The question isn't "does this work in the demo?"

The question is "what happens when this encounters reality?"

Between those two questions lives the judgment that keeps systems from breaking in production.