Why the most common mistake I see in AI projects is treating the model like a storage box — and why the human brain is the best user manual we have.
Across finance, e-commerce, and logistics, I keep watching the same mistake play out: when an AI agent underperforms, teams reach for one lever — more data. "Feed it everything we have," they say. The files pile up, and the answers get worse. Not because there's too little data, but because there's too little understanding.
More data doesn't fix a model that doesn't know what the data is, how it connects, and what to do with it. Fixing that requires the same thing you'd give a new employee: a proper onboarding.
Here's the uncomfortable truth: most teams treat AI models like storage boxes, but AI models work more like human brains. And once you understand that, everything changes — how you structure data, how you write prompts, how you evaluate results.
Why the Brain Analogy Is the Right One
