At one of our early AI projects, we had a strange problem:
Everyone was celebrating progress… but no one could agree on what progress meant.
The data team showed charts.
The product team talked about features.
The business team asked, “So when does this actually go live?”
It wasn’t chaos, but it was close to it. That’s when we tried something new.
We called it The Eval Board, a monthly review, but not for status updates or vanity metrics.
It was where data scientists, engineers, and product leads came together to ask one simple question: Is the model actually getting better for the user?
Not just technically. But in real-world outcomes.
We borrowed the idea from product roadmap reviews, where each feature has to justify its existence.
Here, every model has to justify its performance, its fairness, and its cost.
And what changed?
→ Fewer models in production, but each one mattered more.
→ Less noise, more alignment.
→ AI became a product, not a science project.
Every organization building with AI needs this rhythm.
Because evaluation isn’t just a phase, it’s the culture.
