A MODEL CAN BE STATISTICALLY RIGHT YET COMMERCIALLY WRONG

The real test of AI isn’t how smart it sounds in a demo.
It’s how consistently it earns trust in production.

That trust doesn’t come from intuition. It comes from evals.
Not academic scorecards, but the discipline of proving your AI performs where it matters.

In B2B manufacturing and distribution, that proof is survival.
A single wrong quote can erase months of goodwill, millions in pipeline, and the credibility that built them.

Evals are the operating system for enterprise AI trust.
They keep learning systems aligned with business reality.

THE PRACTICAL SIDE OF EVALS

1. Measure what moves the business.
Forget vanity metrics. Track what changes outcomes: quote accuracy, delivery prediction, issue resolution rate, margin consistency.
If it doesn’t tie to revenue, cost, or trust, it’s noise.

2. Define what “good enough” means before chasing perfect.
In operations, reliability beats brilliance.
An 85% model that’s explainable often outperforms a 95% black box.
Progress compounds faster than perfection.

3. Design with domain sense.
Evals work best when co-built with pricing analysts, engineers, and account managers.
They catch what is statistically right but commercially wrong.

4. Track drift before it tracks you.
Models age like milk, not wine.
Monitor changes in inputs, accuracy, and customer feedback.
Evals surface decay before customers do.

THE STRATEGIC TRUTH

Skipping evals isn’t speed. It’s deferred risk.
They aren’t bureaucracy. They are brakes and headlights, the systems that make speed possible.

Evals don’t slow innovation. They sharpen it.
They show where your AI is learning, where it is drifting, and where it is bluffing.

If you can’t measure what good looks like,
you’re not building intelligence. You’re automating uncertainty.