Simple things cause AI features to fail.

Here’s a real world example and how we solved it:

My team was launching a forecasting feature for a key customer.

Instead of putting the forecast into production, we chose to pilot it first.

We shared a mockup. The user loved it.

The pilot started.

The user refused to adopt.

They thought the model was unreliable and inaccurate.

But that wasn’t true.

The model was highly accurate.

The issue: UI confusion.

Our forecast UI turned out to be more confusing than we realized.

We simplified the forecast UI.

The user loved it.

The forecast went to production.

👉 Moral of the story: Little things cause AI features to fail. Catch them early.

“Pilot before Production” is my mantra.

Questions about piloting analytics and AI features? Have an experience to share?

Drop it in the comments and I’ll respond.


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