Honestly, this is the exact tension I’ve seen play out in almost every org that matures from basic analytics to advanced ML. The moment accuracy jumps, trust drops—and not because people don’t believe in data, but because they lose visibility into why the model thinks the way it does.

What makes it challenging is that the impact goes far beyond the model itself.
Leaders suddenly feel like they’re signing off on decisions they can’t defend.
Risk teams feel exposed because they can’t trace the logic.
Domain experts feel like they’ve lost the ability to challenge assumptions.
And the data team gets stuck trying to explain a model that wasn’t designed to be explained in the first place.

It becomes less about math and more about comfort, communication, and accountability.