Explainability needs to be there from the get-go. When you prioritize it before an AI system is deployed, that can sometimes actually help you with the development of the system, making sure that it operates in a way that is understandable and reasonable. Once the system has been deployed, the first ones to use it aren't going to get those same explainability benefits, so the effectiveness and usability of the system can change.
What I believe is that the biggest mistake teams make is treating explainability as a reporting layer instead of a reasoning layer. You cannot just add it after deployment and expect it to work. We saw this in a model that flagged risky test configurations. The team added post-hoc explanations like "this test failed due to timeout risk," but users had no idea why the agent made that call. The decision logic was buried and disconnected from what users actually saw. The fix was to restructure the model so it surfaced intermediate signals like feature importance, prior behavior patterns, and confidence levels at the time of prediction. Not after. Explainability has to be built into how the agent thinks. If it cannot show why it acted when it did, no dashboard will fix the disconnect. You build trust by making reasoning visible from the start, not by adding it later.