In our survey of 1,200 enterprise users, 73% said they had stopped using an AI feature because they didn't trust its outputs. Not because the feature was inaccurate—but because they couldn't tell whether it was accurate or not.
The Trust Stack
Trust in AI products operates on three levels, which we call the Trust Stack: competence trust ("does it work?"), process trust ("do I understand how it works?"), and intent trust ("is it working for me?").
Most teams focus exclusively on competence trust—improving accuracy, reducing errors. But our research shows that process trust has the highest impact on sustained usage. Users who understand the "why" behind AI recommendations use the feature 4x more frequently.
Designing for Transparency
Transparency doesn't mean showing users a confidence score or a technical explanation. It means giving them enough context to make an informed decision about whether to act on the AI's suggestion.
The best implementations we've seen use progressive disclosure: a simple recommendation at the surface, with the ability to drill into supporting evidence, alternative suggestions, and the reasoning chain.
The Calibration Problem
Even accurate AI can erode trust if it's poorly calibrated. A model that says "I'm 90% confident" but is only right 60% of the time destroys trust faster than a model that honestly says "I'm 60% confident" and is right 60% of the time.
Calibration is a product decision, not just a technical one. PMs need to decide what confidence threshold triggers a recommendation versus a suggestion versus silence.