Measuring and monitoring trust levels

Trust isn't static. Teams need ways to detect trust problems before users abandon the product entirely. Monitoring how user confidence evolves over time reveals issues that direct questions might miss.

Behavioral signals reveal trust better than surveys. Watch if users double-check AI suggestions against other sources. See if they use manual overrides frequently. Notice when they stop using certain features. These actions show trust levels more honestly than asking directly.

Different metrics matter at different stages. New users skipping onboarding might indicate overconfidence in their AI understanding. Experienced users suddenly switching to manual modes suggests trust erosion. The same behavior means opposite things depending on users' history.

Trust varies by feature within products. Users might fully trust music recommendations while staying skeptical of playlist titles. They might love photo organization but avoid face grouping. This granular view helps teams improve specific problem areas.

Regular check-ins catch drift early. Monthly reviews of override rates, feedback patterns, and feature usage reveal slow trust changes. Sudden spikes in support tickets about specific features flag acute problems. Both patterns need attention but different solutions.