In order for an AI product to be successful, users need to understand when the system is useful, when its output deserves verification, and when human judgement should take precedence. After launch, adoption becomes another source of evidence: how people accept, ignore, correct, override, or work around AI outputs can reveal whether the product is earning the right level of trust.

What's Included:

• Trust Signals

• Reliance Patterns

• Continuous Evaluation

AI products create a different adoption challenge: more usage does not necessarily mean more success. A user who accepts every recommendation without scrutiny may be just as concerning as one who ignores the system entirely.

The goal is calibrated trust, where reliance reflects the system's actual capabilities and limitations. That means examining not only whether people use the AI, but how they respond to its outputs, where they hesitate, where they intervene, and whether patterns of reliance remain appropriate as the product and its environment change. Those signals become part of the product's ongoing evaluation loop.