Principle
Measure adoption, not just whether AI was consulted
Opening the advisor and following the advisor are different behaviors.
When it fits
- A team reports AI usage from clicks, chats or consultations and calls that evidence of AI influence.
When to avoid it
- Some AI value comes from stimulating thought without visible text adoption; use qualitative evidence where necessary.
Why it matters
Track whether the AI recommendation changed the final action, wording, score or decision, not only whether the user viewed it. Separate ignored, partially adopted and fully adopted advice. Pair adoption with advice quality so high usage does not look positive when poor advice is being followed.
An example
A consultant may open AI on every ticket but reject most suggestions; chat count alone overstates reliance.
Check your result
The analytics can show how often AI actually changed work and whether those changes helped.
Keep this limit in mind
- Some AI value comes from stimulating thought without visible text adoption; use qualitative evidence where necessary.
Evidence and sources
A 2026 two-study paper found trust predicted both consultation and adoption of ChatGPT input, while perceived expertise reduced reliance even when perceived expertise did not necessarily equal actual skill.
The relationship between perceived expertise and true competence is task-specific.
Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making · Abstract highlights
The same 2026 study found decision performance depended on AI recommendation quality and that adopting poor advice could reduce performance.
Viewing advice without adopting it is behaviorally different from relying on it.
Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making · Abstract highlights