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AI Product Tools / MIF Explorer / Library / UX
Truth Layer
DirectionalLeadingAI-Sensitive Truth Layer
The Truth Layer is the badge system that tells you how trustworthy, directional, or risky a measure is.
Why it matters: It helps teams separate meaningful signals from vanity, misuse, or AI distortion before they optimize the wrong thing.
Example: A metric can be Meaningful, Leading, or Vanity Risk.
Feature Adoption Rate
The percentage of active users who use a specific feature within a given time period.
Evaluation method
users_who_used_feature / total_active_users × 100
Signal type
leading
What it is best for
Evaluating feature launch success
What it tells you +
Whether a feature is reaching the users it was designed for. A direct measure of feature product-market fit.
What it does not tell you +
Tell you whether users found the feature valuable or whether it solved their problem.
When to use it +
- Evaluating feature launch success
- Identifying underutilized features for improvement or deprecation
- Prioritizing investment across feature areas
When not to use it +
- To compare features with fundamentally different audience sizes
- For features that are intentionally niche or expert-only
How leaders misuse it +
- Treating low adoption as failure without considering discoverability
- Comparing adoption rates of core features with optional power-user features
- Using adoption as a proxy for satisfaction
Anti-patterns +
- Adding aggressive prompts or forced tooltips to inflate adoption numbers
- Counting accidental clicks as feature usage
AI interpretation risks +
Scenario: AI recommendation engine surfaces features automatically
What happens: Feature adoption rate rises because AI pushes users toward features
What it really means: Higher adoption may reflect AI-driven exposure, not user-initiated discovery. Users may not have sought the feature independently.
Recommendation: Track organic adoption separately from AI-prompted adoption. Compare retention across both groups.
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Define "use" clearly — a single click versus meaningful engagement. Track per-feature over consistent time windows.
Sample events
feature_opened, feature_action_completed Examples +
A design tool launches a new auto-layout feature. After 4 weeks, 23% of MAU have used it. Heatmap analysis shows the entry point is buried in a submenu.
Suggested decisions +
- Below 10%: investigate discoverability. Is the feature easy to find?
- Between 10-40%: check whether the feature matches the right user segment
- Above 60%: feature is well-adopted. Measure quality of engagement next.