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AI Product Tools / MIF Explorer / Library / UX
Truth Layer
MeaningfulLaggingAI-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.
Task Success Rate
The percentage of users who complete a defined task without critical errors.
Evaluation method
successful_completions / total_attempts × 100
Signal type
lagging
What it is best for
Evaluating whether a redesign improved core workflows
What it tells you +
Whether people can accomplish what they came to do. A direct signal of whether the core experience works.
What it does not tell you +
Tell you why users failed, how much effort it took, or whether they felt confident during the process.
When to use it +
- Evaluating whether a redesign improved core workflows
- Comparing usability across product versions
- Setting usability benchmarks for critical user journeys
When not to use it +
- When tasks are poorly defined or have multiple valid endpoints
- As a standalone metric without understanding failure reasons
- For exploratory features where there is no single correct path
How leaders misuse it +
- Celebrating high completion rates on tasks that are too easy to fail
- Comparing rates across tasks with very different difficulty levels
- Ignoring partial completions that still delivered value to the user
Anti-patterns +
- Measuring only happy-path completions and excluding edge cases
- Setting success criteria so loosely that almost everything counts
AI interpretation risks +
Scenario: AI assistant completes steps for the user
What happens: Task success rate inflates because the AI did the work
What it really means: High completion may reflect automation, not user capability or understanding
Recommendation: Track AI-assisted vs unassisted success separately. Add a user understanding check after AI-assisted completions.
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Define clear start and end events for each task. Track partial completions separately from full successes.
Sample events
task_started, task_completed, task_abandoned Examples +
An e-commerce checkout has an 87% task success rate. The 13% failure is concentrated at the payment step, suggesting a form usability issue.
A SaaS onboarding wizard shows 62% completion. Segment analysis reveals new users with technical backgrounds complete at 85% while non-technical users complete at 41%.
Suggested decisions +
- If below 80%, investigate failure points and prioritize UX fixes
- If above 95%, check whether the task definition is too broad
- Compare across user segments to find who struggles most