| name | task-success-metrics |
| description | Measuring whether the AI actually helped users accomplish their goals. |
Task Success Metrics
Output quality doesn't guarantee task success. The AI might produce a beautiful response that doesn't actually help the user do what they came to do. Task success metrics measure the end-to-end outcome.
Defining Task Success
For each user task, define:
- What does success look like? The user completed their goal (sent the email, found the information, finished the design)
- What are the success criteria? Specific, observable conditions that indicate the task is done
- What's the time expectation? How long should this task take with AI assistance vs. without?
- What's the quality bar? Not just done, but done well enough
Task Success Metrics
- Task completion rate: Percentage of users who complete the task (not just get a response)
- Time to completion: How long from first input to task done
- Turns to completion: How many back-and-forth exchanges needed
- First-attempt success rate: Did the AI's first response accomplish the task, or did it require iteration?
- Intervention rate: How often did the user need to correct, redirect, or override the AI?
- Abandonment rate: How often did users give up before completing the task?
Measuring Task Success
- Direct measurement: Track task completion through product analytics (user clicked "done", saved the output, moved to next step)
- Inferred measurement: Infer success from proxy signals (session length, return rate, output edits)
- Self-reported measurement: Ask users whether the AI helped them accomplish their goal
- Comparative measurement: Compare task success with AI vs. without AI, or with version A vs. version B
Task Success vs. Output Quality
These can diverge:
- High output quality, low task success: The AI's answer is well-written but doesn't address the real need
- Low output quality, high task success: The AI's answer is rough but gives the user exactly what they needed
- Both matter: Track both and investigate when they diverge
Design Artefacts
- Task success definitions per key user task
- Metrics framework with measurement methods
- Success criteria specifications
- Baseline measurements (before AI, or current version)
- Task success dashboard specifications
