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