Measuring AI Feature Success

PM-Friendly Explanations of Precision and Recall

Engineers talk about precision and recall. Here is what they mean in PM language.

Precision: Of the things the AI suggested, how many were actually good? If the AI suggests 10 products and 8 are products users actually want, precision is 80%.

Recall: Of all the good options that exist, how many did the AI find? If there are 20 products users might want and the AI finds 15 of them, recall is 75%.

High precision means fewer false positives. Users get fewer bad suggestions.

High recall means fewer false negatives. Users do not miss good options.

Often you must choose. You can make precision very high by only suggesting when very confident. But then recall drops because you miss good options.

Your product goal determines which matters more. If false suggestions hurt users, maximize precision. If missing good options hurts users, maximize recall.

User Satisfaction

The most important metric is whether users think the feature is helpful.

After users interact with the AI feature, ask: "Was this suggestion helpful?"

Track the percentage of interactions where users say yes.

Example: AI suggests 100 tasks to users. 75 users say the suggestions were helpful. Satisfaction is 75%.

Target: 70% minimum. Below that, the feature is not good enough.

Task Completion Rate

For features that suggest actions, measure if users actually take the action.

Example: AI suggests 5 tasks. Did the user complete any of them?

Metric: Of all suggested tasks, what percentage do users complete within one week?

If completion rate is 0%, suggestions are wrong. If it is 30%, suggestions are somewhat helpful.

Target: 20-30% or higher depending on how ambitious your suggestions are.

Time Saved

Measure whether the AI feature actually saves time.

Before AI feature: User reads 30 support tickets and manually finds themes. Time spent: 60 minutes.

After AI feature: AI clusters tickets and suggests themes. User validates themes. Time spent: 15 minutes.

Time saved: 45 minutes per batch of 30 tickets.

Track this. If the feature does not save time, the value proposition is broken.

Sample survey: "Did this feature save you time? If yes, roughly how many minutes?"

Defining "Good Enough" Before Launch

Before you ship, define what success looks like.

Example definition: "The task suggestion feature is successful if: (1) 70% of users find suggestions helpful (survey), (2) 20% of users follow suggestions (action metric), (3) Users save at least 15 minutes per week (time estimate)."

Without this definition, you cannot know if the feature is working.

Define metrics before launch. Measure after launch. Compare.

A/B Testing AI Features

You want to know if the AI feature actually improves user outcomes.

Setup: 50% of users get the feature. 50% do not. Measure the same things for both groups.

Example:

• Control group: No AI task suggestions. They use the task list normally.
• Treatment group: See AI task suggestions.
• Measure: Completion rate (do they complete more tasks?), retention (do they stay longer in the product?), time to completion (do tasks get done faster?).

If treatment group completes more tasks, the feature works. If there is no difference, reconsider the feature.

Sample Success Criteria

Here is an example of success criteria for an AI summarization feature:

"AI Summary Feature Success Criteria

Launch: Minimum viable version (basic summaries).

Success metrics after 2 weeks:

• 65% of daily active users use the summary feature
• 75% of users find summaries helpful (post-use survey)
• 80% summary accuracy on test set (manually checked)

Go/no-go decision:

• If any metric below target, pause and iterate
• If all metrics hit target, launch broadly
• Plan V2 improvements for month 2

Month 1 targets:

• 70% DAU adoption
• 80% satisfaction
• 85% accuracy"

This is clear. You know what good looks like.

Guardrails and Rollback Criteria

Set rules for when to roll back the feature.

Example rollback criteria:

• If satisfaction drops below 50%, disable feature
• If accuracy drops below 70%, notify customers and flag for review
• If feature breaks for users with long histories (500+ tasks), rollback until fixed

Having these rules in advance makes rollback decisions faster and less emotional.