LinkedIn · AI product · Mid · 45 to 60 minutes

How should LinkedIn measure new AI features?

LinkedIn is a professional network where people build profiles, share knowledge, find jobs, and recruit talent. Answer this product management case and get it graded line by line against a weighted rubric.

Metric design
AI evaluation
Product judgment
Trade-off analysis
Written communication

The situation

You are a PM on LinkedIn's consumer product team. Leadership is preparing to launch several generative AI experiences for members, including AI-written conversation starters for collaborative articles, AI help inside the Premium experience, and AI suggestions for profile writing. Your VP asks you to define how the team will judge whether these launches are successful and safe enough to expand after the initial rollout. This is hard because output quality matters a lot, but the worst failures may be rare. Explicit member feedback may be helpful, but only a small share of users will leave it, and those responses may be biased. Usage data will cover everyone, but it may not tell you whether problems come from weak AI output, low product need, or confusing design. The three features also have very different interaction modes, so a single scorecard may not fit all of them. You need a measurement plan that can work before launch and during rapid iteration, catch trust issues early, and give leaders clear rollout decisions.

What to produce

Write a 1 to 2 page launch measurement memo.

Your answer must

Define the member goal for each of the three AI features and the product outcome LinkedIn should optimize.
Propose a measurement framework that covers pre-launch quality checks, post-launch member feedback, and usage metrics.
Choose feature-specific primary and secondary metrics for collaborative articles, Premium AI help, and profile writing suggestions, and explain why they differ.
Describe how you would collect and interpret in-product feedback, including how you would handle low response volume and skewed sentiment.
Set rollout decision rules, including launch blockers, guardrails for trust and harmful output, and what the team should do when metrics conflict.

Constraints

Do not assume reliable automated evaluation already exists for these new experiences.
Most members will not leave explicit feedback, so feedback data will be sparse and not fully representative.
The products are in early launch and may change weekly, so the plan must support fast iteration.
Success should reflect member value and trust, not just more time spent in the product.

Format

Use sections for goals, measurement plan, feature-specific metrics, guardrails, and decision rules. A simple table that maps each feature to its main metrics and risks will help.

How LinkedIn actually played it

LinkedIn describes how it defines success for generative AI launches such as collaborative articles, AI takeaways for Premium subscribers and profile writing suggestions. It uses three quantitative methods: human review of output quality, in-product feedback, and product usage metrics. A practical measurement plan for any consumer AI feature.

Combine human review, user feedback and usage metrics.
Define success criteria before launch.
Add red-teaming and trust checks beside quality metrics.

Built from Evaluating the Success of Consumer Generative AI Products by Bonnie Barrilleaux.