Google · AI product · Entry · 45 to 60 minutes

How should Search test a stronger AI mode?

Google is a technology company whose Search product helps people find information on the web. Answer this product management case and get it graded line by line against a weighted rubric.

user insight
prioritization
experimentation
risk management
product judgment

The situation

You are an entry level PM on Google Search. A senior product lead asks you to recommend how the team should approach a new AI focused search experience for users who seem to want richer, more direct AI help inside Search. Recent research suggests some people are already trying to get more AI style responses from current search behavior, but the team does not yet know how broad that demand is, which users need it most, or what product shape would best fit Search. The opportunity feels urgent because AI expectations are rising quickly, but the risk is also high. Search serves a very broad audience, and any change that feels confusing, low quality, or overreaching could hurt user trust. You need a plan that helps the team learn fast without assuming the final design, rollout path, or target audience. Your recommendation should cover who to learn from first, how to expose the experience safely, what feedback matters most, and how to decide whether to expand or change direction.

What to produce

Write a 1 page product recommendation memo, about 600 to 800 words.

Your answer must

Define the core user problem and explain what signals suggest unmet demand for more AI help in Search.
Identify the first user segments you would prioritize for learning, and explain why they are the best starting point.
Propose a testing and rollout plan with stages, including how much exposure each stage should get and what decisions each stage should unlock.
List the key qualitative and quantitative signals you would use to judge product quality, usefulness, and risk.
Recommend a go, iterate, or stop framework for deciding whether to broaden access, and explain the main risks and mitigations.

Constraints

You only have early research signals, not proof of broad market demand.
You must protect trust in Search for mainstream users while exploring a new AI experience.
You cannot assume a full launch, major org restructure, or new external partnerships.
Your plan should be feasible for one feature area, not a company wide AI strategy.

Format

Use five short sections with headings: problem, target users, test plan, success signals, decision rule.

How Google actually played it

Google describes how AI Mode in Search grew from user research that showed people trying to trigger more AI responses. The team worked with AI power users and trusted testers, released it in Labs, and refined it before the US launch a few months later. A short example of using a staged release and early adopters to shape an AI product.

Look for users already working around the product to get more of what you plan to build.
Involve power users early and iterate in a Labs-style channel.
Use staged exposure before a wide launch.
Refine design based on feedback, not only on internal reviews.

Built from How Google developed the design for AI Mode in Search by Ari Marini, Google.