A product request can sound like a decision when it is still only a proposed solution:

Customers keep asking to export all feedback. We should add CSV export soon.

AI can quickly organize the feedback, recalculate product metrics, compare team claims, and draft a polished recommendation. That speed is useful when the material is scattered. It also creates a risk: repeated records can look like broad demand, recorded behavior can be mistaken for motivation, and a confident draft can close questions that the evidence never answered.

In this Course, you take responsibility for one continuous product decision at EchoDesk, a fully synthetic B2B software product. You prepare a recommendation for Priya, Head of Product, using Course-prepared feedback, aggregate usage metrics, team memos, and product constraints. The practice materials contain no real customer or company data.

You will:

• turn an urgent feature request into the decision Priya can make now;
• ask AI to organize feedback without losing record IDs, repeated accounts, indirect notes, or counterexamples;
• check calculations and keep recorded behavior separate from team claims and inference;
• decide which needs belong in the current product question and keep materially different responses available;
• inspect a first recommendation, compare it with an independent evidence challenge, and revise what the sources do not support.

AI helps with structure, extraction, calculation, comparison, drafting, and revision. You remain responsible for what material may be shared, whether a claim matches its source, which trade-offs are acceptable, and what should stay unknown. Priya retains the allocation decision; Engineering, Design, and Security/Privacy retain the confirmations that require their authority.

The final result is a Product Decision Brief ready for accountable human review. It is not a build approval, roadmap commitment, PRD, or proof that the recommendation will succeed.

You do not need a particular AI product, analytics platform, or prioritization framework. You should already be comfortable reading basic product feedback and metrics. The method is designed to help you use AI without handing it the product decision.