# Example 16 — Case study: ops wants an AI gimmick feature

## Why this case matters

A lot of product waste starts with features that sound engaging but are strategically weak.
This is where PM judgment is supposed to show up.

## Scene

Operations is pushing a daily AI fortune card feature to drive engagement.
The PM suspects it is novelty, not durable value.

## Raw messy input

> “Ops wants a daily AI fortune card feature to improve engagement. I’m worried it’s a gimmick. Help me evaluate it.”

## What a weak default answer often does badly

- mirrors the excitement of the request
- lists pros and cons without a real conclusion
- ignores opportunity cost
- confuses “might increase clicks” with “worth building now”

## What `pm-workbench` should do better

- ask for the minimum critical premises if the judgment depends on them
- assess user value, strategic fit, business consequence, and opportunity cost together
- produce a clear go / hold / no-go / experiment recommendation
- explain why now or why not now

## Example response shape

- bottom line recommendation
- what problem this feature is actually trying to solve
- likely upside and why it may be shallow
- opportunity cost versus more strategic work
- suggested path: no-go, hold, or tightly scoped experiment

## Suggested output shape

**Feature Evaluation Memo**

## Why this is valuable in real PM work

This is the difference between “feature discussion” and “product judgment.”
A strong PM response protects the roadmap from short-term excitement that does not hold up.
