Recommendation Canvas

## Purpose
Evaluate and propose AI product solutions using a structured canvas that assesses business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, risks, and value justification. Use this to build a comprehensive, defensible recommendation for stakeholders and decision-makers—especially when proposing AI-powered features or products that carry higher uncertainty and risk.

This is not a feature spec—it's a strategic proposal that articulates *why* this AI solution is worth building, *what* assumptions need validating, and *how* you'll measure success.

## Input

**Works best with:** The AI product or feature idea being evaluated.
**Also useful:** Target customer, expected business outcome, known risks, and who the recommendation must convince.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended `ARGUMENTS:` line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

**Arriving empty-handed? That works too.** The skill asks for the idea and the decision-maker, then works through the canvas boxes.

**Example invocation:** `Recommendation canvas: AI-suggested reorder quantities for warehouse managers — VP Ops wants a go/no-go next month.`

## Key Concepts

### The Recommendation Canvas Framework
Created for Dean Peters' Productside "AI Innovation for Product Managers" class, the canvas synthesizes multiple PM frameworks into one strategic view: