recommendation-canvas

SKILL.md

Structured canvas for evaluating AI product ideas across outcomes, hypotheses, risks, and positioning.

Synthesizes 10 strategic components: business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, assumptions, PESTEL risks, value justification, success metrics, and next steps

Designed for AI-specific uncertainty; treats solutions as testable bets rather than commitments, with lightweight "Tiny Acts of Discovery" experiments built in

Outcome-driven framework that articulates why an AI solution deserves investment, what assumptions need validation, and how success will be measured

Best used after initial discovery work to align cross-functional stakeholders (product, engineering, data science, business) on strategic direction before committing engineering resources

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.

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: