recommendation-canvas
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:
Core Components:
- Business Outcome: What's in it for the business?
- Product Outcome: What's in it for the customer?
- Problem Statement: Persona-centric problem framing
- Solution Hypothesis: If/then hypothesis with experiments
- Positioning Statement: Value prop and differentiation
- Assumptions & Unknowns: What could invalidate this?
- PESTEL Risks: Political, Economic, Social, Technological, Environmental, Legal
More from deanpeters/product-manager-skills
prd-development
Build a structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.
1.7Kuser-story
Create user stories with Mike Cohn format and Gherkin acceptance criteria. Use when turning user needs into development-ready work with clear outcomes and testable conditions.
1.7Kroadmap-planning
Plan a strategic roadmap across prioritization, epic definition, stakeholder alignment, and sequencing. Use when turning strategy into a release plan that teams can execute.
1.5Kcompany-research
Create a company research brief with executive quotes, product strategy, and org context. Use when preparing for interviews, competitive analysis, partnerships, or market-entry work.
1.3Kproduct-strategy-session
Run an end-to-end product strategy session across positioning, discovery, and roadmap planning. Use when a team needs validated direction before committing to execution.
1.2Kprioritization-advisor
Choose a prioritization framework based on stage, team context, and stakeholder needs. Use when deciding between RICE, ICE, value/effort, or another scoring approach.
1.1K