Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.

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: Core Components: 1. Business Outcome: What's in it for the business? 2. Product Outcome: What's in it for the customer? 3. Problem Statement: Persona centric problem framing 4. Solution Hypothesis: If/then hypothesis with experiments 5. Positioning Statement: Value prop and differentiation 6. Assumptions & Unknowns: What could invalidate this? 7. PESTEL Risks: Political, Economic, Social, Technological, Environmental, Legal 8. Value Justification: Why this is worth doing 9. Success Metrics: SMART metrics to measure impact 10. What's Next: Strategic next steps Why This Works Outcome driven: Forces clarity on business AND customer value Hypothesis centric: Treats solution as a bet to validate, not a commitment Risk explicit: Makes assumptions and risks visible upfront Executive friendly: Comprehensive but structured for C level review AI appropriate: Especially useful for AI features with high uncertainty Anti Patterns (What This Is NOT) Not a PRD: This is strategic framing, not detailed requirements Not a business case (yet): It informs the business case but needs validation first Not a feature list: Focus on outcomes, not capabilities When to Use This Proposing a new AI powered product or feature Pitching to execs or securing budget/sponsorship Evaluating whether an AI solution is worth pursuing Aligning cross functional stakeholders (product, engineering, data science, business) After completing initial discovery (you need context to fill this out) When NOT to Use This For trivial features (don't over engineer small tweaks) Before any discovery work (you need user research and problem validation first) As a replacement for experimentation (canvas informs experiments, not vice versa) Application Use template.md for the full fill in structure. Step 1: Gather Context Before filling out the canvas, ensure you have: Problem understanding: User research, pain points (reference skills/problem statement/SKILL.md ) Persona clarity: Who experiences the problem? (reference skills/proto persona/SKILL.md ) Market context: Competitive landscape, category positioning Business constraints: Budget, timelines, strategic priorities If missing context: Run discovery work first. This canvas synthesizes insights—it doesn't create them. Step 2: Define Outcomes Business Outcome What's in it for the business? Use this format: [Direction] [Metric] [Outcome] [Context] [Acceptance Criteria] Example: "Increase by 15% the monthly recurring revenue from enterprise customers within 12 months" Quality checks: Measurable: Can you track this metric? Time bound: Within what timeframe? Ambitious but realistic: Not "10x revenue in 1 month" Product Outcome What's in it for the customer? Use this format: [Direction] [Metric] [Outcome] [Context from persona's POV] [Acceptance Criteria] Example: "Reduce by 60% the time spent manually processing invoices for small business owners" Quality checks: Customer centric: Written from user perspective ("I," not "we") Outcome, not feature: "Reduce time spent" not "Use AI automation" Step 3: Frame the Problem Use the problem framing narrative from skills/problem statement/SKILL.md : Quality checks: Empathetic: Does this sound like the user's voice? Specific: Not "users want better tools" but "Sarah spends 8 hours/month..." Validated: Based on real user research, not assumptions Step 4: Define the Solution Hypothesis Hypothesis Statement Use the epic hypothesis format from skills/epic hypothesis/SKILL.md : Example: "If we provide AI powered invoice reminders that auto send at optimal times for freelance designers, then we will reduce time spent on payment follow ups by 70%" Tiny Acts of Discovery Define lightweight experiments to validate the hypothesis: Quality checks: Fast: Days/weeks, not months Cheap: Prototypes, concierge tests, not full builds Falsifiable: Could prove you wrong Proof of Life Define validation measures: Step 5: Define Positioning Use the positioning statement format from skills/positioning statement/SKILL.md : Step 6: Document Assumptions & Unknowns Quality checks: Explicit: Make hidden assumptions visible Testable: Each assumption can be validated via experiments Step 7: Identify PESTEL Risks Risks to Investigate (High Priority) Risks to Monitor (Lower Priority) Step 8: Justify the Value Step 9: Define Success Metrics Use SMART metrics (Specific, Measurable, Attainable, Relevant, Time Bound): Step 10: Define Next Steps Examples See examples/sample.md for a full recommendation canvas example. Mini example excerpt: Common Pitfalls Pitfall 1: Vague Outcomes Symptom: "Business outcome: increase revenue. Product outcome: improve UX." Consequence: No measurability or accountability. Fix: Use the outcome formula: [Direction] [Metric] [Outcome] [Context] [Acceptance Criteria]. Be specific. Pitfall 2: Solution First Thinking Symptom: Problem statement is "We need AI powered X" Consequence: You've jumped to solution without validating the problem. Fix: Frame problem from user perspective. Let the solution hypothesis emerge from validated pain points. Pitfall 3: Skipping Tiny Acts of Discovery Symptom: Hypothesis → straight to roadmap, no experiments Consequence: High risk of building the wrong thing. Fix: Define 2 3 lightweight experiments. Test before committing engineering resources. Pitfall 4: Generic PESTEL Risks Symptom: "Political: regulations might change" Consequence: Risk analysis is theater, not actionable. Fix: Be specific: "GDPR compliance for storing client email timing data requires legal review." Pitfall 5: Weak Value Justification Symptom: "This is valuable because customers will like it" Consequence: Not convincing to execs. Fix: Use data: "Addresses 1 pain point per user research. 20% churn reduction = $500k ARR. Low tech risk." References Related Skills skills/problem statement/SKILL.md — Informs the problem narrative skills/epic hypothesis/SKILL.md — Informs the solution hypothesis structure skills/positioning statement/SKILL.md — Informs positioning section skills/proto persona/SKILL.md — Defines target persona skills/jobs to be done/SKILL.md — Informs customer outcomes External Frameworks Osterwalder's Value Proposition Canvas — Influences problem/solution framing PESTEL Analysis — Risk assessment framework SMART Goals — Success metrics structure Dean's Work AI Recommendation Canvas Template (created for Productside "AI Innovation for Product Managers" class) Provenance Adapted from prompts/recommendation canvas template.md in the https://github.com/deanpeters/product manager prompts repo. Skill type: Component Suggested filename: recommendation canvas.md Suggested placement: /skills/components/ Dependencies: References skills/problem statement/SKILL.md , skills/epic hypothesis/SKILL.md , skills/positioning statement/SKILL.md , skills/proto persona/SKILL.md , skills/jobs to be done/SKILL.md

recommendation-canvas