| name | recommendation-canvas |
| description | Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation. |
| intent | 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. |
| type | component |
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
- Value Justification: Why this is worth doing
- Success Metrics: SMART metrics to measure impact
- 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]
## Business Outcome
- [e.g., "Reduce by 25% the churn of existing customers using our existing product"]
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]
## Product Outcome
- [e.g., "Increase the speed of finding patients when I know the inclusion and exclusion 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:
## The Problem Statement
### Problem Statement Narrative
- [Persona description: 2-3 sentences telling the persona's story from their POV]
- [Example: "Sarah is a freelance designer managing 10 clients. She spends 8 hours/month manually tracking invoices and chasing late payments. By the time she follows up, some clients have already moved to other designers, costing her revenue and damaging relationships."]
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:
## Solution Hypothesis
### Hypothesis Statement
**If we** [action or solution on behalf of target persona]
**for** [target persona]
**Then we will** [attain or achieve desirable outcome]
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:
### Tiny Acts of Discovery
**We will test our assumption by:**
- [Experiment 1: Prototype AI reminder system and test with 5 freelancers]
- [Experiment 2: A/B test manual vs. AI-timed reminders for 20 users]
- [Experiment 3: Survey users on perceived value after 2 weeks]
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:
### Proof-of-Life
**We know our hypothesis is valid if within** [timeframe]
**we observe:**
- [Quantitative outcome: e.g., "80% of users send reminders via the AI system"]
- [Qualitative outcome: e.g., "8 out of 10 users report saving 5+ hours/month"]
Step 5: Define Positioning
Use the positioning statement format from skills/positioning-statement/SKILL.md:
## Positioning Statement
### Value Proposition
**For** [target customer/user persona]
**that need** [statement of underserved need]
[product name]
**is a** [product category]
**that** [statement of benefit, focusing on outcomes]
### Differentiation Statement
**Unlike** [primary competitor or competitive arena]
[product name]
**provides** [unique differentiation, focusing on outcomes]
Step 6: Document Assumptions & Unknowns
## Assumptions & Unknowns
- **[Assumption 1]** - [Description, e.g., "We assume users will trust AI-generated reminders"]
- **[Assumption 2]** - [Description, e.g., "We assume payment timing optimization increases response rates"]
- **[Unknown 1]** - [Description, e.g., "We don't know if users prefer email or SMS reminders"]
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)
## Issues/Risks to Investigate
- **Political:** [e.g., "Regulatory changes to AI-generated communications"]
- **Economic:** [e.g., "Economic downturn reduces willingness to pay for premium features"]
- **Social:** [e.g., "Users may perceive AI reminders as impersonal or pushy"]
- **Technological:** [e.g., "AI model accuracy may degrade over time without retraining"]
- **Environmental:** [e.g., "Energy costs of AI processing"]
- **Legal:** [e.g., "GDPR compliance for storing customer email patterns"]
Risks to Monitor (Lower Priority)
## Issues/Risks to Monitor
- **Political:** [e.g., "Potential AI regulation in EU markets"]
- **Economic:** [e.g., "Exchange rate fluctuations affecting international customers"]
- **Social:** [e.g., "Changing norms around automated communication"]
- **Technological:** [e.g., "Emerging AI competitors with better models"]
- **Environmental:** [e.g., "Carbon footprint concerns from stakeholders"]
- **Legal:** [e.g., "Future data privacy laws"]
Step 8: Justify the Value
## Value Justification
### Is this Valuable?
- [Absolutely yes / Yes with caveats / No with suggested alternatives / Absolutely NO!]
### Solution Justification
<!-- Write these to convince C-level executives -->
We think this is a valuable idea. Here's why:
1. **[Justification 1]** - [Description, e.g., "Addresses the #1 pain point for our target segment"]
2. **[Justification 2]** - [Description, e.g., "Differentiates us from competitors who only offer manual reminders"]
3. **[Justification 3]** - [Description, e.g., "Low technical risk—leverages existing AI infrastructure"]
Step 9: Define Success Metrics
Use SMART metrics (Specific, Measurable, Attainable, Relevant, Time-Bound):
## Success Metrics
1. **[Metric 1]** - [e.g., "80% of active users adopt AI reminders within 3 months"]
2. **[Metric 2]** - [e.g., "Average time spent on payment follow-ups decreases by 50% within 6 months"]
3. **[Metric 3]** - [e.g., "Net Promoter Score for invoicing feature increases from 6 to 8 within 6 months"]
Step 10: Define Next Steps
## What's Next
1. **[Next step 1]** - [e.g., "Run 2-week prototype test with 10 beta users"]
2. **[Next step 2]** - [e.g., "Build lightweight AI model for reminder timing optimization"]
3. **[Next step 3]** - [e.g., "Conduct legal review of GDPR implications"]
4. **[Next step 4]** - [e.g., "Present findings to exec team for go/no-go decision"]
5. **[Next step 5]** - [e.g., "If validated, add to Q2 roadmap"]
Examples
See examples/sample.md for a full recommendation canvas example.
Mini example excerpt:
### Business Outcome
- Increase by 20% MRR from freelance users within 12 months
### Solution Hypothesis
**If we** provide AI-powered invoice reminders
**for** freelance designers
**Then we will** reduce time spent on follow-ups by 70%
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 narrativeskills/epic-hypothesis/SKILL.md — Informs the solution hypothesis structureskills/positioning-statement/SKILL.md — Informs positioning sectionskills/proto-persona/SKILL.md — Defines target personaskills/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
