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recommendation-canvas

评估一个AI产品想法的成果、假设、风险和定位。在决定是否值得投资或推荐某个AI解决方案时使用。

person作者: jakexiaohubgithub
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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]
## 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 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