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Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
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SOC 職業分類に基づく
SKILL.md を表示中
| name | recommendation-canvas |
| argument-hint | [AI product idea] |
| 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 |
| theme | validation-experiments |
| best_for | ["Deciding whether an AI product idea deserves real investment","Surfacing the risks and hypotheses behind an AI feature request","Comparing AI solution options on outcomes rather than novelty"] |
| scenarios | ["Leadership wants an AI feature and I need to evaluate whether it's worth building","I have three AI solution options and need to compare them on outcomes and risk"] |
| estimated_time | 30-45 min |
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.
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.
Created for Dean Peters' Productside "AI Innovation for Product Managers" class, the canvas synthesizes multiple PM frameworks into one strategic view:
Core Components:
Use template.md for the full fill-in structure.
Before filling out the canvas, ensure you have:
skills/problem-statement/SKILL.md)skills/proto-persona/SKILL.md)If missing context: Run discovery work first. This canvas synthesizes insights—it doesn't create them.
What's in it for the business? Use this format:
## Business Outcome
- [e.g., "Reduce by 25% the churn of existing customers using our existing product"]
Example:
Quality checks:
What's in it for the customer? Use this format:
## Product Outcome
- [e.g., "Increase the speed of finding patients when I know the inclusion and exclusion criteria"]
Example:
Quality checks:
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:
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:
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:
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"]
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]
## 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:
## 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"]
## 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"]
## 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"]
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"]
## 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"]
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%
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.
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.
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.
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."
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."
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 outcomesprompts/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