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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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
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:
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?
## 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]
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 Metrics1.**[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 Next1.**[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%
