recommendation-canvas

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:

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