recommendation-canvas skill

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