/recommendation-canvas

name: recommendation-canvas
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

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)

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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.

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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"

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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

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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 solut