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]

```markdown
## 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]

```markdown
## 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`:

```markdown
## 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`:

```markdown
## 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:

```markdown
### 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