/recommendation-canvas

name: recommendation-canvas
argument-hint: "[AI product idea]"
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
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:** 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