AI Product Canvas Skill

AI Product Canvas Skill

Define AI products with the same rigour as any product decision — but with additional layers for data, model, evaluation, and responsible AI. This canvas prevents the most common AI product failure: building a technically impressive feature that doesn't solve a real problem.

AI Product Anti-Patterns to Check First

Before building, flag if any of these apply:

• ❌ "We should add AI to [existing feature]" — with no user problem defined
• ❌ Accuracy target undefined before build begins
• ❌ No plan for what happens when the model is wrong
• ❌ User-facing AI output with no human review or fallback
• ❌ Training data not audited for bias or quality
• ❌ No evaluation metric — "we'll know it when we see it"

AI Product Canvas Output Format

AI Product Canvas — [Feature Name] — [Date]

PM Owner: [Name] ML/AI Lead: [Name] Status: Discovery / Design / Build / Evaluation / Live

1. Problem Definition

User problem being solved:

[What specific situation is the user in? What job are they trying to get done?]

Why AI?

[What makes this problem require AI vs a deterministic solution? If the answer is "because we can," stop here.]

Success for the user looks like:

[What outcome does the user experience when the AI feature is working well?]

2. AI Approach