ai-product-canvas

# 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 **Task type:** - [ ] Classification - [ ] Generation (text, image, code) - [ ] Summarisation / extraction - [ ] Recommendation - [ ] Search / retrieva...