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 / retrieval
• [ ] Prediction / forecasting
• [ ] Conversation / agent

Model approach:

• [ ] LLM API (GPT-4, Claude, Gemini, etc.) — specify: [Model name + version]
• [ ] Fine-tuned model on own data
• [ ] Custom model trained from scratch
• [ ] RAG (retrieval-augmented generation)
• [ ] Embedding + vector search

Rationale for chosen approach: [Why this, not alternatives]

3. Data Requirements

| Data Type | Source | Volume | Quality Status | Bias Risk | |---|---|---|---|---| | [Training data] | [Where it comes from] | [Volume] | [Audit status] | H/M/L | | [Evaluation data] | [Where it comes from] | [Volume] | [Audit status] | H/M/L |

Data gaps: [What's missing and plan to get it] Privacy considerations: [Any PII in training or inference data] Data ownership: [Do we own this data? Can we use it for training?]

4. Evaluation Framework

Primary metric: [The number that defines success — accuracy, F1, BLEU, user rating, task completion rate] Minimum acceptable threshold: [Below X, the feature does not ship] Human evaluation plan: [How will humans review model outputs? Sampling rate? Review panel?]

| Evaluation Type | Method | Cadence | Owner | |---|---|---|---| | Offline (pre-launch) | [Test set, benchmark] | Pre-launch | ML Lead | | Online (post-launch) | [A/B test, user feedback] | Weekly | PM + ML | | Adversarial | [Red-team, edge cases] | Pre-launch | Safety reviewer |

5. User Experience Design

How is AI output presented?

• [ ] Direct output shown to user (high trust required)
• [ ] AI-assisted with user confirmation
• [ ] Suggestion user can accept/reject
• [ ] Background action with audit log

Confidence and uncertainty handling:

• What happens when confidence is low? [Show alternative, ask for clarification, fallback to manual]
• How is uncertainty communicated to the user? [UI pattern]

Fallback plan:

• If the model fails or returns an error: [Specific fallback behaviour]
• If accuracy degrades below threshold: [Kill switch or graceful degradation plan]

6. Responsible AI Checklist

• [ ] Bias audit completed on training data
• [ ] Demographic fairness evaluated (does performance differ by user group?)
• [ ] Hallucination / confabulation risk assessed and mitigated
• [ ] User can see and correct AI output
• [ ] Opt-out mechanism exists (can user disable the AI feature?)
• [ ] Output provenance visible when relevant (does user know AI generated this?)
• [ ] PII not used in ways user didn't consent to
• [ ] Regulatory review completed (GDPR, AI Act, sector-specific)
• [ ] Model cards / documentation completed

7. Launch & Monitoring Plan

Rollout: [% of users, with staged expansion criteria] Monitoring metrics:

• Model performance: [Metric + alert threshold]
• User engagement with AI output: [Acceptance rate, override rate, feedback score]
• Error rate: [% of failed inferences]
• Latency: [P95 target]

Model refresh cadence: [How often is the model retrained or updated?] Drift detection: [How will you know when model performance degrades in production?]

Guidelines

• Never skip the "Why AI?" section — it's the most important question in AI product development
• The fallback UX is not optional — what happens when AI fails defines your product's trustworthiness
• Responsible AI checklist must be completed before launch, not after
• Include latency in success metrics — a 5-second AI response is often worse than no AI at all
• Recommend starting with a human-in-the-loop design and automating only when accuracy is proven

Required Inputs

Ask the user for these if not provided:

• Feature or product description (what the AI is intended to do)
• User problem (what problem the AI is solving for users)
• Available data (what training/inference data exists)
• ML/AI lead (who owns the technical implementation)

Quality Checks

• [ ] "Why AI?" is answered clearly (not "because we can")
• [ ] Minimum acceptable accuracy threshold is defined before build begins
• [ ] Fallback UX is specified for model failures or low-confidence outputs
• [ ] Responsible AI checklist is completed (not deferred to post-launch)
• [ ] Monitoring plan includes both model performance and user engagement metrics