pm-claude-skills/exports/aider/pm-advanced/ai-product-canvas/ai-product-canvas.md

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"

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AI Product Canvas Output Format

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

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

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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?]

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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]

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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?]

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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

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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]

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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

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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?]

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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)

Anti-Patterns

• Do not skip the "Why AI?" question — if the answer is "we want to use AI," stop and reframe around the user problem first
• Do not launch with an undefined accuracy threshold — "good enough" is not a threshold; set a number before build begins
• Do not design the UX to hide AI-generated output as if it were system truth — users need to know when AI is involved so they can override it
• Do not defer the Responsible AI checklist to post-launch — bias and privacy issues are far harder to fix in production than in design
• Do not treat model latency as a post-launch optimisation — a 6-second AI response that replaces a 1-second rule-based response is a regression, not a feature

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