Mohit Aggarwal 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)
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