Why this matters
As an AI Product Manager, your job is not to add models everywhere; it is to find problems where AI can reliably improve decisions, save time, or create new user value. Strong use-case selection protects budget, accelerates learning, and reduces risk.
- Prioritize opportunities that move a core metric (revenue, cost, quality, speed, satisfaction).
- Match pains to AI patterns (classification, prediction, ranking, recommendation, generation, extraction).
- Estimate value vs. feasibility before committing engineering time.
Concept explained simply
AI is best when you must make many small, uncertain decisions using data (e.g., which ticket goes to which team, which customer is likely to churn, which item to recommend).
Mental model
- Input: data + a question under uncertainty.
- AI pattern: a reusable solution shape (classify, predict, rank, recommend, generate, extract).
- Action: who acts on the output (automation or assistive workflow).
- Value: measurable impact = frequency × uplift × unit impact − costs.
- Feasibility: do we have the data, guardrails, and resources?
A simple process to identify high‑value AI use cases
Step 1 — Clarify the outcome: What metric will change if this works? Examples: First Response Time, CSAT, AOV, conversion rate, claim cycle time.
Step 2 — Map pains to AI patterns:
- Classification: route, tag, approve/deny.
- Prediction/Scoring: churn, default, demand.
- Ranking/Recommendation: top-N content or items.
- Generation/Transformation: summarize, write drafts, translate.
- Information Extraction: pull entities/fields from text/images.
Step 3 — Size the value quickly:
- Value ≈ frequency × expected uplift × unit impact.
- ROI ≈ (annual benefit − annual cost) ÷ annual cost.
- Include costs: data work, model development, inference, integration, QA, monitoring.
Step 4 — Feasibility check:
- Data: available, accessible, labeled? quality?
- Safety: privacy, bias, hallucinations, compliance.
- Operational fit: where does the AI output go? human-in-the-loop?
Step 5 — Decide next action: prototype, collect data, or pause.
Quick value sizing template
One-liner: For [user], use [pattern] to [action] so that [metric] improves.
Value: frequency × uplift × unit impact
Costs: data + build + inference + integration + QA + monitoring
Decision: proceed to prototype / collect data / pause
Worked examples
E‑commerce support triage
- User pain: Agents manually route emails; slow first response time (FRT).
- Pattern: Text classification (topic + priority).
- Action: Auto-route to the right queue; flag high-priority.
- Value sizing: 20k tickets/month × 30% faster handling × 1.5 minutes saved ≈ 15k minutes saved/month (~250 hours). Plus CSAT gain.
- Feasibility: 6 months of labeled tickets exist; PII handling required; human override available.
- Metric: FRT, re-routing rate, agent handle time, CSAT.
Churn prediction for subscription app
- User pain: High churn in months 2–3.
- Pattern: Prediction/Scoring (likelihood of churn in 30 days).
- Action: Trigger retention offers or nudges for top-risk deciles.
- Value sizing: 100k subs × 5% uplift on at-risk cohort × $8 margin/month ≈ $40k/month benefit. Varies by country/company; treat as rough ranges.
- Feasibility: Rich behavioral logs; need experiment guardrails to avoid discount abuse.
- Metric: Retention uplift vs. control, offer ROI, net revenue.
Contract summarization for sales
- User pain: Reps spend hours reading standard terms.
- Pattern: Generation + extraction (summarize, highlight risk clauses).
- Action: Draft summary with citations; legal approves.
- Value sizing: 800 contracts/year × 45 minutes saved ≈ 600 hours/year; reduced cycle time.
- Feasibility: Documents available; require RAG with citations; human-in-the-loop mandatory.
- Metric: Cycle time, hours saved, error rate, legal approval rate.
Fraud anomaly flags for marketplace
- User pain: Manual review misses coordinated fraud bursts.
- Pattern: Anomaly detection + ranking.
- Action: Prioritize top suspicious accounts for review.
- Value sizing: Prevented loss − review cost; monitor false positives.
- Feasibility: Transaction graphs exist; labeling sparse; semi-supervised approach.
- Metric: Precision at N, loss prevented, reviewer productivity.
Checklists and self‑check
Use‑case fit checklist
- There is a clear decision or action after the AI output.
- We can name a specific metric to move.
- The problem repeats frequently (not a one‑off).
- Data exists or can be collected ethically.
- There is a safe workflow (guardrails, overrides).
Data readiness checklist
- We know sources, schemas, and ownership.
- Quality is acceptable (coverage, duplicates, drift).
- Labels/examples exist or we have a plan to get them.
- Privacy/compliance constraints are documented.
Value sizing checklist
- Frequency estimated (volume per week/month).
- Uplift is realistic (based on baselines or pilots).
- Unit impact monetized or time‑savings valued.
- Costs include data, build, inference, integration, QA, monitoring.
Exercises
Do these to practice selecting and sizing AI use cases. Your progress is saved if you are logged in. Everyone can take the quick test.
- Exercise 1: Map pains to patterns and size value for a customer support scenario. See details below.
- Exercise 2: Score feasibility for three candidate use cases and choose one to prototype.
Exercise 1 — Support operations
A company receives 15k emails/month: refunds (40%), shipping issues (30%), product questions (30%). Agents do manual triage; average first response time is 9 hours. Identify AI patterns and estimate value for a 25% FRT reduction. Outline guardrails.
Exercise 2 — Feasibility scoring
Three ideas: (A) Auto-generate product descriptions, (B) Predict out-of-stock 7 days ahead, (C) Detect fraudulent returns. Score each 1–5 on data availability, data quality, risk/safety, cost to start. Choose one to pilot and justify.
Common mistakes and how to self‑check
- Choosing novelty over value: Self‑check: Can you name the metric and expected uplift?
- Skipping workflow design: Self‑check: Who uses the output? Where does it appear? What is the fallback?
- Underestimating inference cost: Self‑check: Estimate calls/day × cost/call; include peak loads.
- Ignoring safety: Self‑check: List bias/privacy/hallucination risks and mitigations.
- Boiling the ocean: Self‑check: Can you ship a narrow slice in 2–4 weeks?
Practical projects
- Ticket triage MVP: Train a small classifier on past tickets, add confidence threshold, route high‑confidence only; measure re‑routing rate and FRT.
- Churn actioning pilot: Score top 10% at risk; run a 2‑week experiment with a single nudge; track retention uplift and net revenue impact.
- Contract summary with citations: Implement RAG using your document store; show highlighted clauses and links; require human approval; measure cycle time and error rate.
Learning path
- Problem discovery and outcome definition.
- Data discovery and labeling strategy.
- Metric design (offline/online) and experiment planning.
- Rapid prototyping with guardrails (human‑in‑the‑loop).
- Operationalization: monitoring, cost control, and iteration.
Who this is for
- AI Product Managers and PMs moving into AI work.
- Founders and leads scoping first AI features.
- Analysts and designers partnering on AI workflows.
Prerequisites
- Comfort with product metrics and basic experimentation.
- Basic understanding of data sources in your product.
- Willingness to prototype and iterate with stakeholders.
Next steps
- Complete the exercises and check against the solutions.
- Take the Quick Test below to confirm understanding.
- If logged in, your progress is saved; otherwise, the test is still available to everyone.
Mini challenge
Pick one workflow in your product with measurable pain (e.g., slow responses, low conversion). Write a one‑liner, pattern choice, value sizing, and the first risky assumption to test in a 2‑week spike.
Before you take the Quick Test
Reminder: The Quick Test is available to everyone. Log in to save your progress and resume later.