Module 10: AI Product and Transformation

Goal

Turn business problems into AI opportunities only when AI is appropriate, then define feasibility, KPIs, ROI, risks, and an implementation roadmap.

Discovery Flow

Business Problem -> User Problem -> AI Opportunity -> Feasibility -> Prototype -> Pilot -> Production -> ROI

Product Discovery Topics

• Stakeholder mapping.
• User interviews.
• Customer journey.
• Pain points.
• Requirements.
• User stories.
• Acceptance criteria.
• Prioritization.

AI Product Questions

• Is AI necessary?
• Is deterministic software enough?
• Is ML required?
• Is an LLM appropriate?
• Is RAG required?
• Does the system need an agent?
• What data is available?
• What are the risks?
• What is the expected ROI?
• What happens if the AI is wrong?

Metrics

• Business: revenue, cost reduction, time saved, conversion, satisfaction, productivity.
• AI: accuracy, precision, recall, faithfulness, hallucination rate, task success.
• Technical: latency, throughput, availability, error rate, token usage, cost/request.

Study Tasks

• Pick one business process.
• Map current state.
• Identify pain points.
• Identify AI and non-AI solutions.
• Prioritize opportunities by impact, feasibility, risk, and cost.
• Define KPIs.
• Estimate ROI.
• Write a pilot plan.

Project

Build projects/08-ai-transformation-case-study/.

Tips

• The strongest AI product decision is sometimes rejecting AI.
• Always define what failure looks like.
• Separate prototype success from production readiness.
• ROI should include engineering, operations, model inference, and maintenance costs.

Completion Checklist

• Can write clear user stories.
• Can define measurable KPIs.
• Can estimate ROI.
• Can prioritize opportunities.
• Can create an implementation roadmap.