Module 125 minutes
AI Product Strategy: When AI Makes Sense
Determine if AI is right for your product. Learn to identify good AI use cases and avoid common pitfalls.
product-strategyai-productsuse-cases
Learning Objectives
- âś“Identify problems AI can solve well
- âś“Recognize when NOT to use AI
- âś“Evaluate AI product opportunities
- âś“Define success metrics for AI features
Not Every Problem Needs AI
AI is powerful but not magic. Learn when it makes business sense.
Good AI Use Cases
- Pattern recognition at scale
- Natural language understanding
- Content generation
- Personalization
- Predictions from data
Bad AI Use Cases
- Simple rule-based logic
- When deterministic outputs required
- Highly regulated without AI explainability
- When training data unavailable
Evaluation Framework
Ask:
- Does this need understanding/generation?
- Is there enough data?
- Can we accept probabilistic outputs?
- What's the cost of errors?
- What's the baseline we're beating?
Key Takeaways
- →Use AI for pattern recognition, language tasks, and personalization—not simple rules
- →Ensure you have data and can accept probabilistic outputs
- →Define success metrics before building
- →Start with MVP, not full product
- →Plan for AI limitations and edge cases
Practice Exercises
Apply what you've learned with these practical exercises:
- 1.Evaluate 3 potential AI features for your product
- 2.Calculate baseline metrics to beat
- 3.Identify data requirements
- 4.Define success criteria