Not every problem needs AI, and not every AI application is a good product. The best AI products solve real problems where AI provides a step-change improvement over existing solutions.
The AI Product Opportunity Framework
Ask these questions to evaluate an AI product idea:
1. Is there a real problem? • Who has this problem? How often? How painful is it? • What do they currently do about it? (The "status quo" is your real competitor) • Would they pay to solve it? How much?
2. Does AI meaningfully improve the solution? • Is the task repetitive, data-heavy, or requiring pattern recognition? • Does AI provide 10x improvement, not just 10% improvement? • Could this be solved with traditional software? (If so, AI adds complexity without benefit)
3. Is the problem bounded enough? • Can you define clear success criteria? • Is the domain narrow enough for current AI capabilities? • Are errors acceptable or catastrophic? (Healthcare diagnosis vs. email drafting)
Finding Opportunities
Workflow automation: Look for tasks that knowledge workers do repeatedly: • Summarizing documents, emails, or meetings • Categorizing and routing requests • Drafting standard communications • Extracting data from unstructured sources
Augmentation over automation: The best AI products enhance human capability rather than replace it: • Writing assistants that suggest, not write • Analysis tools that surface patterns for human decision-making • Creative tools that generate options for human curation
The "Last Mile" problem: Many industries have manual "last mile" processes that AI can address: • A lawyer reviews contracts → AI highlights risky clauses for human review • A doctor reads medical images → AI flags anomalies for doctor confirmation • A recruiter screens resumes → AI surfaces top candidates for human interview
Common Pitfalls
- Solution looking for a problem — "We have AI, what should we build?" (backwards)
- Demo magic — Looks amazing in a demo but fails on real-world data
- Accuracy assumptions — Assuming AI will be 99% accurate when it's actually 85%
- Ignoring the human — Building fully autonomous systems when human oversight is needed