Strategy: Diagnose · Add-on
Find and rank AI opportunities
AI is useful for some problems and wasteful for others. Find where it could help with your core challenge, and rank the opportunities by value, feasibility and risk.
About 3-5 daysFast-movingLast reviewed 5 November 2026
Who this applies to
- Skills it builds
- Strategy and visionTechnical and AI fluencyPrioritisation and roadmapping
- PM types
- AI
- Product types
- Data or AI product
- Starting points
- All
- Ways of working
- All
- Experience
- All
- Size of organisation
- All
Steps, by experience
Beginner (under 1 year)
- List the tasks and decisions in your product or organisation that are slow, repetitive or data-heavy.
- For each, ask whether AI could genuinely help.
- Score each one for value, feasibility and risk.
- Pick the top two or three to explore.
- Check each one against your organisation's AI rules.
Intermediate (1 to 4 years)
- Check data availability and quality for each opportunity.
- Estimate running cost as well as build cost.
- Plan small experiments to test the top opportunities.
Experienced (5+ years)
- Build an AI opportunity portfolio across products.
- Balance quick wins with deeper bets.
- Link AI opportunities to the strategy's core challenge.
Who can do this
Owner: Product manager
Can be delegated to
- Data Scientist / ML Engineer: Assess feasibility and data needs
- Policy / Legal / Compliance: Check legal and ethical risks
Reviewed by: Data Scientist / ML Engineer
Tool examples
- Miro
- Plot opportunities on a value and feasibility grid.
- Claude
- Use an AI assistant to brainstorm use cases, then check them with experts.
Definition of done
- Opportunities listed and scored
- Top opportunities chosen
- Experiments planned
Common pitfalls
- Using AI because it is fashionable
- Ignoring data quality
- Forgetting running costs