AI Use Case Prioritiser — Which to Build First

Question 1 of 5

What stage is your company at?

Pre-seed or seed — validating product-market fit, limited runway

Series A — scaling sales, proving repeatability, expanding team

Series B+ — optimising unit economics, building defensibility, expanding markets

Bootstrapped / profitable — capital-efficient, protecting margins

Question 2 of 5

What's your biggest product pressure right now?

Churn or activation — users aren't getting value fast enough

Competitive pressure — rivals are shipping AI features into their pitch decks

Operational efficiency — support costs, manual processes eating margin

Revenue growth — need AI features to justify enterprise pricing or expand ACV

Question 3 of 5

What data do you have clean and accessible today?

Structured product usage data — events, sessions, user behaviours in a database

Text / documents — support tickets, knowledge base, user-generated content

Mixed — both structured metrics and unstructured text, reasonably clean

Honestly messy — data lives in silos, no clear owner, inconsistent schemas

Question 4 of 5

Who would primarily use the AI feature?

Your end users — it's a product feature they interact with directly

Your internal team — support, sales, or ops agents use it to serve customers

Both — customer-facing feature with internal ops benefits

Question 5 of 5

What's your AI engineering capacity right now?

None — we're engineers but haven't shipped ML in production before

Partial — one person with some ML experience, rest are product engineers

Dedicated — one or more ML engineers, but no production deployment experience

Experienced — team has shipped ML to production, needs strategic direction

The AI Use Case Prioritiser