All use cases

Use case

AI product strategy & sequencing

The failure mode is familiar: a thin business case, an optimistic roadmap, and an LLM cheerleading both. PMHub is meant to inject structure: named frameworks, documented tradeoffs, and failure patterns you can cite when leadership pushes for the wrong first AI bet.

How to work this in Claude

  1. Start with a blunt problem statement — customer, constraint, timeline, team skills.
  2. Call search_frameworks with that narrative to surface relevant playbooks.
  3. Shortlist two approaches and use compare_frameworks with your context in use_case.
  4. Run ai_readiness_assessment when the debate is about whether you can execute — not just what to build.

Example prompt

We are a B2B SaaS with 8 engineers, limited ML experience, and leadership wants a customer-facing AI feature in two quarters. What should we sequence first and what usually breaks?