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
- Start with a blunt problem statement — customer, constraint, timeline, team skills.
- Call
search_frameworkswith that narrative to surface relevant playbooks. - Shortlist two approaches and use
compare_frameworkswith your context inuse_case. - Run
ai_readiness_assessmentwhen 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?