Start with the hypothesis
Diagnose retention by connecting the recurring customer job to product usage and cancellation reasons. Treat the first release as a way to learn something specific, not as a smaller copy of the eventual company. State what you believe, who must care, and what evidence would change your mind.
Reduce scope before increasing speed
AI can make implementation faster, but faster implementation does not make unnecessary features useful. Keep the core workflow intact and remove secondary roles, edge-case automation and polish that does not affect the test.
Connect product evidence to business evidence
A working app proves that something can be built. It does not prove that people will discover it, switch to it, pay for it or keep using it. Track the evidence that corresponds to the risk you are testing: interviews, activation, repeated usage, conversion or retention.
Decide the next build from what happened
After launch, group feedback into missing value, usability friction, reliability problems and requests from non-target users. Fix the constraint that blocks the core promise before widening the roadmap.