Case study Framework / GTM experimentation
AI Venture & GTM Lab
Experiment
A route from a product hypothesis to evidence.
Early AI ventures often build before they understand who pays, why now, and how the product reaches the market. Enthusiasm substitutes for evidence.
Editorial illustration of the project’s focus.
01 / Context
The problem
A technical prototype and a commercially viable business answer different questions.
02 / Ownership
My contribution
Structured a seven-stage evaluation framework connecting customer discovery, product choices and commercial assumptions.
The framework
- Define the market, buyer, costly problem and alternative before selecting a narrow ICP.
- Write positioning and pricing hypotheses, then specify evidence needed from validation, distribution and retention tests.
Decision boundaries
- Treat each stage's signal as evidence to seek, rather than proof that an experiment has already succeeded.
- Use explicit stop or revise criteria before increasing investment.
What is available
- A documented discovery-to-retention framework and evidence criteria, shown below.
The idea to carry forward
A growth decision is stronger when the assumption, test and evidence threshold are stated separately.
An experimentation framework. Successful launches, paid customers and retention outcomes are not claimed.