How do you separate an AI product MVP from future enhancements?
Set a clear first-release boundary and preserve later ideas without letting them expand the current milestone.
How do you separate an AI product MVP from future enhancements?
An MVP should test a useful product hypothesis with a coherent experience. It is not simply the smallest amount of code you can ship.
Define the promise of the first release
For an illustrative video tool, the promise might be creating one reviewed narrated video in one supported format. That requires a complete input-to-output journey, including basic recovery and access controls. It does not automatically require multiple destinations, collaborative editing, or a large template catalog.
Keep a visible deferred list
For every proposed addition, ask whether it is necessary to fulfill the current promise. Record deferred items with the condition that would justify revisiting them, such as repeated requests from creators. This makes scope decisions easier to explain and prevents the same discussion from recurring. Evaluate the release with representative tasks and track where users need help. Expand the product based on those observations rather than on how many features competitors advertise.
No. A smaller feature set does not remove the need to protect private user data.
Evidence that it removes an important obstacle or serves a demonstrated need after the core workflow works.
No. It may test interest, but it does not demonstrate that users can generate a useful result.
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