Why it matters for your business
An MVP limits the money spent before you know whether people will use the product. Choosing what to leave out is the hard part: the MVP should do one job well enough that users come back, while resting on foundations that can grow, such as a clean data model, authentication and basic monitoring. For AI products, the MVP should include enough logging and evaluation to learn from real usage. Teams that skip that discipline often build features nobody asked for, or rebuild the MVP from scratch later.
Example
A startup building a scheduling app for dog groomers scopes its MVP to online booking, automated text reminders and a daily schedule view. Payments, inventory, multi-location support and an AI assistant wait for later. Ten salons use the MVP for two months, and their booking patterns and feedback decide which feature gets built next.