Your first AI MVP should answer one question

A strong AI MVP begins with a question that a real user can help answer. Will a support agent use an assisted draft? Can a founder turn an interview into a useful product brief? Will a team trust a search answer enough to open its source? A clear question makes scope easier to defend.

Write down the task, the person doing it, and the moment when the result becomes useful. A feature list is easier to evaluate once those three things are explicit. Choose an initial journey that users can finish from beginning to end, including the point where the system needs help.

Before building, collect a small set of representative examples using permitted data. Include straightforward cases, ambiguous requests, missing context, and a few cases the product should decline. Define what a useful result looks like for each example. These examples become a shared reference for product and engineering decisions.

The first interface should make the AI output easy to inspect. Show the information a user needs to make a decision, let them correct errors, and explain what happens after approval. If an incorrect output could create a consequential action, design an explicit human checkpoint.

Measure the whole task. A fast model response does not necessarily mean a useful product. Track whether the user completes the task, how much correction is needed, where they abandon the journey, and the cost of running it. Collect only the data needed to understand those questions.

Set a review point before the pilot begins. Decide what evidence would support continuing, changing direction, or stopping. A successful MVP can reveal that an idea needs a different workflow or a simpler approach. The value lies in learning enough to make the next decision with confidence.

This editorial article is starter content for the Shaige website; its examples are illustrative and do not describe client outcomes.