There are two kinds of beta tests. Most products run the first kind and think they’ve run the second.

The first kind tests whether the software works: can users complete the task, does the output match expectations, are there crashes or errors? These are important things to know. They’re also not the things that predict whether a professional tool will get used.

The second kind tests whether the workflow works: does the tool fit into how professionals actually do their jobs, does the output get used or set aside, does the analyst trust it enough to build on it rather than redo the work?

Professional tools fail at the workflow level even when they succeed at the software level. An AI lease abstraction tool that produces 95% accurate output but requires the analyst to spend fifteen minutes formatting it for their internal template has failed the workflow test. A tool that produces 90% accurate output with source citations in a format the analyst can immediately share with the client has passed it.

The way to run the second kind of beta test is to watch what happens to the output, not just how it’s produced. Does the analyst put the numbers directly into their model? Do they share the output with their team? Do they reference it in their recommendation memo? Or do they use it as a starting point for their own manual work, treating the AI as a first pass they have to clean up?

If they’re cleaning up, you haven’t passed. You’ve built a slightly faster version of the thing they were already doing.

The beta test that matters is: does the analyst’s workflow change because of your tool? Does something they used to do get eliminated, not just accelerated?

If yes, you have a workflow-native tool. If no, you have a feature.

Design for the workflow test, not the software test. +++