Always Have a Working Model
A Lean hypothesis is a Bayesian inference: it is based on an assumption, such as a belief of what users need. Thus, another way to state the hypothesis is, “Given that users need to do XYZ, if we give them a capability to do XYZ, then they will love it”.
If you then create a product that does XYZ and test market it, and if your Bayesian assumption is true, then the market response will either support your Bayesian premise or not: that is, it will support or invalidate your hypothesis.
We are explaining it this way to make the point that underlying your vision there are assumptions, and there is an implicit cause-and-effect model: your assumption that users need to do XYZ, and the hypothesis that if you give them that, they will buy it. Assumption (they need to do XYZ), cause (you give them that capability), effect (they buy).
There are actually more hidden assumptions, such as what people are willing to pay, and that your product features actually enable someone to do XYZ, but our point here is simply that there is a hypothetical causal model in your mind, whether you think of it that way or not. This point becomes important later when you consider what to measure and what kinds of dashboard to create.
Your vision needs to be refined so that it is expressed as a hypothesis. For example, “We think that if we give such-and-such group of users this kind of capability, they will love it”.