Classification of potential customers
What you described is the more-or-less standard approach, with a couple of caveats.

Leakers

Remember that we live in a stochastic world, so a perfect model is immediately suspect of using leakers, i.e., variables which have a causal connection with the target. In your case, e.g., it would be the presence of a service contract for the machine (which would indicated that the company actually does own one). This, building an interpretable model and examining the most powerful variable for such causal connections (and eliminating them) would be a good idea.

Better Approach

However, one could improve on this approach in a very powerful way by replacing the current state of the company with its historical state at the time of the machine purchase. This way one eliminates the leakers automatically and also gets a better model because the predictor values which are more relevant: after all, you are trying to predict which company is ready to buy the machine now rather that which company bought it in the past when its conditions were very different.

In fact, your approach works precisely because it is an approximation of this approach (approximating the company state at the time it bought the machine with its current state) and the absurdity/uselessness of the perfect model that you mention is the artifact of your approach being an approximation of The Right Thing.