How do you manage expectations at work?
Rational business people don't pay for accuracy, they pay to either 


save money on a profitable process (thereby making it more profitable), or by
creating new money (creating new profitable processes). 


So any project that is undertaken has to be couched in terms that
   reflect this. The first step is always understanding which of the two
   processes you are working on, and you should have a clear idea of how
   that might be achieved, while keeping in mind that as you make
   progress the details of how you do that might change.

If you can improve the accuracy of a process, you can probably make money for the firm and the business people will invest in your progress. The only rational reason a business person could have for insisting on 99 percent accuracy and reject 90 percent is if they already had a way of doing it that was better than the 90 percent.  If that is the case they are of course justified in their position.  

Understanding and presenting the business case of the projects you are working on in terms that the Business People understand is part of the maturing process of any Engineer. It is not unique to Data Science at all, though Data Science has some unique aspects (like lower maturity but higher probability of fortuous serendipity discovery - at least in today's environment.

A relevant process that is close to Data Science that makes this step explicit can be found here:https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining 

But most Enterprise Architecture Frameworks are similarly applicable.