For any work in science, engineering, industry or commerce to make an impact it must be presented in a form that can convince others of the validity of its results and conclusions. This might include: a pitch within a corporation seeking continued financial support for a project, the submission of a research paper to a journal or the presentation of a GP-based product to potential customers.
The burden of proof is on the users of GP, and it is important to use the customer's language. If the fact that GP discovers a particular chemical is important in a reaction or drug design, for example, one should make this stand out during the presentation. A great advantage of GP over many AI techniques in that its results are often simple equations. Ensure these are intelligible to your customer, e.g., by simplification. Also make an effort to present your results using your customer's terminology. Your GP system may produce answers as trees, but if the customers use spreadsheets, consider translating the tree into a spreadsheet formula. Alternatively, your customer may not be particularly interested in the details of the solution, but instead care a great deal about which inputs the evolutionary process tended to use.
Also, one should try to discover how the customers intend to validate GP's answer. Do not let them invent some totally new data which has nothing to do with the data they supplied for training ("just to see how well it does..."). Avoid customers with contrived data: GP is not omnipotent and knows nothing about things it has not seen. At the same time you should be scrupulous about your own use of holdout data. GP is a very powerful machine learning technique, and with this comes the ever present danger of over-fitting. One should never allow performance on data reserved for validation to be used to choose which answer to present to the customer.