This case study explored perceived artificial intelligence (AI) performance and intended future use (IFU) in users of AI-based applications. Users could become less motivated to use these applications if AIs do not clearly communicate their actions. A prototype, a user test, and a structured interview were iteratively developed. Eight students participated in the final iteration, which was thematically analyzed. The results indicate that an AI-based application that shows recommendations can positively affect perceived AI performance and IFU. Possibly, the recommendations increased users’ understanding of AI decisions, as well as their satisfaction. Therefore, recommendations could be a potential design element for increasing perceived AI performance and IFU. Finally, time-saving functionality is a design element that could lead to higher IFU in AI-based applications, possibly only for other tasks than examining recommendations. Further research needs to test these findings under different circumstances.