After quick successful POCs, the productive rollout of AI solutions often comes with unexpected challenges, especially for human-in-the-loop applications. The presentation will illustrate a holistic solution in three sections, starting with an end-to-end overview with the example of ML-based assistance systems, followed by deep-dives into the two main pain points: Imputation approaches for dealing with imperfect data and the psychology behind motivators for human interaction with such systems.