This case study article describes the iterative design process of an AIbased mixed-initiative calendaring tool with embedded artificial intelligence. We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users. These findings motivated the redesign and enhancement of our intelligent system. Lessons learned from the study--namely, that AI systems must be usable to gain widespread adoption and retention and that simple problems that perhaps do not necessitate complex AIbased solutions should not go unattended merely because of their inherent simplicity--conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system. We are working within the infrastructure of a general-purpose, computerized office assistant named CALO (Myers et al. 2007).