They Requested It. I Built It. Nobody Ever Used It.

Data science projects often fail to be adopted by stakeholders for several key reasons. Complex models can be untrustworthy "black boxes," so providing clear, non-technical explanations is crucial for building trust with users like clinicians. Projects that take too long to develop risk being obsolete upon delivery, making rapid iteration and frequent communication essential. A model will also be ignored if its predictions are not seamlessly integrated into the user's existing workflow, as added complexity creates a significant barrier to adoption.