The headline‑grabbing triumph of a question‑answering system over human champions created an expectation that any domain could be transformed by a single, powerful model. The allure lay in the belief that raw predictive ability alone would replace the need for domain expertise, data pipelines, and human judgment. In practice, the model’s performance collapsed when it encountered the messy, regulated, and privacy‑heavy world of clinical decision support.

Engineers discovered that the system could not integrate with electronic health records, could not explain its recommendations in medically acceptable language, and often suggested actions that conflicted with established protocols. The root cause was a mismatch between a technology that excelled at pattern matching on public text and a workflow that demanded provenance, auditability, and tight governance. The episode taught a broader lesson: when an AI’s strength is speed and scale, the cost of retrofitting it into a high‑stakes, compliance‑driven process can outweigh its headline value.

The hidden trade‑off is not between automation and insight, but between a model’s native environment and the institutional scaffolding it must inherit.