Healthcare organizations are spending heavily on AI prototypes that never reach clinical use. The pattern is familiar: a promising tool gets built, tested in a controlled environment, then quietly shelved. The reasons range from poor integration with existing workflows to a lack of clinician buy-in at the outset.
Experts point to a fundamental misalignment between the teams building these tools and the people expected to use them. When developers prioritize technical performance over practical usability, the result is a product that works in theory but fails in the real world. Cost overruns and unclear return on investment accelerate the decision to cut losses.
The fix, according to those close to the problem, starts before a single line of code is written. Involving frontline staff early, setting realistic deployment benchmarks, and treating adoption as part of the build process are the steps most often skipped. Without them, even technically sound AI tools end up collecting dust.
