Healthcare organizations are spending heavily on AI prototypes that never make it past the pilot stage. Despite significant investment, many tools are abandoned before reaching clinical deployment, raising serious questions about how the industry evaluates and adopts new technology.
The core problem is a disconnect between what AI vendors build and what clinical teams actually need. Prototypes often perform well in controlled settings but fall apart when exposed to messy real-world data, inconsistent workflows, and skeptical frontline staff. Without strong buy-in from clinicians early in development, even technically sound tools struggle to survive.
Experts point to a lack of clear success metrics, poor integration with existing electronic health record systems, and insufficient change management as the main culprits. For AI to stick in healthcare, organizations need to involve end users from day one and define what 'working' actually looks like before writing a single line of code.
