Unlike a visible system failure (e.g., a server crash), an AI 'decision failure' is silent. The system continues to operate and the output appears reasonable, but the underlying recommendation is wrong. This makes monitoring for decision trustworthiness more critical than monitoring for system uptime.
When AI reviews charts or makes clinical recommendations, it's behaving like labor, not software. Organizations must define its responsibilities, authority, supervision, and escalation pathways, just as they would for a new employee, to move from experimentation to true deployment.
For years, clinicians have been forced to work around clunky technology like EMRs. The next generation of successful AI tools will reverse this dynamic by being designed around how people actually work—accounting for interruptions, competing priorities, and handoffs—rather than forcing humans to adapt to the software.
The most successful organizations won't have the most accurate AI models, but rather the clearest plans for when those models are wrong, unavailable, or receive incomplete data. The core challenge is managing uncertainty, not just optimizing for success.
When employees use unapproved AI tools, it's evidence that they have found value faster than the organization has provided governed alternatives. Leaders should see this not as insubordination, but as a roadmap for identifying and deploying high-value, sanctioned solutions.
Smart organizations don't ask 'Where can we deploy AI?'. Instead, they ask 'Where is our work breaking down today?' They identify areas of friction—like patient wait times or administrative burdens—and apply AI as a specific solution rather than deploying technology in hopes of finding value.