Generative AI at Work / Unit 6 GAI 147 review, 2 to 3 minutes
The Reality Check in the Room
The idea
The M-CaReM-AI method's composite hospital case includes a validation session that shows why AI can help the room but must never be the room. The case is illustrative, not a real event.
In practice
In the scene, the team is validating the lifecycle of an incident record with the people who will use it. AI has done useful work beforehand: summarizing the draft definition on one page for frontline staff, generating realistic scenarios, including an awkward one, and listing where earlier comments disagreed. During the session, a frontline nurse asks a question nobody had planned for: can an incident be closed for clinical purposes while staying open administratively? The draft lifecycle has one closed state, and the AI-generated material treats closure as a single event, because that is how generic documentation describes it. The nurse's question shows a real distinction in how the hospital works, and the team changes the design. The method draws the general rule from sessions like this. AI is the scribe, the summarizer and the draft editor; it may highlight conflicts and prepare material. It must not validate a model by itself on the basis of generic patterns, resolve policy or legal disagreements, or write domain definitions without the organization's own context.
Where people slip
No amount of checking the AI's output against itself would have found the nurse's point. It came from someone who does the work. The human loop exists to bring that knowledge in, which is why the method insists on real people in the room, not only real people signing at the end. AI prepared the room well; the people in it supplied what no document contained.
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