· 1 min read
What 100 AI Use Cases Taught Us About Production
A reflection on the JCorp use-case portfolio featured in the Microsoft customer story — what survived the journey from idea to production, and the pattern in what didn't.
- case-study
- ai-strategy
- johor
- aiops
Mapping 100+ AI use cases sounds, in retrospect, like the easy half of the work. It was not — it took the better part of a year and the discipline of a steering committee that refused to add an entry to the map without a value hypothesis attached. But the harder half was watching which use cases survived the journey from idea to production. The pattern, once we had enough cases to read it, was not the one we expected.
The cases that survived shared three properties. They had a clear owner of the loss — a named operator who paid the cost if the model was wrong. They had a value signal that updated quickly — within a sprint, not within a quarter. And they had a fallback path that didn't depend on the AI. The cases that stalled, even the technically elegant ones, were missing one of those three.
What this taught the operating team is that productionising AI is mostly a governance exercise dressed up as a technical one. The model choice matters less than the loop. The architecture choice matters less than the ownership choice. The hundredth use case isn't twice as hard as the fiftieth; it's just the one where you finally see the pattern, and you stop adding cases that don't conform to it.