Développement Fullstack

Why Operational AI Systems Break When Granularity Scales

Enterprise AI systems often work at the first level of granularity, then become fragile when the business asks for more precision. A field lesson from pharmaceutical supply optimization on why incremental architectures matter.

Par

Cyril Noirot

13 mai 2026

9 min de lecture

One of the most dangerous moments in an enterprise AI project is when the first version works.

That sounds counterintuitive. A working first version is supposed to be the turning point: the model solves, the dashboard loads, stakeholders can see the recommendation, and the business starts asking how far the system can go.

The first version often proves that the method is possible. The second version proves whether the architecture can survive operational reality.

Sometimes they do. But granularity is not free. In operational AI systems, moving from a coarse decision layer to a fine-grained one can change the entire computational and architectural shape of the problem.

À propos de l'auteur

Cyril Noirot

Lead Data Scientist

Data scientist freelance. Je conçois et déploie des systèmes de décision — prévision, pricing, marketing measurement, optimisation.

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