Applied AI after the pilot: what separates production systems from demos
Most generative AI pilots stall between proof-of-concept and production. The reasons are mostly organizational, and fixable.
9 min read
A working demo answers the question 'can a model do this?' A production system answers a harder set: can it do this reliably, at acceptable cost, with measurable quality, inside our security boundary, and with someone accountable when it is wrong?
Evaluation before expansion
Teams that succeed define evaluation sets early — real examples, graded by people who understand the domain — and treat model changes like code changes, with regression checks.
Data access is the real project
Retrieval, permissions and data freshness consume more engineering time than prompt design. Budget accordingly.
The model is the easiest component to replace. The workflow around it is not.
The organizations seeing durable returns are the ones that picked narrow, high-frequency workflows, instrumented them properly, and expanded only once the numbers held.
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