Organisations invest in modelling talent and then find models never reach production. The obstacles are almost always infrastructural, and they are predictable enough to design against.
Data access is the first bottleneck
Practitioners routinely spend the majority of their time locating data, understanding it and getting permission to use it. That is a cataloguing, lineage and access problem, and no amount of modelling capability compensates for it.
Training and serving diverge
A feature computed one way in a training notebook and another way in the serving path produces a model that performs well in evaluation and badly in production. This mismatch is the single most common cause of unexplained degradation.
Nothing is reproducible
Without recorded data versions, code versions, parameters and environment, a result cannot be reproduced or explained. That makes debugging guesswork and makes any regulated use impossible.
Action step
Take one production model and try to reproduce its training run exactly. Note every thing you cannot recover.
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