One of the biggest misconceptions about AI is the idea that once a model is deployed, the job is “done.”
In reality, deployment is only the beginning.

Models live in an environment that never stops changing.
User behavior shifts.
Market dynamics fluctuate.
Seasonality evolves.
Data pipelines drift.
New patterns emerge that were never part of the training set.

What worked flawlessly a month ago can suddenly break or worse, continue producing results that look correct but are no longer grounded in reality.

This is why modern AI systems require the same rigor as any complex, living ecosystem.