Every enterprise roadmap now has an AI initiative on it. Far fewer have solved the unglamorous problem underneath it: getting clean, governed, well-modeled data in front of the model in the first place.
The Model Is the Easy Part
Picking a model, calling an API, and shipping a demo can happen in an afternoon. Making that same feature reliable in production, on real customer data, with real latency and cost constraints, is a data engineering problem wearing an AI costume.
What actually determines whether an AI feature survives contact with production
- Data quality and lineage the model can be audited against
- Access controls that respect who is allowed to see what
- A feedback loop that turns production failures into retraining signal
- Cost visibility per query, not just per model
Teams that treat the data platform as the product, and the model as a replaceable component sitting on top of it, ship AI features that keep working after the initial demo excitement fades.
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