AI & Data
Enterprise AI infrastructure: the data foundation that determines whether your AI program succeeds
The majority of enterprise AI projects fail before a model is trained. The root cause is almost always data infrastructure. This paper defines the data estate requirements for production AI at enterprise scale.
Why enterprise AI programs stall at the data layer
In our experience working with enterprise AI programs across financial services, healthcare, and manufacturing, the bottleneck is almost never the model. The most common failure mode is that the data required to train and serve the model doesn't exist in a form the model can use. It's locked in operational databases not designed for analytical access, it lacks the lineage metadata required for model governance, it's stored in formats that change without notice, or it's subject to access controls that the ML training pipeline can't satisfy. Fixing these problems takes 12–18 months and is often underestimated by orders of magnitude in project planning.
The four data infrastructure requirements for production AI
Requirement 1: A feature store that serves consistent feature values between training and inference. Training/serving skew is the single most common cause of model performance degradation in production and is almost entirely an infrastructure problem. Requirement 2: Data lineage at the column level, for every feature, tracing back to the source system. Required for model governance under EU AI Act, NIST AI RMF, and most internal risk frameworks. Requirement 3: A streaming data layer capable of serving features with sub-100ms latency for real-time inference use cases. Requirement 4: Data access controls that satisfy both ML pipeline requirements and enterprise governance policies — these are frequently in conflict, and the conflict must be resolved architecturally before model development begins.
The AI operations platform your AI program actually needs
The platforms enterprises typically deploy for MLOps — experiment tracking, model registries, deployment pipelines — address the model development lifecycle but not the data lifecycle that the model depends on. A complete MLOps platform requires: a feature platform (not just a feature store) that handles feature engineering, serving, and monitoring; a data quality monitoring layer that can detect distribution shift before it affects model performance; a model monitoring layer that connects model performance metrics back to the underlying data quality metrics; and a governance layer that can produce audit evidence for every prediction made by every model in production.
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Paper details
Authors
Priya Nair, Head of Data Engineering
Marcus Webb, AI Infrastructure Lead
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