Data & Analytics
What actually makes enterprise data AI-ready
Grounding an AI system requires less exotic infrastructure and more ordinary data discipline than most roadmaps assume.
CloudSight Analytics6 min read
Teams often assume that AI readiness means new infrastructure: a vector database, a model platform, an orchestration layer. In practice the constraint is almost always ordinary data discipline that was deferred while the warehouse grew.
The properties that matter
- Descriptions: tables and fields that a retrieval layer can interpret without tribal knowledge
- Lineage: the ability to trace a generated claim back to a source record
- Access control: entitlements expressed at the data layer, so retrieval can honour them
- Freshness guarantees: a stated expectation, monitored, per dataset
- Stable identifiers: entity resolution that holds across systems
Each of these is unglamorous, and each determines whether a grounded system can answer a question correctly and prove it. A model cannot compensate for a corpus whose permissions are unknowable or whose freshness is unstated.
The practical implication for sequencing: scope the first AI use case around a data domain that already has these properties, and let the broader data work proceed in parallel rather than blocking on it.