Why AI Analytics Is Becoming a Data Infrastructure Problem

Most AI analytics discussions focus on models.

In practice, many failures come from the surrounding data environment.

A model can understand natural language and generate SQL.

That doesn't mean it understands your business.

Enterprise environments contain:

disconnected systems
undocumented relationships
conflicting metric definitions
hidden business logic
What AI actually needs is context.

Business Question
↓
AI Agent
↓
Semantic Definitions
↓
Metadata
↓
Lineage
↓
Relationship Discovery
↓
Enterprise Data

The question is no longer:

"Can the model generate SQL?"

It's:

"Can the model understand the environment behind the SQL?"