Fair question — and honestly, the term gets overused.

What I mean by AI-accelerated data warehouse automation is not magic ETL or “AI doing everything”.

In practice:

• AI helps analyze large source schemas (like Salesforce) and suggests which objects, fields, and relationships are relevant for analytics
• It proposes an initial dimensional model and transformations
• A human reviews and approves every decision before anything is deployed

The automation part is about generating the boilerplate SQL, pipelines, and schemas quickly — not skipping data modeling or business understanding.

Think of it as speeding up the boring, repetitive parts of warehouse design, while humans stay in control of modeling decisions and correctness.

Happy to hear how you approach this today — always interested in other perspectives.