Governed & scalable AI-assisted analytics with dbt

AI can accelerate analytics work—but only if it’s governed and reviewable. In this course, you’ll learn practical patterns for enabling AI-assisted workflows in and around dbt without creating new risk. We’ll cover what guardrails to set, how to structure review, and how to keep your analytics codebase and definitions trustworthy as AI usage scales.

After this course, you will be able to:

Describe where AI helps most in analytics engineering (and where it can introduce risk)

Apply a review framework for AI-assisted changes (correctness, performance, maintainability, documentation)

Identify governance decisions needed before scaling AI usage (who can access what context, what outputs are acceptable, how changes get approved)

Establish team norms that prevent “AI drift” in definitions, naming, and documentation