Kick-start new analytics domains by letting AI generate your data models from natural language requirements. Provide business goals, key entities, and example metrics; the assistant proposes dimensions, facts, and grains, along with naming standards and constraints. It scaffolds DDL and sample pipelines, plus unit tests for conformance and referential integrity. Engineers then adjust surrogate keys, SCD handling, and partitioning, while analysts validate definitions in the semantic layer. Because models are versioned as code, you can iterate safely via pull requests and automated checks. This approach doesn’t replace expertise—it removes the blank page, compressing days of setup into minutes so teams can focus on correctness, performance, and adoption.