Finance teams are often asked to move faster, explain variance sooner, and support automation, but many organizations are still working from fragmented pipelines, inconsistent metric definitions, and dashboard-heavy reporting processes. Before finance can safely use AI or agentic workflows, the data foundation has to be trusted, governed, and explainable.

This session shows how to design an AI-ready finance lakehouse using Databricks, dbt, Unity Catalog, and governed metrics. We will walk through a practical architecture where finance data is transformed through dbt staging, intermediate, and mart layers, with reusable SQL models, macros, tests, documentation, and exposures creating a reliable path from raw source data to finance-ready outputs.

The session will also show how Unity Catalog strengthens governance through lineage, access control, classification, and auditability, while governed metric definitions help keep reporting, Power BI consumption, and AI-assisted analysis aligned to the same trusted business logic. We will discuss how this foundation can support future agentic finance use cases such as variance investigation, anomaly triage, and automated action without allowing AI to invent financial calculations.

Attendees will leave with a practical design pattern for building finance data products that are not only dashboard-ready, but AI-ready.