Generative and agentic AI promises to transform business intelligence more now than at any time since it rose to prominence in the 1980s. Up until recently, BI tools and platforms offered variations on a theme: a report designer for power users to design reports and dashboards with (pivot) tables, charts, and graphs and publish them to business stakeholders. These reporting platforms included features to easily exploit the Star Schema data model—like providing a “drag and drop” experience for authors to select numeric columns (referred to as “measures”) and any combination of dimensions for aggregation. Now with the emergence of agentic AI, there are opportunities to significantly extend and augment capabilities well beyond what traditional business intelligence can deliver. One of the first and most compelling use cases is transforming how organizations conduct research and analysis—moving from static dashboards and manual data exploration to AI-powered agents that can autonomously investigate complex questions, synthesize insights from multiple sources, and generate comprehensive reports that would traditionally require days of an analyst’s time.