Dashboards, long considered a hallmark of being data-driven, have traditionally been used as a workaround to deal with messy data and limited analytical tools, offering visibility but often falling short in delivering genuine insight or strategic decisions. While they provided a creative outlet for data teams and served as a primary method for reporting KPIs and operational signals, dashboards mostly raised more questions than they answered and were not equipped to perform deep analytical reasoning or root-cause analysis. As the limitations of dashboard-based self-service become more apparent, a shift towards agentic analytics is emerging, where systems are designed to operate directly on data warehouses with embedded context, enabling faster and more meaningful insights. This evolution allows data analysts to focus on teaching systems the logic and context necessary for delivering reliable and timely answers, moving beyond static reports to a more dynamic and insightful data-driven approach.