The global AI conversation is changing. Companies are no longer asking only whether large language models are powerful. They are asking a more practical question: can AI agents actually enter enterprise workflows, connect to real data, understand business context, and produce reliable results? This shift matters a lot for enterprise data analytics. Most companies do not lack data. They already have databases, dashboards, BI tools, and reporting systems. The real problem is that data is fragmented across systems, business terms are inconsistent, metric definitions are unclear, and table relationships often live only in the heads of experienced data engineers. A business user may ask a simple question: “Which customers are growing the fastest?” or “Where is inventory risk concentrated?” But behind that question, a data team may need to identify the right tables, confirm metric definitions, write SQL, validate joins, and explain the results.…