AI agents can handle complex audience segmentation requests and deliver insights in seconds.
Marketing teams rely on data but accessing live information often means delays, technical bottlenecks, or confusion. AI agents are stepping into this gap by acting as interpreters between analytics needs and structured query logic. They can translate natural language requests into complex queries across large data sets, even in deeply nested or historic tables.
Imagine an analytics lead asking, "Give me customers with three-year-old smartphones, who own TVs, and have clicked a marketing email in the past year." A well-trained AI agent, with access to metadata and business logic, can convert that into efficient SQL, retrieve the data, apply filters, and return a clean dataset ready for segmentation. Better yet, it can log that query for repeat use, visualize the trend, and surface anomalies.
Retail and media brands are already using agents to generate dynamic cohorts and campaign lookalikes. One media firm described it this way: "We used to wait three days for lists. Now, our planner gets what they need in 30 seconds." The ability to self-serve data for targeting is very possible when AI bridges the gap between data and decisions, every team becomes more agile, precise, and powerful.