Data teams have long aimed to facilitate self-service analytics, but traditional methods often resulted in bottlenecks and frustration due to slow response times. The emergence of AI technologies has shifted this dynamic by enabling team members to independently conduct analyses and answer questions using natural language processing, although it introduces risks of potential inaccuracies. Despite the inherent unpredictability of large language models (LLMs), they empower users to quickly obtain insights, reducing dependency on data teams and allowing these teams to focus on more complex strategic issues. To support this AI-driven self-service model, there is a need for well-structured data systems, comprehensive documentation, and clearly defined semantic layers to ensure consistency and accuracy. This evolution highlights the data team's role as architects of reliable systems rather than mere gatekeepers of information, ultimately broadening the impact of data across the organization and encouraging more strategic use of analytics.