Agentic AI is poised to redefine how value is created from data by enabling new capabilities, accelerating insights, and expanding the reach of data-driven decision-making. Data engineers will experience a disruption in routine tasks, but their roles will shift towards more strategic activities, focusing on designing overall data architectures, managing data platforms for performance and governance, building business automations based on data insights, and acting as enablement and support for the insight generation process. Data scientists will redefine their role, shifting from solely solving predefined problems to proactively framing complex business challenges, requiring a deeper understanding of business nuances and upskilling in areas like agentic AI frameworks, reinforcement learning, and multimodal AI. Data analysts will enhance productivity by automating tasks and speeding up insights generation, while business users will benefit from natural language querying, powered by AI and semantic layers, enabling non-technical users to explore data and discover insights through simple questions. Agentic AI promises to disrupt traditional manual data governance processes, automate access control, policy enforcement, regulatory compliance, and data security, and enhance data trust by automating data quality validation, assigning trust scores to datasets, and ensuring regulatory compliance. Ultimately, AI agents will drive a significant evolution in data roles and the data lifecycle, automating many existing tasks, enhancing efficiency and capabilities, and enabling new ways of interacting with and leveraging data that were previously impractical or impossible.