Public sector agencies are under immense pressure to modernize for AI-driven operations, yet leadership is still forced to make critical mission decisions based on fragmented data. The reality is clear: the AI revolution is a data evolution, and current AI ambitions are outpacing data readiness. Innovation stalls because agencies are held back by complex, fragmented environments; true mission-readiness only exists when enterprise systems are securely connected, timely, and governed.

By breaking down these silos and establishing a trusted single source of truth, organizations can provide AI agents with secure, contextual data. As the public sector transitions from basic digital services to agentic workflows, establishing this secure context is no longer optional—it is the only way to move from reactive operations to proactive, autonomous outcomes. Viewers will learn how government leaders operationalize trusted context from generative "answers" into intelligent "actions."

This session will explore the three-stage structural framework designed to move public sector data from trapped to transformed:

● Unlock: Breaking down technical and bureaucratic silos. Whether data resides in legacy mainframes or modern cloud environments, agencies must ensure information is secure, accessible, and available for the mission in real time.

● Trust: Moving from probability to certainty. This stage focuses on data provenance, lineage, and deduplication—ensuring records are validated and that both human operators and AI agents have the proper authorization to view them.

● Activate: Putting data into motion. By feeding a continuous cycle of trusted context directly into everyday workflows, analytics, and service-delivery applications, agencies can confidently empower both their workforce and tomorrow's digital agents.