Scaling Mission AI: 3 Lessons for Public Sector

Public sector organizations face growing pressure to adopt AI, yet many struggle to move beyond pilots to scalable, mission-critical impact. At Snowflake Summit, leaders highlighted that the foundation for valuable AI—data infrastructure, governance, and organizational alignment—must be established before deploying models. Without this groundwork, AI initiatives risk exposing flaws in data quality and reliability, undermining their potential benefits.

The University of Florida discovered only 10-15% of its data was semantically defined when exploring AI, rendering insights unreliable. To address this, the team developed a process using NaviGator AI and Snowflake components to generate natural language definitions at scale. This effort, estimated to take 83 weeks manually, enabled the creation of Navigator Insights—a conversational analytics tool within Snowflake that allows natural language queries and reveals data trends affecting students.

For Leidos, a defense and government services company, scaling AI required shifting from siloed pilots to enterprise-grade solutions. Chief Data and Analytics Officer Alan Sim found that aligning AI projects with leadership priorities and rethinking problem-solving approaches—such as distinguishing between automation and process re-engineering—was key to transitioning from pilots to production. This discipline separates scalable AI from isolated experiments.

General Dynamics Mission Systems improved its sales quote processing system by moving from a training-based approach to zero-shot prompting with Snowflake Cortex AI, boosting accuracy from 50% to 85%. The focus remained on tangible outcomes like speed, accuracy, and cost savings in a workflow critical to mission delivery, rather than model performance alone.