Public sector agencies face escalating pressures to leverage generative AI for improved service delivery and operational efficiency. However, they grapple with data silos, inconsistencies, and biases that hinder the reliability of AI-generated insights. This is compounded by increasing mandates for data transparency, accountability, and security, as outlined in frameworks like the Federal Data Strategy and the NIST AI Risk Management Framework. These mandates accelerate the need for agencies to modernize their data management practices to effectively support AI initiatives.

Attendees were able to learn how to:

• Understand the critical role of data quality and governance in achieving reliable GenAI outputs
• Determine how to mitigate bias, inaccuracies, and sensitive data risk with fit-for-purpose data feeds to LLMs
• Discover how to implement self-service analytics with a modern, governed AI platform, aligning with government mandates
• Explore how to streamline application modernization and cloud migration to support advanced analytics and AI initiatives