AI Forward Series: From Pilot Programs to Real Government Operations

As agencies move to implement AI throughout their organizations, most are finding their AI efforts disrupted by fragmented, low-quality, or inaccessible data. This is a problem government shares with the private sector – a recent survey found that 79% of respondents said their AI initiatives are being hindered by limited access to data across environments.

These are not new problems for the government. There are still issues with data fragmentation and silos, poor data quality, complexities in applying governance and security requirements, and technical debt – legacy systems aren’t designed for modern analytics needs. And. of course, the pace of data generation continues to accelerate, adding to the pressure to clean and restructure massive numbers of datasets. That recent survey found that 60% of AI projects may be abandoned due to poor data readiness.

Join us as thought leaders in government and industry discuss their experiences addressing data readiness, the tradeoffs between speed and control, their data ownership and governance challenges, and their first-hand experience balancing policy expectations with the reality of “trusted AI.”