Agencies are confronting a challenge as federal AI initiatives continue to scale. The success of this ever-growing list of initiatives relies less on the AI model itself and more on connecting to distributed enterprise data scattered across legacy systems, cloud platforms, and SaaS applications. The traditional solution has been to centralize and consolidate that data, but this approach is more difficult sustain as AI demands real-time access to constantly changing mission data, and every new pipeline also adds cost and complexity. Universal connectivity – a foundation that enables secure, reusable access across distributed systems to deliver context without the need to rebuilt architecture each time - is emerging as a viable alternative.

In this episode of the Government Technology Insider podcast, host Lucas Hunsicker spoke with Kevin Bohan, Director of Product Marketing at Denodo, about what it takes to build an AI data layer. Bohan explained why treating consolidation as the default answer for every new AI initiative doesn’t solve the underlying problem, and how legacy infrastructure and fragmented systems leave AI with only a partial view of people, assets, and missions. He also discussed how reusable, secure connectivity, rather than one-off point-to-point integrations, gives AI the operational context it needs to deliver accurate, cost-effective results, and why this AI data layer is what allows evolving AI technologies reach distributed enterprise data without constant rebuilding.