“The real value is when you give AI the right context for the decisions being made. What ends up happening is not only you’re getting more accurate actions being taken, more accurate responses coming back, but you’ll also find that it’s reducing costs.” – Kevin Bohan, Director of Product Marketing at Denodo.
Agencies face a connectivity problem as federal AI scales
Federal AI initiatives are multiplying, and with that growth comes a familiar but intensifying obstacle: the data those systems need is spread across legacy systems, cloud platforms, and SaaS applications. According to the Government Technology Insider podcast episode featuring Lucas Hunsicker and Kevin Bohan, the core challenge for agencies is not the AI model itself but the ability to connect to distributed enterprise data so AI can see people, assets, and missions with operational completeness.
Centralization as default — and why it is faltering
Historically, the answer has been to centralize and consolidate data into new pipelines or repositories. Bohan told Hunsicker that treating consolidation as the default for every new AI initiative “doesn’t solve the underlying problem.” The podcast argument is straightforward: consolidation is increasingly difficult to sustain as AI demands real-time access to constantly changing mission data, and every new pipeline adds both cost and complexity.
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See what we buildUniversal connectivity and the AI data layer
As an alternative, the episode lays out a different architecture: universal connectivity powered by an AI data layer. The term describes a foundation that enables secure, reusable access across distributed systems to deliver the context AI needs without rebuilding underlying architecture for each new project. Bohan framed this AI data layer as a way for evolving AI technologies to reach distributed enterprise data without constant rebuilding — a reusable plane of connectivity rather than a series of one-off, point-to-point integrations.
Operational context, accuracy, and cost implications
Bohan emphasized the operational consequences of providing or withholding context. When AI systems work from a partial view because legacy infrastructure and fragmented systems restrict access, the result is diminished accuracy and an incomplete picture of missions and assets. By contrast, reusable secure connectivity can give AI the operational context that leads to more accurate actions and responses while reducing the costs associated with building and maintaining multiple, bespoke pipelines. That trade-off — context and reusability for fewer bespoke integrations — was presented as the principal value of the AI data layer approach.
What this means for technologists, procurement leaders, and end users
- Technologists and security teams: They will need to prioritize secure, reusable connectors to legacy, cloud, and SaaS systems rather than repeatedly building point-to-point integrations; the episode frames this as the route to preserving real-time access without proliferating costly pipelines.
- Procurement leaders and program managers: They face a choice between funding repeated consolidation projects or investing in an AI data layer and universal connectivity that can be reused across initiatives — a decision the podcast ties directly to long-term cost and complexity.
- End users and mission owners: When AI receives richer operational context through universal connectivity, the episode argues users should see more accurate outputs and more cost-effective services, because the systems act on a fuller view of people, assets, and missions.
The conversation on Government Technology Insider presents universal connectivity not as a theoretical nice-to-have but as a practical alternative to the perpetual rebuild cycle that centralization encourages. Kevin Bohan’s summary — that giving AI the right context improves accuracy and cuts costs — is the through-line: connect, don’t reconsolidate.
The podcast leaves a clear implementation question in front of agencies and program leaders: will they continue to default to consolidation for each new AI effort, or will they adopt reusable connectivity and an AI data layer to deliver the context modern AI demands?
