AI is revolutionizing the public sector in the U.S., impacting areas such as city planning, cybersecurity, and citizen services. Despite visionary applications, the primary obstacle to AI adoption is not funding or skills, but integration issues with legacy infrastructure. Many agencies experience stalled AI projects due to fragmented data locked in silos across various systems. Effective deployment of AI relies heavily on a robust and integrated data infrastructure, as integration is essential for the successful scaling of AI initiatives in government functions.
According to NetApp's AI Space Race survey, the top barrier to AI adoption isn't imagination, skill gaps or cost. The challenge is integration - specifically, connecting new AI systems with existing legacy infrastructure.
Government data is often locked in silos: some stored on legacy systems, others spread across the cloud or on-premises environments. These silos aren't just inconvenient; they actively block AI systems from scaling effectively.
AI is only as effective as the infrastructure supporting it. For the U.S. public sector, the difference between a successful AI deployment and a stalled one often comes down to this fundamental truth.
Integration is treated as a technical "backend" detail left for IT specialists to figure out later. But that approach creates systemic inefficiencies, especially when government agencies need secure, reliable performance across complex workflows.
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