Why Federal AI Efforts Are Falling Behind: The Hidden Data and Infrastructure Gap
REDWOOD CITY, CA, UNITED STATES, February 5, 2026 /EINPresswire.com/ -- As federal agencies accelerate efforts to adopt artificial intelligence, a new report finds that data maturity and infrastructure are among the major constraints holding agencies back from scaling AI beyond pilot programs.
The Federal AI Readiness Gap: Insights on Federal Infrastructure Challenges and the Way Forward, commissioned by MinIO, the data foundation for enterprise AI and conducted in partnership with GovNavigators, draws on input from federal data, IT, and infrastructure leaders across multiple cabinet agencies and independent organizations. The findings reveal a government eager to deploy AI, but often lacking the foundational data governance, storage, and compute capabilities required to do so responsibly and at scale.
“Agencies are not short on AI ambition,” said Cameron Chehreh, President and General Manager, MinIO Government. “What they lack are the data inventories, lineage, governance structures, and infrastructure clarity needed to move from experimentation to enterprise deployment.”
“Our research confirms what a lot of sources are telling us: agencies aren’t getting access to AI like they should,” said GovNavigators President and former Senior Advisor to the Federal CIO Council Adam Hughes. “And ready access to secure data is just one of the reasons.”
Key Findings
The report identifies several consistent themes across agencies:
* AI adoption remains largely confined to pilots, with limited enterprise-wide implementation.
* Infrastructure constraints, especially compute capacity and secure storage, are the dominant bottlenecks.
* Many agencies lack a clear understanding of total cloud cost of ownership, creating financial risk as data volumes grow.
* Data maturity remains uneven, with some agencies unable to fully identify or govern their data assets.
* Data custody and sovereignty are emerging as decisive considerations, reshaping how agencies evaluate cloud and hybrid architectures.
These findings closely align with recent Government Accountability Office (GAO) reporting on generative AI, which cited limited access to advanced computing resources, funding gaps, and workforce challenges as systemic barriers to federal AI adoption.
Operational Pressures Are Rising
Respondents indicated that mission operations, fraud detection, and customer service functions are expected to place the greatest near-term strain on infrastructure. Interviews conducted as part of the research suggest that the challenge is less about raw data volume and more about data complexity, sprawl, and weak governance across hybrid environments. Agencies today operate across traditional on-prem systems, multiple commercial clouds, SaaS platforms, and legacy file shares—often without unified architectural oversight or governance.
Implications for Federal Agencies and Industry
The report concludes that meaningful AI adoption will require agencies to prioritize foundational investments before scaling advanced capabilities.
Key recommendations include:
* Establishing enterprise data catalogs and lineage systems
* Designing hybrid architectures that support secure, bidirectional data movement
* Developing internal workforce pathways for modern data and AI operations
* Implementing more rigorous frameworks for evaluating cloud costs
For industry partners, the research signals a need to move beyond abstract architectures and proprietary solutions, toward offerings that emphasize portability, transparency, and alignment with federal workforce realities.
Download the full report to see additional data and insights.
About the Research
The research is based on structured questions and qualitative interviews with federal officials responsible for data, analytics, storage, and infrastructure at 14 of the federal government’s largest departments and agencies, including participants from the Departments of Agriculture, Commerce, Housing and Urban Development, and Treasury, as well as NASA, the Social Security Administration, and the Small Business Administration.
About MinIO
MinIO is the data foundation for enterprise AI. Built for exascale performance and limitless scale, MinIO AIStor delivers a secure, sovereign, and AI-ready data store that spans from edge to core to cloud. With rampant adoption across the Fortune 100 and 500, MinIO is redefining how organizations and government agencies store, manage, and mobilize all of their data in the AI era. MinIO is backed by Jerry Yang's AME Cloud Ventures, Dell Technologies, General Catalyst, Index Ventures, Intel Capital, Softbank Vision Fund 2 and others. Learn more at: https://www.min.io/
About GovNavigators
GovNavigators helps organizations understand evolving federal management policies and position themselves to deliver results across government. Learn more at: https://www.govnavigators.com/
The Federal AI Readiness Gap: Insights on Federal Infrastructure Challenges and the Way Forward, commissioned by MinIO, the data foundation for enterprise AI and conducted in partnership with GovNavigators, draws on input from federal data, IT, and infrastructure leaders across multiple cabinet agencies and independent organizations. The findings reveal a government eager to deploy AI, but often lacking the foundational data governance, storage, and compute capabilities required to do so responsibly and at scale.
“Agencies are not short on AI ambition,” said Cameron Chehreh, President and General Manager, MinIO Government. “What they lack are the data inventories, lineage, governance structures, and infrastructure clarity needed to move from experimentation to enterprise deployment.”
“Our research confirms what a lot of sources are telling us: agencies aren’t getting access to AI like they should,” said GovNavigators President and former Senior Advisor to the Federal CIO Council Adam Hughes. “And ready access to secure data is just one of the reasons.”
Key Findings
The report identifies several consistent themes across agencies:
* AI adoption remains largely confined to pilots, with limited enterprise-wide implementation.
* Infrastructure constraints, especially compute capacity and secure storage, are the dominant bottlenecks.
* Many agencies lack a clear understanding of total cloud cost of ownership, creating financial risk as data volumes grow.
* Data maturity remains uneven, with some agencies unable to fully identify or govern their data assets.
* Data custody and sovereignty are emerging as decisive considerations, reshaping how agencies evaluate cloud and hybrid architectures.
These findings closely align with recent Government Accountability Office (GAO) reporting on generative AI, which cited limited access to advanced computing resources, funding gaps, and workforce challenges as systemic barriers to federal AI adoption.
Operational Pressures Are Rising
Respondents indicated that mission operations, fraud detection, and customer service functions are expected to place the greatest near-term strain on infrastructure. Interviews conducted as part of the research suggest that the challenge is less about raw data volume and more about data complexity, sprawl, and weak governance across hybrid environments. Agencies today operate across traditional on-prem systems, multiple commercial clouds, SaaS platforms, and legacy file shares—often without unified architectural oversight or governance.
Implications for Federal Agencies and Industry
The report concludes that meaningful AI adoption will require agencies to prioritize foundational investments before scaling advanced capabilities.
Key recommendations include:
* Establishing enterprise data catalogs and lineage systems
* Designing hybrid architectures that support secure, bidirectional data movement
* Developing internal workforce pathways for modern data and AI operations
* Implementing more rigorous frameworks for evaluating cloud costs
For industry partners, the research signals a need to move beyond abstract architectures and proprietary solutions, toward offerings that emphasize portability, transparency, and alignment with federal workforce realities.
Download the full report to see additional data and insights.
About the Research
The research is based on structured questions and qualitative interviews with federal officials responsible for data, analytics, storage, and infrastructure at 14 of the federal government’s largest departments and agencies, including participants from the Departments of Agriculture, Commerce, Housing and Urban Development, and Treasury, as well as NASA, the Social Security Administration, and the Small Business Administration.
About MinIO
MinIO is the data foundation for enterprise AI. Built for exascale performance and limitless scale, MinIO AIStor delivers a secure, sovereign, and AI-ready data store that spans from edge to core to cloud. With rampant adoption across the Fortune 100 and 500, MinIO is redefining how organizations and government agencies store, manage, and mobilize all of their data in the AI era. MinIO is backed by Jerry Yang's AME Cloud Ventures, Dell Technologies, General Catalyst, Index Ventures, Intel Capital, Softbank Vision Fund 2 and others. Learn more at: https://www.min.io/
About GovNavigators
GovNavigators helps organizations understand evolving federal management policies and position themselves to deliver results across government. Learn more at: https://www.govnavigators.com/
Adam Hughes
GovNavigators LLC
+1 202-320-3254
ahughes@govnavigators.com
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