Federal agencies are no longer content with isolated AI experiments that stall at the proof-of-concept stage—a common issue often referred to as “pilot purgatory.” The U.S. government is now driving a significant shift, requiring AI investments to move quickly from experimentation to measurable, mission-aligned outcomes that directly support agency objectives.
As emphasized during the Carahsoft AI for Government 2025 Summit, the focus has turned to governance, return on investment (ROI), and shared infrastructure as the pillars for scaling AI across federal agencies responsibly. Agencies are expected to prove the value of AI not just through innovation but through real-world impact, operational efficiency, and public accountability.
From Pilots to Missions: Why ROI Must Be Proven in Every AI Project
Federal agencies are now required to move beyond isolated AI pilots and demonstrate measurable, mission-driven ROI for every AI project. It is no longer acceptable to deploy AI without clear evidence of how it improves service delivery, operational efficiency, or cost savings. Projects that fail to show this value risk being deprioritized or defunded.
This shift is directly supported by the OMB Memo M-25-21, which mandates that all federal AI initiatives must deliver quantifiable outcomes that support agency missions. AI must now be tied to specific, trackable performance indicators and align with the Federal Data Strategy to ensure responsible, ethical use of data.
A proven example comes from the U.S. Navy, which implemented an AI monitoring platform to automate model oversight. This reduced model update cycles by 97%, significantly improving operational speed and resource efficiency. For federal agencies to scale AI effectively, ROI measurement and continuous performance tracking must be integrated from the beginning. Defining success criteria early and maintaining governance throughout the AI lifecycle are now essential for moving from pilots to fully operational, impactful deployments.
Building Trust: The Critical Role of AI Governance in Federal Scaling
Building trust is essential for scaling AI across federal agencies, and that trust must be anchored in strong, transparent governance. Agencies are now expected to fully operationalize the NIST AI Risk Management Framework (AI RMF) to ensure AI systems are safe, reliable, and aligned with public values. Governance boards, ethics reviews, and detailed, transparent documentation are no longer optional—they are critical components of every federal AI deployment.
Standardized governance frameworks guide this process. The NIST AI RMF provides a structured approach for identifying, assessing, and managing AI risks across the lifecycle. This is reinforced by executive orders on trustworthy AI, which require fairness, accountability, and explainability in all federal AI systems.
To support scalable oversight, the Unified Control Framework (UCF) is emerging as a key tool for cross-agency alignment. It allows agencies to manage multiple compliance requirements efficiently, ensuring that privacy, security, and ethical standards are consistently applied across diverse AI initiatives.
Without strong governance, federal AI programs cannot build or sustain public trust. Agencies must embed governance from the start, not as a final check, but as a core operational requirement at every stage of development and deployment.
Shared Sandboxes: Accelerating Safe, Responsible AI Deployment
Shared sandboxes are multi-tenant, phased environments—typically structured as development, test, and production stages—that allow agencies to prototype, validate, and deploy AI solutions within secure, controlled settings. These enable agencies to experiment with AI while minimizing operational risks and ensuring policy compliance from the outset.
For federal agencies, shared sandboxes offer several critical benefits:
- Controlled risk: Secure environments minimize the chance of unintended consequences during AI testing.
- Centralized licensing and infrastructure: Reduce costs by avoiding duplication of software, hardware, and resources across teams and agencies.
- Rapid experimentation: Enable faster AI development cycles while maintaining necessary oversight and governance.
- Built-in governance guardrails: Ensure policy compliance, ethical standards, and security controls are enforced throughout the AI lifecycle.
- Cross-agency accessibility: Facilitate collaboration by providing a shared space for multiple teams to develop and test AI solutions safely.
Successful implementations include the Burnes Center sandbox model, which has helped state and local agencies securely test AI applications aligned with public sector priorities. Similarly, the Microsoft Federal Responsible & Secure AI Sandbox provides federal agencies with secure environments for evaluating AI tools under strict governance and security controls. These examples demonstrate that shared sandboxes are more than just technical solutions—they are strategic enablers for scaling AI responsibly across the federal landscape.
Shared Infrastructure: Building AI-Ready Government Foundations
Shared infrastructure is a critical foundation for scaling AI across federal agencies. It typically includes centralized compute environments, composable orchestration platforms, and standardized development, testing, and production pipelines. These components allow agencies to deploy AI solutions efficiently, with consistent security, performance, and governance controls.
Centralized infrastructure helps agencies avoid redundant investments by consolidating compute resources and enabling multiple teams to access shared AI environments. Composable orchestration platforms provide modular, scalable tools that integrate AI lifecycle management, monitoring, and human-in-the-loop checkpoints. Standardized pipelines ensure that AI systems can move from development to production quickly while maintaining security and compliance at each phase. Key enablers of this approach include the General Services Administration’s Technology Modernization Fund (TMF), which offers federal agencies dedicated funding to modernize legacy systems and invest in AI-ready infrastructure.
For long-term success, AI infrastructure must also align with the Federal Enterprise Architecture to ensure interoperability across systems and agencies. This alignment supports seamless data exchange, consistent governance, and the scalability needed for enterprise-level AI deployment.
Conclusion: From Purgatory to Progress
The era of indefinite AI pilots in the federal government is over. Agencies must now deliver proven, mission-driven AI deployment outcomes and demonstrate clear ROI to sustain AI investment. Moving forward requires shifting from small-scale experiments to fully operational systems that drive measurable improvements in efficiency, service delivery, and public trust. By investing in governance, shared sandboxes, and scalable infrastructure, agencies can deploy AI solutions that are both fast and accountable. These strategies enable federal leaders to safely accelerate AI adoption while ensuring security, ethical integrity, and alignment with public service priorities.
Federal agencies must now balance urgency with responsible oversight. The path is clear: move quickly, govern effectively, and focus on sustainable, mission-aligned AI at scale. To successfully navigate this journey, federal leaders can benefit from the right expertise and technology partnerships.
TechSur Solutions is ready to help your agency build scalable, compliant AI environments that deliver real impact. Connect with TechSur today to advance your AI mission with confidence.