By Anand Trivedi, REI Systems’ AI Offering Lead
The White House Office of Science and Technology Policy recently released Winning the AI Race: America’s AI Action Plan (July 2025), outlining the United States’ strategic roadmap for global leadership in artificial intelligence. Structured around three pillars—innovation, infrastructure, and international leadership—the plan calls on federal agencies to treat AI as a core enabler of modern governance, mission execution, and national competitiveness.
At REI Systems, we see this as a natural alignment with our longstanding mission: to help agencies move beyond pilots and meaningfully drive AI innovation—safely, responsibly, and with measurable mission outcomes. Over the years, REI has partnered with agencies such as GSA, FDA, HRSA, NASA, USPTO, and OCC–Treasury, helping them modernize operations, make data more actionable, and bring advanced technology into production environments. The AI Action Plan validates many of these priorities and provides a federal framework to scale them across government.
Pillar 1: Accelerating Innovation Across Government
The Action Plan emphasizes one clear priority: move fast but responsibly. Federal agencies are encouraged to streamline procurement, remove unnecessary regulatory barriers, and demonstrate leadership in adopting artificial intelligence.
Key takeaways for agencies:
Adopt Open, Interoperable AI Tools
To avoid vendor lock in and foster innovation, the plan urges agencies to adopt open-source solutions and shared infrastructure. This supports broader goals of transparency, reusability, and alignment with OMB’s AI policy guidance. The plan also favors open model weights and code reusability to strengthen collaboration across agencies and sectors.
Upskill the Workforce
Building internal AI fluency is essential. This includes training current staff, attracting AI capable talent, and developing cross- functional understanding of AI’s potential and limitations. The plan also calls for broader workforce policies such as government–industry talent exchanges, apprenticeships, and STEM curriculum development to build a sustainable AI-ready labor pipeline.
Advance High Impact Use Cases
Agencies should begin by identifying their most promising use cases, but also ensure that each use case is governed by appropriate evaluation standards and oversight. Regulatory sandboxes will allow mission owners to safely test AI capabilities in sensitive areas such as healthcare and financial oversight. At the same time, procurement guidance and toolboxes will make it easier for contracting officers to access responsible AI solutions..
Bottom line: Innovation is no longer aspirational. It is foundational. But it must be implemented with transparency, trust, and structure.
Pillar 2: Building the Infrastructure to Scale AI
Artificial intelligence cannot thrive on outdated systems. The Action Plan underscores the need for a modern, secure digital backbone capable of supporting compute intensive workloads and enterprise scale adoption.
What this means for agencies:
Modernize IT Environments
Agencies should assess current infrastructure for scalability, performance, and compatibility with modern AI applications. Cloud based, modular architectures will better support evolving data and processing demands.
Ensure Secure and Resilient Systems
Agencies must plan now for where and how they will host their most sensitive AI workloads, adopting secure-by-design standards to protect against emerging risks such as model poisoning or adversarial attacks. Agencies are also expected to integrate AI incident response into their cybersecurity frameworks and participate in AI-specific information-sharing networks.
Plan for Strategic Investment
The plan calls for national investments in computing power, energy capacity, and data infrastructure. Agencies should proactively evaluate and prepare for funding and partnership opportunities to expand AI capabilities. It also prioritizes fast-tracked regulatory approvals for semiconductor plants and power grid modernization, acknowledging the foundational role of compute infrastructure.
Agencies in states that impose burdensome AI regulations may also be ineligible for future federal AI funding, per the plan’s proposed conditional funding mechanism.
Agencies that prioritize scalable, secure, and future ready infrastructure will be best positioned to meet mission demands and deliver long term impact.
Pillar 3: Leading in Global AI Standards and Security
AI is not only a domestic priority—it is a strategic global asset. The Action Plan outlines how the United States must lead in setting international AI norms, promoting democratic values, and safeguarding critical technologies.
Agencies can support this by:
Protecting Sensitive Models and Data
Agencies are expected to protect U.S. leadership in AI by aligning with national export-control policies, contributing to international standards-setting bodies, and safeguarding against security risks such as misuse of frontier AI models in biosecurity or defense contexts.
Coordinating with International and Security Partners
Agencies that engage internationally—whether through science, trade, or regulation—must ensure their AI collaborations reflect U.S. values of openness, fairness, and security. The plan emphasizes tighter export controls and protection of intellectual property, especially for cutting edge AI chips and software, to prevent adversarial acquisition of U.S. technologies.
It also directs updates to NIST’s AI Risk Management Framework to remove ideological terms such as DEI or misinformation, reinforcing the administration’s focus on perceived neutrality and objectivity in government AI systems.
This pillar reinforces that leadership in AI requires not only innovation, but also trust, transparency, and global alignment.
Next Steps: A Framework for Agency Readiness
Agencies cannot afford to treat the AI Action Plan as an abstract vision—it demands concrete action. REI recommends an eight-step readiness framework:
Mission-First Use-Case Portfolio: Identify a handful of high-value, feasible AI use cases per bureau—mixing “quick wins” with transformative “lighthouse” initiatives.
Governance and Roles: Empower the Chief AI Officer, establish an AI Steering Group, and align with NIST evaluation and risk frameworks.
Data Foundations: Build AI-ready datasets with strong lineage, privacy controls, and quality standards; contribute to shared national datasets where possible.
Procurement and Platforms: Leverage GSA’s AI procurement toolbox and design multi-model platforms with policy-as-code guardrails.
Evaluation and Monitoring: Stand up evaluation labs with pre-deployment tests, domain red-teaming, and continuous monitoring dashboards.
Security and Resilience: Adopt secure-by-design patterns, participate in AI-ISAC, and integrate AI incident response into existing cybersecurity playbooks.
Workforce Enablement: Provide broad but governed access to frontier models, with role-based training and apprenticeship pathways for AI infrastructure roles.
Scaling and Benefits Tracking: Track cycle-time, accuracy, satisfaction, and cost-to-serve metrics; promote cross-agency wins and reinvest savings into new use cases.
REI’s Role in the Federal AI Journey
At REI Systems, we see the AI Action Plan not as a challenge but as a roadmap for action. Our value lies in bridging policy, technology, and mission execution:
Strategy and Governance: We help agencies establish CAIO-led operating models, build AI use-case inventories, and define policy frameworks aligned to NIST and OMB guidance.
Data and Platforms: We design AI-ready data pipelines, secure enclaves, and multi-model platforms that combine open-weight and closed models responsibly.
Evaluation and Assurance: We establish evaluation labs that cover benchmarking, fairness, red-teaming, and continuous monitoring.
Security and Incident Response: We embed secure-by-design practices and connect agencies to AI-specific information-sharing communities.
Workforce and Change Management: We deliver role-based training and help staff use AI responsibly—whether that means clinical evidence synthesis at FDA, acquisition copilots at GSA, or examiner support tools at USPTO.
Program Delivery at Scale: We ensure that successful pilots transition into full production environments with measurable service improvements.
Conclusion: From Planning to Deployment
The National AI Action Plan is not just guidance—it is a national mandate for action.
Agencies must now move from planning to implementation by aligning infrastructure, workforce, data strategy, and governance. When done right, AI can become a powerful tool for solving complex public challenges and improving service delivery across government.
By combining responsible innovation with technical execution, agencies can build sustainable AI programs that deliver value and maintain public trust.
REI Systems is committed to supporting this transition with practical, secure, and results driven solutions that meet the evolving needs of federal missions.
The opportunity is here. Let’s shape what comes next, together.
