America's AI Action Plan outlines a comprehensive strategy for the nation's leadership in AI. Part of this plan is aimed at accelerating the adoption of AI in the federal government. But there are gaps in that vision. Government agencies have been slow to adopt AI tools to better serve their citizens. The biggest barrier to deploying and scaling trustworthy AI is not policy or computational power, but the underlying infrastructure. How government stores, accesses, and manages records will determine whether AI succeeds or stalls. These records are not just for storage. These are the fuel needed for AI models to increase operational efficiency through streamlined workflows and uncover mission insights that enable timely and accurate decision-making. Without robust digitization and data governance, federal records cannot serve as the reliable fuel that AI models need to drive innovation.
Before AI adoption takes hold, agencies need to do something less glamorous, but absolutely essential: modernize their records. Many companies still need to automate record management, starting with opening archive boxes, assessing their contents, and deciding what's worth keeping. This critical process transforms inaccessible, unstructured records into structured, connected datasets that AI models can actually use. Without this, agencies will not only delay AI adoption but also build on a weak foundation that crumbles under the weight of daily mission demands.
If you don't know what's inside the box, how sure can you be that its records aren't important for AI-powered process automation? From an AI perspective, with the help of models like OpenAI, the results are only as good as the digitized data behind them. The larger the knowledge base, the faster AI can be deployed and scaled to positively impact public services. Here, agencies can begin preparing their records (knowledge bases) to lay a defensible foundation for AI deployment.
Step 1: Inventory and prioritize what you already have
Many government agencies store decades of records in a mix of storage boxes, shared drives, aging databases, and poorly managed digital repositories. These records often lack consistent metadata, classification tags, and digital traceability, making them difficult to search, difficult to manage, and nearly impossible to automate.
This fragmentation is not new. According to NARA's 2023 FEREM report, only 61% of institutions were rated as low risk in electronic records management, indicating that many institutions still face gaps in easily accessible records, digitization, and data governance. This leaves thousands of unstructured repositories vulnerable to security risks and unable to be fed into AI models. A comprehensive inventory allows agencies to review their holdings, determine what is mission-critical, and prioritize record cleanup. Not everything needs to be digital. But everything needs to be considered. This early triage allows digitization, automation, and analytics to focus on the right things, maximizing benefits while minimizing risk.
Without this step, agencies risk building powerful AI models based on unreliable data, a setup that undermines success and introduces compliance pitfalls.
Step 2: Make digitalization the foundation of modernization
One of the biggest misconceptions about modernization is that digitalization is a tactical compliance task with limited strategic value. In reality, digitization is about turning idle content into usable data. This is the gateway to AI-driven automation across government agencies, including one-click records management and data-driven policymaking.
By focusing on high-impact records, records that intersect with mission-critical workflows, freedom of information laws, cybersecurity enforcement, and policy enforcement, agencies can go beyond just being compliant and begin building a foundation for the future. These records form the connective tissue between systems, employees, data, and decision-making.
The Government Accountability Office estimates that up to 80% of the federal government's IT budget is still spent maintaining legacy systems. Reallocating resources could help fund strategic digitization and deliver real efficiency gains. The opportunity cost of delay increases exponentially every day.
Step 3: Align records governance with your AI strategy
Modern AI adoption is not just about models and calculations. It's about trust, traceability and compliance. That's why strong information governance is essential.
Government agencies that are the earliest to work on AI are combining records management modernization with evolving governance frameworks to synchronize classification structures, retention schedules, and access controls with broader digital strategies. The Office of Management and Budget’s 2025 AI Risk Management Guidance is clear. Explainability, reliability, and auditability must be built in from the beginning.
When AI adoption evolves in tandem with careful records management programs centered around data governance, government agencies can accelerate innovation, build public trust, and avoid costly rework. For example, labeling records with standardized metadata from the start enables rapid digital search during audits and investigations. This need will only grow as the use of AI expands. This alignment is critical as government agencies adopt FedRAMP Moderate certified platforms to run sensitive workloads and meet compliance requirements. These platforms provide a higher baseline of performance and security, but that only matters if the data passing through them is available, well-managed, and trusted.
Infrastructure Integrity: The Hidden Foundation of AI
Strengthening your digital backbone is only half of the modernization equation. Agencies must also ensure that the physical infrastructure supporting their systems can withstand increased operational, environmental, and cybersecurity demands.
Colocation data centers play a critical role in this continuity, providing a secure, federally compliant environment that protects sensitive data and maintains uptime for mission-critical systems. These capabilities provide the stability, scalability, and redundancy needed to sustain AI-driven workloads, bridging the gap between digital transformation and operational resiliency.
By combining strong information governance with resilient colocation infrastructure, agencies can build a true foundation for AI, ensuring that innovation is not only possible, but sustainable, even in the most complex mission environments.
Melissa Carson is the General Manager of Iron Mountain Government Solutions.
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