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AI in the Federal Government: A Fragmented Reality

Despite bipartisan presidential directives and significant investment, federal AI use remains limited by siloed agency adoption. To realize the potential of AI, the federal government requires a more agile and comprehensive AI strategy.

U.S. President Donald Trump delivers remarks on artificial intelligence at the "Winning the AI Race" Summit in Washington D.C. Kent Nishimura/Reuters

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The United States aims for AI leadership, a goal underscored by presidential directives from both the Trump and Biden administrations. President Trump’s “Removing Barriers to American Leadership in Artificial Intelligence” Executive Order and President Biden’s Executive Order 14110, which directed all government departments to “develop AI strategies and pursue high-impact AI use cases,” underscore a bipartisan recognition of AI’s transformative potential. These directives have been accompanied by significant financial investment, with federal agencies spending $831 million on AI-related software contracts in 2023 alone—a figure poised for substantial growth. 

Despite this clear mandate and investment, the federal government’s implementation of AI remains an early work in progress. Our examination of the Department of Justice (DOJ) use cases highlights a fragmented AI strategy that can lead to inefficiencies and hinder information flows. Addressing this fragmentation presents an opportunity to leverage the benefits AI promises: improved efficiency, accelerated analysis, and enhanced decision-making for public service and national security. 

Federal Use Cases 

By the end of 2024, the Federal AI Use Case Repository listed over 2,100 supposed “use cases” across major government entities. However, this figure is likely inflated because individual AI systems are often conflated with actual use cases. For instance, the Department of Energy recorded an AI-assisted word processor three separate times as distinct “use cases,” even though it represents one. Our analysis will account for these categorization differences. 

DOJ: A Case Study in Fragmentation 

The Department of Justice, with 240 reported AI systems supporting approximately 100 to 110 unique use cases, exemplifies the challenges of fragmented AI implementation. These systems span diverse applications, from pattern recognition in violent crime data to predicting inmate security levels and transforming audio into text. While 70 percent of these systems are operational, a closer look reveals fragmentation across two critical dimensions: bureaus and workflows. 

  1. Fragmentation Across Bureaus: Less than five percent of the DOJ’s AI systems are department-wide, and almost 70 percent are confined to a single bureau. This may cause redundancies, with distinct AI systems serving similar functions in different parts of the organization. For instance, the record shows 12 different license plate reader systems and nine AI systems for audio and video transcription. This bureau-specific approach can add also friction when sharing information. Consider for example the FBI, DEA, ATF, and the Office of the Inspector General each use different license plate reading systems. Such fragmentation, while possibly reflecting organizational boundaries, can limit AI’s ability to effectively distribute crucial information, hindering collaboration, and comprehensive intelligence gathering.
  2. Fragmentation Across Workflows: Most AI systems are designed for narrow tasks, rather than being integrated across an entire workflow. Consider, for example, an FBI analyst investigating a criminal case. To analyze diverse evidence—photos, videos, documents, and public data—they might use as many as seven different AI systems: one for text extraction and translation, another for processing that information, three facial recognition tools, one for categorizing public data, and yet another for broader analysis. This approach, while effective for individual tasks, often requires manual and time-consuming data reformatting and transfer between systems, which can lead to inefficiencies and introduce errors.

These fragmentations, compounded by an outdated four-year-old AI strategy, are also costly as each individual system demands separate acquisition process, updates, support, and training, straining departmental budgets and personnel. 

Harnessing Artificial Intelligence in the Federal Government 

Advocating for AI adoption is not enough. To truly harness the power of AI, the federal government—especially agencies like the DOJ—needs a comprehensive and forward-looking strategy. This strategy must prioritize two critical areas: 

  1. Agencies must create acquisition and deployment pathways for AI systems that are both fast enough to keep pace with AI’s evolution and enable continuous AI fine-tuning as new data emerges, and visible across the organization to reduce inefficiencies.
  2. Agencies must reimagine workflows by shifting beyond task-specific AI. This means integrating AI across various tasks and bureaus, which helps to reduce inefficiencies and break down persistent information silos.

Addressing these elements will position the federal government agencies to maximize AI’s potential, improving the speed and quality of their decisions and actions, strengthening national security, and better serving the American public.