As is every large organization, the U.S. government is assessing how to best integrate artificial intelligence into its procedures and workflows. While AI has undeniable risks, it also has the potential to make work significantly more efficient and effective in a broad range of ways, from automating simpler tasks to unearthing unexpected insights.
Over the past decade, the federal government has made the adoption of AI a priority. Both the Biden administration and the two Trump administrations have emphasized the need for federal government AI adoption to improve service delivery, foster data-driven analysis, promote national competitiveness, and strengthen national security.
New research from the Brookings Institution has found that while the scope and pace of this adoption have accelerated over the past three years, AI use across the federal government remains concentrated in a few large agencies More widespread adoption has been slowed by several factors, including workforce capacity constraints, a risk-averse culture, funding challenges, and a lack of trust in AI's usefulness and safety.
"While the federal government has made progress on using AI, there's still a long way to go," says Brookings fellow Valerie Wirtschafter, the author of the report. To understand the current state of AI adoption across the federal government, she analyzed data on federal government AI use from 2023 to 2025 as well as federal jobs data. In addition, she interviewed current and former technology specialists across eight federal agencies.
Over the past few years, AI use by the federal government has grown. More agencies are using it, and the amount they use it has also increased. In 2023, 21 agencies, including 13 large agencies and eight midsize agencies, reported using AI; no small agencies participated. By last year, 41 agencies (13 large, 17 midsize, and 11 small) reported AI use. In 2025, 41 agencies documented more than 3,600 distinct projects that used AI, a 69% increase from the previous year and five times the number reported in 2023. While many of these cases focused on streamlining operations and facilitating back-office processes, others involved more mission-oriented work, including benefits delivery, health and medical services, and law enforcement.
However, there are still significant disparities among agencies. Over the past three years, five agencies accounted for over half of the total AI use. In 2025, large agencies (more than 15,000 employees) accounted for more than three-quarters of all AI use. While more small and midsize agencies are starting to experiment with AI, large agencies are scaling their efforts more aggressively. It is important to note that overall, AI-focused workers continue to represent a small fraction of the overall federal technological workforce.
Wirtschafter identified several key bottlenecks to adoption. Some of these apply only to certain agencies, such as those handling sensitive health or security data. [NL1] Others stem from issues that have hindered federal adoption of technology for decades, such as outdated equipment and infrastructure.
Hiring challenges remain a key obstacle to integrating AI into federal agencies. Among the issues: The federal government has a slow hiring timeline, and limited pathways for career advancement for technologists. The Executive branch has rolled out efforts to improve hiring timelines, and Congress has explored possibilities for improving AI-focused hiring across agencies.
It is worth noting that since the second Trump administration laid off nearly 300,000 federal workers last year, the number of AI-focused federal job listings has dropped significantly, part of an overall decline in hiring. Wirtschafter argues that these layoffs may have undermined efforts to recruit AI expertise into the federal government because many recent hires were still probationary. She says that it's likely that the layoffs led to the departure of at least some AI-focused employees.
Moreover, the federal government tends to have a risk-averse culture that discourages experimentation and innovation. In addition, the opaqueness of AI processes—it's often unclear how a program came to its conclusions—can undermine trust and deter use, especially for sensitive work. Moreover, the growing politicization of some large language models (LLMs) is another challenge that could impede the adoption process. For example, Grok, developed by Elon Musk's xAI, has a well-documented history of reflecting his political values and generating questionable content, while Anthropic's Claude has been dubiously labeled a "supply chain risk" by the Department of Defense following contract disagreements with the agency.
Wirtschafter offers a series of recommendations to help the federal government more effectively adopt AI. These include:
- Streamlining the hiring process for AI-related jobs;
- Creating new job paths so AI-focused workers have a chance to advance;
- Investing in AI literacy and treating it as a core job requirement;
- Documenting and sharing AI success stories across the government;
- Increasing transparency around AI usage across agencies; and
- Focusing AI investment on high‑impact projects that clearly improve people's lives.
Read the full report here .
[NL1] which agencies?
