Federal AI Use Soars, But Bottlenecks Threaten Momentum Amid Public Skepticism: Brookings

The use of artificial intelligence within the U.S. federal government has increased dramatically in recent years, but significant obstacles—from talent shortages to public skepticism—are slowing responsible integration of the technology into government services, according to a new U.S. government report. Brookings Institution.

Wednesday’s report is based on inventories of AI use cases from 2023 to 2025, federal jobs data, Office of Management and Budget memorandums and interviews with current and former federal technologists from eight agencies.

The numbers tell a story of rapid acceleration. In 2025, 41 agencies documented more than 3,600 individual AI use cases – 69% more than the total reported in 2024 and five times the number reported in 2023. The applications span a wide range of government functions: more than half of the Social Security Administration’s reported use cases support service delivery and benefits processing, while more than half of the Department of Justice’s inventory supports law enforcement efforts.

Yet growth is far from evenly distributed. Over the past three years, five major agencies accounted for more than half of all reported AI use cases, and major agencies contributed 76% of the total inventory in 2025. Smaller agencies are barely keeping pace: The eleven small agencies reporting in 2025 collectively submitted only 60 use cases, representing just 2% of the total inventory.

The report identifies several structural barriers that prevent broader adoption. One of the most pressing is the lack of specialized talent. Of the more than 56,000 technical vacancies posted by the federal government since 2016, just over 1,600 – less than 3% – explicitly refer to AI capabilities.

A Biden-era workforce surge was intended to close this gap, but workforce reductions in early 2025 may have undermined these efforts, as at least 25% of AI-specific job openings were posted as of 2024 — meaning many of these newly hired workers could have been among the most recent and easily laid off.

In addition to staffing levels, the report points to an entrenched culture of risk aversion within federal agencies. Nearly 60% of all AI use cases are in the pilot or pre-deployment phase, indicating that the federal AI landscape is still in a rapid growth phase – a phase that requires dedicated time for education and experimentation that many agencies may find difficult. The report also notes that the Trump administration’s explicit linking of AI deployment to the workforce is hindering employment Ministry of Government Efficiency (DOGE) can reinforce this hesitation.

Another concern is the lack of accountability. More than 85% of all high-impact AI use cases deployed in 2025 lack any required mitigation information, despite explicit requirements from OMB.

Public trust remains a challenge. According to recent data from the Pew Research Center, about half of Americans now say they are more concerned than excited about the growing prominence of AI, up from 37% four years ago, and only 17% of the American public believes AI will have a positive impact on the US over the next 20 years.

The report warns that the stakes are high. Public confidence in the federal government remains at an all-time low, with recent data showing that only 16% of Americans say they trust Washington to do the right thing most or almost all of the time. Against that backdrop, the authors argue that poorly executed AI implementations can cause serious damage, but that well-designed applications focused on tangible service improvements can conversely help restore trust in government institutions.

To achieve that, Brookings recommends expanding AI literacy training across agencies, reforming procurement rules designed for more static software systems, strengthening transparency practices around risky AI uses, and prioritizing use cases that deliver clear, positive benefits to the public.