Most federal chief AI officers stay on the same career path, Brookings finds

What the Brookings analysis examined

The study comes from Kevin C. Desouza and colleagues at the Center for Technology Innovation, published through Brookings' TechTank series. It follows an earlier report on the backgrounds of federal chief AI officers (CAIOs), which found that the Biden and Trump administrations assembled different combinations of expertise to advance their approaches to AI governance. This time the authors looked at what happens after officials leave the post.

The team used data from Leadership Connect and publicly available professional biographies to examine the career histories of 55 current and former federal CAIOs. Because many current CAIOs are still in government, only 27 people had post-government careers sufficiently observable for a full analysis. According to the authors, the remaining officials offer a chance to test whether the patterns continue over time.

Three measures and three career archetypes

The researchers assessed each career along three dimensions:

• Domain continuity: whether the person's field of expertise, such as health care, cybersecurity, defense, finance, data management or technology policy, stays the same.
• Sector continuity: movement among government, private industry, academia and research, nonprofits and other levels of government. The framework deliberately separates sector moves from substantive career change.
• AI depth: whether AI becomes materially more central to the person's professional identity, responsibilities or later position.

From these, they defined three archetypes. Reinforcement means CAIO service strengthens an established path without changing the domain or the centrality of AI. Amplification means the person stays in the same domain but AI becomes materially more central. Inflection means the substantive domain after government differs meaningfully from the one before.

Findings: reinforcement dominates, inflection is rare

Fourteen of the 27 observable transitions were reinforcement, making it the most common outcome. These individuals kept essentially the same professional path, though some changed organizations or sectors. The authors say that what changes is the organization, not the expertise. They compare the pattern to an earlier study of successful chief information officers, who tended to evolve rather than dramatically change their careers.

Eleven of the 27 cases fit amplification. Technology leaders became AI transformation executives, data leaders took on broader enterprise AI responsibilities, and policy and governance specialists built stronger AI portfolios. The authors suggest the CAIO role can function as a career accelerator.

Only two cases showed a substantial change in professional direction after federal AI service. The researchers found no examples of complete transformational shifts across both professional field and institutional setting, and several theoretically possible outcome categories did not appear at all. In their reading, federal AI leadership generally builds on existing human capital rather than replacing it.

What it means

The authors argue that moving between organizations is not the same as changing profession. A health care technology executive who serves as a federal CAIO and later joins a health care organization, for example, crosses institutional boundaries while staying on a consistent path. That challenges the common assumption that movement between government and industry represents a significant career change.

The study also points to growing demand for leaders who understand both the technology and how to deploy it within large, complex institutions. Federal AI leadership now sits at the intersection of government, industry, academia, consulting and research, and the authors say CAIO service may create a distinctive form of expertise that stays valuable after officials leave government. The sample is small, and the analysis covers only the 27 people whose later careers could be observed.