Where do chief AI officers go after federal service? – Brookings
Research
October 30, 2026
Artificial Intelligence
U.S. States and Territories
Governance Studies
Center for Technology Innovation (CTI)
As artificial intelligence (AI) becomes increasingly embedded across the federal government, the role of chief AI officer (CAIO) has emerged as one of the most influential technology leadership positions in government. Agencies now rely on CAIOs to help govern AI, oversee implementation, coordinate across organizational boundaries, and translate emerging technologies into mission outcomes. Yet while considerable attention has been devoted to the responsibilities of the role, far less attention has been paid to the individuals who occupy it.
In a recent report, we examined the backgrounds of federal CAIOs and found the Biden and Trump administrations assembled different combinations of expertise to advance their respective approaches to AI governance. The analysis focused on who was selected to govern federal AI and what those selections revealed about evolving government priorities.
Now, we turn to what happens to the careers of the people who occupy these increasingly important positions after they leave. As the CAIO role becomes more established across the federal government, enough officials have now entered and exited the position to begin identifying broader career patterns. Examining those patterns provides insight not only into the future of the CAIO role itself but also into how AI leadership expertise moves across sectors. Understanding that movement reveals whether federal AI service primarily reinforces existing expertise, amplifies the role of AI within established careers, or serves as a turning point that reshapes professional trajectories
That finding matters because federal AI leadership increasingly sits at the intersection of government, industry, academia, consulting, and research. As AI adoption grows, so does demand for leaders who understand not only the technology itself but also how to deploy it within large, complex institutions. Service as a federal CAIO may therefore create a distinctive form of expertise that remains valuable long after an individual leaves government.
Drawing on career histories from 55 current and former federal CAIOs, we examine whether service in the role primarily reinforces existing career trajectories, strengthens the role of AI within those careers, or fundamentally changes professional direction.
Using data from Leadership Connect and publicly available professional biographies, we examined the career histories of 55 current and former federal CAIOs. Because many current CAIOs remain in government service, only 27 individuals have sufficiently observable post-government careers to support a complete analysis. The remaining officials provide an opportunity to test whether current patterns continue over time.
We looked for three things in our examination of those who left federal CAIO roles:
The data also let us examine three broad career archetypes:
The clearest finding is that federal AI leadership rarely sends people in entirely new directions. Of the 27 former CAIOs whose post-government careers can be observed, 25 continued working in fields closely related to the ones that defined their careers before entering government.
This pattern is visible across health care, cybersecurity, defense, enterprise technology, data management, and public administration. Individuals changed jobs and sometimes changed sectors, but they typically did not abandon the expertise that brought them into AI leadership in the first place. Government service simply added another layer of experience rather than supplanting what they already knew.
This is an important distinction. Discussions of public-private mobility often focus on movement between organizations, but our findings suggest that movement between organizations is not necessarily the same thing as a change in profession. A health care technology executive who serves as a federal CAIO and later joins a health care organization has crossed institutional boundaries but remains on a remarkably consistent career path.
For a substantial number of former CAIOs, government service did more than reinforce an existing career path—it increased the prominence of AI within that path. Eleven of the 27 observed cases fit this pattern.
These individuals did not emerge from government with entirely new professional identities. Rather, they became more closely associated with AI than they had been beforehand. Technology leaders became AI transformation executives, data leaders adopted broader enterprise AI responsibilities, and policy and governance specialists developed stronger AI portfolios. The underlying expertise remained largely intact, but AI moved closer to the center of their professional identity.
For many officials, the CAIO role appears to function as a career accelerator. It provides an opportunity to apply existing expertise to one of the most important technology challenges facing large organizations today.
The most common pattern was straightforward reinforcement.
Fourteen of the 27 observable transitions involved individuals continuing along essentially the same professional path they had been pursuing before entering federal AI leadership. Some changed organizations, and some moved between sectors, but their underlying expertise remained largely unchanged.
This finding is noteworthy because public discussion often assumes movement between government and industry represents a significant career change. We saw this pattern when we examined rock star chief information officers (CIOs): The most successful CIOs were those who evolved rather than dramatically changed their careers. The career histories in our sample CAIOs suggest that, like their successful CIO brethren, individuals moved across organizational boundaries while maintaining highly consistent professional identities.
For example, a health care technology leader might move from industry to government and later to academia while continuing to focus on health care technology. Similarly, a cybersecurity executive may transition between government and private-sector roles without fundamentally changing professional direction. What changes is the organization, not the expertise.
Only two observable cases reflected a substantial change in professional direction following federal AI service. No examples were identified that represented complete transformational shifts across both professional field and institutional settings. Several theoretically possible outcome categories were not observed at all.
The evidence suggests that federal AI leadership generally builds upon existing human capital rather than replacing it. Individuals enter the role because of prior expertise, and they typically leave with that expertise enhanced rather than fundamentally altered. In short, for most of our dataset, their stint as a CAIO was simply another step in their career.
Perhaps the most significant takeaway is the type of expertise government service appears to create. Many organizations can hire people who understand AI technology, but fewer can hire people who have experience implementing AI in large, highly regulated environments where governance requirements, procurement processes, cybersecurity concerns, legal reviews, and mission needs must all be balanced simultaneously. Federal CAIOs gain that experience firsthand.
What appears to become portable is not simply knowledge of AI, rather, it is knowledge of how to make AI work inside a given institution. Former CAIOs leave government having confronted the practical realities of implementation, and that experience increasingly has value across industry, consulting, academia, and other government organizations.
The findings also suggest that successful CAIOs are rarely selected solely because of their AI expertise. Instead, most successful leaders combine AI knowledge with expertise in domains such as enterprise technology, cybersecurity, health care, defense, finance, procurement, analytics, or organizational transformation. Their value comes from connecting AI capabilities to agency missions. This concept very closely aligns with our previous work on rock star CIOs, which found that the best performing CIOs were ones that had in-depth organizational and technical skills versus being simply a splashy name.
This raises the possibility that the CAIO role will increasingly resemble other enterprise leadership positions. As AI becomes a standard business capability, future agencies may integrate AI responsibilities more closely with CIO, chief data, technology, or digital and artificial intelligence officer functions. The standalone CAIO position could become less distinct as AI becomes more deeply embedded throughout government operations. In short, a CAIO may be just a reflection of the nascent stage of AI development, and, over time, their tasks may be subsumed into already existing organization roles.
Our findings show that agencies may benefit from thinking differently about the CAIO role. The evidence does not point to a single profile for successful AI leaders. In fact, many successful CAIOs appear to have been selected because of expertise that extends well beyond AI itself. They brought experience in health care, cybersecurity, enterprise technology, analytics, defense, finance, procurement, or organizational transformation and then applied that expertise to AI-related challenges. In short, they are battle-tested executives rather than flashy rock stars.
For agencies considering internal candidates, it may involve looking more closely at individuals who already understand the mission, systems, and operational realities of the organization. For agencies considering external candidates, the more important question may not be who has the strongest AI credentials, but who can translate those credentials into outcomes within the unique constraints of government. In short, domain expertise may be far more salient than tech credentials. The overriding lesson from current and former CAIOs is that AI leadership is rarely just about AI. It is about connecting technology to mission execution.
Our analysis also shows agency heads should not hire a CAIO as a generic AI expert position. The more important question is whether the candidate possesses the domain knowledge, institutional authority, and adjacent capabilities needed to convert AI into mission outcomes. The career evidence points out that successful CAIO appointments are usually built on existing professional capital rather than on AI expertise alone. In short, what we said about rock star CIOs equally applies to AI leadership: Domain knowledge is more important than purely technical knowledge.
For internal candidates, agency heads should look primarily for people whose existing responsibilities place them close to the organizational foundations on which AI depends: data, enterprise architecture, cybersecurity, mission systems, analytics, procurement, digital transformation, or technology governance. These individuals already possess something difficult for an outsider to acquire quickly such as knowledge of the agency’s mission, legacy systems, decision processes, stakeholders, constraints, and organizational culture. The CAIO appointment can then amplify that established expertise by adding AI responsibility rather than requiring the individual to abandon their existing professional identity. For internal appointments, therefore, the key selection criterion should probably be adjacency to AI plus demonstrated capacity to lead institutional change, rather than whether the candidate already has the deepest AI resume.
For external candidates, the selection problem is somewhat different. Because outsiders do not initially possess federal or agency-specific implementation knowledge, their comparative advantage should be sufficiently distinctive to justify the additional institutional learning required. This might be deep technical AI expertise, large-scale commercial deployment experience, sector-specific expertise, cloud and platform engineering, cybersecurity, or experience transforming complex organizations. The strongest external candidates are therefore unlikely to be generic technology executives, rather, they should bring capabilities that the agency cannot readily develop internally while also demonstrating an ability to work in regulated, security-conscious, mission-driven environments. The career histories of commercial technologists entering government suggest that outside expertise can be valuable, but it must be converted into public-sector AI implementation capital. Candidates with strong AI credentials but who offer little evidence that they can operate effectively across interagency boundaries may be less suitable than their technical accomplishments initially suggest. Indeed, this was the lesson for the rock star CIOs that we previously studied.
So, agency heads should appoint CAIOs for the capabilities their agency lacks, not simply for their AI credentials. Internal candidates offer institutional and mission knowledge that AI leadership can amplify while external candidates can inject scarce technical or commercial capabilities but must learn how to translate them into the constraints of government. In both cases, the most valuable CAIO is likely to be a boundary spanner who can combine domain expertise with sufficient AI depth and the organizational capacity to turn technology into mission outcomes.
In closing, the current CAIOs are really the first generation of AI leadership in government, but clear themes are emerging. By understanding these themes, agency heads can position the incoming CAIO to be successful in both their new role as well as in future roles, all while ensuring the organization succeeds. Given the amount of spending on AI, these outcomes are crucial.
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