Saving lives after a natural disaster. Pre-empting the next pandemic. Thwarting cyberattacks. Winning in drone and information warfare. The power of AI to ensure our nation’s prosperity and security is enormous.
Realizing this power requires a strong public sector data and AI workforce. But despite years of admiring the problem, the federal government still lacks a dedicated cadre of AI talent.
The need for such a cadre has been apparent since at least 2019, when a series of high-profile reports were published that initially caught attention from Congress. Five years later, we still cannot clearly define or measure this workforce.
In the absence of progress, the October 2023 AI Executive Order provided two great motivators: urgency and accountability. A newly formed AI Talent Task Force carried out an “AI talent surge” that has to date overseen the hiring of over 200 AI experts through programs such as the U.S. Digital Service, U.S. Digital Corps, Presidential Innovation Fellowship (PIF) program, and the Department of Homeland Security’s AI Corps. The federal hiring experience memo looks to tackle issues with onboarding.
This progress is important but not enough. While the Task Force serves as a forcing function for recruiting AI talent into government, it stopped short of the finish line: defining and institutionalizing a formal data and AI workforce. As the stewards and experts of this workforce, federal chief digital and artificial intelligence officers (CDAOs) and chief data officers (CDOs) should work together and take the lead to tackle three priorities.
Create career pathways
It is nearly impossible to build and sustain a workforce that is not defined or measured. There are two reasons for this. First, without a formal occupation or career field, there is no dedicated pathway for advancement. Second, without being part of the formal budgeting and planning process, there is no clear way to get appropriated funding for data and AI positions. Both have serious implications for retention, which undermines the progress from the AI EO. Both also create a national security risk, because there is no way to ensure the government has sufficient access to this talent, including foreign-born talent, when and where it is needed.
CDAOs and CDOs should push to formalize a designated set of occupations that constitute data and AI professions. In the current taxonomy, this should at minimum include data scientists, operations researchers, computer scientists, computer engineers, mathematicians and statisticians. Talent in these occupations should be managed as a functional community, with opportunities for professional development and career advancement. There should be two-way talent bridges with industry for rotational assignments, instead of viewing it as a competition.
As a complement, CDAOs and CDOs should also oversee the coding of data and AI work roles, similar to what chief information officers (CIOs) did for the cybersecurity workforce. This is also needed because the reality is data and AI talent are sprinkled across dozens of occupations. It is important to understand what data and AI skill needs are, even if not part of a formal occupation group. There is no AI occupation, and official data shows only 700 data scientists as of February 2024 (out of 2.2 million federal employees; most data positions are in other occupations). The need for work roles is particularly acute in the military, where efforts to define and manage data and AI talent generally remain stalled outside of a new Navy robotics rating.
Make every agency a data and AI agency
Prioritizing a data and AI workforce requires making these skills a core part of organizational identity. That can only happen through building partnerships and coalitions within and across agencies to showcase the benefits of AI adoption. This is a systemic mindset. For example, a data-driven organization will value these skills in-house and not default to contracting them out.
CDAOs and CDOs must build relationships with key enablers like the CIO, general counsel, acquisition executives, and chief human capital officer. They should also join the AI EO Talent Task Force and the Office of Personnel Management’s Tech to Gov working group. In the Defense Department, a tri-chaired monthly Digital Talent Management Task Force seeks to share information across organizational stakeholders.
Building a coalition of partners also means showcasing how data and AI can reduce people’s burdens and meet their goals faster. For example, make AI tools accessible by hosting show-and-tells on completing mundane tasks like managing email, responding to tedious taskers, and drafting read-aheads.
Institutionalize progress
Pilots, initiatives and experiments — even task forces — offer critical insights but are not forever. CDAOs and CDOs must build institutional infrastructure through official policies, strategies, and doctrines to enable advocacy for resources and hold senior leaders accountable. Strategies like the DoD CDAO’s Data, Analytics, and AI Adoption Strategy and the Commerce Department’s Data Governance Board FY2023 Action Plan that explicitly make data and AI talent a core pillar enable accountability and align incentives through inclusion in organizational key performance indicators and action plans.
A final way to build a workforce is to build a dedicated community. This could include creating a one-stop shop to be the home for data and AI talent and their enablers. Such a platform would allow members to sandbox projects and build applications, celebrate skills with awards and competitions, and share job opportunities by integrating tools like GigEagle, a GitHub for talent.
Much is at stake to have a world-class public sector data and AI workforce amid the current global uncertainty and fierce talent competition. We must build on the momentum from the AI EO to formalize this workforce in a three-pronged effort led by federal CDAOs and CDOs. Then leaders can move on to other critical pieces like ensuring the entire federal workforce is data- and AI-ready and that the U.S. stands ready to lead and succeed in the AI competition.
Diana Gehlhaus is a director for economy at the Special Competitive Studies Project. She is also an adjunct policy researcher at the RAND Corporation. Diana was previously a senior advisor in the Department of Defense Chief Digital and Artificial Intelligence Office.
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