Digital WorkforceGrowing the U.S. AI Workforce

A new policy brief Georgetown University’s Center for Security and Emerging Technology (CSET) addresses the need for a clearly defined artificial intelligence education and workforce policy. The brief provides recommendations designed to grow, sustain, and diversify the U.S. AI workforce. The authors employ a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals. They note that their recommendations are designed to leverage opportunities within the U.S. education and training system while mitigating its challenges, and prioritize equity in access and opportunity to AI education and AI careers.

Here is the brief’s Executive Summary:

The U.S. artificial intelligence workforce, which stood at 14 million people in 2019, or 9 percent of total U.S. employment, has grown rapidly in recent years. This trend is likely to continue, as AI occupational employment over the next decade is projected to grow twice as fast as employment in all occupations.

Such an important and increasing component of the U.S. workforce demands dedicated education and workforce policy. Yet one does not exist. To date, U.S. policy has been a piecemeal approach based on inconsistent definitions of the AI workforce. For some, current policy is focused on top-tier doctorates and immigration reform. For others, the conversation quickly reverts to STEM education.

This report addresses the need for a clearly defined AI education and workforce policy by providing recommendations designed to grow, sustain, and diversify the domestic AI workforce. We use a comprehensive definition of the AI workforce––technical and nontechnical occupations––and provide data-driven policy goals.

Our policy goals and recommendations build off of previous CSET research along with new research findings presented here. Previous research in this series defined the AI workforce, described and characterized these workers, and assessed the relevant labor market dynamics. For example, we found that the demand for computer and information research scientists appears to be higher than the current supply, while for software developers and data scientists, evidence of a supply-demand gap is mixed.

To understand the current state of U.S. AI education for this report, we manually compiled an “AI Education Catalog” of curriculum offerings, summer camps, after-school programs, contests and challenges, scholarships, and related federal initiatives. To assess the current landscape of employer demand and hiring experiences, we also interviewed select companies engaged in AI activities.