Incorporating AI impacts in BLS employment projections: occupational case studies

Discussions about advances in artificial intelligence (AI) have become commonplace. In recent years, such advances have received constant news coverage, especially since the deployment of Open AI’s ChatGPT in November 2022. ChatGPT, a natural language processing tool driven by AI technology that allows users to have humanlike conversations, was the first to offer many users the opportunity to directly experience the potential power of AI in their lives. Millions of Americans have since used ChatGPT. Although AI and chatbots are not new, ChatGPT and similar tools have opened a new portal into the world of large language models (LLMs) and their wide-ranging applications. Generative AI (GenAI) tools are becoming increasingly powerful in creating prose, images, videos, and sound. Because of the sheer potential of these tools, many people have been asking how they might affect our future, including that of workers and employment.

The U.S. Bureau of Labor Statistics (BLS) Employment Projections (EP) program approaches AI in the same way as it does other technologies. Established technologies and other structural changes to the labor market have impacts that register in the historical data. BLS projection methods are designed to measure and reflect structural technological changes, and these changes and their employment impacts tend to occur gradually. There have been many claims about new technologies displacing jobs, and although such displacement has occurred in the past, it tends to take longer than technologists typically expect. Various technologies have had occupational impacts throughout recent history, but many affected occupations have still seen employment growth. Although it is always possible that future developments will deviate from historical patterns, BLS projection methods are not designed to capture extremely rapid technological change and, therefore, assume that the overall pace of technological change will be consistent with past experience.

Projecting future employment involves substantial uncertainty, especially in the case of evaluating the future impacts of a developing technology. BLS acknowledges this uncertainty in analyzing the relative likelihood of employment impacts across occupations. In this article, we use case studies to illustrate how BLS considers various factors of uncertainty in its employment projections. The next section provides more details, along with illustrative examples, on how BLS has approached this type of projections work in the past. The section after that presents several case studies, based on EP research done for the 2023–33 projections cycle, examining the potential employment impacts of AI technological advancements on selected occupations in the computer, legal, business and financial, and architecture and engineering occupational groups. The final section concludes, summarizing our results and noting EP’s plans for future research.