According to a recent study by the Massachusetts Institute of Technology, artificial intelligence systems in the United States are capable of completing tasks that are equivalent to 11.7% of the country’s total workforce. This translates to approximately $1.2 trillion in earnings linked to work that could potentially be automated in industries such as finance, healthcare, and professional services.
The research, conducted in collaboration with Oak Ridge National Laboratory, introduced a new tool named the Iceberg Index, which examines how AI tools interact with a workforce of 151 million individuals to assess the exposure of technical tasks rather than predicting job eliminations. The analysis is the first to map the capabilities of AI systems across the entire U.S. labor market at a county level.
Tennessee has already integrated these findings into its Artificial Intelligence Advisory Council Action Plan, while Utah is in the process of developing a similar report based on this research. The study also revealed that only 2.2% of wage value exposure is attributed to visible AI adoption in computing and technology, accounting for around $211 billion.
The Iceberg Index matches AI capabilities with occupational skill requirements by analyzing over 13,000 AI tools and aligning them with Bureau of Labor Statistics taxonomies covering 32,000 competencies across 923 different occupations in approximately 3,000 counties. Each skill within an occupation is assessed based on its importance, automatability, and wage value.
The deployment of AI is mainly concentrated in technology-related roles employing around 1.9 million individuals, with software engineers, data scientists, and program managers being the most impacted professions. The study highlights that while some jobs may be fully automated by AI systems, others may see a shift towards hybrid models where AI supports workers in executing repetitive or low-judgment tasks.
MIT economist David Autor expressed during the MIT AI Conference that AI is more likely to enhance human expertise rather than replace it entirely. The report also emphasizes that exposure to AI is not limited to tech hubs on the coasts but extends to states like South Dakota, North Carolina, and Utah when considering administrative and financial sectors.
Additionally, industrial states like Tennessee and Ohio exhibit significant cognitive-task exposure due to administrative coordination and professional services intertwined within manufacturing supply chains. The study indicates that while some regions might experience concentrated exposure in specific industries like finance and technology, others display broader patterns across various sectors such as logistics, production, administration, and services.
Furthermore, MIT researchers have developed an interactive simulation tool that enables states to model different policy scenarios related to training investments and workforce funding before implementation. A separate report by PYMNTS Intelligence highlights varying levels of readiness for AI adoption within enterprises, with 60% of chief financial officers claiming their companies are somewhat prepared for managing AI-driven changes.