Companion dashboard — Mapping AI Exposure Across the U.S. Workforce
An interactive tool for browsing the exposure data by occupation, work activity, and over time.
- Teddy A. Wright Undergraduate author Kahlert School of Computing, University of Utah · Office of AI Policy, Utah Department of Commerce
- Alice C. Schwarze Office of AI Policy, Utah Department of Commerce · Department of Mathematics, Dartmouth College
- Zach M. Boyd Office of AI Policy, Utah Department of Commerce · Department of Mathematics, Brigham Young University
Abstract
Companion dashboard to the workforce automation exposure paper. It measures how AI capabilities map onto the U.S. occupational task structure: for a given occupation, work activity, or job category, what share of the work could be affected by current AI systems — and how many workers and wage dollars does that represent.
The dashboard triangulates across five independent AI scoring sources (Anthropic Economic Index conversation and API data, MCP server logs, and Microsoft Copilot exposure data) so no single methodology drives the result, and combines that with O*NET task structure and BLS employment and wage statistics. Eight pages cover occupation- and work-activity-level exploration, two-group side-by-side comparisons, time-series trends, and task-level diffs between dataset versions.
Built for Utah's Office of AI Policy. Intended for policymakers, workforce analysts, and informed members of the public. The companion paper makes the substantive argument; this dashboard is the place to interrogate the underlying numbers.