Our Methodology

Data Sources

PlainWorkforce combines two official federal government datasets to provide a comprehensive view of occupational employment outlook and artificial intelligence workforce impact:

AI Exposure Score Calculation

AI exposure scores (0–100%) represent the estimated proportion of an occupation's work content that is susceptible to current and near-term AI automation or augmentation. The methodology:

  1. Task extraction: For each occupation, we retrieve all documented task statements from O*NET Database 30.0.
  2. Task classification: Each task is evaluated against four AI automability criteria:
    • Routine data processing or structured information handling
    • Pattern recognition from standardized inputs
    • Rule-based or algorithmic decision making
    • Predictable physical actions in constrained environments
  3. Weighted scoring: Task importance weights from O*NET are applied. The occupation's AI exposure score is the weighted proportion of its task content meeting automability criteria.

These scores reflect current and near-term AI capabilities as of the dataset vintage. They are analytical estimates — not predictions of employment outcomes.

Employment Projections

Employment projections are taken directly from BLS as published, without modification. BLS projects employment levels for 2024 and 2034, the difference being the 10-year change. We also present the BLS-published median annual wage from the May 2024 OEWS survey as context.

Occupation Classification

Occupations use Standard Occupational Classification (SOC) codes. BLS projections and O*NET data are matched by SOC code. Where SOC codes differ between BLS and O*NET editions, we use the BLS projections coding as the primary key.

Important Caveats

Understanding the limitations of both employment projections and exposure scores is essential for responsible interpretation:

  • Artificial intelligence exposure scores measure task content susceptibility — not the probability that any given worker will actually lose their position.
  • Actual workforce effects depend on many factors including adoption speed, economic conditions, regulatory environment, and augmentation models where artificial intelligence assists workers rather than replacing them entirely.
  • Bureau of Labor Statistics projections are inherently uncertain — they assume continuation of current economic trends and policies over the projection period.
  • High exposure does not necessarily mean job loss; many high-exposure occupations are still projected to grow in employment due to other favorable economic factors and increasing demand.

Processing Pipeline

Our ETL pipeline combines BLS employment projections with O*NET task data through a multi-step process:

  • Download BLS Employment Projections tables for the 2024–2034 projection cycle, extracting employment levels, growth rates, wages, and education requirements for each occupation
  • Download O*NET Database 30.0 task statements and work activity importance ratings for each SOC-coded occupation
  • Match BLS occupations to O*NET occupations by SOC code, handling code crosswalk differences between database editions
  • Apply the AI automability classification to each O*NET task statement and compute weighted exposure scores
  • Build industry-level views from BLS industry projections tables with growth rate and employment change data
  • Compute sector and education-level aggregations for overview dashboards

Employment projections and wage data are presented exactly as published by BLS. AI exposure scores are our computed analytical metric based on O*NET task analysis.

Not Affiliated

PlainWorkforce is not affiliated with the Bureau of Labor Statistics, the U.S. Department of Labor, or any government agency.