Primary Employment Wage Data

Bureau of Labor Statistics (BLS)

• Source: U.S. Bureau of Labor Statistics - Occupational Employment and Wage

Statistics (OEWS)

• Dataset: May 2024 National Occupational Employment and Wage Estimates

• URL: https://www.bls.gov/oes/current/oes_nat.htm

• Specific Data Used:

o Employment counts by occupation (SOC codes)

o Median annual wages by occupation

o Occupation titles and descriptions

• Coverage: ~830 detailed occupations representing the entire U.S. workforce

• Data Retrieved: November 2025

• Reliability: Official government statistics, Gold standard for U.S. employment data

Automation Risk AI Impact Research

1. Frey Osborne (2013)

• Study: The Future of Employment: How Susceptible are Jobs to Computerization

• Authors: Carl Benedikt Frey and Michael A. Osborne, Oxford Martin School

• URL:

https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

• Data Used:

o Occupation-specific automation probability scores (0-100%)

o Task analysis (routine vs. non-routine)

o Engineering bottlenecks for automation

• Methodology: Machine learning classification of 702 occupations based on nine key variables

• Key Finding: 47% of U.S. employment at high risk of automation

2. Goldman Sachs Research (2023-2025)

• Reports: The Potentially Large Effects of Artificial Intelligence on Economic

Growth (2023)

o Generative AI and the Future of Work (2023-2024)

o Annual AI impact updates (2024-2025)

• URL: https://www.goldmansachs.com/intelligence/pages/generative-ai-couldraise-global-gdp-by-7-percent.html

• Data Used:

o Generative AI exposure scores by occupation

oTask-level automation potential

o Timeline estimates for AI capability development

• Key Finding: 300 million jobs globally could be affected by AI automation, with 25% of work tasks automatable by 2030

3. McKinsey Global Institute (2024)

• Study: Generative AI and the future of work in America"

• URL: https://www.mckinsey.com/mgi/our-research/generative-ai-and-thefuture-of-work-in-america

• Data Used:

o Occupation automation timelines (early, mid, late adoption scenarios)

o Activity-level automation analysis o Sector-specific displacement projections

• Methodology: Analysis of 2,000+ detailed work activities across 800+ occupations

• Key Finding: By 2030, activities accounting for up to 30% of hours worked could be automated

4. World Economic Forum (WEF)

• Report: Future of Jobs Report 2023 and Future of Jobs Report 2025

• URL: https://www.weforum.org/publications/the-future-of-jobs-report-2023/

• Data Used:

o Job displacement and creation projections

o Skills gap analysis

o Technology adoption timelines by industry

o Reskilling priorities and pathways

• Methodology: Survey of 803 companies employing 11.3 million workers across 27 industry clusters and 45 economies

• Key Finding: 83 million jobs may be eliminated while 69 million new jobs could be created by 2027

5. MIT Work of the Future Task Force (2024)

• Study: The Work of the Future: Building Better Jobs in an Age of Intelligent Machines

• Authors: MIT Task Force on the Work of the Future

• URL: https://workofthefuture.mit.edu/

• Data Used:

o Task complementarity analysis (human-AI collaboration)

o Industry transformation patterns

o Skill requirements evolution

• Key Finding: Emphasis on augmentation over replacement; middle-skill jobs most vulnerable

6. OECD Employment Outlook (2023-2024)

• Report: "OECD Employment Outlook 2023: Artificial Intelligence and the Labour

Market

• URL: https://www.oecd.org/employment-outlook/

• Data Used:

o International comparative automation risk scores

o Policy implications and workforce transitions

o Cross-country employment trends

• Key Finding: 27% of jobs face high risk of automation across OECD countries

7. Brookings Institution Research

• Study: Automation and Artificial Intelligence: How machines are affecting people and places (2019-2024 updates)

• Authors: Mark Muro, Robert Maxim, and Jacob Whiton

• URL: https://www.brookings.edu/research/automation-and-artificial-intelligence/

• Data Used:

o Metropolitan area automation risk profiles

o Demographic vulnerability analysis

o Regional economic impacts

• Key Finding: Automation exposure varies significantly by geography and demographics

8. Burning Glass Technologies / Lightcast

• Data: Labor market analytics and real-time job posting analysis

• URL: https://lightcast.io/

• Data Used:

o Emerging skill requirements

o Job transition pathways

o Real-time labor market demand signals

o Skill adjacency analysis

Task Skill Classification

O*NET Database

• Source: U.S. Department of Labor O*NET (Occupational Information Network)

• URL: https://www.onetonline.org/

• Data Used:

o Detailed task descriptions by occupation

o Skills, knowledge, and abilities requirements

o Work activities and context

o Technology skills required

• Usage: Task classification into routine/cognitive/creative/interpersonal categories

• Coverage: 974 occupations with detailed task-level data

World Economic Forum Skills Taxonomy

• Source: WEF Future of Jobs Report Skills Framework

• Data Used:

o Core competencies by occupation

o Emerging skills demand

o Skills stability and disruption ratings

Technology Adoption Diffusion

International Federation of Robotics (IFR)

• Report: World Robotics Report 2024

• URL: https://ifr.org/

• Data Used:

o Industrial robot adoption rates by sector

o Automation technology penetration timelines

• Key Finding: Robot density increasing 5% annually; 4 million industrial robots by 2025

Gartner Technology Hype Cycles

• Report: Annual Hype Cycle for Emerging Technologies

• URL: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle

• Data Used:

o Technology maturity timelines o Adoption curve estimates

o Plateau of productivity projections for AI/ML technologies

Reskilling Workforce Transition

Coursera Skills Report

• Report: Global Skills Report 2024"

• URL: https://www.coursera.org/skills-reports/

• Data Used:

o Popular reskilling pathways

o Skill proficiency benchmarks

o Learning duration estimates

• Usage: Realistic reskilling program costs and timelines

LinkedIn Economic Graph Research

• Data: and workforce transition analytics

• URL: https://economicgraph.linkedin.com/

• Data Used:

o Successful career transition pathways

o Skills transferability analysis Job-to-job mobility patterns

• Usage: Reskilling success rate estimates

U.S. Department of Labor - CareerOneStop

• Source: Career exploration and training resources

• URL: https://www.careeronestop.org/

• Data Used:

o Training program costs

o Certification requirements

o Educational pathway timelines

Association for Talent Development (ATD)

• Data: Corporate training cost benchmarks

• Usage: Enterprise reskilling cost estimates and ROI projections

Economic Wage Projections

BLS Employment Projections (2023-2033)

• Source: Bureau of Labor Statistics Employment Projections Program

• URL: https://www.bls.gov/emp/

• Data Used:

o 10-year occupational growth projections

o Industry employment trends

o Labor force participation forecasts

• Methodology: Economic modeling and demographic analysis

Federal Reserve Economic Data (FRED)

• Source: Federal Reserve Bank of St. Louis

• URL: https://fred.stlouisfed.org/

• Data Used:

Wage growth trends

o Labor market indicators

o Economic productivity metrics

Methodology Notes

Automation Risk Score Calculation

The automation risk scores (0-100) in the WDF database represent a composite assessment derived from:

1. Frey-Osborne base probability (40% weight)

2. Goldman Sachs generative AI exposure (25% weight)

3. McKinsey automation timeline (20% weight)

4. Task routine-ness analysis from O*NET (15% weight) Scores are normalized to a 0-100 scale where:

• 0-30: Low automation risk (highly resistant to automation)

• 31-60: Moderate risk (partially automatable)

• 61-100: High risk (highly automatable in near to medium term)

Task Breakdown Methodology

Task percentages (routine, cognitive, creative, interpersonal) are derived from:

1. O*NET work activities analysis

2. WEF core competencies framework

3. Expert synthesis of task characteristics

Percentages sum to 100% for each occupation.

Technology Adoption Speed

Classification of adoption speed (slow/moderate/rapid) based on:

1. IFR robotics adoption data

2. Gartner technology maturity assessments

3. Historical technology diffusion patterns in similar sectors

4. Current AI capability advancement rates

Displacement Projections

Displacement estimates combine:

1. Automation risk scores

2. Technology adoption timelines

3. Current employment levels (BLS data)

4. Sector-specific economic modeling

Data Quality Limitations

Strengths

• Official government statistics (BLS) as employment/wage foundation

• Peer-reviewed research (Oxford, MIT) for automation analysis

• Leading consulting firms (McKinsey, Goldman Sachs) for current AI impact assessments

• International organizations (WEF, OECD) for global context

Limitations

• Automation predictions are probabilistic and subject to uncertainty

• Technology adoption rates can vary significantly by region, company size, and sector

• New job creation from AI (not fully captured) may offset displacement

• Regulatory, social, and economic factors not fully modeled

• Data reflects 2024-2025 knowledge; AI capabilities advancing rapidly

Data Currency

• BLS employment/wage data: May 2024 (most recent official release)

• Automation research: 2023-2025 publications

• AI impact assessments: Ongoing updates as of November 2025

• Next BLS OEWS update: Expected May 2025

Citation Guidelines

When using WDF data in reports, presentations, or publications, please cite:

For Employment/Wage Data:

U.S. Bureau of Labor Statistics. (2024). Occupational Employment and Wage Statistics.

Retrieved from https://www.bls.gov/oes/

For Automation Risk Analysis:

Workforce Disruption Forecaster database compiled from: Frey, C. B., & Osborne, M. A.

(2013); Goldman Sachs Research (2023-2025); McKinsey Global Institute (2024); World

Economic Forum (2023-2025).

For Comprehensive Citation:

Data sourced from Workforce Disruption Forecaster (WDF), integrating U.S. Bureau of

Labor Statistics employment data with automation risk research from Oxford University,

McKinsey Global Institute, Goldman Sachs, World Economic Forum, and other

authoritative sources (2024-2025).

Contact Updates

For questions about data sources, methodology, or to report data issues:

• Review the complete methodology documentation

• Check for updated research publications from key sources

• Monitor BLS for annual OEWS data releases (typically May each year)

Last Updated: November 2025 Next Review: May 2025 (BLS data update cycle)

HR Rebooted Workforce Navigator Data Sources

Numbers are updated on a quarterly basis to ensure continued veracity. v.1 12/08/25