Our Methodology
This page explains how AI Workforce Watch analyzes the impact of artificial intelligence on different jobs and industries. Our approach combines quantitative analysis with qualitative research to provide balanced, evidence-based assessments.
Core Framework
1. Task Analysis
We begin by breaking down each job into its component tasks. For each task, we assess:
- What percentage of time is spent on this task
- Whether AI can meaningfully perform this task today
- The timeline for AI capability development
- Whether automation would be economically viable
2. Automation Potential Assessment
We evaluate three key dimensions for each job:
- Technical Feasibility: Can AI actually do this work?
- Economic Viability: Is it cost-effective to automate?
- Regulatory Feasibility: Are there legal/ethical barriers?
3. Displacement Timeline
We categorize impact into three timeframes:
- Near-term (0-3 years): Changes already visible in the market
- Medium-term (3-10 years): High confidence automation will occur
- Long-term (10+ years): Likely to happen but less certainty
Data Sources
Our analysis draws from multiple evidence streams:
- Academic research on automation and employment
- U.S. Bureau of Labor Statistics occupational data
- Industry reports and corporate announcements
- Published AI capabilities and limitations research
- Case studies of successful automation implementations
- Expert interviews with domain specialists
Limitations & Caveats
Important: AI development is unpredictable. Breakthroughs could accelerate timelines significantly. Our assessments represent our best judgment based on current evidence, not predictions.
Other important limitations:
- We focus on technical and economic feasibility, not social or political barriers to automation
- Our analysis assumes rational economic decision-making, which may not always occur
- We cannot predict new job creation that will emerge alongside automation
- We focus on job displacement risk, not overall job market health
Transparency
We believe in transparency about our analysis:
- All jobs include our confidence level in our assessment
- We cite sources for key claims
- We update analyses as new evidence emerges
- We welcome expert feedback and corrections
Questions?
For questions about our methodology or feedback on specific assessments, please contact us. We welcome constructive criticism and expert input.