For researchers: the willitreplace.me dataset
WillItReplace.me publishes a task-level AI automation-exposure dataset covering 535 occupations in 30 industries, with 2,172 individual task scores. Each occupation is broken into core tasks; every task receives a 0-100 score for how likely current or near-future AI (LLMs, agents, computer vision, robotics) can perform it. The role-level score is the aggregation of its task scores.
The dataset is intended as a transparent, citable indicator of task-level AI exposure - useful for exploration, teaching, journalism, and comparison against peer-reviewed studies - not as a forecast of job losses.
Download the dataset
Free to use with attribution. JSON preserves full detail (tasks, timelines, strategies); CSV is a flat task-level table with one row per task.
- jobs-535.json - full dataset, 535 occupations (JSON, ~1.1 MB)
- jobs-535.csv - flat table: job_name, category, overall_risk_pct, task_name, task_ai_probability_pct (~2,172 rows)
Methodology
- Each occupation is decomposed into its core tasks (typically 3-6 per role).
- Each task is scored 0-100 for AI automability, weighing: current AI capability, near-future trajectory (5-10 years), task complexity (physical dexterity, emotional intelligence, creative judgment), regulatory barriers, and real-world adoption.
- The role-level risk score aggregates task scores; full details on the methodology page.
The task-based approach follows Frey & Osborne (2013) and McKinsey Global Institute's work-activity framework, re-scored for generative AI. Scores are produced by structured model-assisted assessment against these rubrics; they are not survey measurements or official statistics.
Frey, C. B. & Osborne, M. A. (2013). "The Future of Employment: How Susceptible Are Jobs to Computerisation?"
Oxford Martin School, University of Oxford
The foundational task-based framework: probability of computerisation for 702 US occupations. WillItReplace.me adapts its task-level approach to current AI capabilities rather than reusing its 702-occupation probabilities directly.
PDF (oxfordmartin.ox.ac.uk) →McKinsey Global Institute (2017). "Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation."
McKinsey & Company
Work-activity-level (not job-level) analysis of automation potential across 46 countries; found roughly half of work activities could be automated with existing technology.
Report page (mckinsey.com) →McKinsey Global Institute (2023). "The Economic Potential of Generative AI: The Next Productivity Frontier."
McKinsey & Company
Updates automation estimates for generative AI; estimates current technology could automate work hours absorbing 60-70% of employee time, with large uncertainty ranges.
Report page (mckinsey.com) →Goldman Sachs (2023). "The Potentially Large Effects of Artificial Intelligence on Economic Growth."
Goldman Sachs Research
Widely cited estimate that roughly 300 million full-time jobs globally are exposed to automation by generative AI, and ~25% of US work hours could be automated. Exposure here means task overlap, not job elimination.
Article (goldmansachs.com) →World Economic Forum (2025). "Future of Jobs Report 2025."
World Economic Forum
Employer-survey-based: 170M roles created vs 92M displaced by 2030 (net +78M); 41% of employers plan workforce reductions where AI automates work.
Report page (weforum.org) →US Bureau of Labor Statistics — Occupational Outlook Handbook
US Department of Labor
Used for occupation definitions and salary benchmarks, not for risk scoring.
bls.gov/ooh →Limitations
- Task-level, not job-level. The scores measure exposure of tasks. A high score does not mean the job disappears; history shows automation typically restructures roles (see WEF 2025 for net job-change estimates).
- An indicator, not a prediction. Scores express capability-based exposure under stated assumptions. They are not probabilities of job loss and carry no confidence intervals.
- Model-assisted scoring. Task scores are produced by structured AI-assisted assessment against the rubric, then human-reviewed. They have not been validated against realized labor-market outcomes.
- US-centric framing. Occupation definitions and salary context are US-based (BLS); regulatory and adoption barriers vary by country.
- Snapshots age quickly. AI capability moves faster than annual surveys; treat scores as of their review date.
Citing this dataset
If you use the data, please cite the site (no formal authors are published; credit the site as publisher and include your access date). The underlying studies above have their own citations and should be cited separately.
BibTeX
@dataset{willitreplace2026jobs535,
title = {willitreplace.me AI Job Automation Dataset: 535 Occupations, Task-Level Exposure Scores},
author = {{WillItReplace.me}},
year = {2026},
url = {https://willitreplace.me/research},
note = {Accessed: <your access date>}
}Plain text
WillItReplace.me (2026). willitreplace.me AI Job Automation Dataset: 535 occupations with task-level AI exposure scores. https://willitreplace.me/research (accessed <your access date>).