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AI Job Check: how much does your occupation overlap with AI?

For 785 occupations, Microsoft Research measured how much of the typical work matches what people actually use artificial intelligence for today. Search for your job and see where it lands.

    Type an occupation and pick it from the list. The titles come from the US occupational classification — if yours is missing, try a related term.

    No occupation selected yet. Type one in above or click a dot in the graphic below — you then see how strongly it is affected and where it sits among all 785 occupations.

    Strongly affectedClearly affectedPartly affectedLittle affectedBarely affected

    barely affected strongly affected
    – of all occupations are less affected
    – of 785 occupations
    – Reading

    How is this measured?

    Microsoft Research analysed around 200,000 anonymised conversations with an AI assistant over nine months and mapped each one to the activities recorded for an occupation.

    That shows, for every occupation, how much of its typical work matches what people actually use AI for today - and how successfully. Occupations can then be compared with each other, which is exactly what the percentage above shows.

    What this is not: not the share of your working time that AI takes over, and not a statement about whether the occupation gets replaced. It measures overlap, not displacement.

    All occupations compared
    Strongly affected Partly affected Barely affected Strongly affected Partly affected Barely affected

    Every dot is one of the 785 occupations studied. Point at one to see which, click it to run it through the card above — or split the cloud by occupational group.

    Ranking

    Both ends of the scale

    At the top, the occupations with the largest overlap; below, those where today's AI contributes next to nothing.

    1. 1 Interpreters and Translators 100.0 %
    2. 2 Historians 99.9 %
    3. 3 Writers and Authors 99.7 %
    4. 4 Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel 99.6 %
    5. 5 Computer Numerically Controlled Tool Programmers 99.5 %
    6. 6 Broadcast Announcers and Radio Disc Jockeys 99.4 %
    7. 7 Customer Service Representatives 99.2 %
    8. 8 Telemarketers 99.1 %
    9. 9 Political Scientists 99.0 %
    10. 10 Mathematicians 98.9 %
    11. 11 News Analysts, Reporters, and Journalists 98.7 %
    12. 12 Passenger Attendants 98.6 %
    13. 13 Technical Writers 98.5 %
    14. 14 Concierges 98.3 %
    15. 15 Proofreaders and Copy Markers 98.2 %
    16. 16 Editors 98.1 %
    17. 17 Business Teachers, Postsecondary 98.0 %
    18. 18 Public Relations Specialists 97.8 %
    19. 19 Data Scientists 97.7 %
    20. 20 Personal Financial Advisors 97.6 %
    21. 21 Web Developers 97.4 %
    22. 22 Advertising Sales Agents 97.3 %
    23. 23 Management Analysts 97.2 %
    24. 24 Geographers 97.1 %
    25. 25 Brokerage Clerks 96.9 %

    Occupational groups

    By occupational group

    Average across all occupations in each group. The number in brackets is how many occupations the group contains.

    • IT and Mathematics (21) 86.6 %
    • Sales (21) 85.1 %
    • Education and Library (60) 84.3 %
    • Office and Administration (50) 78.3 %
    • Community and Social Service (13) 77.8 %
    • Business and Finance (32) 73.8 %
    • Arts, Media and Sports (36) 73.5 %
    • Architecture and Engineering (35) 71.8 %
    • Science and Research (47) 71.6 %
    • Personal Care and Service (29) 59.9 %
    • Food Preparation and Serving (15) 54.4 %
    • Management (37) 52.7 %
    • Protective Service (23) 44.3 %
    • Healthcare Practitioners (69) 38.5 %
    • Installation and Repair (50) 35.9 %
    • Legal (7) 34.0 %
    • Transportation and Logistics (46) 34.0 %
    • Production (100) 30.3 %
    • Cleaning and Grounds Maintenance (8) 26.1 %
    • Farming, Fishing and Forestry (12) 22.9 %
    • Construction and Extraction (57) 14.8 %
    • Healthcare Support (17) 10.8 %

    Method

    Where the numbers come from — and what they do not say

    The basis is a 2025 study by Microsoft Research. It analysed around 200,000 anonymised conversations with the AI assistant Bing Copilot over nine months. Each conversation was mapped to the work activities that the US occupational classification O*NET records for occupations.

    That produces one value per occupation, built from three parts: how often AI is called on for a task, how successfully it handles it, and how large a share of the occupation that task represents. No occupation reaches even half of what would theoretically be possible.

    What matters is what the value is not: it is not a forecast about jobs. The researchers stress that a high overlap does not mean an occupation gets replaced. An interpreter - the occupation with the largest overlap of all - does not disappear, but the work will look different in five years.

    Two caveats are worth carrying with you. First, the data comes from the US and from a single AI assistant; for the Austrian or German labour market it is an indication, not a measurement. Second, the study captures the state of 2025 — AI moves faster than any occupational classification.

    Data: Kiran Tomlinson et al., “Working with AI: Measuring the Applicability of Generative AI to Occupations”, Microsoft Research 2025, published under CC BY 4.0. Paper: https://arxiv.org/abs/2507.07935 — Data: https://github.com/microsoft/working-with-ai