One of the biggest questions to arise since the launch of ChatGPT and the widespread emergence of GenAI has been, what will the impact be on the job market?
The truth is that there will be a fundamental shift in the work we do and how we do it. Some tasks will be left to AI, and the human in the loop could well disappear; others may not be affected at all. While most of this is speculation, there has been solid research that can predict what some of these outcomes will be.
The latest comes from a team of Microsoft researchers led by Kiran Tomlinson in the paper, “Working with AI: Measuring the Occupational Implications of Generative AI.” Let’s take a look at its findings.
AI Agent & Copilot Summit is an AI-first event to define opportunities, impact, and outcomes with Microsoft Copilot and agents. Building on its 2025 success, the 2026 event takes place March 17-19 in San Diego. Get more details.
GenAI’s Impact
The ultimate output from the research is refreshingly tangible. It lists the 40 professions most likely and least likely to be impacted by GenAI. However, before we arrive at that list, it’s essential to understand the methods the team used to get there.
The team gathered real user data from Microsoft Copilot and analyzed 200,000 anonymized U.S. user conversations between January 1, 2024, and September 30, 2024., “…understanding the effects of AI on the economy is one of society’s most important questions,” reads the paper’s abstract. “In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities.
“We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system.”
The team used a GPT-4o-based LLM classification pipeline to identify all intermediate work activities (IWAs) within the conversations based on the framework laid out by the U.S. Department of Labor’s O*NET database, which is used to standardize job roles across close to 1,000 occupations in the U.S.
Finally, to measure a potential impact on a specific occupation, each one was given an AI applicability score. The higher the score, the more likely the occupation is to be impacted by GenAI. “The score captures whether AI is being used (with sufficient activity share) for the work activities of an occupation and whether that usage tends to be successful (completion rate) and cover a moderate share of the work activity (scope).”
The Findings
Top 40 occupations with highest AI applicability score
| Job Title (Abbrv.) | Coverage | Cmpltn. | Scope | Score | Employment |
|---|---|---|---|---|---|
| Interpreters and Translators | 0.98 | 0.88 | 0.57 | 0.49 | 51,560 |
| Historians | 0.91 | 0.85 | 0.56 | 0.48 | 3,040 |
| Passenger Attendants | 0.80 | 0.88 | 0.62 | 0.47 | 20,190 |
| Sales Representatives of Services | 0.84 | 0.90 | 0.57 | 0.46 | 1,142,020 |
| Writers and Authors | 0.85 | 0.84 | 0.60 | 0.45 | 49,450 |
| Customer Service Representatives | 0.72 | 0.90 | 0.59 | 0.44 | 2,858,710 |
| CNC Tool Programmers | 0.90 | 0.87 | 0.53 | 0.44 | 28,030 |
| Telephone Operators | 0.80 | 0.86 | 0.57 | 0.42 | 4,600 |
| Ticket Agents and Travel Clerks | 0.71 | 0.90 | 0.56 | 0.41 | 119,270 |
| Broadcast Announcers and Radio DJs | 0.74 | 0.84 | 0.60 | 0.41 | 25,070 |
| Brokerage Clerks | 0.74 | 0.89 | 0.57 | 0.41 | 48,060 |
| Farm and Home Management Educators | 0.77 | 0.91 | 0.55 | 0.41 | 8,110 |
| Telemarketers | 0.66 | 0.89 | 0.60 | 0.40 | 81,580 |
| Concierges | 0.70 | 0.88 | 0.56 | 0.40 | 41,020 |
| Political Scientists | 0.77 | 0.87 | 0.53 | 0.39 | 5,580 |
| News Analysts, Reporters, Journalists | 0.81 | 0.81 | 0.56 | 0.39 | 45,020 |
| Mathematicians | 0.91 | 0.74 | 0.54 | 0.39 | 2,220 |
| Technical Writers | 0.83 | 0.82 | 0.54 | 0.38 | 47,970 |
| Proofreaders and Copy Markers | 0.91 | 0.86 | 0.49 | 0.38 | 5,490 |
| Hosts and Hostesses | 0.60 | 0.90 | 0.57 | 0.37 | 425,020 |
| Editors | 0.78 | 0.82 | 0.54 | 0.37 | 95,700 |
| Business Teachers, Postsecondary | 0.70 | 0.90 | 0.52 | 0.37 | 82,980 |
| Public Relations Specialists | 0.63 | 0.90 | 0.60 | 0.36 | 275,550 |
| Demonstrators and Product Promoters | 0.64 | 0.88 | 0.53 | 0.36 | 50,790 |
| Advertising Sales Agents | 0.66 | 0.90 | 0.53 | 0.36 | 108,100 |
| New Accounts Clerks | 0.72 | 0.87 | 0.51 | 0.36 | 41,180 |
| Statistical Assistants | 0.85 | 0.84 | 0.49 | 0.36 | 7,200 |
| Counter and Rental Clerks | 0.62 | 0.90 | 0.52 | 0.36 | 390,300 |
| Data Scientists | 0.77 | 0.86 | 0.51 | 0.36 | 192,710 |
| Personal Financial Advisors | 0.69 | 0.88 | 0.52 | 0.35 | 272,190 |
| Archivists | 0.66 | 0.88 | 0.49 | 0.35 | 7,150 |
| Economics Teachers, Postsecondary | 0.68 | 0.90 | 0.51 | 0.35 | 12,210 |
| Web Developers | 0.73 | 0.86 | 0.51 | 0.35 | 85,350 |
| Management Analysts | 0.68 | 0.90 | 0.54 | 0.35 | 838,140 |
| Geographers | 0.77 | 0.83 | 0.48 | 0.35 | 1,460 |
| Models | 0.64 | 0.89 | 0.53 | 0.35 | 3,090 |
| Market Research Analysts | 0.71 | 0.90 | 0.52 | 0.35 | 846,370 |
| Public Safety Telecommunicators | 0.66 | 0.88 | 0.53 | 0.35 | 97,820 |
| Switchboard Operators | 0.68 | 0.86 | 0.52 | 0.35 | 43,830 |
| Library Science Teachers, Postsecondary | 0.65 | 0.90 | 0.51 | 0.34 | 4,220 |
Bottom 40 occupations with lowest AI applicability score
| Job Title (Abbrv.) | Coverage | Cmpltn. | Scope | Score | Empl. |
|---|---|---|---|---|---|
| Phlebotomists | 0.06 | 0.95 | 0.29 | 0.03 | 137,080 |
| Nursing Assistants | 0.07 | 0.85 | 0.34 | 0.03 | 1,351,760 |
| Hazardous Materials Removal Workers | 0.04 | 0.95 | 0.35 | 0.03 | 49,960 |
| Helpers–Painters, Plasterers, … | 0.04 | 0.96 | 0.38 | 0.03 | 7,700 |
| Embalmers | 0.07 | 0.55 | 0.22 | 0.03 | 3,380 |
| Plant and System Operators, All Other | 0.05 | 0.93 | 0.38 | 0.03 | 15,370 |
| Oral and Maxillofacial Surgeons | 0.05 | 0.89 | 0.34 | 0.03 | 4,160 |
| Automotive Glass Installers and Repairers | 0.04 | 0.93 | 0.34 | 0.03 | 16,890 |
| Ship Engineers | 0.05 | 0.92 | 0.39 | 0.03 | 8,860 |
| Tire Repairers and Changers | 0.04 | 0.95 | 0.35 | 0.02 | 101,520 |
| Prosthodontists | 0.10 | 0.90 | 0.29 | 0.02 | 570 |
| Helpers–Production Workers | 0.04 | 0.93 | 0.36 | 0.02 | 181,810 |
| Highway Maintenance Workers | 0.03 | 0.96 | 0.32 | 0.02 | 150,860 |
| Medical Equipment Preparers | 0.04 | 0.96 | 0.31 | 0.02 | 66,790 |
| Packaging and Filling Machine Op. | 0.04 | 0.91 | 0.39 | 0.02 | 371,600 |
| Machine Feeders and Offbearers | 0.05 | 0.89 | 0.36 | 0.02 | 44,500 |
| Dishwashers | 0.03 | 0.95 | 0.30 | 0.02 | 463,940 |
| Cement Masons and Concrete Finishers | 0.03 | 0.92 | 0.39 | 0.01 | 203,560 |
| Supervisors of Firefighters | 0.04 | 0.88 | 0.39 | 0.01 | 84,120 |
| Industrial Truck and Tractor Operators | 0.03 | 0.94 | 0.28 | 0.01 | 778,920 |
| Ophthalmic Medical Technicians | 0.04 | 0.89 | 0.33 | 0.01 | 73,390 |
| Massage Therapists | 0.10 | 0.91 | 0.32 | 0.01 | 92,650 |
| Surgical Assistants | 0.03 | 0.78 | 0.29 | 0.01 | 18,780 |
| Tire Builders | 0.03 | 0.93 | 0.40 | 0.01 | 20,660 |
| Helpers–Roofers | 0.02 | 0.94 | 0.37 | 0.01 | 4,540 |
| Gas Compressor and Gas Pumping Station Op. | 0.01 | 0.96 | 0.47 | 0.01 | 4,400 |
| Roofers | 0.02 | 0.94 | 0.38 | 0.01 | 135,140 |
| Roustabouts, Oil and Gas | 0.01 | 0.95 | 0.39 | 0.01 | 43,830 |
| Maids and Housekeeping Cleaners | 0.02 | 0.94 | 0.34 | 0.01 | 836,230 |
| Paving, Surfacing, and Tamping Equipment Op. | 0.01 | 0.96 | 0.29 | 0.01 | 43,080 |
| Logging Equipment Operators | 0.01 | 0.95 | 0.36 | 0.01 | 23,720 |
| Motorboat Operators | 0.01 | 0.93 | 0.39 | 0.00 | 2,710 |
| Orderlies | 0.00 | 0.76 | 0.18 | 0.00 | 48,710 |
| Floor Sanders and Finishers | 0.00 | 0.94 | 0.34 | 0.00 | 5,070 |
| Pile Driver Operators | 0.00 | 0.98 | 0.24 | 0.00 | 3,010 |
| Rail-Track Laying and Maintenance Equip. Op. | 0.00 | 0.96 | 0.27 | 0.00 | 18,770 |
| Foundry Mold and Coremakers | 0.00 | 0.95 | 0.36 | 0.00 | 11,780 |
| Water Treatment Plant and System Op. | 0.00 | 0.92 | 0.44 | 0.00 | 120,710 |
| Bridge and Lock Tenders | 0.00 | 0.93 | 0.39 | 0.00 | 3,460 |
| Dredge Operators | 0.00 | 0.99 | 0.22 | 0.00 | 940 |
Note: Metrics reported as mean of user goal and AI action score.
Closing Thoughts
There is certainly a lot to digest here. The variety of jobs that will be impacted is significant. However, the data suggests that the roles most likely to be affected are those that require assistance, advice, or content creation, such as teaching, communications, and media.
Jobs that require manual labor or a physical presence may seem safe and secure, but the distinction isn’t as straightforward as it appears. Roles that are considered at risk from AI have been highlighted, yet the extent of AI’s impact does not imply a complete takeover.
The data indicates that AI will not fully replace these jobs; instead, it suggests that AI tools will augment them. The key concept here is augmentation. AI is expected to improve job effectiveness by providing greater insights and efficiency. The challenge for human workers lies in their ability to adapt. Perhaps we have a useful projection regarding the future of AI: Successful AI = augmentation + adaptation.
Ask Cloud Wars AI Agent about this analysis
