Human Resources Specialists
In short: today's best AI models could do about 63% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 69% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 31% of the time, stays with people.
Recruit, screen, interview, or place individuals within an organization. May perform other activities in multiple human resources areas.
912,430 people do this job in the US. $75,940 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on1 h
- A person checks AI's work24 h
- Desk work AI can't do yet2 h
- Stays with people13 h
Human Resources Specialists, now: of a 40-hour week, AI could take on 1 hours, 24 hours become checking its work, 2 hours are desk work it can't do yet, and 13 hours stay with people.
The largest pieces: Search for job candidates (about 3 h, a person checks); Analyze employment data and report (about 3 h, a person checks); Match applications to job requirements (about 2 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 50%
- 18%
- 30%
- 2%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 63% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 31%. Today's estimate: 63%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.021% went to this job's tasks in May 2026, the 317th largest of the 447 jobs that showed up at all; 2 of its 26 tasks showed up. Within each of Anthropic's methods, never across a change: 0.14% to 0.081% (released February and March 2025); 0.057% to 0.15% (August 2025 to February 2026); 0.023% to 0.021% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.14% | 119 of 708 | 14 of 26 |
| Released March 2025 | 0.081% | 190 of 703 | 16 of 26 |
| The week of Aug 4, 2025 (released September 2025) | 0.057% | 226 of 655 | 11 of 26 |
| The week of Nov 13, 2025 (released January 2026) | 0.13% | 121 of 672 | 15 of 26 |
| The week of Feb 5, 2026 (released March 2026) | 0.15% | 121 of 677 | 14 of 26 |
| April 2026 (released June 2026) | 0.023% | 309 of 434 | 2 of 26 |
| May 2026 (released June 2026) | 0.021% | 317 of 447 | 2 of 26 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
31% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Advise on recruiting and retention programs, the conversation, 5%
- Develop recruiting strategies, the conversation, 3%
- Hire employees and process paperwork, the conversation, 3%
- Explain policies, laws and regulations, the conversation, 2%
- Select applicants and recommend hires, the conversation, 2%
- Search for job candidates, the conversation, 2%
What becomes checking the machine
64% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 3% is desk work that is small if wrong, which can be handed over with lighter checks.
- Search for job candidates, costly if wrong, 8%
- Analyze employment data and report, costly if wrong, 8%
- Develop recruiting strategies, costly if wrong, 7%
- Match applications to job requirements, costly if wrong, 6%
- Advise on recruiting and retention programs, costly if wrong, 5%
- Evaluate selection criteria against standards, costly if wrong, 5%
What to learn
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 50% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- The conversations. About 31% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Handling protected records. Much of the work touches protected personal records: knowing what may be shared with a model, and how, is part of the job.
- Running AI inside the business systems. About 18% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 26 tasks from .
| Task | ||||
|---|---|---|---|---|
| Advise management on organizing, preparing, or implementing recruiting or retention programs.Advice in meetings, with the analysis behind it.Done more than yearly by most who do it. | 11% | 2.5 h50 min to 1 work day | Documents 50%, conversation 50% | 46%Desk part half by now |
| Develop or implement recruiting strategies to meet current or anticipated staffing needs.Planning on paper, with conversations to shape it.Done more than yearly by most who do it. | 10% | 5 h1.7 h to 2.1 work days | Documents 70%, conversation 30% | 51%Desk part half by now |
| Perform searches for qualified job candidates, using sources such as computer databases, networking, Internet recruiting resources, media advertisements, job fairs, recruiting firms, or employee referrals.Searching databases and sites, postings, and some calls and fairs.Done more than monthly by most who do it. AI observed on this task. | 10% | 2.5 h38 min to 6.7 h | Documents 50%, systems 30%, conversation 20% | 72%Desk part half by now |
| Analyze employment-related data and prepare required reports.Analysis and reports from the personnel data.Done more than weekly by most who do it. AI observed on this task. | 8% | 3.3 h50 min to 6.7 h | Documents 80%, systems 20% | 91%Half by now |
| Hire employees and process hiring-related paperwork.Offer calls, and hiring entries in the system.Done more than monthly by most who do it. | 7% | 1.3 h38 min to 3.3 h | Systems 60%, conversation 40% | 60%Desk part half by now |
| Review employment applications and job orders to match applicants with job requirements.Reading applications against the job order.Done more than monthly by most who do it. AI observed on this task. | 6% | 38 min17 min to 1.7 h | Documents 80%, systems 20% | 100%Half by now |
| Administer employee benefit plans.Enrollments and changes in the benefits system, and staff questions answered.Done more than monthly by most who do it. | 5% | 2.5 h25 min to 5 h | Systems 70%, conversation 30% | 61%Desk part half by now |
| Evaluate recruitment or selection criteria to ensure conformance to professional, statistical, or testing standards, recommending revisions, as needed.Analysis on paper.Done more than yearly by most who do it. AI observed on this task. | 5% | 3.3 h1.7 h to 6.7 h | Documents 100% | 100%Half by now |
| Review and evaluate applicant qualifications or eligibility for specified licensing, according to established guidelines and designated licensing codes.Reading qualifications against written guidelines.Done several times a day by most who do it. | 5% | 50 min17 min to 1.7 h | Documents 80%, systems 20% | 100%Half by now |
| Select qualified job applicants or refer them to managers, making hiring recommendations when appropriate.Weighing the candidates, and a conversation with the hiring manager.Done more than monthly by most who do it. AI observed on this task. | 5% | 50 min17 min to 1.7 h | Documents 60%, conversation 40% | 60%Desk part half by now |
| Evaluate selection or testing techniques by conducting research or follow-up activities and conferring with management or supervisory personnel.Research and analysis, and conversations with managers.Done more than yearly by most who do it. | 4% | 1.3 work days3.3 h to 3.1 work days | Documents 70%, conversation 30% | 37%Desk part half by now |
| Interpret and explain human resources policies, procedures, laws, standards, or regulations.Answering staff questions, with the policy read first.Done more than weekly by most who do it. AI observed on this task. | 4% | 38 min17 min to 1.7 h | Conversation 60%, documents 40% | 40%Desk part half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Human Resources Assistants, Except Payroll and Timekeeping70% now, 70% by 2028
- Human Resources Managers39% now, 60% by 2028
- Eligibility Interviewers, Government Programs62% now, 62% by 2028
- First-Line Supervisors of Office and Administrative Support Workers54% now, 58% by 2028
- Compensation, Benefits, and Job Analysis Specialists43% now, 80% by 2028
- Industrial-Organizational Psychologists21% now, 74% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Human Resources Specialists is within reach of AI now?
- An estimated 63% of the working time is within reach of AI models now and 69% by the end of 2028, on METR's long-run pace at four in five (68% to 69% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Human Resources Specialists come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Search for job candidates (72% of the task's time now, 80% by the end of 2028); Analyze employment data and report (91% of the task's time now, 100% by the end of 2028); and Match applications to job requirements (100% of the task's time now).
- Which tasks of Human Resources Specialists stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 31% of the working time.
- What should Human Resources Specialists learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; briefing a model and checking its drafts; and the conversations. By the end of 2028 on the long-run pace at four in five, about 31% of the working time stays with people, 64% becomes checking a model's work and 3% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Human Resources Specialists?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.021% went to this job's tasks, the 317th largest share of the 447 jobs that showed up; 2 of its 26 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 40%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Human Resources Specialists?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 26 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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