Occupation · SOC 13-1071.00
Recruit, screen, interview, or place individuals within an organization. May perform other activities in multiple human resources areas.
Also called: HR Coordinator (Human Resources Coordinator) · HR Generalist (Human Resources Generalist) · Human Resources Specialist (HR Specialist) · Recruiter · Corporate Recruiter · Employment Representative · HR Analyst (Human Resources Analyst) · Human Resources Representative (HR Rep) · Personnel Analyst · Personnel Officer · Business Intelligence Engineer · Career Development Specialist
Job family: Business and Financial Operations Occupations
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A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
88th-percentile task overlap — yet about 81,800 openings a year (+6.2% projected, BLS), and observed AI use leans 4375% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 93rd | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 87th | 1.0 | |
| AI assistant applicability (Microsoft) High | 68th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.6), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Contact job applicants to inform them of the status of their applications. | 1.8% | |
| Interpret and explain human resources policies, procedures, laws, standards, or regulations. | 1.0% | |
| Evaluate recruitment or selection criteria to ensure conformance to professional, statistical, or testing standards, recommending revisions, as needed. | 0.9% | |
| 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. | 0.8% | |
| Review employment applications and job orders to match applicants with job requirements. | 0.8% | |
| Interview job applicants to obtain information on work history, training, education, or job skills. | 0.5% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +6.2% by 2034 |
| Projected annual openings | 81,800 |
| Employment 2024 → 2034 | 944,300 → 1,002,700 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Personnel and Careers Professionals · 2423 | 45% | Gradient 2 |
| Employment agents and contractors · 3333 | 39% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 43.8% working with AI · 42.8% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.8 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 89.0% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Review employment applications and job orders to match applicants with job requirements. | Directive | 1.8% |
| Contact job applicants to inform them of the status of their applications. | Directive | 1.8% |
| Select qualified job applicants or refer them to managers, making hiring recommendations when appropriate. | Directive | 1.4% |
| Interview job applicants to obtain information on work history, training, education, or job skills. | Iteration | 1.4% |
| Interpret and explain human resources policies, procedures, laws, standards, or regulations. | Learning | 1.3% |
| 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. | Directive | 1.2% |
| Prepare or maintain employment records related to events such as hiring, termination, leaves, transfers, or promotions, using human resources management system software. | Directive | 0.9% |
| Maintain and update human resources documents, such as organizational charts, employee handbooks or directories, or performance evaluation forms. | Iteration | 0.8% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Contact job applicants to inform them of the status of their applications. | 100.0% | |
| 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. | 99.2% | |
| Review employment applications and job orders to match applicants with job requirements. | 98.9% | |
| Interview job applicants to obtain information on work history, training, education, or job skills. | 98.6% | |
| Select qualified job applicants or refer them to managers, making hiring recommendations when appropriate. | 97.1% | |
| Provide management with information or training related to interviewing, performance appraisals, counseling techniques, or documentation of performance issues. | 97.1% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me review employment applications and job orders to match applicants with job requirements. From: Review employment applications and job orders to match applicants with job requirements. · 1.8% of measured AI use · directive
Help me contact job applicants to inform them of the status of their applications. From: Contact job applicants to inform them of the status of their applications. · 1.8% of measured AI use · directive
Help me select qualified job applicants or refer them to managers, making hiring recommendations when appropriate. From: Select qualified job applicants or refer them to managers, making hiring recommendations when appropriate. · 1.4% of measured AI use · directive
Help me interview job applicants to obtain information on work history, training, education, or job skills. From: Interview job applicants to obtain information on work history, training, education, or job skills. · 1.4% of measured AI use · task iteration
All 26 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Personnel and Human Resources | 4.6 | |
| Administrative | 3.9 | |
| Administration and Management | 3.8 | |
| English Language | 3.8 | |
| Customer and Personal Service | 3.5 | |
| Law and Government | 3.3 | |
| Education and Training | 3.1 |
| Speaking | 4.1 | |
| Reading Comprehension | 4.0 | |
| Active Listening | 4.0 | |
| Writing | 3.9 | |
| Critical Thinking | 3.9 | |
| Active Learning | 3.3 | |
| Learning Strategies | 3.0 | |
| Monitoring | 3.0 |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 3.9 | |
| Problem Sensitivity | 3.9 | |
| Deductive Reasoning | 3.9 | |
| Speech Recognition | 3.9 | |
| Speech Clarity | 3.9 | |
| Near Vision | 3.6 | |
| Inductive Reasoning | 3.3 | |
| Information Ordering | 3.3 | |
| Category Flexibility | 3.1 | |
| Selective Attention | 2.9 | |
| Fluency of Ideas | 2.8 |
| Social Perceptiveness | 3.4 | |
| Service Orientation | 3.4 | |
| Judgment and Decision Making | 3.3 | |
| Instructing | 3.1 | |
| Complex Problem Solving | 3.1 | |
| Coordination | 3.0 | |
| Negotiation | 3.0 | |
| Time Management | 3.0 | |
| Persuasion | 2.9 | |
| Systems Analysis | 2.9 | |
| Management of Personnel Resources | 2.9 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 81.
Showing the top 40 of 172.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Business, Management, Marketing, and Related Support Services , Psychology . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Bachelor's Degree | 46.6% | |
| High School Diploma | 13.5% | |
| Associate's Degree (or other 2-year degree) | 7.2% | |
| Some College Courses | 4.8% | |
| Post-Baccalaureate Certificate | 1.7% | |
| Post-Secondary Certificate | 1.3% |
The interests and personal qualities O*NET associates with people who do this work.
| Human Resources | 6.8 | |
| Office Work | 5.1 | |
| Management/Administration | 4.8 | |
| Professional Advising | 3.9 | |
| Law | 3.2 | |
| Public Speaking | 2.9 | |
| Teaching/Education | 2.5 | |
| Business Initiatives | 2.5 |
| Enterprising | 5.7 | |
| Conventional | 5.5 | |
| Social | 3.8 | |
| Investigative | 2.5 |
| Dependability | 5.0 | |
| Attention to Detail | 4.0 | |
| Integrity | 3.0 | |
| Cooperation | 2.3 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $45,440 |
| 25th percentile | $55,870 |
| Median (50th) | $72,910 |
| 75th percentile | $97,270 |
| 90th percentile | $126,540 |
| People employed | 917,460 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Administrative and Support and Waste Management and Remediation Services · Sector | 176,700 | $59,500 |
| Professional, Scientific, and Technical Services · Sector | 133,160 | $81,330 |
| Health Care and Social Assistance · Sector | 100,820 | $62,060 |
| Temporary Help Services · National industry | 85,200 | $56,650 |
| Management of Companies and Enterprises · Sector | 70,720 | $79,110 |
| Manufacturing · Sector | 66,400 | $77,570 |
| Finance and Insurance · Sector | 48,250 | $83,060 |
| Educational Services · Sector | 45,880 | $66,650 |
| Information · Sector | 30,350 | $102,050 |
| Wholesale Trade · Sector | 29,080 | $79,360 |
| Retail Trade · Sector | 27,320 | $59,310 |
| Transportation and Warehousing · Sector | 26,580 | $67,100 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Temporary Help Services · National industry | 5.4× | 85,200 |
| Management of Companies and Enterprises · Sector | 4.23× | 70,720 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 3.29× | 176,700 |
| Professional, Scientific, and Technical Services · Sector | 2.08× | 133,160 |
| Direct Health and Medical Insurance Carriers · National industry | 1.8× | 4,810 |
| Information · Sector | 1.75× | 30,350 |
| Solar Electric Power Generation · National industry | 1.57× | 130 |
| Engineering Services · National industry | 1.56× | 10,750 |
Part of the Management & Entrepreneurship career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Human Resources Specialists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 78th percentile of 427 international occupations.
Human Resources Specialists show 88th-percentile AI task overlap — and about 81,800 annual U.S. openings
Human Resources Specialists show 88th-percentile AI task overlap — and about 81,800 annual U.S. openings • Human Resources Specialists rank in the 88th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 81,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+6.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $72,910, across about 917,460 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 44% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Human Resources Specialists". https://singulariki.com/roles/role-13-1071-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Human Resources Specialists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-13-1071-00
Singulariki. (2026). Human Resources Specialists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-13-1071-00
@misc{singulariki-role-13-1071-00,
title = {Human Resources Specialists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-13-1071-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.