As of August 2026, Human Resources Managers has an AI-exposure score of 63/100 (High exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Human Resources Managers

63/100
High exposure
LowModerateElevatedHighVery High

More exposed than 75% of the roles we track. Median pay ~US$149,280. About 17,900 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Human Resources Managers?

No exposure score can predict whether AI will replace this role. The 63/100 score means our current model estimates high task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role is in the High exposure band, but its assessed task mix is not automation-heavy. The study found the decline concentrated where AI was more likely to automate rather than augment work, so the headline figure should not be applied directly.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Plan, direct, or coordinate human resources activities and staff of an organization.

Common titles
Employee Relations ManagerHR Director (Human Resources Director)HR Manager (Human Resources Manager)HR VP (Human Resources Vice President)HR Admin Director (Human Resources Administration Director)HR Ops Manager (Human Resources Operations Manager)
O*NET job-zone preparation
Job Zone 4 · Considerable Preparation Needed Most of these occupations require a four-year bachelor's degree, but some do not. A considerable amount of work-related skill, knowledge, or experience is needed for these occupations.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
Management of Personnel ResourcesReading ComprehensionSpeakingCoordinationWritingActive Learning
Work context
Frequent contact with othersDecision latitudeIndoor controlled settingRepeating tasks

Source: O*NET 29.1 exact occupation - Human Resources Managers, SOC 11-3121.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Human Resources Managers tasks, by AI exposure

Automatable
  • Maintain records and compile statistical reports concerning personnel-related data such as hires, transfers, performance appraisals, and absenteeism rates.
  • Conduct exit interviews to identify reasons for employee termination.
  • Provide current and prospective employees with information about policies, job duties, working conditions, wages, opportunities for promotion, and employee benefits.
Augmentable
  • Analyze and modify compensation and benefits policies to establish competitive programs and ensure compliance with legal requirements.
  • Administer compensation, benefits, and performance management systems, and safety and recreation programs.
14 more augmentable tasks locked in the report.
Durable
  • Represent organization at personnel-related hearings and investigations.

We analyzed all 20 Human Resources Managers tasks - 3 automatable, 16 augmentable and 1 durable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.

Social and Community Service Managers
40% skills overlap; 6 points lower - lower exposure; Elevated band; ~US$80,390
View path
57
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Maintain records and compile statistical reports concerning personnel-related data such as hires, transfers, performance appraisals, and absenteeism rates.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

AI was the most-cited reason for U.S. layoffs through mid-2026 - the workers who adapt earliest fare best. - Challenger, Gray & Christmas, 2026The upside: Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Human Resources Managers - median pay by US state (BLS OEWS, USD)

New York: US$176,650California: US$170,080Florida: US$137,790Texas: US$136,500

Median annual wage, in USD. US national: US$149,280. More states are being added.

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