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AI Automation Index
Task-level automation exposure for Compensation and Benefits Managers: 22 O*NET tasks scored against their work activities, plus the simulator.
Automation Exposure
22 of 22 tasks scored
Median Salary
BLS OEWS, May 2025 — this exact occupation
Employment Level
22,940 people hold this job — size, not growth
Projected 2025–35
1,600 openings a year · BLS line item
BLS OEWS publishes 46 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$89,250 – $199,190
Highest medians
Lowest medians
State figures come from the 11-3111 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Importance-weighted mean of the scored tasks. This is the headline index.
Share of weighted tasks in hands-on or machine-operating work activities.
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Share in documenting, recording, and administrative activities.
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
22 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
11 tasks
8 tasks
3 tasks
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.
interpersonal — HR management is relational and accountability-bearing
Design, evaluate, and modify benefits policies to ensure that programs are current, competitive, and in compliance with legal requirements.
data analysis — Program metrics analysis, human owns conclusions
Fulfill all reporting requirements of all relevant government rules and regulations, including the Employee Retirement Income Security Act (ERISA).
data analysis — Reports and budgets drafted; human owns assumptions
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.
information retrieval — AI monitors news and events affecting operations
Identify and implement benefits to increase the quality of life for employees by working with brokers and researching benefits issues.
data analysis — Rules-based benefits admin; human handles exceptions
Manage the design and development of tools to assist employees in benefits selection, and to guide managers through compensation decisions.
interpersonal — HR management is relational and accountability-bearing
Administer, direct, and review employee benefit programs, including the integration of benefit programs following mergers and acquisitions.
data analysis — Program metrics analysis, human owns conclusions
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.
interpersonal — Cross-department liaison is relational brokering
Plan, direct, supervise, and coordinate work activities of subordinates and staff relating to employment, compensation, labor relations, and employee relations.
interpersonal — HR management is relational and accountability-bearing
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.
text generation — Document organizational procedures from provided content
Develop methods to improve employment policies, processes, and practices, and recommend changes to management.
decision support — Drafts policy options; political judgment owned by human
Formulate policies, procedures and programs for recruitment, testing, placement, classification, orientation, benefits and compensation, and labor and industrial relations.
decision support — AI drafts strategies; human owns operational judgment
Study legislation, arbitration decisions, and collective bargaining contracts to assess industry trends.
information retrieval — AI summarizes developments; human retains expertise
Plan and conduct new-employee orientations to foster positive attitude toward organizational objectives.
interpersonal — Program design aided, delivery and engagement are human
Prepare budgets for personnel operations.
data analysis — Budget drafting from assumptions; human validates assumptions
Negotiate bargaining agreements.
interpersonal — Labor dispute negotiation needs authority and presence
Prepare personnel forecasts to project employment needs.
data analysis — Labor estimates depend on context and human calibration
Maintain records and compile statistical reports concerning personnel-related data, such as hires, transfers, performance appraisals, and absenteeism rates.
data analysis — Compiling operational data from sources is automatable
Analyze statistical data and reports to identify and determine causes of personnel problems, and develop recommendations for improvement of organization's personnel policies and practices.
decision support — Personnel decisions carry accountability and bias risk
Contract with vendors to provide employee services, such as food services, transportation, or relocation service.
interpersonal — Negotiation needs authority and live give-and-take
Advise management on such matters as equal employment opportunity, sexual harassment, and discrimination.
decision support — Compliance guidance drafted; human verifies and owns
Represent organization at personnel-related hearings and investigations.
interpersonal — External representation requires human presence and authority
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<script src="https://giniloh.com/embed.js" async></script> Community Sentiment
Do you agree with the AI automation risk score calculated for this occupation?
Method
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
See the full method →Related Article
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