A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-11-3111-00/context.md directly.
AI work map
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.
Often handed to AI
Task areas most often handled directively in observed AI conversations —
candidates to delegate with light review.
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan. · 0.6%
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies. · 0.5%
Develop methods to improve employment policies, processes, and practices, and recommend changes to management. · 0.3%
Task areas where a human was still judged necessary in a large share of observed
conversations — not a safety ruling, an observed-need signal.
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies. · 100.0% need a human
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions. · 98.8% need a human
Design, evaluate and modify benefits policies to ensure that programs are current, competitive and in compliance with legal requirements. · 96.8% need a human
↔70th-percentile task overlap — yet
about 1,500 openings a year
(+0.2% projected, BLS), and
observed AI use leans 4277% copilot, not hand-off (AEI)
. What exposure means →
AI & job outlook
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.
Exposure to current AI
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
87th
1.3
LLM task exposure, γ (OpenAI / Eloundou) High
77th
0.9
AI assistant applicability (Microsoft) Moderate
46th
0.1
OpenAI's exposure study scores tasks three ways: with a language model alone
(α 0.1), with simple added tooling
(β 0.5), and including AI-powered software
(γ 0.9). 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.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 1.0 ·
91st percentile among occupations ·
High
How AI is actually used in this job
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.
Develop methods to improve employment policies, processes, and practices, and recommend changes to management.
0.8%
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.
0.6%
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.
0.3%
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.
0.3%
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.
0.3%
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 —
a labor-market forecast, not an AI-impact forecast.
Outlook
About average · +0.2% by 2034
Projected annual openings
1,500
Employment 2024 → 2034
20,900 → 20,900
“Annual openings” counts new jobs plus replacements for workers who leave the
occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
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 occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task
overlap, not automation, adoption, or jobs lost.
Compensation and Benefits Managers sits at the 66th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
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.
Working with AI in this job
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
42.8% working with AI · 36.0% handed to AI
Most common way people use AI here
Directive · AI does it; you give the instruction
Typical AI autonomy
3.5 / 5
· higher = AI acts more independently
Used for work (vs. personal / coursework)
88.1%
What people delegate to AI
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
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.
Iteration
0.9%
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.
Directive
0.6%
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.
Directive
0.5%
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.
Iteration
0.5%
Develop methods to improve employment policies, processes, and practices, and recommend changes to management.
Directive
0.3%
Design, evaluate and modify benefits policies to ensure that programs are current, competitive and in compliance with legal requirements.
—
0.3%
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful
read on the current boundary, not a guarantee.
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.
100.0%
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.
98.8%
Design, evaluate and modify benefits policies to ensure that programs are current, competitive and in compliance with legal requirements.
96.8%
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.
92.0%
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.
88.1%
Develop methods to improve employment policies, processes, and practices, and recommend changes to management.
82.4%
What people most often hand AI here
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 mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.
From: Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions. · 0.9% of measured AI use · task iteration
Help me analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.
From: Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan. · 0.6% of measured AI use · directive
Help me direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.
From: Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies. · 0.5% of measured AI use · directive
Help me prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.
From: Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers. · 0.5% of measured AI use · task iteration
Tasks
All 24 tasks O*NET lists for this occupation, ordered by importance.
Each links to its own page with AI-exposure and observed-use detail.
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.
A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
Preparation level
SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.
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).
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI
exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Compensation and Benefits Managers — not advice or a forecast. Each is a real
cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
▸Write a report on thisheadline · factoids · citation
Compensation and Benefits Managers show 70th-percentile AI task overlap — and about 1,500 annual U.S. openings
Compensation and Benefits Managers rank in the 70th 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 1,500 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 (+0.2%) from 2024 to 2034.BLS Employment Projections 2024–34
Median annual pay is $140,360, across about 20,070 U.S. workers.BLS OEWS (May 2024)
Of the AI use actually observed for this work, 43% 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
Copy the whole kit
Compensation and Benefits Managers show 70th-percentile AI task overlap — and about 1,500 annual U.S. openings
• Compensation and Benefits Managers rank in the 70th 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 1,500 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 (+0.2%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $140,360, across about 20,070 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 43% 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 — "Compensation and Benefits Managers". https://singulariki.com/roles/role-11-3111-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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.
Sources for this page
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.
O*NET 30.3U.S. Department of Labor / National Center for O*NET Development
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Compensation and Benefits Managers." 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; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-11-3111-00
APA
Singulariki. (2026). Compensation and Benefits Managers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-11-3111-00
BibTeX
@misc{singulariki-role-11-3111-00,
title = {Compensation and Benefits Managers},
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; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-11-3111-00}
}
Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.
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