Occupation · SOC 41-3021.00
Sell life, property, casualty, health, automotive, or other types of insurance. May refer clients to independent brokers, work as an independent broker, or be employed by an insurance company.
Also called: Insurance Agent · Insurance Broker · Sales Agent · Sales Representative · Insurance Sales Agent · Sales Associate · Underwriting Sales Representative · Account Representative · Account Specialist · Auto Insurance Agent (Automotive Insurance Agent) · Bilingual Insurance Sales Agent · Bond Writer
Job family: Sales and Related 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.
93rd-percentile task overlap — yet about 47,000 openings a year (+3.7% projected, BLS), and observed AI use leans 5933% 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 | 91st | 1.3 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 75th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), 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.
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 0.9 · 82nd percentile among occupations · High
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.
| Customize insurance programs to suit individual customers, often covering a variety of risks. | 1.5% | |
| Explain necessary bookkeeping requirements for customer to implement and provide group insurance program. | 1.2% | |
| Develop marketing strategies to compete with other individuals or companies who sell insurance. | 0.9% | |
| Confer with clients to obtain and provide information when claims are made on a policy. | 0.3% | |
| Install bookkeeping systems and resolve system problems. | 0.2% | |
| Interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.7% by 2034 |
| Projected annual openings | 47,000 |
| Employment 2024 → 2034 | 568,800 → 589,800 |
“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 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.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Insurance Representatives · 3321 | 53% | Gradient 3 |
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 | 59.3% working with AI · 26.1% handed to AI |
| Most common way people use AI here | Learning · you ask AI to explain or teach |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 40.7% |
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 |
|---|---|---|
| Interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage. | Directive | 0.9% |
| Customize insurance programs to suit individual customers, often covering a variety of risks. | Iteration | 0.9% |
| Explain necessary bookkeeping requirements for customer to implement and provide group insurance program. | Learning | 0.8% |
| Confer with clients to obtain and provide information when claims are made on a policy. | Learning | 0.8% |
| Call on policyholders to deliver and explain policy, to analyze insurance program and suggest additions or changes, or to change beneficiaries. | Learning | 0.5% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Confer with clients to obtain and provide information when claims are made on a policy. | 100.0% | |
| Interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage. | 98.9% | |
| Explain necessary bookkeeping requirements for customer to implement and provide group insurance program. | 98.8% | |
| Call on policyholders to deliver and explain policy, to analyze insurance program and suggest additions or changes, or to change beneficiaries. | 96.3% | |
| Customize insurance programs to suit individual customers, often covering a variety of risks. | 92.0% |
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 interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage. From: Interview prospective clients to obtain data about their financial resources and needs, the physical condition of the person or property to be insured, and to discuss any existing coverage. · 0.9% of measured AI use · directive
Help me customize insurance programs to suit individual customers, often covering a variety of risks. From: Customize insurance programs to suit individual customers, often covering a variety of risks. · 0.9% of measured AI use · task iteration
Help me explain necessary bookkeeping requirements for customer to implement and provide group insurance program. From: Explain necessary bookkeeping requirements for customer to implement and provide group insurance program. · 0.8% of measured AI use · learning
Help me confer with clients to obtain and provide information when claims are made on a policy. From: Confer with clients to obtain and provide information when claims are made on a policy. · 0.8% of measured AI use · learning
All 19 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).
| Reading Comprehension | 4.0 | |
| Active Listening | 3.9 | |
| Speaking | 3.9 | |
| Critical Thinking | 3.8 | |
| Writing | 3.6 | |
| Mathematics | 3.0 | |
| Active Learning | 3.0 | |
| Monitoring | 2.9 |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Speech Clarity | 3.8 | |
| Written Expression | 3.6 | |
| Speech Recognition | 3.6 | |
| Near Vision | 3.5 | |
| Problem Sensitivity | 3.3 | |
| Deductive Reasoning | 3.3 | |
| Inductive Reasoning | 3.1 | |
| Information Ordering | 3.1 | |
| Category Flexibility | 3.1 | |
| Mathematical Reasoning | 3.1 | |
| Number Facility | 3.0 |
| Persuasion | 3.6 | |
| Time Management | 3.5 | |
| Service Orientation | 3.4 | |
| Negotiation | 3.3 | |
| Social Perceptiveness | 3.1 | |
| Coordination | 3.0 | |
| Judgment and Decision Making | 3.0 | |
| Complex Problem Solving | 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 43.
Showing the top 40 of 64.
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 . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Associate's Degree (or other 2-year degree) | 15.8% | |
| High School Diploma | 14.8% | |
| Post-Secondary Certificate | 14.5% | |
| Some College Courses | 8.1% | |
| Post-Baccalaureate Certificate | 7.4% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 8.0 | |
| Attention to Detail | 7.0 | |
| Integrity | 6.0 | |
| Achievement Orientation | 5.0 | |
| Social Orientation | 4.0 | |
| Perseverance | 3.0 |
| Sales | 6.6 | |
| Business Initiatives | 4.0 | |
| Finance | 3.8 | |
| Office Work | 3.7 | |
| Public Speaking | 3.5 | |
| Marketing/Advertising | 3.2 | |
| Personal Service | 2.9 |
| Enterprising | 6.0 | |
| Conventional | 5.5 | |
| Social | 3.7 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $36,390 |
| 25th percentile | $45,520 |
| Median (50th) | $60,370 |
| 75th percentile | $91,150 |
| 90th percentile | $135,660 |
| People employed | 469,480 |
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 |
|---|---|---|
| Finance and Insurance · Sector | 451,930 | $60,400 |
| Insurance Agencies and Brokerages · National industry | 353,250 | $59,580 |
| Direct Health and Medical Insurance Carriers · National industry | 21,460 | $74,060 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 6,830 | $46,390 |
| Management of Companies and Enterprises · Sector | 3,600 | $80,250 |
| Professional, Scientific, and Technical Services · Sector | 2,490 | $57,000 |
| Retail Trade · Sector | 1,760 | $39,680 |
| Real Estate and Rental and Leasing · Sector | 640 | $46,940 |
| Temporary Help Services · National industry | 240 | $57,130 |
| Health Care and Social Assistance · Sector | 230 | $66,820 |
| Other Services (except Public Administration) · Sector | 220 | $48,530 |
| Manufacturing · Sector | 50 | — |
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 |
|---|---|---|
| Insurance Agencies and Brokerages · National industry | 117.16× | 353,250 |
| Finance and Insurance · Sector | 23.84× | 451,930 |
| Direct Health and Medical Insurance Carriers · National industry | 15.69× | 21,460 |
| Management of Companies and Enterprises · Sector | 0.42× | 3,600 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.25× | 6,830 |
| Real Estate and Rental and Leasing · Sector | 0.09× | 640 |
| Professional, Scientific, and Technical Services · Sector | 0.08× | 2,490 |
| Retail Trade · Sector | 0.04× | 1,760 |
Part of the Financial Services 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 Insurance Sales Agents — 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 90th percentile of 427 international occupations.
Insurance Sales Agents show 93rd-percentile AI task overlap — and about 47,000 annual U.S. openings
Insurance Sales Agents show 93rd-percentile AI task overlap — and about 47,000 annual U.S. openings • Insurance Sales Agents rank in the 93rd 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 47,000 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 (+3.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $60,370, across about 469,480 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 59% 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 — "Insurance Sales Agents". https://singulariki.com/roles/role-41-3021-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
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. "Insurance Sales Agents." 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-41-3021-00
Singulariki. (2026). Insurance Sales Agents. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-41-3021-00
@misc{singulariki-role-41-3021-00,
title = {Insurance Sales Agents},
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-41-3021-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.