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Insurance sales agents

Also called Insurance agent, Insurance broker, Life insurance agent, Property and casualty agent, P&C agent

In brief

In May 2025, BLS counted 479,100 US insurance sales agents, with median pay of $62,280 a year. Of the BLS counts since 2003, the May 2025 count is the highest. The earliest figure here is a Census count of 7,000 from 1870, not measured the same way as today's count. The last full technology era for insurance sales agents: Insurtech and AI comparison platforms, 2010 to 2022.

What the sources say

Official outlook

BLS projects employment to grow 3.3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Insurance sales agents: jobs and pay since 1870 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/41-3021

Chicago

Future History. "Insurance sales agents: jobs and pay since 1870." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/41-3021.

BibTeX

@misc{futurehistory_41_3021, author = {{Future History}}, title = {Insurance sales agents: jobs and pay since 1870}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/41-3021}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1850187519001925195019752000now
Country
2026
Known then as Insurance Agent / Soliciting Agent (antebellum mutual company era)
Known then as Insurance Agent / Insurance Canvasser (Gilded Age expansion era)
Known then as Insurance Agent / Debit Agent / Life Insurance Agent (agency reform era)
Known then as Insurance Agent / Property and Casualty Agent / Life Agent (postwar captive-agency era)
Known then as Insurance Agent / Independent Agent / Insurance Broker (deregulation and direct-writer era)
Known then as Insurance Sales Agents (BLS SOC 41-3021)
Known today as Insurance Sales Agents (BLS SOC 41-3021)
Projected to still be called Insurance Sales Agents (BLS SOC 41-3021)
Data from the May 2025 survey
US Employment
479K

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$62,280
Est.

≈ $60,683 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1843

The modern insurance sales agent emerges from mutual life insurance companies chartering across the Northeast. The Mutual Life Insurance Company of New York (1842), Mutual Benefit Life of New Jersey (1845), and Connecticut Mutual Life (1846) deploy "soliciting agents" to canvass prospective policyholders door to door. The shift from passive advertising to active solicitation marks the birth of the insurance sales profession as a distinct occupation. By 1845, Mutual Benefit Life's first field agent was appointed in Norwich, Connecticut, with agents following in Ohio, Washington DC, Massachusetts, and Georgia within months.

Beat · 1871

The Great Chicago Fire of October 1871 burns 3.3 square miles of the city. Of 129 insurance companies covering Chicago properties, only 14 are local; 51 companies fail to pay claims, 16 partially pay, and only 62 pay in full. The catastrophe is a pivotal moment for both the insurance industry and the agent's role: it exposes the problem of agents placing coverage with financially weak carriers and catalyzes a decades-long push for state insurance regulation and carrier solvency standards that agents would increasingly be required to navigate.

Beat · 1896

The National Association of Local Fire Insurance Agents is founded, becoming the first national professional association for insurance agents (later renamed multiple times, becoming the Independent Insurance Agents and Brokers of America in 2002). The founding in 1896 reflects the maturation of independent insurance agency as a distinct professional identity separate from the carriers agents represented. The association would grow to represent over 300,000 agents and their employees.

Beat · 1905

The Armstrong Investigation, commissioned by the New York State Legislature with Charles Evans Hughes as chief counsel, conducts 51 sessions examining the largest life insurance companies. It exposes excessive agent commissions, "twisting" (encouraging policyholders to lapse existing policies and buy new ones, generating fresh commissions at the policyholder's expense), deferred dividend manipulation, and massive political contributions. New York's 1906 reforms outlawed tontine policies, prohibited excessive agent commissions, and created a template for insurance regulation that most US states adopted. The reforms professionalized the occupation by eliminating its worst practices but also reduced the income of individual agents who had relied on abusive compensation structures.

Beat · 1945

The McCarran-Ferguson Act of 1945 returns primary insurance regulation to the states after the Supreme Court's 1944 ruling that the federal government could regulate insurance. State-by-state regulation creates a patchwork of licensing requirements that agents must navigate, making multi-state insurance sales require separate licenses in each state. For much of the 20th century this regulatory structure was the primary barrier to entry that protected agent income from out-of-state competition; it would eventually become a compliance burden as agents sought to serve clients across state lines.

Beat · 1982

Applied Systems launches as the first widely adopted agency management system software, enabling independent insurance agencies to maintain digital policy records on personal computers. The AMS revolution of the 1980s fundamentally reorganizes independent agency operations: agents can now efficiently manage 300-500 accounts on a desktop system that previously required dedicated clerical staff. The productivity gain accelerates independent-agency consolidation and sets the technical foundation for the comparative rating platforms that would follow.

Beat · 1999

The Gramm-Leach-Bliley Act of 1999 repeals the Glass-Steagall Act provisions separating banking from insurance and securities, allowing banks to cross-sell insurance products through their branch networks. The act creates new competition for independent agents from bank-affiliated insurance operations but also creates new distribution opportunities: some agents affiliate with banks as "bancassurance" arrangements, gaining access to bank customer bases for insurance cross-selling. The act also establishes new privacy requirements (GLBA privacy provisions) that insurance agents must comply with in handling client financial information.

Beat · 2010

The Affordable Care Act of 2010 (ACA) creates a new and complex market for individual and small-group health insurance through state and federal exchanges. For insurance agents the ACA is simultaneously a threat and an opportunity: the exchange marketplaces allow consumers to buy health coverage directly online without an agent, but the complexity of plan design, subsidy calculation, and network adequacy creates strong demand for brokers who can navigate the options for individuals and small employers. Health insurance and employee benefits brokers become one of the fastest-growing segments of the occupation through the 2010s and 2020s.

Beat · 2024

The NAIC Model AI Bulletin, adopted in modified form by multiple states in 2023-2024, creates new obligations for insurance producers who recommend AI-assisted or algorithmically-underwritten products: agents must be able to explain adverse underwriting decisions to applicants and document their compliance with fairness requirements. The AI Bulletin represents the first time insurance regulation has directly addressed the agent's role in AI-mediated insurance decisions, creating a new compliance competency that distinguishes knowledgeable brokers from order-takers.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace insurance sales agents? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+3.3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 41-3021 (Insurance sales agents): 572.6 thousand jobs in 2025, projected 591.4 thousand in 2035, a change of +18.8 thousand (+3.3%). Matrix employment counts all jobs in the occupation, including self-employed workers (11.8% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
53%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 41-3021.00 Insurance Sales Agents. Shown: human-rated β = 0.53, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.333 and γ (all LLM-assisted) 0.727; GPT-4-rated β 0.576. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for insurance sales agents

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereManage policyholder records, certificates of insurance, policy documents, and endorsement changes in an Agency Management System (AMS) such as HawkSoft or Applied Epic: process mid-term changes (vehicle additions, address updates, coverage modifications), issue COIs for commercial clients needing proof of insurance for contracts, and maintain accurate coverage records that satisfy E&O documentation standards, tasks that AI-assisted AMS workflows are accelerating but that still require agent-level accuracy sign-off.

Manage policyholder records, certificates of insurance, policy documents, and endorsement changes in an Agency Management System (AMS) such as HawkSoft or Applied Epic: process mid-term changes (vehicle additions, address updates, coverage modifications), issue COIs for commercial clients needing proof of insurance for contracts, and maintain accurate coverage records that satisfy E&O documentation standards, tasks that AI-assisted AMS workflows are accelerating but that still require agent-level accuracy sign-off.[8],[2]

Where your edge is

AMS platforms are automating the mechanical steps of certificate issuance and policy document filing faster each year: HawkSoft and Applied Epic both have AI features that auto-populate endorsement change requests from email. Your role is shifting to quality-control and exception handling: flagging coverage changes that require underwriter approval, catching requests that would create a coverage gap, and maintaining the accuracy of a policy record that drives E&O exposure. Build AMS fluency at the power-user level; agents who know their AMS inside-out handle books larger than those doing manual data entry.

AI is sitting alongside you hereGenerate multi-carrier P&C quotations for personal lines and small-commercial accounts using EZLynx or Applied Rater: enter applicant data once and receive simultaneous quotes from 40+ admitted and surplus-lines carriers in seconds

Generate multi-carrier P&C quotations for personal lines and small-commercial accounts using EZLynx or Applied Rater: enter applicant data once and receive simultaneous quotes from 40+ admitted and surplus-lines carriers in seconds; compare coverage terms, deductibles, and premium side-by-side; present the ranked results to the client with a recommendation that prioritizes coverage adequacy over price alone, the judgment the AI comparison engine cannot supply.[9],[2]

Tools picking this up
Where your edge is

Comparative rating is now table stakes: any agency that does not use a rater is already at a competitive disadvantage on speed. The residual human value is coverage interpretation: explaining why a liability limit is inadequate for a client whose net worth is far higher, or why the carrier offering the lowest premium uses an exclusion that voids coverage in the most probable loss scenario for that client's business. Develop coverage literacy (ISO policy forms, endorsements, exclusions) deep enough to spot those gaps before the client does.

AI is sitting alongside you hereManage policy renewals and X-date follow-up across a book of 300–800 clients using AgencyZoom: configure automated renewal sequences (90-day, 60-day, 30-day, and 7-day touches) with AI-personalized SMS, email, and call-reminder workflows

Manage policy renewals and X-date follow-up across a book of 300–800 clients using AgencyZoom: configure automated renewal sequences (90-day, 60-day, 30-day, and 7-day touches) with AI-personalized SMS, email, and call-reminder workflows; review AgencyZoom's book-analysis dashboard to identify accounts at risk of non-renewal, competitor solicitation flags, or premium increases requiring proactive agent outreach before the renewal date.[10]

Tools picking this up
Where your edge is

AgencyZoom can automate the renewal-touch calendar that previously required an account manager for a large book; agents who deploy it report retaining more renewals than those relying on manual follow-up. Your value on the renewal is not the reminder: it is the annual coverage review conversation that identifies gaps (umbrella needed, flood excluded, business income limit unchanged since 2019) and deepens the relationship before a competing agent can get an X-date appointment.

Where insurance sales agents go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Sales Managers

Top-performing insurance producers who build and lead sales teams (training new agents, managing pipeline metrics, setting production goals, and owning carrier relationship management for the agency) are the natural pipeline for Sales Manager roles. Independent agency principals and regional sales managers at carriers follow exactly this path. AI is changing the Sales Manager role (Salesforce Agentforce and Gong generate forecasts and call analytics automatically), but quota accountability, talent development, and strategic account oversight remain irreducibly human. BLS projects 6% growth for insurance sales agents vs. a flat outlook for sales managers overall: the move is lateral-to-slightly-declining in job growth but can offer significantly higher income for high-producing agents. The transition requires shifting from personal production to team leverage: a different skill than individual selling.

What you'd add
  • · Agency sales pipeline management: Salesforce or HubSpot pipeline analytics, production forecasting, carrier production reporting
  • · Sales coaching methodology using Gong call reviews: structured feedback frameworks and carrier-specific objection-handling playbooks
  • · Carrier relationship management: production agreements, contingency bonus thresholds, profit-sharing structures
  • · Hiring and onboarding: producer licensing timelines, E&O requirements, state-by-state non-compete considerations
  • · AgencyZoom or similar platform administration: configuring automation sequences, book analytics, and producer scorecards
What it takesSome new skills to pick up
Tools of the era

How has the work of insurance sales agents changed since 1843?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Paper policy ledger, mortality tables, and door-to-door canvas (pre-telephone soliciting agent era)

    The antebellum insurance agent worked entirely without technology beyond printed mortality tables, paper application forms, and a rate book issued by the carrier. Prospecting meant physically walking door to door in a territory. Policy proposals required arithmetic on mortality tables and premium calculation performed by hand. Applications were completed in ink, mailed to the home office for underwriting review, and policies returned by post. The agent knew every client personally and collected premiums on periodic rounds of a defined territory, recording everything in a handwritten ledger. The occupation required trust, persistence, and numeracy more than any specialized tool.

  • Telephone prospecting and typewriter-produced proposals (mass-market agency era)

    The telephone, widely available in American business by 1910, transformed insurance prospecting from a purely physical door-to-door canvas into a two-step process: phone first to qualify the prospect, then visit in person to close. The typewriter produced professional-looking policy summaries and proposals that replaced handwritten documents and helped agents project institutional authority. Metropolitan Life and Prudential, the largest industrial insurance carriers, built systematic telephone-based referral networks. The Armstrong Investigation of 1905-06 reformed commission structures and outlawed the most abusive sales practices, professionalizing the occupation's outward presentation even as its core methods remained relationship-based.

  • Mainframe-backed rate books and IBM tabulating systems at carriers (postwar captive-agency era)

    Travelers Insurance Company was among the first insurance carriers to install an IBM mainframe computer in the 1960s, using it to automate premium calculations, policy administration, and claims processing at the home office. For agents in the field this era changed the home-office backend without yet touching the sales encounter itself: agents still quoted from printed rate books, completed handwritten or typewritten applications, and mailed submissions to underwriters. What changed was turnaround time and policy accuracy: the mainframe-processed policy arrived at the customer faster and with fewer arithmetic errors than the manual system it replaced. IBM tabulating machines at larger carriers had already, by the 1930s, enabled agents' book-level analytics by policy number and premium.

  • Agency Management Systems (Applied Systems, TAM, AMS) and personal computer quoting

    Applied Systems launched in the early 1980s as the first widely adopted agency management system (AMS) software for independent agencies, enabling agents to maintain digital policy records, generate renewal notices, track commissions, and produce certificates of insurance from a desktop PC. Personal computers brought the power of the mainframe to the agency office for the first time. For independent agents this was transformative: a single agent or small office could now efficiently manage a book of hundreds of accounts that previously required dedicated clerical staff. By the late 1980s, comparative rating software allowed agents to generate quotes from multiple carriers simultaneously rather than consulting each carrier's printed rate manual separately. The era vastly increased individual agent productivity and accelerated independent-agency growth at the expense of captive agency networks.

    Effect on the work

    Agency management systems enabled individual agents to manage more accounts than the pre-computer era, contributing to consolidation of smaller agencies into larger independents. The Independent Insurance Agents of America (renamed 1975 from NAIA) grew its membership to represent over 300,000 agents and their employees as the independent system expanded.

  • Direct-to-consumer telephone and internet insurance (GEICO 1993, Progressive internet 1999, online comparison 2000s)

    In 1993 Progressive became the first insurer to offer comparison rates and phone purchase of auto insurance, directly bypassing agents for commodity personal auto policies. By 1999 insurance comparison websites had launched, and by the early 2000s consumers could obtain, compare, and bind personal auto and homeowners insurance without any agent involvement. The displacement was concentrated in the commodity personal lines segment: straightforward auto and home policies with standard risks. Complex commercial insurance, life insurance with estate-planning applications, and group employee benefits resisted disintermediation because the complexity exceeded what a form and an algorithm could handle.

    Effect on the work

    Between the 2000s peak and 2024, direct-to-consumer personal lines disintermediation was a contributing factor to the occupation's failure to grow despite broader economic expansion. However, commercial lines and benefits broker employment grew over the same period, partially offsetting the personal-lines agent contraction.

  • Insurtech and AI comparison platforms (Insurify 2013, The Zebra 2012, Lemonade 2015, comparative rater APIs)

    The insurtech wave of the 2010s applied machine learning to insurance distribution at scale. Insurify (founded 2013) used AI to personalize multi-carrier quote comparisons across many carriers for personal auto and home insurance; The Zebra (founded 2012) built a real-time auto insurance comparison engine used by millions. Lemonade (founded 2015) used AI-driven chatbots to underwrite and bind renters and home insurance in seconds. EZLynx and Applied Rater brought automated comparative rating to independent agents for P&C lines, and AgencyZoom brought AI-powered renewal automation to agency books of business. The decade bifurcated the occupation sharply: commodity personal lines agents faced structural substitution while independent commercial and benefits brokers gained powerful AI tools that amplified their productivity.

  • Generative AI tools for proposals, CRM intelligence, and compliance (ChatGPT, Claude, Salesforce Einstein, Gong)

    ChatGPT's launch in November 2022 and Claude's in March 2023 gave insurance agents general-purpose assistants for drafting proposal narratives, coverage comparison summaries, and client follow-up communications. Salesforce Financial Services Cloud, which unifies data from insurance platforms around the customer, embedded AI in the CRM that many larger agencies and carrier sales teams used. Gong's conversation-intelligence platform let agents review recorded discovery calls and improve their qualifying technique. The generative AI era strengthened independent commercial agents and benefits brokers who could leverage document-generation and pipeline-management AI while adding only marginal uplift to commodity personal lines agents whose sales are increasingly handled by comparison engines.

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Insurance Sales Agents employment and pay by year

On record since1843
Latest US employment479,100 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$62,280 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+3.3% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 63100)

    Employment, 2020
    77,515, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 63100 Insurance agents and brokers, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$60,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 63100 Insurance agents and brokers, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.

  • Australia (ANZSCO 6112)

    Employment, 2025
    16,200, ABS Labour Force Survey Official statistic Report an errorABS Labour Force Survey (Detailed), table EQ08: employed persons in ANZSCO 6112 Insurance Agents, national (summed across sex and state/territory), annual average of 4 quarterly estimates.
    Median pay, 2025
    A$81,068, ABS Employee Earnings May 2025 Official statistic Report an errorAnnualised (x52) from A$1,559 median weekly earnings. ABS Survey of Employee Earnings and Hours, May 2025: median pay for full-time non-managerial employees paid at the adult rate, before tax. Surfaced via the Jobs and Skills Australia occupation profile.
View all 29 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
18707,000n/aDecennial Census 1870 Researched contextReport
190053,000n/aDecennial Census 1900 Researched contextReport
1930151,000n/aDecennial Census 1940 Researched contextReport
1960312,000$6,500Decennial Census 1960 (employment) Researched contextEst., University of Missouri Libraries (pay) Researched contextReport
1990415,000$28,000BLS CPS Researched contextReport
2000240,830$38,750BLS OEWS 2000 Official statisticReport
2003277,120$40,040BLS OEWS May 2003 Official statisticReport
2004285,390$41,720BLS OEWS May 2004 Official statisticReport
2005299,470$42,340BLS OEWS May 2005 Official statisticReport
2006311,380$43,870BLS OEWS May 2006 Official statisticReport
2007321,920$44,110BLS OEWS May 2007 Official statisticReport
2008327,780$45,430BLS OEWS May 2008 Official statisticReport
2009325,710$45,500BLS OEWS May 2009 Official statisticReport
2010318,800$46,770BLS OEWS May 2010 Official statisticReport
2011321,780$47,450BLS OEWS May 2011 Official statisticReport
2012336,740$48,150BLS OEWS May 2012 Official statisticReport
2013354,460$48,210BLS OEWS May 2013 Official statisticReport
2014374,700$47,860BLS OEWS May 2014 Official statisticReport
2015386,140$48,200BLS OEWS May 2015 Official statisticReport
2016385,700$49,990BLS OEWS May 2016 Official statisticReport
2017386,320$49,710BLS OEWS May 2017 Official statisticReport
2018393,830$50,600BLS OEWS May 2018 Official statisticReport
2019410,050$50,940BLS OEWS May 2019 Official statisticReport
2020409,950$52,180BLS OEWS May 2020 Official statisticReport
2021422,600$49,840BLS OEWS May 2021 Official statisticReport
2022445,540$57,860BLS OEWS May 2022 Official statisticReport
2023457,510$59,080BLS OEWS May 2023 Official statisticReport
2024469,480$60,370BLS OEWS May 2024 Official statisticReport
2025479,100$62,280BLS OEWS May 2025 Official statisticReport
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