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Customer service representatives

Also called Customer service rep, CSR, Customer support specialist, Customer care representative, Call center agent

In brief

In May 2025, BLS counted 2,595,750 US customer service representatives, with median pay of $44,770 a year. Of the BLS counts since 2003, the highest was 2,919,230, in May 2019. The earliest figure here is a Census count of 178,000 from 1920, not measured the same way as today's count. The last full technology era for customer service representatives: Generative AI chatbots, 2022 to 2024.

What the sources say

Official outlook

BLS projects employment to decline 5.3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 70.5% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 0.55 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013

Since the 2013 prediction

In 2013, Frey and Osborne put this job's probability of computerisation at 0.55. BLS counted 2,389,580 of these jobs in May 2013 and 2,595,750 in May 2025 (+9%, a change derived by Future History).

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). Customer service representatives: jobs and pay since 1920 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/43-4051

Chicago

Future History. "Customer service representatives: jobs and pay since 1920." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/43-4051.

BibTeX

@misc{futurehistory_43_4051, author = {{Future History}}, title = {Customer service representatives: jobs and pay since 1920}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/43-4051}}, 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
19001925195019752000now
Country
2026
Known then as Telephone Operator / Switchboard Operator
Known then as Telephone Operator (declining) / Order-Taker / Information Clerk
Known then as Customer Service Representative / Call Center Agent
Known then as Customer Service Representative
Known today as Customer Service Representative
Projected to still be called Customer Service Representative
Data from the May 2025 survey
US Employment
2.60M
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$44,770

≈ $43,622 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1876

Alexander Graham Bell is awarded US Patent 174,465 for the telephone on March 7, 1876. The patent is one of the most contested in US history (Elisha Gray filed the same day); Bell prevails and licenses the technology exclusively, creating the conditions for AT&T's operator monopoly.

Beat · 1878

Boston Telephone Dispatch hires Emma Nutt on September 1, 1878: the world's first female telephone operator. Her sister Stella followed hours later as the second. The company had started with boys as operators but replaced them within months because they were rude and prone to pranks. Emma Nutt reportedly had a soothing voice and outstanding memory; she stayed with the company for 33 years.

Beat · 1951

The first Direct Distance Dialing call in the United States is placed on November 10, 1951, from Englewood, New Jersey to Alameda, California. Mayor M. Leslie Denning of Englewood dials the call himself without operator assistance. This moment begins the 27-year phase-out of long-distance telephone operators.

Beat · 1967

AT&T launches commercial InWATS (1-800) toll-free service in 1967, enabling businesses to receive national inbound calls at their own expense. This single infrastructure change created the economic basis for the dedicated customer-service call center.

Beat · 1973

Continental Airlines installs a Rockwell Galaxy Automatic Call Distributor: one of the first large-scale ACD systems deployed for a dedicated business call center. The system routes reservation calls across agent pools automatically; Continental uses it for 23 years. The same year marks the first documented use of the term "call center" in the industry.

Beat · 1999

Salesforce launches on March 8, 1999, as the first cloud-based CRM: a browser-accessible customer database that agents could reach from any computer. It eventually displaces Siebel Systems (founded 1993) as the dominant enterprise platform and becomes the operating system of the customer-service agent desktop.

Beat · 2024

February 14, 2024: British Columbia's Civil Resolution Tribunal rules in Moffatt v. Air Canada that the airline is liable for false information provided by its AI chatbot, the chatbot had incorrectly told a bereaved traveler he could claim a bereavement fare retroactively. Air Canada's argument that the chatbot was "a separate legal entity responsible for its own actions" was rejected. The ruling establishes that companies cannot disclaim responsibility for what their AI agents say.

Beat · 2024

February 27, 2024: Klarna announces that its OpenAI-powered AI assistant handled 2.3 million customer service conversations in its first month of operation, two-thirds of all Klarna customer service volume, with an average resolution time of under 2 minutes (vs. 11 minutes for human agents), a 25% drop in repeat contacts, and a customer satisfaction score equivalent to human agents. The company estimates the chatbot does the work of 700 full-time customer service employees and projects a $40 million profit improvement for the year. This is the most cited real-world substitution data point in any occupation as of 2024.

Beat · 2025

Sierra (Bret Taylor and Clay Bavor's AI customer-agent company, launched February 2024) reaches $100M annual recurring revenue in seven quarters: among the fastest revenue ramps in enterprise software history. Its clients include Prudential, Cigna, Blue Cross Blue Shield, Rocket Mortgage, and one in three of the world's largest banks. By May 2026, Sierra raises $950M at a $15.8B valuation. Decagon (founded August 2023) reaches $4.5B valuation by January 2026. The customer-service AI industry has moved from pilot to production to consolidation in under 24 months.

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 customer service representatives? 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
-5.3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 43-4051 (Customer service representatives): 2,666.0 thousand jobs in 2025, projected 2,524.1 thousand in 2035, a change of -141.8 thousand (-5.3%). Matrix employment counts all jobs in the occupation, including self-employed workers (0.5% 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)
70.5%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 43-4051.00 Customer Service Representatives. Shown: human-rated β = 0.705, 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) 0.545 and γ (all LLM-assisted) 0.864; GPT-4-rated β 0.568. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Frey & Osborne (2013), "The Future of Employment"
2013 study · Probability of computerisation (2013 model)
55%
probability
What this measures

Published appendix value for SOC 2010 43-4051 Customer Service Representatives: probability of computerisation 0.55, rank 315 of 702 (rank 1 is least computerisable). Frey and Osborne estimated in 2013, with a Gaussian-process classifier on O*NET variables, how likely the whole occupation is to be technically automatable over "some unspecified number of years, perhaps a decade or two". It is a probability for the occupation, not a share of tasks and not a forecast of jobs lost.

Official statisticReport an error
Today, in this role

What's changing day to day for customer service representatives

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

AI is taking this onHandle escalated inquiries that AI agents (Fin, Zendesk AI, Salesforce Agentforce) were unable to resolve (multi-system edge cases, ambiguous account situations, and callers who explicitly requested a human) applying judgment the AI's playbook doesn't cover.

Handle escalated inquiries that AI agents (Fin, Zendesk AI, Salesforce Agentforce) were unable to resolve (multi-system edge cases, ambiguous account situations, and callers who explicitly requested a human) applying judgment the AI's playbook doesn't cover.[4],[9],[1]

Where your edge is

Your value is no longer handling volume: it's handling the hard ones. Develop deep product knowledge and a structured escalation checklist so your resolution rate on AI-fallthrough cases is measurably higher than the AI's. Document every resolution pattern you develop: those patterns feed back into the AI's training data and make you indispensable.

AI is taking this onHandle inbound phone calls transferred from voice AI systems (Cresta AI Agent, IVR-to-Hume pipelines): calls where the AI identified a complexity threshold, detected high customer distress, or hit a policy decision point requiring human authorization.

Handle inbound phone calls transferred from voice AI systems (Cresta AI Agent, IVR-to-Hume pipelines): calls where the AI identified a complexity threshold, detected high customer distress, or hit a policy decision point requiring human authorization.[11],[12],[13]

Where your edge is

As voice AI handles the easy calls, every call that reaches you is harder than before. Invest in listening skills: when a caller has already been through an AI, they are often frustrated. Lead with acknowledgment before problem-solving. Your ability to recover a frustrated caller from an AI-failed interaction is the clearest value you provide.

AI is sitting alongside you hereReview and correct AI-generated CRM case summaries and interaction logs in Salesforce or Zendesk, verifying resolution accuracy, adding context the AI missed, and tagging records with the right disposition codes for downstream quality analysis.

Review and correct AI-generated CRM case summaries and interaction logs in Salesforce or Zendesk, verifying resolution accuracy, adding context the AI missed, and tagging records with the right disposition codes for downstream quality analysis.[14],[15]

Where your edge is

Think of every case note you write or correct as training data for the AI. Flag patterns where the AI consistently mis-categorizes or misses context: those flags are how you demonstrate value to CX operations leadership and position yourself for a QA or AI trainer role.

Where customer service representatives go next

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

What each move takes

A direction you could grow

Training and Development Specialists

Customer service reps already understand exactly what AI agents get wrong, because they see the fallthrough calls every shift. That domain knowledge is the core input to a conversation designer or AI trainer role, which are classified under Training and Development Specialists (13-1151.00). Companies deploying AI customer service agents need people who can translate real call patterns into training data, write knowledge base articles, and QA conversational flows. The Conversation Design Institute and Coursera both offer sub-$1,000 certification paths. This pivot is available in-company (transition without changing employers) for CSRs working at AI-forward companies, and available externally through an expanding job market for AI trainers and conversation designers.

What you'd add
  • · Conversation design fundamentals (Conversation Design Institute certification, $400-500)
  • · Intent classification and entity extraction basics (no coding required)
  • · Writing and editing AI knowledge base articles and response templates
  • · Measuring AI agent performance (resolution rate, CSAT, fallthrough patterns)
  • · Adult learning principles for internal AI tool training programs
What it takesSome new skills to pick up
Tools of the era

How has the work of customer service representatives changed since 1878?

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

  • Manual cord switchboard

    The original customer-facing telephone interface: a human operator manually connected callers by inserting cords into the correct jacks on a physical switchboard. Every call required a live person. The job was simultaneously the most technologically sophisticated role available to young women and the most physically demanding: operators stood or sat at boards covering walls, answering requests with split-second precision. Ringing, routing, and disconnecting were all manual.

    Effect on the work

    Drove rapid employment growth from near-zero in 1878 to 178,000 female operators by 1920. AT&T became the largest US employer; operators were over half its workforce.

  • Dial telephone (step-by-step switching)

    The first dial telephone exchange opened in Chesapeake & Potomac Telephone Co. in Norfolk, Virginia in 1919. Callers could now place local calls without an operator. The technology spread city by city over the following decades, mechanizing the most routine part of operator work. By 1930, a sizeable share of Bell telephones were on dial. Across several cities studied by BLS, mechanization reduced operator headcount relative to what employment would have been at 1921 productivity levels.

    Effect on the work

    Operator employment peaked around 350,000 (AT&T, late 1940s) and then began declining sharply in the 1950s as dial penetration hit critical mass. This is the most precisely documented prior technology-displacement in the occupation lineage.

  • Direct Distance Dialing (DDD)

    The first Direct Distance Dialing call in the US was placed on November 10, 1951 (Englewood, NJ to Alameda, CA). DDD eliminated the need for long-distance operators by letting callers direct-dial area codes nationwide. Long-distance call volume had been one of the last high-skilled segments of operator work: complex routing, time-zone math, pricing look-ups. Its automation removed the final pillar of the occupation. By the late 1970s, telephone-company operator employment had collapsed from its 1950 peak to near-zero.

    Effect on the work

    The occupation of telephone operator (43-2021) would shrink between 1950 and 2021. The 1978 full-phase-out is the clearest historical precedent for AI-driven occupational elimination: a headcount decline over decades.

  • AT&T 1-800 InWATS toll-free number

    AT&T launched commercial toll-free InWATS (Inward Wide Area Telephone Service) in 1967, allowing businesses to receive calls at their own expense. This created the economic infrastructure for dedicated inbound customer service: hotels, airlines, mail-order catalogs, and banks could now invite nationwide calls without the caller paying long-distance charges. The 1-800 number is what made "call us anytime" a viable business model and defined the call-center workforce as a distinct occupation rather than a telephone-company function.

  • Automatic Call Distribution (ACD) + dedicated call centers

    Continental Airlines installed the Rockwell Galaxy Automatic Call Distributor in 1973: one of the first large-scale ACDs used for a dedicated business call center (the airline used it for decades). ACD technology routed incoming calls automatically across agent pools, enabling organizations to run purpose-built operations with hundreds of seats rather than routing everything through a hotel-style switchboard. This is the origin of the modern call center as a business unit.

  • IVR (Interactive Voice Response): "Press 1 for sales"

    DTMF-based IVR systems reached mass commercial deployment in the early 1980s after hard drive costs fell enough to make digitized voice storage affordable. Leon Ferber's Perception Technology was the first mainstream vendor. IVR deflected the simplest customer inquiries (account balances, store hours, order status) away from live agents, establishing the "self-service before human" call-flow pattern that defines the industry to this day. By the mid-1990s, IVR was ubiquitous in banking, utilities, and telecoms.

    Effect on the work

    IVR diverted a sizeable share of inbound call volume that would otherwise have reached live agents in mature deployments. It compressed per-agent call complexity upward: the easiest calls disappeared; the harder ones remained.

  • CRM software: Siebel (1993), Salesforce (1999)

    Tom Siebel left Oracle in 1993 to build Siebel Systems, the first dedicated sales-force and customer-service automation platform. It gave agents a unified view of the customer across channels for the first time. Salesforce launched on March 8, 1999 as the first cloud CRM, moving the agent desktop from installed client software to a browser tab. CRM standardized and scripted the agent's work: issue categories, resolution paths, escalation rules, and SLA timers. The agent role shifted from judgment-heavy to protocol-following.

  • Offshore call-center wave (India + Philippines)

    The offshore customer-service industry industrialized in the early 2000s, concentrated in India (Convergys, Genpact, Wipro BPO, Infosys BPO) and the Philippines (which surpassed India in voice services by 2012). Labor arbitrage, CSRs at a sizeable share of US wage equivalents, put permanent downward pressure on domestic agent wages and employment. The model worked for high-volume, scripted, English-language contact; it eroded quality on complex or culturally-nuanced interactions, generating the consumer backlash that fueled demand for domestic agents in premium segments.

    Effect on the work

    Net US domestic employment was still growing in aggregate (demand from e-commerce more than offset offshore migration), but the wage floor was structurally suppressed.

  • Email + live chat + ticketing: Zendesk (2007), Intercom (2011)

    Zendesk was founded in Copenhagen in 2007 as a web-based ticket-management platform; Intercom followed in August 2011 with a messaging-first approach. Both platforms moved customer service off the phone and onto typed digital channels: email, live chat, in-app messaging. This broadened the addressable market for CSR work (non-phone-comfortable customers could now get help) while also creating richer interaction logs that could be mined for automation training. The multichannel agent, handling simultaneous chats rather than sequential calls, became the modal hire.

  • Rule-based chatbots: Ada, Drift, early Intercom Fin

    The mid-2010s chatbot wave used decision-tree logic and keyword matching rather than language models. Platforms like Ada (founded 2016), Drift (2014), and early Intercom automation offered "FAQ deflection", intercepting common queries before they reached an agent. These systems handled a sizeable share of inbound volume on well-implemented deployments, but broke immediately on anything outside their scripted paths. Customer frustration with bot loops ("I want to speak to a human") created a strong consumer backlash that constrained adoption.

  • Generative AI chatbots: ChatGPT, Intercom Fin (2023)

    ChatGPT (November 30, 2022) was the first LLM that could handle open-ended customer service conversations without a decision tree. Intercom launched "Fin", its GPT-4-powered support agent, in March 2023, allowing it to resolve customer issues directly from a company's knowledge base. Within months, every major customer service platform had announced an AI layer. The qualitative change: rule-based bots broke on edge cases; LLMs handled edge cases natively while occasionally hallucinating facts, introducing a new failure mode (AI confident-wrong) alongside massive deflection capacity.

  • AI customer agents at scale: Sierra, Decagon, Klarna, GPT-4o voice

    The 2024 enterprise AI-customer-service deployment wave was structurally different from prior chatbot experiments: it was backed by CFO sign-off, not pilot teams. Sierra (founded February 2024 by Bret Taylor and Clay Bavor) built purpose-designed AI agents for Prudential, Cigna, and one in three of the world's largest banks. Decagon (founded August 2023) replaced entire support-tier workflows for enterprises including Avis Budget Group and Deutsche Telekom. Klarna's February 27, 2024 announcement was the landmark: its OpenAI-powered chatbot handled 2.3 million conversations in its first month, equivalent to hundreds of full-time agents, resolving issues in minutes, with a 25% drop in repeat contacts. OpenAI launched GPT-4o on May 13, 2024 with explicit real-time voice customer-service capability. The Air Canada chatbot lawsuit (BC Civil Resolution Tribunal, February 14, 2024) established that companies remain liable for AI chatbot misinformation: the first major legal constraint on the space.

    Effect on the work

    Data USA reports 43-4051 headcount fell through 2024 in the BLS OEWS May 2024 data: a 140,000-position decline in a single year. Sierra reached $100M ARR in seven quarters, faster than almost any enterprise software company in history.

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Customer Service Representatives employment and pay by year

On record since1878
Latest US employment2,595,750 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$44,770 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook-5.3% 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 64409)

    Employment, 2020
    243,205, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 64409 Other customer and information services representatives, 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$48,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 64409 Other customer and information services representatives, 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 5412)

    Employment, 2025
    112,400, ABS Labour Force Survey Official statistic Report an errorABS Labour Force Survey (Detailed), table EQ08: employed persons in Customer Service Representatives (Call or Contact Centre Workers and Information Officers), national (summed across sex and state/territory), taken as the sum of the cited cells for ANZSCO 5411 + 5412 (the US occupation has no single ANZSCO equivalent), annual average of 4 quarterly estimates.
    Median pay, 2025
    A$76,024, ABS Employee Earnings May 2025 Official statistic Report an errorAnnualised (x52) from A$1,462 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. This is the cited median of the dominant constituent group (ANZSCO 5412 Information Officers) within the combined Customer Service Representatives (Call or Contact Centre Workers and Information Officers) (ANZSCO 5411 + 5412), not a blend of the constituent medians.
View all 30 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1920178,000n/aDecennial Census 1920 Researched contextReport
1950350,000n/aEst., Conversable Economist Researched contextReport
1970420,000n/aEst., Wikipedia Researched contextReport
19901,200,000n/aEst., CallCentreHelper.com Researched contextReport
1999n/a$26,913Census 2000 Researched contextReport
20001,907,890n/aBLS OEWS 2000 Official statisticReport
2001n/a$27,450 (average)BLS OEWS 2001 Official statisticReport
20031,902,850$26,500BLS OEWS May 2003 Official statisticReport
20042,021,350$27,020BLS OEWS May 2004 Official statisticReport
20052,067,700$27,490BLS OEWS May 2005 Official statisticReport
20062,147,770$28,330BLS OEWS May 2006 Official statisticReport
20072,193,430$29,040BLS OEWS May 2007 Official statisticReport
20082,233,270$29,860BLS OEWS May 2008 Official statisticReport
20092,195,860$30,290BLS OEWS May 2009 Official statisticReport
20102,146,120$30,460BLS OEWS May 2010 Official statisticReport
20112,212,820$30,610BLS OEWS May 2011 Official statisticReport
20122,299,750$30,580BLS OEWS May 2012 Official statisticReport
20132,389,580$30,870BLS OEWS May 2013 Official statisticReport
20142,511,130$31,200BLS OEWS May 2014 Official statisticReport
20152,595,990$31,720BLS OEWS May 2015 Official statisticReport
20162,707,040$32,300BLS OEWS May 2016 Official statisticReport
20172,767,790$32,890BLS OEWS May 2017 Official statisticReport
20182,871,400$33,750BLS OEWS May 2018 Official statisticReport
20192,919,230$34,710BLS OEWS May 2019 Official statisticReport
20202,833,250$35,830BLS OEWS May 2020 Official statisticReport
20212,787,070$36,920BLS OEWS May 2021 Official statisticReport
20222,879,840$37,780BLS OEWS May 2022 Official statisticReport
20232,858,710$39,680BLS OEWS May 2023 (employment) Official statisticBLS OEWS May 2023 (pay) Official statisticReport
20242,725,930$42,830BLS OEWS May 2024 Official statisticReport
20252,595,750$44,770BLS OEWS May 2025 Official statisticReport
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