Occupation · SOC 41-9041.00
Solicit donations or orders for goods or services over the telephone.
Also called: Telemarketer · Telephone Sales Representative (TSR) · Telephone Service Representative (TSR) · Telesales Representative (Telesales Rep) · Call Agent · Inside Sales Representative (Inside Sales Rep) · Telemarketing Sales Representative (Telemarketing Sales Rep) · Telesales Specialist · Call Center Agent · Call Center Operator · Call Center Representative (Call Center Rep) · Call Center Sales Agent
Job family: Sales and Related Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-41-9041-00/context.md directly.
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 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.
96th-percentile task overlap — yet about 6,500 openings a year (-22.1% projected, BLS), and observed AI use leans 5742% 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 | 79th | 1.1 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 99th | 0.4 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.6), with simple added tooling (β 0.8), 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 1.0 · 99th 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.
| Explain products or services and prices, and answer questions from customers. | 3.5% | |
| Adjust sales scripts to better target the needs and interests of specific individuals. | 0.5% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -22.1% by 2034 |
| Projected annual openings | 6,500 |
| Employment 2024 → 2034 | 67,400 → 52,500 |
“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 |
|---|---|---|
| Contact Centre Salespersons · 5244 | 61% | Gradient 4 |
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 | 57.4% working with AI · 29.7% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 28.9% |
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 |
|---|---|---|
| Explain products or services and prices, and answer questions from customers. | Learning | 2.0% |
| Adjust sales scripts to better target the needs and interests of specific individuals. | Iteration | 0.5% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Explain products or services and prices, and answer questions from customers. | 100.0% | |
| Adjust sales scripts to better target the needs and interests of specific individuals. | 96.3% |
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 explain products or services and prices, and answer questions from customers. From: Explain products or services and prices, and answer questions from customers. · 2.0% of measured AI use · learning
Help me adjust sales scripts to better target the needs and interests of specific individuals. From: Adjust sales scripts to better target the needs and interests of specific individuals. · 0.5% of measured AI use · task iteration
All 12 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).
| Speaking | 4.1 | |
| Active Listening | 4.0 | |
| Reading Comprehension | 3.3 | |
| Critical Thinking | 3.0 | |
| Writing | 2.5 | |
| Monitoring | 2.3 |
| Persuasion | 4.1 | |
| Service Orientation | 3.5 | |
| Social Perceptiveness | 3.3 | |
| Negotiation | 3.0 | |
| Coordination | 2.5 | |
| Complex Problem Solving | 2.4 | |
| Judgment and Decision Making | 2.4 | |
| Time Management | 2.4 | |
| Instructing | 2.3 |
| Oral Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Speech Clarity | 3.8 | |
| Speech Recognition | 3.6 | |
| Written Comprehension | 3.1 | |
| Selective Attention | 3.1 | |
| Problem Sensitivity | 2.9 | |
| Near Vision | 2.9 | |
| Written Expression | 2.8 | |
| Deductive Reasoning | 2.8 | |
| Inductive Reasoning | 2.6 | |
| Information Ordering | 2.6 | |
| Fluency of Ideas | 2.3 | |
| Originality | 2.3 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Salesforce software | Customer relationship management CRM software | Hot technology |
| Zoom | Video conferencing software | Hot technology |
| Acarda Sales Technologies Acarda Outbound | Helpdesk or call center software | |
| Automatic call distribution software | Helpdesk or call center software | |
| Database Systems Corp Telemation | Customer relationship management CRM software | |
| Microsoft Dynamics | Customer relationship management CRM software | |
| Remote access call center software | Access software | |
| Softphone software | Helpdesk or call center software |
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.
Share of people in this occupation at each level of education.
| High School Diploma | 39.5% | |
| Some College Courses | 37.3% | |
| Less than a High School Diploma | 19.8% | |
| Associate's Degree (or other 2-year degree) | 3.5% |
The interests and personal qualities O*NET associates with people who do this work.
| Sales | 6.5 | |
| Office Work | 5.2 | |
| Marketing/Advertising | 3.3 | |
| Public Speaking | 2.8 | |
| Personal Service | 2.2 | |
| Business Initiatives | 2.1 |
| Enterprising | 5.4 | |
| Conventional | 5.1 | |
| Social | 3.6 | |
| Realistic | 1.9 |
| Social Orientation | 3.0 | |
| Optimism | 2.2 | |
| Perseverance | 2.0 | |
| Self-Control | 1.9 | |
| Achievement Orientation | 1.9 | |
| Stress Tolerance | 1.8 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $24,620 |
| 25th percentile | $29,120 |
| Median (50th) | $34,410 |
| 75th percentile | $38,640 |
| 90th percentile | $48,930 |
| People employed | 66,430 |
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 |
|---|---|---|
| Administrative and Support and Waste Management and Remediation Services · Sector | 47,930 | $33,160 |
| Finance and Insurance · Sector | 4,430 | $47,090 |
| Professional, Scientific, and Technical Services · Sector | 4,230 | $36,360 |
| Construction · Sector | 3,180 | $36,320 |
| Direct Health and Medical Insurance Carriers · National industry | 1,970 | $63,220 |
| Wholesale Trade · Sector | 1,810 | $36,750 |
| Information · Sector | 1,500 | $42,660 |
| Retail Trade · Sector | 1,030 | $35,570 |
| Insurance Agencies and Brokerages · National industry | 880 | $36,250 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 670 | $37,850 |
| Other Services (except Public Administration) · Sector | 550 | $34,300 |
| Management of Companies and Enterprises · Sector | 390 | $43,700 |
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 |
|---|---|---|
| Administrative and Support and Waste Management and Remediation Services · Sector | 12.32× | 47,930 |
| Direct Health and Medical Insurance Carriers · National industry | 10.18× | 1,970 |
| Insurance Agencies and Brokerages · National industry | 2.06× | 880 |
| Finance and Insurance · Sector | 1.65× | 4,430 |
| Plumbing, Heating, and Air-Conditioning Contractors · National industry | 1.23× | 670 |
| Information · Sector | 1.2× | 1,500 |
| Construction · Sector | 0.91× | 3,180 |
| Professional, Scientific, and Technical Services · Sector | 0.91× | 4,230 |
Part of the Marketing & Sales 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 Telemarketers — 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 98th percentile of 427 international occupations.
Telemarketers show 96th-percentile AI task overlap — and about 6,500 annual U.S. openings
Telemarketers show 96th-percentile AI task overlap — and about 6,500 annual U.S. openings • Telemarketers rank in the 96th 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 6,500 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 declining (-22.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $34,410, across about 66,430 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 57% 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 — "Telemarketers". https://singulariki.com/roles/role-41-9041-00 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. "Telemarketers." 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-41-9041-00
Singulariki. (2026). Telemarketers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-41-9041-00
@misc{singulariki-role-41-9041-00,
title = {Telemarketers},
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-41-9041-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.