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Telephone Operators

Occupation · SOC 43-2021.00

Provide information by accessing alphabetical, geographical, or other directories. Assist customers with special billing requests, such as charges to a third party and credits or refunds for incorrectly dialed numbers or bad connections. May handle emergency calls and assist children or people with physical disabilities to make telephone calls.

Also called: 411 Directory Assistance Operator (411 Directory Assistance Op) · Directory Assistance Operator (Directory Assistance Op) · Phone Operator (Telephone Operator) · TELECOM Op (Telecommunications Operator) · Information Specialist · Live Source Operator (Live Source Op) · Long Distance Operator (LD Operator) · PBX Operator (Post Box Exchange Operator) · Phone Secretary (Telephone Secretary) · Toll Operator (Toll Op) · Central Office Operator (CO Op) · Change Number Operator (Change Number Op)

Job family: Office and Administrative Support Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-43-2021-00/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.

Use as a copilot

Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.

  • Perform clerical duties such as typing, proofreading, and sorting mail. · 0.8%
See collaboration patterns →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Perform clerical duties such as typing, proofreading, and sorting mail. · 98.8% need a human
See the boundary tasks →

92nd-percentile task overlap — yet about 300 openings a year (-27.5% projected, BLS), and observed AI use leans 5556% 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 76th 1.0
LLM task exposure, γ (OpenAI / Eloundou) High 85th 0.9
AI assistant applicability (Microsoft) High 96th 0.3

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.8), with simple added tooling (β 0.9), 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.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

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 · 94th 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.

Suggest and check alternate spellings, locations, or listing formats to customers lacking details or complete information. 0.4%
Perform clerical duties such as typing, proofreading, and sorting mail. 0.2%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Declining · -27.5% by 2034
Projected annual openings 300
Employment 2024 → 2034 4,000 → 2,900

“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.

54% mean task exposure (2025)
92nd percentile of 427 placed occupations
−4 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Telephone Switchboard Operators · 4223 54% 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.

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 55.6% working with AI · 32.1% handed to AI
Most common way people use AI here Iteration · you and AI go back and forth
Typical AI autonomy 3.0 / 5 · higher = AI acts more independently
Used for work (vs. personal / coursework) 88.9%

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
Perform clerical duties such as typing, proofreading, and sorting mail. Iteration 0.8%

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.

Perform clerical duties such as typing, proofreading, and sorting mail. 98.8%

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 perform clerical duties such as typing, proofreading, and sorting mail.

    From: Perform clerical duties such as typing, proofreading, and sorting mail. · 0.8% of measured AI use · task iteration

Tasks

All 16 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Customer and Personal Service 4.2
Telecommunications 3.8
Administrative 3.6
English Language 3.2
Computers and Electronics 3.1
Administration and Management 2.6
Public Safety and Security 2.4
Education and Training 2.3
Personnel and Human Resources 2.2
Communications and Media 2.1

Abilities

Oral Expression 4.1
Oral Comprehension 4.0
Speech Recognition 4.0
Speech Clarity 4.0
Written Comprehension 3.0
Problem Sensitivity 3.0
Selective Attention 3.0
Near Vision 3.0
Deductive Reasoning 2.9
Inductive Reasoning 2.9
Written Expression 2.8
Information Ordering 2.8
Finger Dexterity 2.6
Category Flexibility 2.5
Flexibility of Closure 2.5
Auditory Attention 2.5
Perceptual Speed 2.3

Essential skills

Active Listening 4.0
Speaking 4.0
Reading Comprehension 3.0
Critical Thinking 3.0
Monitoring 2.8
Writing 2.3
Active Learning 2.3

Transferable skills

Service Orientation 3.5
Social Perceptiveness 3.1
Complex Problem Solving 2.6
Coordination 2.5
Time Management 2.5
Judgment and Decision Making 2.3

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Office software Office suite software Hot technology In demand
Microsoft Excel Spreadsheet software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
Computer aided dispatch software Helpdesk or call center software
Handheld computer device software Operating system software
Video conference software Video conferencing software

Work context

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.

Telephone Conversations 5.0
Contact With Others 5.0
Deal With External Customers or the Public in General 4.7
Work With or Contribute to a Work Group or Team 4.7
Face-to-Face Discussions with Individuals and Within Teams 4.6
E-Mail 4.5
Indoors, Environmentally Controlled 4.3
Frequency of Decision Making 4.2
Importance of Being Exact or Accurate 4.2
Spend Time Making Repetitive Motions 4.1
Coordinate or Lead Others in Accomplishing Work Activities 3.9
Dealing With Unpleasant, Angry, or Discourteous People 3.9
Importance of Repeating Same Tasks 3.9
Spend Time Sitting 3.8
Impact of Decisions on Co-workers or Company Results 3.7
Freedom to Make Decisions 3.7
Determine Tasks, Priorities and Goals 3.7
Time Pressure 3.7
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 3.5
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.5
Health and Safety of Other Workers 3.3
Conflict Situations 3.1
Work Outcomes and Results of Other Workers 3.1
Written Letters and Memos 3.0
Physical Proximity 2.9
Level of Competition 2.7
Public Speaking 2.7
Pace Determined by Speed of Equipment 2.7
Degree of Automation 2.6
Spend Time Standing 2.5
Consequence of Error 2.3
Spend Time Bending or Twisting Your Body 2.3
Dealing with Violent or Physically Aggressive People 1.7
Spend Time Walking or Running 1.7
Exposed to Contaminants 1.7
Exposed to Extremely Bright or Inadequate Lighting Conditions 1.6
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 1.6
Exposed to Very Hot or Cold Temperatures 1.6
Spend Time Keeping or Regaining Balance 1.4
Exposed to Whole Body Vibration 1.4

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 97.8%
Associate's Degree (or other 2-year degree) 1.9%
Some College Courses 0.2%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Conventional 6.2
Social 4.0
Enterprising 3.3
Realistic 3.2

Interest areas

Office Work 5.7
Personal Service 3.5
Social Service 2.1
Information Technology 1.7
Sales 1.7

Work styles

Dependability 2.3
Cooperation 2.2
Attention to Detail 2.0
Self-Control 1.8
Social Orientation 1.8
Stress Tolerance 1.8
Integrity 1.7

Wages & employment

U.S. · annual wages (BLS OEWS)

$31k10th$36k25th$39kMedian$49k75th$58k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
4k20243k2034 (proj.)-27.5% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $31,440
25th percentile $35,860
Median (50th) $39,130
75th percentile $48,530
90th percentile $57,510
People employed 3,950

Industries that employ this occupation

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
Health Care and Social Assistance · Sector 2,170 $39,030
Information · Sector 380 $47,760
Accommodation and Food Services · Sector 360 $36,910
Management of Companies and Enterprises · Sector 80 $49,140
Educational Services · Sector 80 $39,090
Retail Trade · Sector 70 $37,710
Finance and Insurance · Sector 60 $35,550

Where this work is most concentrated

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
Information · Sector 5.1× 380
Health Care and Social Assistance · Sector 3.67× 2,170
Accommodation and Food Services · Sector 0.99× 360

Part of the Public Service & Safety career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Telephone Operators sits at the 92nd percentile of AI task-overlap and the 12th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Telephone Operators Telecommunications Equipment Installers and Repairers, Except Line Installers Reservation and Transportation Ticket Agents and Travel Clerks Office Clerks, General Receptionists and Information Clerks Public Safety Telecommunicators Dispatchers, Except Police, Fire, and Ambulance Telemarketers Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel AI task-overlap percentile → ↑ Median pay
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 Telephone Operators — 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.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 92nd percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Telephone Operators show 92nd-percentile AI task overlap — and about 300 annual U.S. openings

  • Telephone Operators rank in the 92nd 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 300 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 (-27.5%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $39,130, across about 3,950 U.S. workers.BLS OEWS (May 2024)
  • Of the AI use actually observed for this work, 56% 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
Telephone Operators show 92nd-percentile AI task overlap — and about 300 annual U.S. openings

• Telephone Operators rank in the 92nd 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 300 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 (-27.5%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $39,130, across about 3,950 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 56% 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 — "Telephone Operators". https://singulariki.com/roles/role-43-2021-00
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.

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.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Telephone Operators." 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-43-2021-00

APA

Singulariki. (2026). Telephone Operators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-43-2021-00

BibTeX 
@misc{singulariki-role-43-2021-00,
  title  = {Telephone Operators},
  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-43-2021-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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