Occupation · SOC 43-4171.00
Answer inquiries and provide information to the general public, customers, visitors, and other interested parties regarding activities conducted at establishment and location of departments, offices, and employees within the organization.
Also called: Front Desk Receptionist · Greeter · Office Assistant · Receptionist · Clerk Specialist · Information Assistant (Info Assistant) · Medical Receptionist · Member Services Representative (Member Services Rep) · Registration Clerk · Scheduler · Appointment Clerk · Appointment Scheduler
Job family: Office and Administrative Support Occupations
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A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
76th-percentile task overlap — yet about 128,500 openings a year (+0% projected, BLS), and observed AI use leans 3313% 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 | 75th | 1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 73rd | 0.9 | |
| AI assistant applicability (Microsoft) High | 78th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.5), 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.
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 · 91st 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.
| Analyze data to determine answers to questions from customers or members of the public. | 7.2% | |
| Transmit information or documents to customers, using computer, mail, or facsimile machine. | 3.4% | |
| Calculate and quote rates for tours, stocks, insurance policies, or other products or services. | 2.2% | |
| Schedule appointments and maintain and update appointment calendars. | 0.7% | |
| Process and prepare memos, correspondence, travel vouchers, or other documents. | 0.7% | |
| Provide information about establishment, such as location of departments or offices, employees within the organization, or services provided. | 0.6% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · 0.0% by 2034 |
| Projected annual openings | 128,500 |
| Employment 2024 → 2034 | 1,007,200 → 1,007,600 |
“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 3 occupations 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 Information Clerks · 4222 | 58% | Gradient 3 |
| Inquiry Clerks · 4225 | 57% | Gradient 3 |
| Receptionists (general) · 4226 | 56% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 33.1% working with AI · 55.1% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 76.7% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Process and prepare memos, correspondence, travel vouchers, or other documents. | Iteration | 3.9% |
| Schedule appointments and maintain and update appointment calendars. | Directive | 1.2% |
| Analyze data to determine answers to questions from customers or members of the public. | Directive | 0.7% |
| Schedule space or equipment for special programs and prepare lists of participants. | Directive | 0.5% |
| Perform administrative support tasks, such as proofreading, transcribing handwritten information, or operating calculators or computers to work with pay records, invoices, balance sheets, or other documents. | Directive | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Schedule space or equipment for special programs and prepare lists of participants. | 100.0% | |
| Perform administrative support tasks, such as proofreading, transcribing handwritten information, or operating calculators or computers to work with pay records, invoices, balance sheets, or other documents. | 100.0% | |
| Schedule appointments and maintain and update appointment calendars. | 99.2% | |
| Process and prepare memos, correspondence, travel vouchers, or other documents. | 96.7% | |
| Analyze data to determine answers to questions from customers or members of the public. | 91.7% |
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 process and prepare memos, correspondence, travel vouchers, or other documents. From: Process and prepare memos, correspondence, travel vouchers, or other documents. · 3.9% of measured AI use · task iteration
Help me schedule appointments and maintain and update appointment calendars. From: Schedule appointments and maintain and update appointment calendars. · 1.2% of measured AI use · directive
Help me analyze data to determine answers to questions from customers or members of the public. From: Analyze data to determine answers to questions from customers or members of the public. · 0.7% of measured AI use · directive
Help me schedule space or equipment for special programs and prepare lists of participants. From: Schedule space or equipment for special programs and prepare lists of participants. · 0.5% of measured AI use · directive
All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Oral Expression | 4.0 | |
| Oral Comprehension | 3.9 | |
| Speech Recognition | 3.9 | |
| Speech Clarity | 3.8 | |
| Written Comprehension | 3.6 | |
| Written Expression | 3.4 | |
| Near Vision | 3.1 | |
| Selective Attention | 3.0 | |
| Time Sharing | 2.9 | |
| Problem Sensitivity | 2.8 | |
| Inductive Reasoning | 2.8 | |
| Information Ordering | 2.8 | |
| Far Vision | 2.8 | |
| Deductive Reasoning | 2.6 | |
| Category Flexibility | 2.6 | |
| Perceptual Speed | 2.6 |
| Speaking | 3.9 | |
| Active Listening | 3.8 | |
| Reading Comprehension | 3.1 | |
| Critical Thinking | 3.1 | |
| Writing | 3.0 | |
| Monitoring | 2.9 | |
| Active Learning | 2.8 |
| Service Orientation | 3.6 | |
| Social Perceptiveness | 3.1 | |
| Coordination | 3.0 | |
| Time Management | 2.9 | |
| Complex Problem Solving | 2.8 | |
| Persuasion | 2.6 | |
| Instructing | 2.6 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 46.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Agriculture, Agriculture Operations, and Related Sciences , Business, Management, Marketing, and Related Support Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 49.5% | |
| Some College Courses | 25.7% | |
| Post-Secondary Certificate | 20.9% | |
| Associate's Degree (or other 2-year degree) | 3.7% | |
| Less than a High School Diploma | 0.3% |
The interests and personal qualities O*NET associates with people who do this work.
| Office Work | 6.4 | |
| Personal Service | 4.7 | |
| Health Care Service | 2.5 | |
| Human Resources | 2.1 | |
| Management/Administration | 2.0 | |
| Accounting | 2.0 | |
| Public Speaking | 1.8 |
| Conventional | 6.3 | |
| Enterprising | 4.3 | |
| Social | 3.6 | |
| Realistic | 1.9 |
| Dependability | 3.0 | |
| Cooperation | 2.5 | |
| Social Orientation | 2.3 | |
| Attention to Detail | 1.9 | |
| Empathy | 1.9 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $28,280 |
| 25th percentile | $32,660 |
| Median (50th) | $37,230 |
| 75th percentile | $44,070 |
| 90th percentile | $48,870 |
| People employed | 964,530 |
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 | 449,730 | $38,410 |
| Other Services (except Public Administration) · Sector | 109,250 | $33,950 |
| Professional, Scientific, and Technical Services · Sector | 107,660 | $37,540 |
| Arts, Entertainment, and Recreation · Sector | 61,270 | $31,150 |
| Veterinary Services · National industry | 59,110 | $36,900 |
| Fitness and Recreational Sports Centers · National industry | 51,450 | $30,370 |
| Retail Trade · Sector | 40,400 | $34,770 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 33,630 | $38,330 |
| Educational Services · Sector | 32,020 | $36,120 |
| Finance and Insurance · Sector | 22,740 | $39,320 |
| Real Estate and Rental and Leasing · Sector | 22,740 | $37,390 |
| Offices of Chiropractors · National industry | 18,300 | $37,130 |
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 |
|---|---|---|
| Veterinary Services · National industry | 20.37× | 59,110 |
| Offices of Chiropractors · National industry | 20.05× | 18,300 |
| Offices of Optometrists · National industry | 15.42× | 14,710 |
| Fitness and Recreational Sports Centers · National industry | 13.05× | 51,450 |
| Offices of Physical, Occupational and Speech Therapists, and Audiologists · National industry | 5.08× | 15,160 |
| Other Services (except Public Administration) · Sector | 3.95× | 109,250 |
| Arts, Entertainment, and Recreation · Sector | 3.71× | 61,270 |
| Health Care and Social Assistance · Sector | 3.11× | 449,730 |
Part of the Hospitality, Events, & Tourism 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 Receptionists and Information Clerks — 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 95th percentile of 427 international occupations.
Receptionists and Information Clerks show 76th-percentile AI task overlap — and about 128,500 annual U.S. openings
Receptionists and Information Clerks show 76th-percentile AI task overlap — and about 128,500 annual U.S. openings • Receptionists and Information Clerks rank in the 76th 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 128,500 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 about average (0%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $37,230, across about 964,530 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 33% 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 — "Receptionists and Information Clerks". https://singulariki.com/roles/role-43-4171-00 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. "Receptionists and Information Clerks." 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-43-4171-00
Singulariki. (2026). Receptionists and Information Clerks. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-43-4171-00
@misc{singulariki-role-43-4171-00,
title = {Receptionists and Information Clerks},
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-43-4171-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.