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AI Automation Index
Will AI Replace Receptionists and Information Clerks?
Task-level automation exposure for Receptionists and Information Clerks: 18 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
55%
weighted task index
18 of 18 tasks scored
Median Salary
$38,010 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
910,180 people hold this job — size, not growth
Projected 2025–35
-1.7%
employment change
105,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$22,890 – $49,060
Highest medians
District of Columbia $49,060
Alaska $44,720
Washington $44,710
California $44,100
Massachusetts $43,870
Lowest medians
Puerto Rico $22,890
Guam $23,290
Mississippi $31,160
West Virginia $31,920
Alabama $32,590
State figures come from the 43-4171 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 55%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 5%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 48%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 24%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 5/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
18 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 49%
10 tasks
Medium exposure (≥ 35%) 32%
5 tasks
Low exposure (below 35%) 19%
3 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 08/2023 · Incumbent
Enter and update databases of contact information, such as names, addresses, and phone numbers.
Constituent Tasks
#744 · Core · 76% automatable
Operate telephone switchboard to answer, screen, or forward calls, providing information, taking messages, or scheduling appointments.
decision support — Calendar scheduling automatable; exceptions need human judgment
Schedule appointments. Answer telephones to direct calls or provide information.
#747 · Core · 15% automatable
Greet persons entering establishment, determine nature and purpose of visit, and direct or escort them to specific destinations.
interpersonal — Physical presence greeting customers
Greet customers, patrons, or visitors.
#745 · Core · 35% automatable
Receive payment and record receipts for services.
physical manipulation — Handling cash/payment collection in person
Collect deposits, payments or fees.
#751 · Core · 80% automatable
Schedule appointments and maintain and update appointment calendars.
decision support — Calendar scheduling automatable; exceptions need human judgment
Schedule appointments.
#750 · Core · 75% automatable
Transmit information or documents to customers, using computer, mail, or facsimile machine.
text generation — Sending digital materials automatable; physical mailing human
Send information, materials or documentation.
#748 · Core · 55% automatable
Hear and resolve complaints from customers or the public.
interpersonal — Drafting responses easy; live de-escalation is human
Respond to customer problems or complaints.
#749 · Core · 45% automatable
File and maintain records.
physical manipulation — Filing physical documents requires hands in the world
File documents or records.
#753 · Core · 50% automatable
Provide information about establishment, such as location of departments or offices, employees within the organization, or services provided.
interpersonal — Customer dialogue needs live presence and adaptation
Discuss goods or services information with customers or patrons.
#746 · Core · 59% automatable
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.
text generation — Proofreading text for accuracy is core LLM strength
Proofread documents, records, or other files to ensure accuracy. Operate computers or computerized equipment.
#755 · Core · 14% automatable
Collect, sort, distribute, or prepare mail, messages, or courier deliveries.
physical manipulation — Sorting and delivering physical mail
Sort mail. Distribute incoming mail.
#762 · Core · 5% automatable
Perform duties, such as taking care of plants or straightening magazines to maintain lobby or reception area.
physical manipulation — Physical cleaning of facilities and equipment
Clean facilities or equipment.
#752 · Supplemental · 75% automatable
Analyze data to determine answers to questions from customers or members of the public.
data analysis — Operational/research data analysis; human interprets
Analyze operational or research data.
#756 · Supplemental · 82% automatable
Calculate and quote rates for tours, stocks, insurance policies, or other products or services.
data analysis — Cost calculation from structured inputs
Calculate costs of goods or services.
#754 · Supplemental · 80% automatable
Keep a current record of staff members' whereabouts and availability.
data analysis — Record personnel information into databases
Record personnel information.
#759 · Supplemental · 72% automatable
Schedule space or equipment for special programs and prepare lists of participants.
decision support — Algorithmic scheduling with human oversight of constraints
Schedule operational activities.
#758 · Supplemental · 88% automatable
Process and prepare memos, correspondence, travel vouchers, or other documents.
text generation — Routine business correspondence fully draftable by AI
Prepare business correspondence.
#760 · Supplemental · 70% automatable
Enroll individuals to participate in programs and notify them of their acceptance.
text generation — Routine customer notifications largely automatable
Provide notifications to customers or patrons.
#757 · Supplemental · 75% automatable
Take orders for merchandise or materials and send them to the proper departments to be filled.
text generation — Sending digital materials automatable; physical mailing human
Send information, materials or documentation. Order materials, supplies, or equipment.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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