As of August 2026, Interviewers, Except Eligibility and Loan has an AI-exposure score of 80/100 (Very High exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Interviewers, Except Eligibility and Loan

80/100
Very High exposure
LowModerateElevatedHighVery High

More exposed than 98% of the roles we track. Median pay ~US$45,920. About 15,800 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Interviewers, Except Eligibility and Loan?

No exposure score can predict whether AI will replace this role. The 80/100 score means our current model estimates very high task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role's Very High score and automation-heavy task mix point in the same direction as the study's highest-exposure automation cohort. The measures are not identical, so the study result is context rather than a direct forecast for this role.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Interview persons by telephone, mail, in person, or by other means for the purpose of completing forms, applications, or questionnaires. Ask specific questions, record answers, and assist persons with completing form. May sort, classify, and file forms.

Common titles
Admissions ClerkAdmissions RepresentativeInterviewerRegistration ClerkAdmitting RepresentativeData Collection Assistant
O*NET job-zone preparation
Job Zone 3 · Medium Preparation Needed Most occupations in this zone require training in vocational schools, related on-the-job experience, or an associate's degree. Previous work-related skill, knowledge, or experience is required for these occupations.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
SpeakingReading ComprehensionSocial PerceptivenessCritical ThinkingService OrientationTime Management
Work context
Frequent contact with othersRepeating tasksIndoor controlled settingDecision latitude

Source: O*NET 29.1 exact occupation - Interviewers, Except Eligibility and Loan, SOC 43-4111.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Interviewers, Except Eligibility and Loan tasks, by AI exposure

Automatable
  • Perform office duties, such as telemarketing or customer service inquiries, maintaining staff records, billing patients, or receiving payments.
  • Compile, record, and code results or data from interview or survey, using computer or specified form.
  • Collect and analyze data, such as studying old records, tallying the number of outpatients entering each day or week, or participating in federal, state, or local population surveys as a Census Enumerator.
13 more automatable tasks locked in the report.
Augmentable

No augmentable tasks identified for this role - all 16 of its individually-assessed tasks read as automatable.

Durable

No durable tasks identified for this role - all 16 of its individually-assessed tasks read as automatable.

We analyzed all 16 Interviewers, Except Eligibility and Loan tasks - 16 automatable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.

Patient Representatives
72% skills overlap; 11 points lower - lower exposure; High band; ~US$50,290
View path
69
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Perform office duties, such as telemarketing or customer service inquiries, maintaining staff records, billing patients, or receiving payments.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

AI was the most-cited reason for U.S. layoffs through mid-2026 - the workers who adapt earliest fare best. - Challenger, Gray & Christmas, 2026The upside: Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Interviewers, Except Eligibility and Loan - median pay by US state (BLS OEWS, USD)

California: US$56,880New York: US$55,410Texas: US$43,980Florida: US$43,500

Median annual wage, in USD. US national: US$45,920. More states are being added.

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