As of August 2026, Retail Loss Prevention Specialists has an AI-exposure score of 65/100 (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

Retail Loss Prevention Specialists

65/100
High exposure
LowModerateElevatedHighVery High

More exposed than 80% of the roles we track. Median pay ~US$42,540. About 23,300 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 Retail Loss Prevention Specialists?

No exposure score can predict whether AI will replace this role. The 65/100 score means our current model estimates 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 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

Implement procedures and systems to prevent merchandise loss. Conduct audits and investigations of employee activity. May assist in developing policies, procedures, and systems for safeguarding assets.

Common titles
Loss Prevention AgentLoss Prevention Associate (LPA)Loss Prevention DetectiveLoss Prevention OfficerAsset Protection Associate (APA)Loss Prevention Investigator
O*NET job-zone preparation
Job Zone 2 · Job Zone 1-2: Very Little to Some Preparation Needed Usually requires a high school diploma or GED, though some occupations may not. Some occupations may need little or no previous experience; others require several months to a year of experience.

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

Skills and knowledge
Critical ThinkingMonitoringSpeakingJudgment and Decision MakingSocial PerceptivenessActive Learning
Work context
Frequent contact with othersIndoor controlled settingDecision latitudeRepeating tasksConsequence of error

Source: O*NET 29.1 exact occupation - Retail Loss Prevention Specialists, SOC 33-9099.02. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Retail Loss Prevention Specialists tasks, by AI exposure

Automatable
  • Perform covert surveillance of areas susceptible to loss, such loading docks, distribution centers, or warehouses.
  • Direct work of contract security officers or other loss prevention agents.
  • Implement or monitor processes to reduce property or financial losses.
12 more automatable tasks locked in the report.
Augmentable
  • Inspect buildings, equipment, or access points to determine security risks.
  • Investigate known or suspected internal theft, external theft, or vendor fraud.
3 more augmentable tasks locked in the report.
Durable

No durable tasks identified for this role - its individually-assessed tasks split 75% automatable / 25% augmentable.

We analyzed all 20 Retail Loss Prevention Specialists tasks - 15 automatable and 5 augmentable. 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.

Security Managers
64% skills overlap; 9 points lower - lower exposure; Elevated band; ~US$106,660
View path
56
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Perform covert surveillance of areas susceptible to loss, such loading docks, distribution centers, or warehouses.

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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Retail Loss Prevention Specialists - median pay by US state (BLS OEWS, USD)

New York: US$52,690Florida: US$44,310California: US$39,330Texas: US$33,500

Median annual wage, in USD. US national: US$42,540. More states are being added.

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