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Loss Prevention Managers

Occupation · SOC 11-9199.08

Plan and direct policies, procedures, or systems to prevent the loss of assets. Determine risk exposure or potential liability, and develop risk control measures.

Also called: Asset Protection Manager · Loss Prevention Director · Loss Prevention Manager · Loss Prevention Operations Manager · Logistics Loss Prevention Manager · Loss Control Manager · Loss Prevention Operations Director · Loss Prevention Supervisor · Market Asset Protection Manager · Area Asset Protection Manager · Area Loss Prevention Manager · Asset Protection Leader

Job family: Management Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-11-9199-08/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.

Often handed to AI

Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.

  • Identify potential for loss and develop strategies to eliminate it. · 1.7%
See how AI is used here →

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.

  • Identify potential for loss and develop strategies to eliminate it. · 92.3% need a human
See the boundary tasks →

62nd-percentile task overlap — yet about 106,700 openings a year (+4.5% projected, BLS), and observed AI use leans 5621% 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 72nd 1.0
LLM task exposure, γ (OpenAI / Eloundou) High 67th 0.8
AI assistant applicability (Microsoft) Moderate 49th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.4), and including AI-powered software (γ 0.8). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.

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 0.3 · 37th percentile among occupations · Moderate

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.

Identify potential for loss and develop strategies to eliminate it. 3.0%

Job outlook

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

Outlook About average · +4.5% by 2034
Projected annual openings 106,700
Employment 2024 → 2034 1,333,700 → 1,393,500

“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 6 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.

37% mean task exposure (2025)
68th percentile of 427 placed occupations
+5 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Business Services and Administration Managers Not Elsewhere Classified · 1219 42% Gradient 2
Professional Services Managers Not Elsewhere Classified · 1349 38% Minimal
Senior Officials of Special-interest Organizations · 1114 37% Minimal
Policy and Planning Managers · 1213 36% Not exposed
Mining Managers · 1322 35% Minimal
Sports, Recreation and Cultural Centre Managers · 1431 32% Minimal

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 56.2% working with AI · 39.6% handed to AI
Most common way people use AI here Directive · AI does it; you give the instruction
Typical AI autonomy 4.0 / 5 · higher = AI acts more independently
Used for work (vs. personal / coursework) 70.4%

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
Identify potential for loss and develop strategies to eliminate it. Directive 1.7%

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.

Identify potential for loss and develop strategies to eliminate it. 92.3%

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 identify potential for loss and develop strategies to eliminate it.

    From: Identify potential for loss and develop strategies to eliminate it. · 1.7% of measured AI use · directive

Tasks

All 27 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

Public Safety and Security 4.1
Law and Government 3.9
Administration and Management 3.8
English Language 3.8
Education and Training 3.7
Customer and Personal Service 3.5
Psychology 3.5
Computers and Electronics 3.5
Personnel and Human Resources 3.4

Abilities

Oral Expression 4.1
Problem Sensitivity 4.1
Oral Comprehension 4.0
Near Vision 4.0
Written Comprehension 3.9
Written Expression 3.9
Deductive Reasoning 3.9
Inductive Reasoning 3.9
Speech Recognition 3.8
Speech Clarity 3.8
Originality 3.4

Essential skills

Reading Comprehension 4.0
Active Listening 4.0
Speaking 4.0
Critical Thinking 3.9
Monitoring 3.9
Writing 3.8
Active Learning 3.8
Learning Strategies 3.3

Transferable skills

Instructing 3.9
Complex Problem Solving 3.9
Judgment and Decision Making 3.9
Time Management 3.9
Persuasion 3.8
Systems Analysis 3.8
Systems Evaluation 3.8
Social Perceptiveness 3.6
Coordination 3.6
Service Orientation 3.6
Management of Personnel Resources 3.6
Negotiation 3.5

Skills in demand

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 43.

Tools & technology

Example Category
Google Workspace software Office suite software Hot technology In demand
Microsoft Excel Spreadsheet software Hot technology In demand
Microsoft Office software Office suite software Hot technology In demand
Microsoft Outlook Electronic mail software Hot technology In demand
Microsoft PowerPoint Presentation software Hot technology In demand
Microsoft Access Data base user interface and query software Hot technology
Microsoft Project Project management software Hot technology
Microsoft SharePoint Document management software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
MySQL Data base user interface and query software Hot technology
SAP software Enterprise resource planning ERP software Hot technology
Structured query language SQL Data base user interface and query software Hot technology
Enterprise application integration EAI software Enterprise application integration software In demand
Reporting software Data base reporting software In demand
Enabl-u Technologies APIS Project management software
Financial accounting software Accounting software
IBM Lotus Notes Electronic mail software
Inventory tracking software Inventory management software
MICROS XBR Loss Prevention Business intelligence and data analysis software
Personnel management software Human resources software
Time reporting software Time accounting software
Work scheduling software Calendar and scheduling 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.

E-Mail 5.0
Contact With Others 4.8
Telephone Conversations 4.8
Indoors, Environmentally Controlled 4.7
Face-to-Face Discussions with Individuals and Within Teams 4.7
Health and Safety of Other Workers 4.6
Work With or Contribute to a Work Group or Team 4.4
Freedom to Make Decisions 4.4
Impact of Decisions on Co-workers or Company Results 4.2
Determine Tasks, Priorities and Goals 4.1
Frequency of Decision Making 4.1
Deal With External Customers or the Public in General 4.0
Conflict Situations 4.0
Importance of Being Exact or Accurate 3.9
Time Pressure 3.9
Coordinate or Lead Others in Accomplishing Work Activities 3.6
Written Letters and Memos 3.6
Work Outcomes and Results of Other Workers 3.5
Dealing With Unpleasant, Angry, or Discourteous People 3.5
Spend Time Sitting 3.4
Physical Proximity 3.3
In an Enclosed Vehicle or Operate Enclosed Equipment 3.1
Level of Competition 3.1
Importance of Repeating Same Tasks 3.0
Public Speaking 3.0
Consequence of Error 2.8
Dealing with Violent or Physically Aggressive People 2.8
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.6
Spend Time Standing 2.6
Indoors, Not Environmentally Controlled 2.4
Degree of Automation 2.4
Spend Time Walking or Running 2.3
Spend Time Making Repetitive Motions 2.3
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 2.3
Outdoors, Exposed to All Weather Conditions 2.3
Exposed to Contaminants 2.1
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.0
Exposed to High Places 2.0
Outdoors, Under Cover 1.9
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 1.9

How to get in

Job zone
Zone 4 — Job Zone Four: Considerable Preparation Needed
Education
Most of these occupations require a four-year bachelor's degree, but some do not.
Typical entry-level education
Bachelor's degree · BLS, the typical path — not a requirement
Related experience
A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
Preparation level
SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.

What to study: Business, Management, Marketing, and Related Support Services , Communication, Journalism, and Related Programs , Computer and Information Sciences and Support Services , Health Professions and Related Programs , History , Homeland Security, Law Enforcement, Firefighting and Related Protective Services , Library Science , Multi/Interdisciplinary Studies , Natural Resources and Conservation , Psychology , Public Administration and Social Service Professions , Social Sciences , Theology and Religious Vocations , Visual and Performing Arts . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

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

Bachelor's Degree 54.5%
High School Diploma 13.6%
Post-Secondary Certificate 9.1%
Some College Courses 9.1%
Associate's Degree (or other 2-year degree) 9.1%
Post-Baccalaureate Certificate 4.5%

Interests & work styles

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

Work styles

Dependability 9.0
Attention to Detail 8.0
Integrity 7.0
Cautiousness 6.0
Achievement Orientation 5.0
Self-Control 4.0
Stress Tolerance 3.0

Interest areas

Management/Administration 6.0
Protective Service 4.7
Law 3.1
Accounting 2.8

Career interests (Holland / RIASEC)

Conventional 5.8
Enterprising 5.6
Investigative 3.6
Realistic 3.4
Social 2.9

Wages & employment

U.S. · annual wages (BLS OEWS)

$69k10th$100k25th$137kMedian$179k75th$228k90th
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.
1.33M20241.39M2034 (proj.)+4.5% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $68,860
25th percentile $100,010
Median (50th) $136,550
75th percentile $179,190
90th percentile $227,590
People employed 630,980

Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 11-9199), not for the specialty alone.

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
Professional, Scientific, and Technical Services · Sector 94,490 $164,060
Management of Companies and Enterprises · Sector 50,980 $163,830
Manufacturing · Sector 46,390 $160,640
Finance and Insurance · Sector 44,890 $162,780
Information · Sector 38,680 $167,740
Educational Services · Sector 32,840 $102,450
Administrative and Support and Waste Management and Remediation Services · Sector 32,500 $109,990
Health Care and Social Assistance · Sector 31,360 $108,810
Wholesale Trade · Sector 25,860 $137,780
Construction · Sector 19,840 $110,040
Other Services (except Public Administration) · Sector 19,110 $111,320
Retail Trade · Sector 13,510 $95,720

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
Wind Electric Power Generation · National industry 9.11× 370
Research and Development in the Social Sciences and Humanities · National industry 6.07× 1,510
Management of Companies and Enterprises · Sector 4.43× 50,980
Direct Health and Medical Insurance Carriers · National industry 3.92× 7,200
Solar Electric Power Generation · National industry 3.33× 190
Information · Sector 3.25× 38,680
Nuclear Electric Power Generation · National industry 2.7× 410
Labor Unions and Similar Labor Organizations · National industry 2.65× 1,150

Part of the Arts, Entertainment, & Design , Management & Entrepreneurship and Public Service & Safety career clusters.

Exposure quadrant: AI task-overlap percentile vs Median pay Loss Prevention Managers sits at the 62nd percentile of AI task-overlap and the 96th 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 Loss Prevention Managers Retail Loss Prevention Specialists Security Managers Private Detectives and Investigators Emergency Management Directors Fraud Examiners, Investigators and Analysts Information Security Engineers 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 Loss Prevention Managers — 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.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 68th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Loss Prevention Managers show 62nd-percentile AI task overlap — and about 106,700 annual U.S. openings

  • Loss Prevention Managers rank in the 62nd percentile (Moderate 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 106,700 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 (+4.5%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $136,550, across about 630,980 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
Loss Prevention Managers show 62nd-percentile AI task overlap — and about 106,700 annual U.S. openings

• Loss Prevention Managers rank in the 62nd percentile (Moderate 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 106,700 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 (+4.5%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $136,550, across about 630,980 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 — "Loss Prevention Managers". https://singulariki.com/roles/role-11-9199-08
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. "Loss Prevention Managers." 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-11-9199-08

APA

Singulariki. (2026). Loss Prevention Managers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-11-9199-08

BibTeX
@misc{singulariki-role-11-9199-08,
  title  = {Loss Prevention Managers},
  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-11-9199-08}
}

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

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