Use as a copilot
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
- Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. · 0.6%
Shipping, Receiving, and Inventory Clerks
Occupation · SOC 43-5071.00
Verify and maintain records on incoming and outgoing shipments involving inventory. Duties include verifying and recording incoming merchandise or material and arranging for the transportation of products. May prepare items for shipment.
Also called: Receiver · Receiving Clerk · Shipper · Shipping Clerk · Materials Control Associate · Order Fulfillment Specialist · Receiving Associate · Receiving Coordinator · Shipping Coordinator · Traffic Assistant · Backroom Associate · Booking Clerk
Job family: Office and Administrative Support Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-43-5071-00/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.
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.
- Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. · 93.4% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- Declining employment outlook (-7.7% by 2034)
- 69,300 openings/yr
- Moderate AI exposure
- Observed AI use leans augmentation
- Median pay $43,190/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (97th); today's AI task-overlap is comparatively low (48th)
48th-percentile task overlap — yet about 69,300 openings a year (-7.7% projected, BLS), and observed AI use leans 5082% 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.) Low | 27th | -0.7 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 56th | 0.7 | |
| AI assistant applicability (Microsoft) Moderate | 63rd | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.5), and including AI-powered software (γ 0.7). 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.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
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 1.0 · 97th percentile among occupations · High
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.
| Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. | 0.6% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -7.7% by 2034 |
| Projected annual openings | 69,300 |
| Employment 2024 → 2034 | 862,200 → 795,800 |
“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 2 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.
Shipping, Receiving, and Inventory Clerks sits at the 85th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
47% mean task exposure (2025)
85th percentile of 427 placed occupations
+7 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Clearing and Forwarding Aents · 3331 | 57% | Gradient 3 |
| Stock Clerks · 4321 | 37% | 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 | 50.8% working with AI · 44.3% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 78.7% |
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 |
|---|---|---|
| Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. | Iteration | 0.6% |
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.
| Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. | 93.4% |
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 prepare documents, such as work orders, bills of lading, or shipping orders, to route materials.From: Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. · 0.6% of measured AI use · task iteration
Tasks
All 11 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Examine shipment contents and compare with records, such as manifests, invoices, or orders, to verify accuracy. Core · importance 4.2
- Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. Core · importance 4.0
- Requisition and store shipping materials and supplies to maintain inventory of stock. Core · importance 4.0
- Pack, seal, label, or affix postage to prepare materials for shipping, using hand tools, power tools, or postage meter. Core · importance 4.0
- Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes. Core · importance 4.0
- Confer or correspond with establishment representatives to rectify problems, such as damages, shortages, or nonconformance to specifications. Core · importance 4.0
- Deliver or route materials to departments using handtruck, conveyor, or sorting bins. Core · importance 3.9
- Contact carrier representatives to make arrangements or to issue instructions for shipping and delivery of materials. Core · importance 3.8
- Determine shipping methods, routes, or rates for materials to be shipped. Core · importance 3.8
- Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list. Core · importance 3.7
- Compare shipping routes or methods to determine which have the least environmental impact. Supplemental · importance 3.7
Work activities
- Analyze shipping information to make routing decisions.
- Inspect shipments to ensure correct order fulfillment.
- Package objects for shipping.
- Deliver items.
- Prepare documentation for contracts, transactions, or regulatory compliance.
- Record shipping information.
- Coordinate shipping activities with external parties.
- Respond to customer problems or complaints.
- Store items.
- Order materials, supplies, or equipment.
- Calculate shipping costs.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Near Vision | 3.8 | |
| Oral Expression | 3.3 | |
| Problem Sensitivity | 3.3 | |
| Information Ordering | 3.3 | |
| Oral Comprehension | 3.1 | |
| Arm-Hand Steadiness | 3.1 | |
| Written Comprehension | 3.0 | |
| Written Expression | 3.0 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Category Flexibility | 3.0 | |
| Perceptual Speed | 3.0 | |
| Selective Attention | 3.0 | |
| Manual Dexterity | 3.0 | |
| Speech Recognition | 3.0 | |
| Speech Clarity | 3.0 | |
| Finger Dexterity | 2.9 | |
| Trunk Strength | 2.9 | |
| Far Vision | 2.9 | |
| Fluency of Ideas | 2.8 |
Knowledge
Essential skills
| Speaking | 3.3 | |
| Reading Comprehension | 3.1 | |
| Active Listening | 3.1 | |
| Critical Thinking | 3.0 | |
| Monitoring | 3.0 |
Transferable skills
| Time Management | 3.0 | |
| Social Perceptiveness | 2.9 | |
| Coordination | 2.9 | |
| Complex Problem Solving | 2.9 | |
| Judgment and Decision Making | 2.9 | |
| Persuasion | 2.8 |
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 41.
Tools & technology
Showing the top 40 of 52.
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.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 74.9% | |
| Associate's Degree (or other 2-year degree) | 11.9% | |
| Bachelor's Degree | 10.8% | |
| Some College Courses | 1.9% | |
| Post-Secondary Certificate | 0.5% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Conventional | 7.0 | |
| Realistic | 4.0 | |
| Enterprising | 3.1 |
Interest areas
| Office Work | 6.3 | |
| Physical/Manual Labor | 2.8 | |
| Transportation/Machine Operation | 2.5 | |
| Accounting | 2.5 | |
| Management/Administration | 1.9 | |
| Information Technology | 1.7 | |
| Mathematics/Statistics | 1.5 | |
| Finance | 1.5 | |
| Business Initiatives | 1.4 |
Work styles
| Dependability | 2.5 | |
| Attention to Detail | 2.4 | |
| Integrity | 1.7 | |
| Cautiousness | 1.5 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $32,900 |
| 25th percentile | $37,040 |
| Median (50th) | $43,190 |
| 75th percentile | $49,390 |
| 90th percentile | $60,300 |
| People employed | 857,630 |
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 |
|---|---|---|
| Manufacturing · Sector | 231,730 | $45,870 |
| Retail Trade · Sector | 185,670 | $37,590 |
| Transportation and Warehousing · Sector | 137,140 | $45,050 |
| Wholesale Trade · Sector | 135,030 | $43,720 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 52,100 | $38,080 |
| Temporary Help Services · National industry | 29,930 | $37,590 |
| Health Care and Social Assistance · Sector | 23,480 | $44,240 |
| Professional, Scientific, and Technical Services · Sector | 18,660 | $46,360 |
| Management of Companies and Enterprises · Sector | 10,110 | $46,430 |
| Construction · Sector | 9,980 | $48,530 |
| Other Services (except Public Administration) · Sector | 9,780 | $40,360 |
| Information · Sector | 7,730 | $44,470 |
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 |
|---|---|---|
| Farm and Garden Machinery and Equipment Merchant Wholesalers · National industry | 5× | 3,170 |
| Jewelry and Silverware Manufacturing · National industry | 4.78× | 530 |
| Sporting Goods Retailers · National industry | 4.62× | 7,650 |
| Wholesale Trade · Sector | 4.02× | 135,030 |
| Machine Shops · National industry | 3.97× | 5,730 |
| Transportation and Warehousing · Sector | 3.34× | 137,140 |
| Manufacturing · Sector | 3.26× | 231,730 |
| Retail Trade · Sector | 2.14× | 185,670 |
Related occupations
Part of the Supply Chain & Transportation career cluster.
- Postal Service Clerks · 43-5051.00 Compare →
- Stockers and Order Fillers · 53-7065.00 Compare →
- Cargo and Freight Agents · 43-5011.00 Compare →
- Weighers, Measurers, Checkers, and Samplers, Recordkeeping · 43-5111.00 Compare →
- Postal Service Mail Sorters, Processors, and Processing Machine Operators · 43-5053.00 Compare →
- Production, Planning, and Expediting Clerks · 43-5061.00 Compare →
- Mail Clerks and Mail Machine Operators, Except Postal Service · 43-9051.00 Compare →
- Freight Forwarders · 43-5011.01 Compare →
- Laborers and Freight, Stock, and Material Movers, Hand · 53-7062.00 Compare →
- Order Clerks · 43-4151.00 Compare →
- Procurement Clerks · 43-3061.00 Compare →
- Logistics Analysts · 13-1081.02 Compare →
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 Shipping, Receiving, and Inventory Clerks — 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.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 85th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Shipping, Receiving, and Inventory Clerks show 48th-percentile AI task overlap — and about 69,300 annual U.S. openings
- Shipping, Receiving, and Inventory Clerks rank in the 48th 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 69,300 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 declining (-7.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $43,190, across about 857,630 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 51% 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
Shipping, Receiving, and Inventory Clerks show 48th-percentile AI task overlap — and about 69,300 annual U.S. openings • Shipping, Receiving, and Inventory Clerks rank in the 48th 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 69,300 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 declining (-7.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $43,190, across about 857,630 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 51% 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 — "Shipping, Receiving, and Inventory Clerks". https://singulariki.com/roles/role-43-5071-00 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.
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.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Shipping, Receiving, and Inventory 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-43-5071-00
APA
Singulariki. (2026). Shipping, Receiving, and Inventory Clerks. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-43-5071-00
BibTeX
@misc{singulariki-role-43-5071-00,
title = {Shipping, Receiving, and Inventory 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-43-5071-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.