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AI Automation Index
Will AI Replace Shipping, Receiving, and Inventory Clerks?
Task-level automation exposure for Shipping, Receiving, and Inventory Clerks: 11 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
52%
weighted task index
11 of 11 tasks scored
Median Salary
$45,260 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
816,870 people hold this job — size, not growth
Projected 2025–35
-7.6%
employment change
61,300 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.
$26,050 – $51,590
Highest medians
Washington $51,590
District of Columbia $49,660
Hawaii $49,660
Massachusetts $49,240
Vermont $49,080
Lowest medians
Puerto Rico $26,050
Guam $32,360
Virgin Islands $36,240
Mississippi $38,620
New Mexico $38,860
State figures come from the 43-5071 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 52%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 29%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 33%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 19%
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 16/100
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
11 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) 43%
5 tasks
Medium exposure (≥ 35%) 37%
4 tasks
Low exposure (below 35%) 19%
2 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 →Constituent Tasks
#20276 · Core · 35% automatable
Examine shipment contents and compare with records, such as manifests, invoices, or orders, to verify accuracy.
visual inspection — AI can cross-check orders; physical verification human
Inspect shipments to ensure correct order fulfillment.
#4742 · Core · 43% automatable
Requisition and store shipping materials and supplies to maintain inventory of stock.
decision support — Ordering via systems automatable; human approves spend
Store items. Order materials, supplies, or equipment.
#4737 · Core · 68% automatable
Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials.
text generation — Compliance docs drafted; human owns regulatory accuracy
Prepare documentation for contracts, transactions, or regulatory compliance.
#20277 · Core · 5% automatable
Pack, seal, label, or affix postage to prepare materials for shipping, using hand tools, power tools, or postage meter.
physical manipulation — Manual packing for shipping
Package objects for shipping.
#4739 · Core · 85% automatable
Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes.
data analysis — Record shipping information into structured fields
Record shipping information.
#4741 · Core · 55% automatable
Confer or correspond with establishment representatives to rectify problems, such as damages, shortages, or nonconformance to specifications.
interpersonal — Drafting responses easy; live de-escalation is human
Respond to customer problems or complaints.
#20278 · Core · 10% automatable
Deliver or route materials to departments using handtruck, conveyor, or sorting bins.
situated mobility — Physical delivery of items in real environments
Deliver items.
#4740 · Core · 50% automatable
Contact carrier representatives to make arrangements or to issue instructions for shipping and delivery of materials.
decision support — AI drafts shipping coordination; external negotiation human
Coordinate shipping activities with external parties.
#20279 · Core · 75% automatable
Determine shipping methods, routes, or rates for materials to be shipped.
data analysis — Routing analysis from shipping data; human approves
Analyze shipping information to make routing decisions.
#4744 · Core · 85% automatable
Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list.
data analysis — Shipping cost calculation from rates and weights
Calculate shipping costs.
#19834 · Supplemental · 75% automatable
Compare shipping routes or methods to determine which have the least environmental impact.
data analysis — Routing analysis from shipping data; human approves
Analyze shipping information to make routing decisions.
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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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