AI Automation Index

Will AI Replace Customs and Border Protection Officers?

Task-level automation exposure for Customs and Border Protection Officers: 11 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

45% weighted task index

11 of 11 tasks scored

Median Salary

$76,210 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Very High

670,520 people hold this job — size, not growth

Projected 2025–35

+3.5% employment change

51,500 openings a year · BLS line item

Pay by state

BLS OEWS publishes 53 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$45,510 – $118,880

Highest medians

California $118,880

Washington $102,080

Illinois $100,520

Alaska $99,130

Colorado $97,350

Lowest medians

Puerto Rico $45,510

Mississippi $46,140

Arkansas $47,670

Louisiana $48,380

Alabama $51,270

State figures come from the 33-3051 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 45%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 19%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 12%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 10%

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 67/100 High

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) 38%

4 tasks

Medium exposure (≥ 35%) 25%

3 tasks

Low exposure (below 35%) 37%

4 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

#9450 · Core · 51% automatable

Examine immigration applications, visas, and passports and interview persons to determine eligibility for admission, residence, and travel in the U.S.

visual inspection — Document validity check aided, human verifies authenticity

Interview people to obtain information about actions or status of individuals. Examine personal documentation to ensure that it is valid.

#9451 · Core · 3% automatable

Detain persons found to be in violation of customs or immigration laws and arrange for legal action, such as deportation.

physical manipulation — Physically detaining suspects

Detain suspects or witnesses.

#9454 · Core · 20% automatable

Inspect cargo, baggage, and personal articles entering or leaving U.S. for compliance with revenue laws and U.S. customs regulations.

visual inspection — Cargo hazard inspection requires on-site presence

Inspect cargo to identify potential hazards.

#9452 · Core · 15% automatable

Locate and seize contraband, undeclared merchandise, and vehicles, aircraft, or boats that contain such merchandise.

visual inspection — Spotting suspicious objects needs physical visual search

Locate suspicious objects or vehicles. Confiscate prohibited or dangerous items.

#9453 · Core · 62% automatable

Interpret and explain laws and regulations to travelers, prospective immigrants, shippers, and manufacturers.

information retrieval — AI retrieves and explains regulations; human confirms

Inform others about laws or regulations.

#9456 · Core · 38% automatable

Institute civil and criminal prosecutions and cooperate with other law enforcement agencies in the investigation and prosecution of those in violation of immigration or customs laws.

interpersonal — Law enforcement info sharing needs trusted humans

Investigate illegal or suspicious activities. Collaborate with law enforcement or security agencies to share information.

#9457 · Core · 15% automatable

Testify regarding decisions at immigration appeals or in federal court.

interpersonal — Testimony carries legal accountability and live presence

Testify at legal or legislative proceedings.

#9455 · Core · 80% automatable

Record and report job-related activities, findings, transactions, violations, discrepancies, and decisions.

data analysis — Maintain operational records in structured systems

Maintain operational records.

#9458 · Core · 82% automatable

Determine duty and taxes to be paid on goods.

data analysis — Tax calculation with rules; human review

Calculate tax information.

#9459 · Core · 78% automatable

Collect samples of merchandise for examination, appraisal, or testing.

information retrieval — Property info lookup from databases

Obtain property information.

#9460 · Supplemental · 55% automatable

Investigate applications for duty refunds and petition for remission or mitigation of penalties when warranted.

information retrieval — Legal research strong, judgment and accountability human

Investigate legal issues.

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Community Sentiment

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Method

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

See the full method →

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