AI Automation Index

Will AI Replace Passenger Attendants?

Task-level automation exposure for Passenger Attendants: 12 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

34% weighted task index

12 of 12 tasks scored

Median Salary

$37,720 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Medium

27,110 people hold this job — size, not growth

Projected 2025–35

+6.0% employment change

4,300 openings a year · BLS line item

Pay by state

BLS OEWS publishes 30 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$24,650 – $54,290

Highest medians

Alaska $54,290

District of Columbia $52,730

Kentucky $44,810

Massachusetts $43,350

California $43,340

Lowest medians

Wisconsin $24,650

Utah $25,840

Ohio $26,220

Georgia $26,330

Tennessee $27,380

State figures come from the 53-6061 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 34%

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

Physical work 9%

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

Judgment & influence 47%

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

Routine administration 7%

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 1/100 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

12 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) 16%

2 tasks

Medium exposure (≥ 35%) 35%

4 tasks

Low exposure (below 35%) 50%

6 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

#13176 · Core · 55% automatable

Perform equipment safety checks prior to departure.

decision support — AI can guide safety procedures but human executes driving

Follow safety procedures for vehicle operation.

#13175 · Core · 62% automatable

Explain and demonstrate safety procedures and safety equipment use.

information retrieval — AI explains policies; human handles live questions

Explain regulations, policies, or procedures.

#13177 · Core · 5% automatable

Signal transportation operators to stop or to proceed.

physical manipulation — Physical hand signals to direct vehicle movement

Signal others to coordinate vehicle movement.

#20841 · Core · 15% automatable

Secure passengers for transportation by buckling seatbelts or fastening wheelchairs with tie-down straps.

interpersonal — In-person comfort and safety assurance

Assist customers to ensure comfort or safety.

#13174 · Core · 55% automatable

Respond to passengers' questions, requests, or complaints.

information retrieval — Transport info lookup automatable; live assistance human

Provide transportation information to passengers or customers.

#13183 · Core · 5% automatable

Determine or facilitate seating arrangements.

physical manipulation — Physical boarding assistance

Assist passengers during vehicle boarding.

#13173 · Core · 5% automatable

Open and close doors for passengers.

physical manipulation — Physical boarding assistance

Assist passengers during vehicle boarding.

#13179 · Core · 55% automatable

Greet passengers boarding transportation equipment and announce routes and stops.

information retrieval — Transport info lookup automatable; live assistance human

Provide transportation information to passengers or customers.

#13172 · Core · 5% automatable

Provide boarding assistance to elderly, sick, or injured people.

physical manipulation — Physical boarding assistance

Assist passengers during vehicle boarding.

#13182 · Supplemental · 55% automatable

Provide customers with information on routes, gates, prices, timetables, terminals, or concourses.

information retrieval — Transport info lookup automatable; live assistance human

Provide transportation information to passengers or customers.

#13178 · Supplemental · 81% automatable

Count and verify tickets and seat reservations and record numbers of passengers boarding and disembarking.

data analysis — Record production data into structured logs

Verify information or specifications. Record operational or production data.

#13184 · Supplemental · 15% automatable

Adjust window shades or seat cushions at the request of passengers.

interpersonal — In-person comfort and safety assurance

Assist customers to ensure comfort or safety.

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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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