Transportation and Material Moving · United States (national)
Aircraft Service Attendants
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is less exposed to automation, and a near-term shift looks unlikely.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 23.1
Physical / roboticsrange 0.0 to 12.0
When the change is likely
Largely unaffectedlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Stay sharp; urgency is low
This role looks durable. Keep your skills current and watch for shifts in your field.
2
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
3
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
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Which of my tasks are exposed
Apply de-icing fluid to aircraft from baskets lifted by truck-mounted cranes.
0.0
Change aircraft oil, coolant, or other fluids.
0.0
Clean aircraft interiors by picking up waste, wiping down windows, or vacuuming.
0.0
Climb ladders to reach aircraft surfaces to be cleaned.
0.0
Complete forms describing tasks completed.
11.1
De-grease aircraft exteriors.
0.0
Empty aircraft lavatory systems or refill them with sanitizer fluid.
0.0
Guide aircraft to designated areas using hand signals, batons, or other methods.
0.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Complete forms describing tasks completed.
Task: Apply de-icing fluid to aircraft from baskets lifted by truck-mounted cranes.
How this score is calculated
Map Aircraft Service Attendants to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 11.1, robotics 0.0.
The timeline comes from the higher axis (11.1), shifted for local demand (stable), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.