Will AI replace Transportation Security Screeners?
Most of the work in Transportation Security Screeners still leans on things AI struggles with — research rates its theoretical AI reach at only ~26%, and real-world use lower still.
O*NET-SOC 33-9093
How your 24 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~26%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~26%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 24 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- Inspect carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation.
- Check passengers' tickets to ensure that they are valid, and to determine whether passengers have designations that require special handling, such as providing photo identification.
- Test baggage for any explosive materials, using equipment such as explosive detection machines or chemical swab systems.
- Decide whether baggage that triggers alarms should be searched or should be allowed to pass through.
- Inform other screeners when baggage should not be opened because it might contain explosives.
- Inspect checked baggage for signs of tampering.
- Patrol work areas to detect any suspicious items.
- Record information about any baggage that sets off alarms in monitoring equipment.
- Watch for potentially dangerous persons whose pictures are posted at checkpoints.
- Contact leads or supervisors to discuss objects of concern that are not on prohibited object lists.
- Monitor passenger flow through screening checkpoints to ensure order and efficiency.
- Provide directions and respond to passenger inquiries.
- Search carry-on or checked baggage by hand when it is suspected to contain prohibited items such as weapons.
- Perform pat-down or hand-held wand searches of passengers who have triggered machine alarms, who are unable to pass through metal detectors, or who have been randomly identified for such searches.
- Notify supervisors or other appropriate personnel when security breaches occur.
- Send checked baggage through automated screening machines, and set bags aside for searching or rescreening as indicated by equipment.
- Follow those who breach security until police or other security personnel arrive to apprehend them.
- Ask passengers to remove shoes and divest themselves of metal objects prior to walking through metal detectors.
- Close entry areas following security breaches or reopen areas after receiving notification that the airport is secure.
- Challenge suspicious people, requesting their badges and asking what their business is in a particular areas.
- Contact police directly in cases of urgent security issues, using phones or two-way radios.
- Confiscate dangerous items and hazardous materials found in opened bags and turn them over to airlines for disposal.
- Inform passengers of how to mail prohibited items to themselves, or confiscate these items.
- Direct passengers to areas where they can pick up their baggage after screening is complete.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.