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AI Automation Index
Will AI Replace Gambling Change Persons and Booth Cashiers?
Task-level automation exposure for Gambling Change Persons and Booth Cashiers: 13 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
59%
weighted task index
13 of 13 tasks scored
Median Salary
$36,220 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
21,530 people hold this job — size, not growth
Projected 2025–35
-1.9%
employment change
3,900 openings a year · BLS line item
Pay by state
BLS OEWS publishes 34 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$22,050 – $63,400
Highest medians
Arizona $63,400
Texas $62,400
North Dakota $49,830
New York $48,110
Maryland $41,220
Lowest medians
Puerto Rico $22,050
West Virginia $22,080
Louisiana $23,390
Montana $27,910
Nebraska $28,080
State figures come from the 41-2012 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 59%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 5%
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 32%
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 14/100
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
13 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) 50%
6 tasks
Medium exposure (≥ 35%) 29%
4 tasks
Low exposure (below 35%) 21%
3 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
#6999 · Core · 85% automatable
Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations.
data analysis — Maintain structured sales records via tools
Maintain records of sales or other business transactions.
#21152 · Core · 48% automatable
Exchange money, credit, tickets, or casino chips and make change for customers.
data analysis — Transaction processing software-driven; human handles exceptions
Issue money, credit, or vouchers. Process sales or other transactions.
#6998 · Core · 85% automatable
Count money and audit money drawers.
data analysis — Transaction accuracy checks are rule-based and reliable
Review accuracy of sales or other transactions. Reconcile records of sales or other financial transactions.
#21153 · Core · 71% automatable
Check identifications to verify age of players.
visual inspection — Document validity check aided, human verifies authenticity
Examine personal documentation to ensure that it is valid. Verify patron or staff credentials.
#7003 · Core · 30% automatable
Maintain cage security according to rules.
situated mobility — Security monitoring of work areas needs physical presence
Monitor work areas to provide security.
#7005 · Core · 85% automatable
Reconcile daily summaries of transactions to balance books.
data analysis — Reconciling transaction records is rule-based matching
Reconcile records of sales or other financial transactions.
#7004 · Supplemental · 50% automatable
Obtain customers' signatures on receipts when winnings exceed the amount held in a slot machine.
information retrieval — Drafting aided; authorized signature human
Obtain written authorization to perform activities.
#7007 · Supplemental · 85% automatable
Calculate the value of chips won or lost by players.
data analysis — Gaming win/loss computation from records
Compute gaming wins and losses.
#7009 · Supplemental · 72% automatable
Accept credit applications and verify credit references to provide check-cashing authorization or to establish house credit accounts.
data analysis — Credit data screening against criteria, human decides
Verify customer credit information.
#7008 · Supplemental · 40% automatable
Furnish change persons with a money bank at the start of each shift.
physical manipulation — Issuing money or vouchers often physical and accountable
Issue money, credit, or vouchers.
#7002 · Supplemental · 40% automatable
Listen for jackpot alarm bells and issue payoffs to winners.
physical manipulation — Issuing money or vouchers often physical and accountable
Issue money, credit, or vouchers.
#7006 · Supplemental · 32% automatable
Sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons.
interpersonal — Closing sales requires persuasion and accountability
Sell products or services.
#21154 · Supplemental · 5% automatable
Clean casino areas.
physical manipulation — Hands-on cleaning of work areas
Clean work areas. Clean facilities or equipment.
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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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