US dataswitch to UK
First-Line Supervisors of Gambling Services Workers
monitoring game operations to ensure that house rules, resetting slot machines after payoffs and answering patrons' questions about gaming machine functions and payouts. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: performing minor repairs or making adjustments to slot machines is work software can't reach.
What shifts is performing paperwork required for monetary transactions: the paper around the work, not the work.
Your week, as this page understands it
Directly supervise and coordinate activities of workers in assigned gambling areas. May circulate among tables, observe operations, and ensure that stations and games are covered for each shift. May verify and pay off jackpots. May reset slot machines after payoffs and make repairs or adjustments to slot machines or recommend removal of slot machines for repair. May plan and organize activities and services for guests in hotels/casinos. The job title says “first-line supervisors of gambling services workers”. The real job is the part underneath: performing minor repairs or making adjustments to slot machines. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But first-line supervisors of gambling services workers is not one task. It is 30 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is performing minor repairs or making adjustments to slot machines, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 12%
- changing shape
- 18%
- staying human
- 71%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 25 out of 100 (20–31 allowing for uncertainty): low exposure, across 30 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for first-line supervisors of gambling services workers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 6 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
4 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Performing paperwork required for monetary transactions
This is reading one thing and writing another: paperwork in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Perform paperwork required for monetary transactions.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The transaction paperwork behind cash handling is structured form-filling that software handles very well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Recording the specifics of malfunctioning machines and documenting malfunctions needing repair
This is reading one thing and writing another: the specifics of malfunctioning machines in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Record the specifics of malfunctioning machines and document malfunctions needing repair.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Logging which machine failed and how is structured record-keeping software handles very well.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining how many gaming tables to open each day and scheduling staff accordingly
This is reading one thing and writing another: how many gaming tables in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Determine how many gaming tables to open each day and schedule staff accordingly.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Forecasting how many tables to open and rostering staff is planning work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing operational expenses, budget estimates, betting accounts or collection reports for accuracy
This is reading one thing and writing another: operational expenses, budget estimates in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Review operational expenses, budget estimates, betting accounts, or collection reports for accuracy.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking expenses, budgets and betting accounts for accuracy is exactly what accounting software is built for.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
5 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Explaining and interpreting house rules
The software now makes the first pass at house rules, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Explain and interpret house rules, such as game rules or betting limits, for patrons.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Game rules and betting limits are fully documented, though patrons ask about them at the table.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Answering patrons' questions about gaming machine functions and payouts
The software now makes the first pass at patrons' questions, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Answer patrons' questions about gaming machine functions and payouts.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Machine functions and payout odds are published in detail, but questions come in person on the floor.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Reporting customer-related incidents occurring in gaming areas to supervisors
The software now makes the first pass at customer-related incidents, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Report customer-related incidents occurring in gaming areas to supervisors.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up an incident for supervisors is routine reporting, though the details come from what happened in the room.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
21 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Greeting customers and asking about the quality of service they are receiving
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Greet customers and ask about the quality of service they are receiving.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Greeting guests and asking how their evening is going only works face to face.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Responding to and resolving patrons' complaints
This work happens in the physical world: and resolving patrons' complaints, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Respond to and resolve patrons' complaints.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: A calm, in-person response is what usually settles an unhappy customer at the table.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Observing gamblers' behavior for signs of cheating
This work happens in the physical world: gamblers' behavior, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Observe gamblers' behavior for signs of cheating, such as marking, switching, or counting cards, and notify security staff of suspected cheating.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Camera systems flag suspicious play well, but the supervisor still has to be watching the table.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Show the other 20 tasks
Evaluating workers' performance and preparing written performance evaluations
changing shapeThe software now makes the first pass at workers' performance, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Evaluate workers' performance and prepare written performance evaluations.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Written performance reviews can be drafted well from tracked data and manager notes.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Directing workers compiling summary sheets for each race or event to record amounts wagered and amounts to be paid
changing shapeThe software now makes the first pass at workers compiling summary sheets, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Direct workers compiling summary sheets for each race or event to record amounts wagered and amounts to be paid to winners.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Compiling wagered and payout totals for each event is calculation on data already recorded.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Establishing policies on types of gambling
staying humanThe rules require a named, qualified person to answer for policies, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Establish policies on types of gambling offered, odds, or extension of credit.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Options can be modeled, but gambling offerings and odds have to be approved by accountable people and regulators.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Supervising the distribution of complimentary meals
staying humanThis work happens in the physical world: the distribution of complimentary meals, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Supervise the distribution of complimentary meals, hotel rooms, discounts, or other items given to players, based on length of play and amount bet.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Comp calculations already run on tracked play data, though handing them out happens on the floor.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Maintaining familiarity with the games at a facility and with strategies or tricks used by cheaters at such games
staying humanThe ratings behind this row put familiarity well outside what today's tools can do on their own.
importance 5 · CoreSource: “Maintain familiarity with the games at a facility and with strategies or tricks used by cheaters at such games.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over.
The rating behind it: Reference material on games and cheating methods exists, but the point is the supervisor knowing it themselves.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Establishing and maintaining banks and table limits for each game
staying humanThis work happens in the physical world: banks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Establish and maintain banks and table limits for each game.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Setting bank and table limits is a numbers decision software can make, though the licensed operator remains accountable.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Training, supervising, scheduling and evaluating workers
staying humanThis work happens in the physical world: workers, in a real place. Software cannot follow it there.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Train, supervise, schedule, and evaluate workers.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Rostering can be automated, but training and supervising a floor team happens in person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Recording, issuing receipts for and pay off bets
staying humanThis work happens in the physical world: receipts, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Record, issue receipts for, and pay off bets.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The recording and receipts are automatable, but paying out a bet happens over the counter.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Attaching "out of order" signs to malfunctioning machines
staying humanThis work happens in the physical world: "out of order" signs, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Attach "out of order" signs to malfunctioning machines, and notify technicians when machines need to be repaired or removed.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Notifying a technician is easy, but physically tagging the machine happens on the floor.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring payment of hand-delivered jackpots to ensure promptness
staying humanThis work happens in the physical world: payment of hand-delivered jackpots, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Monitor payment of hand-delivered jackpots to ensure promptness.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Payout timing can be tracked automatically, but hand-delivered jackpots happen on the floor.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring stations and games and moving dealers from game to game to ensure adequate staffing
staying humanThis work happens in the physical world: stations, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Monitor stations and games and move dealers from game to game to ensure adequate staffing.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (5–19 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Deciding to move a dealer depends on watching the pace and mood of live tables.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Interviewing and hiring workers
staying humanThis work happens in the physical world: workers, in a real place. Software cannot follow it there.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Interview and hire workers.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Hiring turns on judgement formed in an interview with a person in front of you.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Monitoring game operations to ensure that house rules
staying humanThis work happens in the physical world: game operations, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Monitor game operations to ensure that house rules are followed, that tribal, state, and federal regulations are adhered to, and that employees provide prompt and courteous service.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Watching that games are run properly and staff are courteous means being on the floor.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Enforcing safety rules
staying humanThis work happens in the physical world: safety rules, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Enforce safety rules, and report or remove safety hazards as well as guests who are underage, intoxicated, disruptive, or cheating.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Removing an underage or disruptive guest requires staff physically present.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Monitoring patrons for signs of compulsive gambling
staying humanThis work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Monitor patrons for signs of compulsive gambling, offering assistance if necessary.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–10 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Noticing that someone’s gambling is getting out of hand, and approaching them well, depends on being there and being trusted.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 1/4.
Performing minor repairs or making adjustments to slot machines
staying humanThis work happens in the physical world: minor repairs, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Perform minor repairs or make adjustments to slot machines, resolving problems such as machine tilts and coin jams.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Clearing a coin jam or resetting a tilted machine needs hands inside it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Resetting slot machines after payoffs
staying humanThis work happens in the physical world: slot machines after payoffs, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Reset slot machines after payoffs.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Resetting a slot machine after a payout is done at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Exchanging currency for customers, converting currency into requested combinations of bills and coins
staying humanThis work happens in the physical world: currency, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Exchange currency for customers, converting currency into requested combinations of bills and coins.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Counting out currency into requested bills and coins is done by hand at the cage.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Cleaning and maintaining slot machines and surrounding areas
staying humanThis work happens in the physical world: slot machines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Clean and maintain slot machines and surrounding areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning and maintaining machines is hands-on work in the shop.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Monitoring functioning of slot machine coin dispensers and filling coin hoppers
staying humanThis work happens in the physical world: slot machine coin dispensers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Monitor functioning of slot machine coin dispensers and fill coin hoppers when necessary.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking coin dispensers and refilling hoppers means opening the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $63,820a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 26,010in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: the specifics of malfunctioning machines in, a record out. The rows above are exactly that shape: performing paperwork required for monetary transactions and recording the specifics of malfunctioning machines and documenting malfunctions needing repair. What it cannot do is be there in the room, and that is still where minor repairs get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: performing minor repairs or making adjustments to slot machines is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 12% of this job's task weight sits in rows the software is already learning, 18% in rows that change shape rather than disappear, and 71% in rows it is nowhere near. That is the position, measured across 30 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Performing paperwork required for monetary transactions is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to the specifics of malfunctioning machines, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether minor repairs are in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near explaining and interpreting house rules. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to first-line supervisors of gambling services workers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was gambling managers: only about 19% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 25/100 here, with only 12% of the task list in the top band, and “greet customers and ask about the quality of service they are receiving” is not work that hands over cleanly. None of them beats deepening what you already have.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Gambling Managers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “explain and interpret house rules”. Across the whole of both lists that adds up to about 19% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 19% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 5,030 of those jobs against 26,010 of yours (OEWS May 2025), 19% as many seats.
Gambling Dealers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange currency for customers, converting currency into requested combinations of bills and coins, and their equivalent is to exchange paper currency for playing chips or coin money. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $34,320 against your $63,820, 46.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Cashiers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet customers and ask about the quality of service they are receiving, and their equivalent is to greet customers entering establishments. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $32,880 against your $63,820, 48.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 12% of its task weight, across 30 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like performing minor repairs or making adjustments to slot machines arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
If you run a team doing this job
If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are performing paperwork required for monetary transactions, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Sports and leisure assistants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Sports and leisure assistants, Betting shop and gambling establishment managers and Financial administrative occupations n.e.c.. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
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Anywhere in the US:
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No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for first-line supervisors of gambling services workers, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 12% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for first-line supervisors of gambling services workers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for first-line supervisors of gambling services workers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace First-Line Supervisors of Gambling Services Workers?
- Not as a job, but it is already doing parts of the work. Across the 30 official task statements scored for First-Line Supervisors of Gambling Services Workers (United States, SOC 39-1013), 12% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 25 out of 100 (range 20–31, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “First-Line Supervisors of Gambling Services Workers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Perform paperwork required for monetary transactions” (81/100, very high); “Review operational expenses, budget estimates, betting accounts, or collection reports for accuracy” (81/100, very high); “Determine how many gaming tables to open each day and schedule staff accordingly” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “First-Line Supervisors of Gambling Services Workers” stay human?
- About 71% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Monitor functioning of slot machine coin dispensers and fill coin hoppers when necessary” (0/100, minimal); “Clean and maintain slot machines and surrounding areas” (0/100, minimal); “Exchange currency for customers, converting currency into requested combinations of bills and coins” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “First-Line Supervisors of Gambling Services Workers” do about AI?
- Start from the ledger rather than the headline: 12% of this job's weighted core work is exposed, and roughly 71% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for First-Line Supervisors of Gambling Services Workers calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 30 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 6 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt-with-imputed)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.