US dataswitch to UK
Gambling Surveillance Officers and Gambling Investigators
monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures, observing casino or casino hotel operations for irregular activities and developing and maintaining log of surveillance observations. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: developing and maintaining log of surveillance observations. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures, stays yours. New tools, same person answering for it.
Your week, as this page understands it
Observe gambling operation for irregular activities such as cheating or theft by either employees or patrons. Investigate potential threats to gambling assets such as money, chips, and gambling equipment. Act as oversight and security agent for management and customers. The job title says “gambling surveillance officers” or “gambling investigators”: officially one job, two names. The real job is the part underneath: monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures. 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 gambling surveillance officers and gambling investigators is not one task. It is 8 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 26%
- changing shape
- 14%
- staying human
- 60%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 41 out of 100 (34–47 allowing for uncertainty): partial exposure, across 8 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 gambling surveillance officers and gambling investigators 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.
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- 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
2 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.
Developing and maintaining log of surveillance observations
This is reading one thing and writing another: log of surveillance observations in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Develop and maintain log of surveillance observations.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Keeping a running log of what was seen is routine record-keeping that software already handles.
The five ratings: output a model can produce 3/4 · 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.
Reviewing video surveillance footage
This is reading one thing and writing another: video surveillance footage in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Review video surveillance footage.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high 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: Searching and reviewing recorded footage is something video software now does quickly, with a person confirming the important clips.
The five ratings: output a model can produce 3/4 · 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
1 taskTasks 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.
Reporting all violations and suspicious behaviors
The software now makes the first pass at all violations, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Report all violations and suspicious behaviors to supervisors, verbally or in writing.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (45–53 allowing for uncertainty): partial 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: Turning what was observed into a written violation report is standard writing work that software drafts well.
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 0/4 · how much data exists 3/4.
Staying human
5 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.
Monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures
The rules require a named, qualified person to answer for establishment activities, and that person cannot be a piece of software.
importance 5 · CoreSource: “Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures.” (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: someone qualified has to answer for it.
The rating behind it: Software can flag odd patterns, but judging whether a casino floor meets state gaming rules still needs a licensed officer watching.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Observing casino or casino hotel operations for irregular activities
The ratings behind this row put casino well outside what today's tools can do on their own.
importance 5 · CoreSource: “Observe casino or casino hotel operations for irregular activities, such as cheating or theft by employees or patrons, using audio and video equipment and one-way mirrors.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Video analytics spot some irregularities, yet recognizing a practiced cheat on a live floor remains a trained human's eye.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inspecting and monitoring audio or video surveillance equipment to ensure it is working appropriately
This work happens in the physical world: audio, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Inspect and monitor audio or video surveillance equipment to ensure it is working appropriately.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 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: System health can be checked automatically, but confirming cameras and microphones physically work means walking the building.
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 0/4 · how much data exists 3/4.
Acting as oversight or security agents for management or customers
This work happens in the physical world: oversight, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Act as oversight or security agents for management or customers.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (0–22 allowing for uncertainty): minimal exposure, low 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: Acting as a security presence for staff and guests depends on actually being there and being trusted.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Supervising or training surveillance observers
The value here is that a specific person handles surveillance observers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Supervise or train surveillance observers.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Training material can be drafted automatically, but supervising and coaching real observers depends on working alongside them.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $43,370a 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
- 9,520in 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: log of surveillance observations in, a record out. The rows above are exactly that shape: developing and maintaining log of surveillance observations and reviewing video surveillance footage. What it cannot do is be answerable: establishment activities need a named person the rules will accept, and software cannot be that person. 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
Your week is splitting in two, and which half fills it is the whole question. Developing and maintaining log of surveillance observations is going; monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures is not.
So, given all that: 26% of this job's task weight sits in rows the software is already learning, 14% in rows that change shape rather than disappear, and 60% in rows it is nowhere near. That is the position, measured across 8 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles monitoring establishment activities to ensure adherence to all state gaming regulations and company policies and procedures, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 gambling surveillance officers and gambling investigators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was audio and video technicians: only about 6% of its durable work is work you already do. Your own job splits about 26/74: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “report all violations and suspicious behaviors to supervisors, verbally or in writing”, and let the exposed end go.
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.
Audio and Video Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect and monitor audio or video surveillance equipment to ensure it is working…, and their equivalent is to perform minor repairs and routine cleaning of audio and video equipment. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Gambling Cage Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already monitor establishment activities to ensure adherence to all state gaming regulations and company…, and their equivalent is to follow all gaming regulations. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $37,580 against your $43,370, 13.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Nuclear Power Reactor Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already develop and maintain log of surveillance observations, and their equivalent is to record operating data, such as the results of surveillance tests. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $122,890 against your $43,370 is 2.83× (OEWS May 2025 (both)), and you would be crossing it holding about 2% of their durable work. A gap that size with an overlap that small is a wish, not a route.
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: 26% of its task weight, across 8 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Legal associate professionals 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 Legal associate professionals, Security guards and related occupations and Betting shop and gambling establishment managers. 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:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for gambling surveillance officers / gambling investigators, 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 26% 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 gambling surveillance officers / gambling investigators. 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 gambling surveillance officers / gambling investigators launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Gambling Surveillance Officers and Gambling Investigators?
- Not as a job, but it is already doing parts of the work. Across the 8 official task statements scored for Gambling Surveillance Officers and Gambling Investigators (United States, SOC 33-9031), 26% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 41 out of 100 (range 34–47, band: partial). 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 “Gambling Surveillance Officers and Gambling Investigators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Develop and maintain log of surveillance observations” (66/100, high); “Review video surveillance footage” (66/100, high); “Report all violations and suspicious behaviors to supervisors, verbally or in writing” (49/100, partial). 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 “Gambling Surveillance Officers and Gambling Investigators” stay human?
- About 60% 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: “Act as oversight or security agents for management or customers” (10/100, minimal); “Supervise or train surveillance observers” (26/100, low); “Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures” (28/100, low). 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 “Gambling Surveillance Officers and Gambling Investigators” do about AI?
- Start from the ledger rather than the headline: 26% of this job's weighted core work is exposed, and roughly 60% 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 Gambling Surveillance Officers and Gambling Investigators 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 8 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
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 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)
- 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.