US dataswitch to UK
Broadcast Announcers and Radio Disc Jockeys
reading news flashes to inform audiences of important events, developing story lines for broadcasts and selecting program content. 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: studying background information to prepare for programs or interviews. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, making promotional appearances at public or private events to represent their employers, stays yours. New tools, same person answering for it.
Your week, as this page understands it
Speak or read from scripted materials, such as news reports or commercial messages, on radio, television, or other communications media. May play and queue music, announce artist or title of performance, identify station, or interview guests. The job title says “broadcast announcers” or “radio disc jockeys”: officially one job, two names. The real job is the part underneath: making promotional appearances at public or private events to represent their employers. 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 broadcast announcers and radio disc jockeys is not one task. It is 24 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is making promotional appearances at public or private events to represent their employers, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 34%
- changing shape
- 23%
- staying human
- 43%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 42 out of 100 (37–48 allowing for uncertainty): partial exposure, across 24 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 broadcast announcers and radio disc jockeys 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 task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
- 3 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
8 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.
Studying background information to prepare for programs or interviews
This is reading one thing and writing another: background information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Study background information to prepare for programs or interviews.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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: Background research for a program or interview is reading and summarizing, an AI strength.
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 4/4.
Identifying stations and introduce or close shows, ad-libbing or using memorized or reading scripts
This is reading one thing and writing another: stations in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Identify stations, and introduce or close shows, ad-libbing or using memorized or read scripts.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Station IDs and show intros are largely scripted, though live ad-libbing is harder to match.
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 1/4 · how much data exists 3/4.
Commenting on music and other matters
This is reading one thing and writing another: music in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Comment on music and other matters, such as weather or traffic conditions.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Short comments on music, weather or traffic follow patterns AI reproduces easily.
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 1/4 · how much data exists 3/4.
Announcing musical selections, station breaks, commercials or public service information and accept requests from listening audience
This is reading one thing and writing another: musical selections, station breaks in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Announce musical selections, station breaks, commercials, or public service information, and accept requests from listening audience.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Station announcements and requests follow a familiar pattern automated systems already handle.
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 1/4 · 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.
Preparing and delivering news, sports or weather reports
The software now makes the first pass at news, sports or weather reports, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Prepare and deliver news, sports, or weather reports, gathering and rewriting material so that it will convey required information and fit specific time slots.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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: Gathering and rewriting news to fit a slot is writing AI does well; delivery still happens in studio.
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 4/4.
Developing story lines for broadcasts
The software now makes the first pass at story lines, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Develop story lines for broadcasts.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 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.
The rating behind it: AI can offer plenty of story ideas, but choosing what will actually work for the audience stays with the team.
The five ratings: output a model can produce 2/4 · 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.
Selecting program content, in conjunction with producers and assistants, based on factors, program specialties, audience tastes or requests from the public
The software now makes the first pass at program content, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Select program content, in conjunction with producers and assistants, based on factors such as program specialties, audience tastes, or requests from the public.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Content can be picked from audience data, but the final call is made with producers together.
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 2/4 · how much data exists 3/4.
Staying human
11 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.
Reading news flashes to inform audiences of important events
This work happens in the physical world: news flashes, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Read news flashes to inform audiences of important events.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (6–26 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
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: News copy is easy to generate, but reading a flash on air is done by the presenter in the studio.
The five ratings: output a model can produce 3/4 · 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.
Making promotional appearances at public or private events to represent their employers
This work happens in the physical world: promotional appearances, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Make promotional appearances at public or private events to represent their employers.” (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 value is that a specific person does it.
The rating behind it: Turning up at events as the station's face is about being physically there.
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 3/4 · how much data exists 2/4.
Providing commentary and conducting interviews during sporting events
This work happens in the physical world: commentary, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Provide commentary and conduct interviews during sporting events, parades, conventions, or other events.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high 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: Live commentary at an event depends on someone watching it happen and reacting.
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 3/4.
Show the other 14 tasks
Keeping daily program logs to provide information on all elements aired during broadcast
shifting to AIThis is reading one thing and writing another: program logs in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Keep daily program logs to provide information on all elements aired during broadcast, such as musical selections and station promotions.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 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: Logging what aired and when is exactly the kind of record-keeping software does automatically.
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 4/4.
Maintaining organization of the music library
shifting to AIThis is reading one thing and writing another: organization of the music library in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Maintain organization of the music library.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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: Organizing a music library is cataloguing work software already does automatically.
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 4/4.
Writing and editing video and scripts for broadcasts
shifting to AIThis is reading one thing and writing another: video in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write and edit video and scripts for broadcasts.” (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: Writing and editing broadcast scripts is text work AI produces to a usable standard.
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.
Giving network cues permitting selected stations to receive programs
shifting to AIThis is reading one thing and writing another: network cues permitting selected stations in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Give network cues permitting selected stations to receive programs.” (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: Sending network cues is a timing signal broadcast automation systems already handle.
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.
Recording commercials for later broadcast
changing shapeThe software now makes the first pass at commercials, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Record commercials for later broadcast.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Recorded commercial reads are increasingly produced with synthetic voices to an acceptable standard.
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 0/4 · how much data exists 3/4.
Locating guests to appear on talk or interviewing shows
changing shapeThe software now makes the first pass at guests, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Locate guests to appear on talk or interview shows.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Finding and researching potential guests is search and outreach work AI supports 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 2/4 · how much data exists 3/4.
Coordinating games, contests or other on-air competitions, performing such duties as asking questions and awarding prizes
staying humanThe value here is that a specific person handles games, contests and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Coordinate games, contests, or other on-air competitions, performing such duties as asking questions and awarding prizes.” (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 value is that a specific person does it.
The rating behind it: On-air contests run to a format, but the live back-and-forth with callers still needs a host.
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 3/4.
Describing or demonstrating products that viewers may purchase through specific shows or in stores
staying humanThis work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Describe or demonstrate products that viewers may purchase through specific shows or in stores.” (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: Demonstrating a product on air means handling it and selling it live.
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.
Discussing various topics over the telephone with viewers or listeners
staying humanThe value here is that a specific person handles various topics over the telephone and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Discuss various topics over the telephone with viewers or listeners.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (14–22 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Talking with listeners on air works because a real person is on the other end.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Operating control consoles
staying humanThis work happens in the physical world: control consoles, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Operate control consoles.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (10–18 allowing for uncertainty): minimal exposure, high 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: Working a control console during a show means hands on the desk in the studio.
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.
Interviewing show guests about their lives
staying humanThis work happens in the physical world: show guests, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Interview show guests about their lives, their work, or topics of current interest.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high 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: A good interview depends on live rapport between the host and the guest.
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 3/4.
Moderating panels or discussion shows on topics
staying humanThis work happens in the physical world: panels, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Moderate panels or discussion shows on topics such as current affairs, art, or education.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 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: Moderating a live panel depends on reading the room and the people in it.
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.
Hosting civic, charitable or promotional events broadcast over television or radio
staying humanThis work happens in the physical world: civic, charitable or promotional events broadcast over television, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Host civic, charitable, or promotional events broadcast over television or radio.” (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 value is that a specific person does it.
The rating behind it: Hosting an event means being on the stage or in the studio with the audience.
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 3/4 · how much data exists 1/4.
Attending press conferences to gather information for broadcast
staying humanThis work happens in the physical world: press conferences, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Attend press conferences to gather information for broadcast.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Attending a press conference to gather material means being in the room.
The five ratings: output a model can produce 1/4 · 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 3/4.
What this job pays, and how many people do it
- Median pay
- $47,340a 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
- 21,240in 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: background information in, a record out. The rows above are exactly that shape: studying background information to prepare for programs or interviews and identifying stations and introduce or close shows. What it cannot do is be there in the room, and that is still where promotional appearances 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
Your week is splitting in two, and which half fills it is the whole question. Studying background information to prepare for programs or interviews is going; making promotional appearances at public or private events to represent their employers is not.
So, given all that: 34% of this job's task weight sits in rows the software is already learning, 23% in rows that change shape rather than disappear, and 43% in rows it is nowhere near. That is the position, measured across 24 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 (making promotional appearances at public or private events to represent their employers) 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 making promotional appearances at public or private events to represent their employers, 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 broadcast announcers and radio disc jockeys (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was sound engineering technicians: only about 3% of its durable work is work you already do. Your own job splits about 34/66: 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 “read news flashes to inform audiences of important events”, 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.
Sound Engineering Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already operate control consoles, and their equivalent is to regulate volume level and sound quality during recording sessions, using control consoles. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Atmospheric and Space Scientists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already host civic, charitable, or promotional events broadcast over television or radio, and their equivalent is to direct forecasting services at weather stations or at radio or television broadcasting facilities. 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. I will not move you off one melting floe onto another: 67% of its own task list already scores in the top exposure band (62/100 in this release), so the same software is eating it. The pay gap is the market pricing a barrier: $99,070 against your $47,340 is 2.09× (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.
Meeting, Convention, and Event Planners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide commentary and conduct interviews during sporting events, parades, conventions, or other events, and their equivalent is to consult with customers to determine objectives and requirements for events. 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.
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: 34% of its task weight, across 24 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 Actors, entertainers and presenters 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
In UK official statistics this job is counted as Actors, entertainers and presenters. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
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No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for broadcast announcers / radio disc jockeys, 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 34% 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 broadcast announcers / radio disc jockeys. 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 broadcast announcers / radio disc jockeys launches. Nothing else.
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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 Broadcast Announcers and Radio Disc Jockeys?
- Not as a job, but it is already doing parts of the work. Across the 24 official task statements scored for Broadcast Announcers and Radio Disc Jockeys (United States, SOC 27-3011), 34% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 42 out of 100 (range 37–48, 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 “Broadcast Announcers and Radio Disc Jockeys” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Keep daily program logs to provide information on all elements aired during broadcast, such as musical selections and station promotions” (88/100, very high); “Study background information to prepare for programs or interviews” (83/100, very high); “Maintain organization of the music library” (83/100, very 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 “Broadcast Announcers and Radio Disc Jockeys” stay human?
- About 43% 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: “Attend press conferences to gather information for broadcast” (0/100, minimal); “Make promotional appearances at public or private events to represent their employers” (0/100, minimal); “Host civic, charitable, or promotional events broadcast over television or radio” (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 “Broadcast Announcers and Radio Disc Jockeys” do about AI?
- Start from the ledger rather than the headline: 34% of this job's weighted core work is exposed, and roughly 43% 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 Broadcast Announcers and Radio Disc Jockeys 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 24 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
About the data on this page
- One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 3 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)
- 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.
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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.