What should someone working in “Reservation and Transportation Ticket Agents and Travel Clerks” do about AI?

Start from the ledger rather than the headline: 53% of this job's weighted core work is exposed, and roughly 33% 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.

US dataswitch to UK

Reservation and Transportation Ticket Agents and Travel Clerks

examining passenger documentation to determine destinations and to assign boarding passes, preparing customer invoices and accept payment and determining whether space is available on travel dates. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: answering inquiries regarding information, such as schedules, accommodations, procedures or policies is work AI now does quickly and cheaply, and providing boarding or disembarking assistance to passengers needing special assistance is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 53%
changing shape: 14%
staying human: 33%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 53 out of 100 (48–58 allowing for uncertainty): partial exposure, across 21 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 reservation and transportation ticket agents and travel clerks 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 2026-08-04. Read the full method.

Which half fills your week decides your exposure. That is more in your control than it sounds.

Your week, as this page understands it

Make and confirm reservations for transportation or lodging, or sell transportation tickets. May check baggage and direct passengers to designated concourse, pier, or track; deliver tickets and contact individuals and groups to inform them of package tours; or provide tourists with travel or transportation information. The job title says “reservation”, “transportation ticket agents” or “travel clerks”: officially one job, several names. The real job is the part underneath: providing boarding or disembarking assistance to passengers needing special assistance. 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 reservation and transportation ticket agents and travel clerks is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is providing boarding or disembarking assistance to passengers needing special assistance, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 53%
changing shape: 14%
staying human: 33%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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 2026-08-04. 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.

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

11 tasks

Tasks 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.

Conferring with customers to determine their service requirements and travel preferences
This is reading one thing and writing another: customers in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Confer with customers to determine their service requirements and travel preferences.” (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: Asking what a customer needs and noting preferences works well through chat or phone.
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.
Answering inquiries regarding information, such as schedules, accommodations, procedures or policies
This is reading one thing and writing another: inquiries regarding information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Answer inquiries regarding information, such as schedules, accommodations, procedures, or policies.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Answering questions about schedules and policies is well suited to automated assistants.
The five ratings: output a model can produce 4/4 · 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 4/4.
Determining whether space is available on travel dates
This is reading one thing and writing another: whether space is available in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine whether space is available on travel dates requested by customers, assigning requested spaces when available.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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 whether seats are free on a date is exactly what a booking system does.
The five ratings: output a model can produce 4/4 · 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 4/4.
Assembling and issuing required documentation
This is reading one thing and writing another: required documentation in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Assemble and issue required documentation, such as tickets, travel insurance policies, or itineraries.” (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: Issuing tickets, itineraries and policies from a booking is standard automated document work.
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.

Changing shape

4 tasks

Tasks 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.

Tracing lost, delayed or misdirected baggage for customers
The software now makes the first pass at lost, delayed or misdirected baggage, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Trace lost, delayed, or misdirected baggage for customers.” (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: Tracing a bag is database searching and customer updates, mostly done at a screen.
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.
Preparing customer invoices and accept payment
The software now makes the first pass at customer invoices, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · Supplemental
Source: “Prepare customer invoices and accept payment.” (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: Invoicing is automated, but taking payment at a counter still involves the customer in person.
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.
Providing clients with assistance in preparing required travel documents and forms
The software now makes the first pass at clients, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Supplemental
Source: “Provide clients with assistance in preparing required travel documents and forms.” (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: Helping fill in travel forms is guided paperwork, mostly done on screen with the customer.
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.

Staying human

6 tasks

Tasks 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.

Examining passenger documentation to determine destinations and to assign boarding passes
This work happens in the physical world: passenger documentation, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Examine passenger documentation to determine destinations and to assign boarding passes.” (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; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Reading travel documents is routine, but the passenger hands them over at the desk.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Providing boarding or disembarking assistance to passengers needing special assistance
This work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Provide boarding or disembarking assistance to passengers needing special assistance.” (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: Helping a passenger board is direct physical assistance.
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 2/4 · how much data exists 2/4.
Announcing arrival and departure information
This work happens in the physical world: arrival, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Announce arrival and departure information, using public address systems.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 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: The announcement text is easy to produce, but someone is on the terminal microphone.
The five ratings: output a model can produce 4/4 · 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 4/4.

Show the other 11 tasks

Planning routes, itineraries and accommodation details and computing fares and fees, using schedules, rate books and computers
shifting to AI
This is reading one thing and writing another: routes, itineraries and accommodation details and computing fares in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Plan routes, itineraries, and accommodation details, and compute fares and fees, using schedules, rate books, and computers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very 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: Building itineraries and working out fares is calculation against schedules and rate tables.
The five ratings: output a model can produce 4/4 · 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.
Making and confirming reservations for transportation and accommodations
shifting to AI
This is reading one thing and writing another: reservations in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Make and confirm reservations for transportation and accommodations, using telephones, faxes, mail, and computers.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Making and confirming bookings is a standard system transaction.
The five ratings: output a model can produce 4/4 · 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 4/4.
Maintaining computerized inventories of available passenger space and providing information on space reserved or available
shifting to AI
This is reading one thing and writing another: computerized inventories of available passenger space in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain computerized inventories of available passenger space and provide information on space reserved or available.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Keeping seat inventory current and reporting what is available is straightforward database work.
The five ratings: output a model can produce 4/4 · 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 4/4.
Contacting customers or travel agents to advise them of travel conveyance changes or to confirm reservations
shifting to AI
This is reading one thing and writing another: customers in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Contact customers or travel agents to advise them of travel conveyance changes or to confirm reservations.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Notifying customers of schedule changes and confirming bookings is routine automated messaging.
The five ratings: output a model can produce 4/4 · 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 4/4.
Contacting motel, hotel, resort and travel operators to obtain current advertising literature
shifting to AI
This is reading one thing and writing another: motel, hotel, resort and travel operators in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Contact motel, hotel, resort, and travel operators to obtain current advertising literature.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 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: Contacting operators for current brochures is routine, repeatable correspondence.
The five ratings: output a model can produce 4/4 · 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.
Informing clients of essential travel information
shifting to AI
This is reading one thing and writing another: clients of essential travel information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Inform clients of essential travel information, such as travel times, transportation connections, or medical and visa requirements.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Travel and visa information is widely published, though wrong answers are costly so it gets checked.
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 4/4.
Providing customers with travel suggestions and information sources
shifting to AI
This is reading one thing and writing another: customers in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Provide customers with travel suggestions and information sources, such as guides, directories, brochures, or maps.” (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: Travel suggestions and guide information are abundant and easy to assemble.
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 1/4 · how much data exists 4/4.
Promoting particular destinations, tour packages and other travel services
changing shape
The software now makes the first pass at particular destinations, tour packages and other travel services, 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 · Supplemental
Source: “Promote particular destinations, tour packages, and other travel services.” (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: 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: Promotional copy is easy to write, but persuading someone to book still leans on a person.
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 4/4.
Checking baggage and cargo and directing passengers to designated locations for loading
staying human
This work happens in the physical world: baggage, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check baggage and cargo and direct passengers to designated locations for loading.” (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: Handling bags and cargo and walking passengers to loading points is physical.
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.
Keeping information facilities clean during operation
staying human
This work happens in the physical world: information facilities clean during operation, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Keep information facilities clean during operation.” (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: Keeping an information desk clean is physical cleaning work.
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 1/4.
Opening or closing information facilities
staying human
This work happens in the physical world: information facilities, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Open or close information facilities.” (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: Opening and locking up an information facility requires someone 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 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $44,390a 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: 118,710in 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: inquiries regarding information in, a record out. The rows above are exactly that shape: answering inquiries regarding information, such as schedules, accommodations, procedures or policies and conferring with customers to determine their service requirements and travel preferences. What it cannot do is be there in the room, and that is still where assistance gets 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

The exposed part of your job is the biggest part, and I am not going to dress that up: answering inquiries regarding information, such as schedules, accommodations, procedures or policies is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 53% of this job's task weight sits in rows the software is already learning, 14% in rows that change shape rather than disappear, and 33% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.

What you have that the software does not is providing boarding or disembarking assistance to passengers needing special assistance, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.

This week: one thing

Sit on the machine's side of the desk. Pick one real piece of inquiries regarding information you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.

What you end up holding: a written list of the machine’s mistakes, in your handwriting
How long it takes: an evening, or an hour if you pick one job rather than one client

If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of inquiries regarding information, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is answering inquiries regarding information, such as schedules, accommodations, procedures or policies” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.

Over the next 12 months

Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of providing boarding or disembarking assistance to passengers needing special assistance you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 reservation and transportation ticket agents and travel clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was flight attendants: only about 7% of its durable work is work you already do. Your own job splits about 53/47: 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 “examine passenger documentation to determine destinations and to assign boarding passes”, 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.

Flight Attendants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide boarding or disembarking assistance to passengers needing special assistance, and their equivalent is to assist passengers entering or disembarking the aircraft. 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.
Look at that job’s page anyway →
Passenger Attendants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide boarding or disembarking assistance to passengers needing special assistance, and their equivalent is to count and verify tickets and seat reservations and record numbers of passengers boarding…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $37,720 against your $44,390, 15.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 27,110 of those jobs against 118,710 of yours (OEWS May 2025), 23% as many seats.
Look at that job’s page anyway →
Cashiers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer inquiries regarding information, and their equivalent is to answer customers' questions, and provide information on procedures or policies. 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. It is a pay cut, in those words: $32,880 against your $44,390, 25.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →

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: 53% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: providing boarding or disembarking assistance to passengers needing special assistance is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
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 Travel agents 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 Travel agents. 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

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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for reservation / transportation ticket agents / travel clerks, 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 53% of the work on this page is already inside what they can do.

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The AI Authority is a general community about working with AI, not a course for reservation / transportation ticket agents / travel clerks. 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.

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No Space for reservation / transportation ticket agents / travel clerks yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

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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 Reservation and Transportation Ticket Agents and Travel Clerks?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Reservation and Transportation Ticket Agents and Travel Clerks (United States, SOC 43-4181), 53% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 53 out of 100 (range 48–58, 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 “Reservation and Transportation Ticket Agents and Travel Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Plan routes, itineraries, and accommodation details, and compute fares and fees, using schedules, rate books, and computers” (93/100, very high); “Assemble and issue required documentation, such as tickets, travel insurance policies, or itineraries” (88/100, very high); “Make and confirm reservations for transportation and accommodations, using telephones, faxes, mail, and computers” (85/100, 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 “Reservation and Transportation Ticket Agents and Travel Clerks” stay human?: About 33% 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: “Open or close information facilities” (0/100, minimal); “Keep information facilities clean during operation” (0/100, minimal); “Check baggage and cargo and direct passengers to designated locations for loading” (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 “Reservation and Transportation Ticket Agents and Travel Clerks” do about AI?: Start from the ledger rather than the headline: 53% of this job's weighted core work is exposed, and roughly 33% 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 Reservation and Transportation Ticket Agents and Travel Clerks 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 21 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.
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 2026-08-04.
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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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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.