US dataswitch to UK
Interpreters and Translators
following ethical codes that protect the confidentiality of information, referring to reference materials and checking translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: referring to reference materials, such as dictionaries, lexicons, encyclopedias and computerized terminology banks is work AI now does quickly and cheaply, and identifying and resolving conflicts related to the meanings of words is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Interpret oral or sign language, or translate written text from one language into another. The job title says “interpreters” or “translators”: officially one job, two names. The real job is the part underneath: identifying and resolving conflicts related to the meanings of words. 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 interpreters and translators is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is identifying and resolving conflicts related to the meanings of words, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 49%
- changing shape
- 19%
- staying human
- 32%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 62 out of 100 (56–67 allowing for uncertainty): high exposure, across 17 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 interpreters and translators 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.
- 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.
Referring to reference materials, such as dictionaries, lexicons, encyclopedias and computerized terminology banks
This is reading one thing and writing another: reference materials in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.” (O*NET task statement)
How this row was scored
Exposure score: 100 out of 100 (96–100 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: Looking terms up in dictionaries and term banks is instant lookup, which is what these tools are built on.
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 4/4.
Compiling terminology and information to be used in translations
This is reading one thing and writing another: terminology in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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: Pulling technical terms out of documents and building a glossary is text work software does thoroughly.
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.
Compiling information on content and context of information to be translated and on intended audience
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compile information on content and context of information to be translated and on intended audience.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Gathering context about a text and its audience is desk work, though the client holds some of the detail.
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.
Checking translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions
This is reading one thing and writing another: translations of technical terms in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 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 a term translated the same way throughout a document is a consistency check software has done for years.
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.
Changing shape
3 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.
Following ethical codes that protect the confidentiality of information
The software now makes the first pass at ethical codes, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Follow ethical codes that protect the confidentiality of information.” (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: Confidentiality rules are written down and easy to apply, though judging a tricky situation still needs a professional.
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.
Listening to speakers' statements to determine meanings and to prepare translations
The software now makes the first pass at speakers' statements, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.” (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: Turning what a speaker says into an accurate translation is exactly what speech and language tools now do.
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.
Discussing translation requirements with clients and determining any fees to be charged for services
The software now makes the first pass at translation requirements, 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 · SupplementalSource: “Discuss translation requirements with clients and determine any fees to be charged for services provided.” (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: Scoping a job and quoting a fee can be drafted automatically, though agreeing terms is a client conversation.
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
6 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.
Translating messages simultaneously or consecutively into specified languages
The value here is that a specific person handles messages and stands behind it. That is earned, not computed.
importance 5 · CoreSource: “Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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: Live speech translation by machine is now good, but interpreting in a room, or in sign, still needs a person.
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 2/4 · how much data exists 3/4.
Identifying and resolving conflicts related to the meanings of words
The value here is that a specific person handles conflicts and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.” (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: Untangling what a word or custom really means in context often needs someone who knows both cultures firsthand.
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.
Educating students, parents, staff and teachers about the roles and functions of educational interpreters
The value here is that a specific person handles students, parents, staff and teachers and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Educate students, parents, staff, and teachers about the roles and functions of educational interpreters.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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: The explanation is easy to write, but convincing teachers and parents how interpreting works happens face to face.
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 2/4 · how much data exists 3/4.
Show the other 7 tasks
Proofreading, editing and revising translated materials
shifting to AIThis is reading one thing and writing another: translated materials in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Proofread, edit, and revise translated materials.” (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: Proofreading and polishing translated text is exactly the kind of editing AI now does 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 4/4.
Reading written materials, such as legal documents, scientific works or news reports and rewrite material into specified languages
shifting to AIThis is reading one thing and writing another: written materials in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.” (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: Written translation of documents and news is where machine translation is strongest, with people checking sensitive parts.
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.
Adapting software and accompanying technical documents to another language and culture
shifting to AIThis is reading one thing and writing another: software in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Adapt software and accompanying technical documents to another language and culture.” (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: Software localisation is highly systematic, and translation tools handle both the strings and the documents well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Checking original texts or conferring with authors to ensure that translations retain the content
shifting to AIThis is reading one thing and writing another: original texts in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.” (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: Comparing a translation against the original for meaning and tone is text-to-text checking AI does capably.
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.
Adapting translations to students' cognitive and grading levels
staying humanThe value here is that a specific person handles translations and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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: Simplifying language to a child's level is well within AI's reach, but working with the school team is personal.
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 2/4 · how much data exists 3/4.
Training and supervising other translators or interpreters
staying humanThe value here is that a specific person handles other translators and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Train and supervise other translators or interpreters.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 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: Training and supervising other translators works through feedback and trust built with each individual.
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 3/4 · how much data exists 3/4.
Travelling with or guiding tourists who speak another language
staying humanThis work happens in the physical world: or guiding tourists who speak another language, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Travel with or guide tourists who speak another language.” (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: Accompanying tourists means physically travelling with them and handling whatever comes up on the day.
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.
What this job pays, and how many people do it
- Median pay
- $60,170a 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
- 52,060in 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: reference materials in, a record out. The rows above are exactly that shape: referring to reference materials, such as dictionaries, lexicons, encyclopedias and computerized terminology banks and compiling terminology and information to be used in translations. What it cannot do is be trusted in person, which is what conflicts run on: someone specific doing it and standing behind it. 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: referring to reference materials, such as dictionaries, lexicons, encyclopedias and computerized terminology banks is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 49% of this job's task weight sits in rows the software is already learning, 19% in rows that change shape rather than disappear, and 32% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
What you have that the software does not is identifying and resolving conflicts related to the meanings of words, 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 reference materials 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 reference materials, 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 referring to reference materials, such as dictionaries, lexicons, encyclopedias and computerized terminology banks” 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 identifying and resolving conflicts related to the meanings of words 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 interpreters and translators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical records specialists: only about 5% of its durable work is work you already do, it is under the same pressure this job is and it pays 15.0% less. I am not going to pretend that is comfortable news: 49% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “follow ethical codes that protect the confidentiality of information” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
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.
Medical Records Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already follow ethical codes that protect the confidentiality of information, and their equivalent is to protect the security of medical records to ensure that confidentiality is maintained. 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. I will not move you off one melting floe onto another: 76% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. It is a pay cut, in those words: $51,140 against your $60,170, 15.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Health Information Technologists and Medical Registrars
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already follow ethical codes that protect the confidentiality of information, and their equivalent is to protect the security of medical records to ensure that confidentiality is maintained. 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.
School Psychologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already adapt translations to students' cognitive and grade levels, collaborating with educational team members…, and their equivalent is to develop individualized educational plans in collaboration with teachers and other staff members. 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: 49% of its task weight, across 17 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: identifying and resolving conflicts related to the meanings of words 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 Authors, writers and translators 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 Authors, writers and translators. 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:
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 interpreters / translators, 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 49% 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 interpreters / translators. 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 interpreters / translators 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 Interpreters and Translators?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Interpreters and Translators (United States, SOC 27-3091), 49% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 62 out of 100 (range 56–67, band: high). 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 “Interpreters and Translators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy” (100/100, very high); “Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions” (93/100, very high); “Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material” (93/100, 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 “Interpreters and Translators” stay human?
- About 32% 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: “Travel with or guide tourists who speak another language” (0/100, minimal); “Train and supervise other translators or interpreters” (24/100, low); “Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors” (26/100, 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 “Interpreters and Translators” do about AI?
- Start from the ledger rather than the headline: 49% of this job's weighted core work is exposed, and roughly 32% 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 Interpreters and Translators 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 17 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.
- 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.
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.