US dataswitch to UK
Historians
gathering historical data from sources, speaking to various groups and teaching and conducting research in colleges. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: conserving and preserving manuscripts, records and other artifacts is work software can't reach.
What shifts is organizing information for publication and for other means of dissemination. This page scores what today's tools actually do, not headlines.
Your week, as this page understands it
Research, analyze, record, and interpret the past as recorded in sources, such as government and institutional records, newspapers and other periodicals, photographs, interviews, films, electronic media, and unpublished manuscripts, such as personal diaries and letters. The job title says “historians”. The real job is the part underneath: conserving and preserving manuscripts, records and other artifacts. 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 historians 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 conserving and preserving manuscripts, records and other artifacts, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 15%
- changing shape
- 27%
- staying human
- 58%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 40 out of 100 (35–47 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 historians 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.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
4 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Organizing information for publication and for other means of dissemination
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: “Organize information for publication and for other means of dissemination, such as via storage media or the Internet.” (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: Structuring finished material for publication or a website is standard document work that AI handles 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.
Tracing historical development in a particular field
This is reading one thing and writing another: historical development in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Trace historical development in a particular field, such as social, cultural, political, or diplomatic history.” (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: Tracing how a field developed is synthesis of published work, which is one of AI’s strongest areas.
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.
Translating or requesting translation of reference materials
This is reading one thing and writing another: translation of reference materials in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Translate or request translation of reference materials.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 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: Machine translation is strong, though archaic wording and old handwriting in historical sources still need a specialist’s check.
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.
Editing historical society publications
This is reading one thing and writing another: historical society publications in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Edit historical society publications.” (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: Editing society publications is standard text work AI handles well, with a human check on historical accuracy.
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.
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.
Organizing data and analyzing and interpreting its authenticity and relative significance
The software now makes the first pass at data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Organize data, and analyze and interpret its authenticity and relative significance.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Software can sort and summarize sources, but judging whether a document is genuine and how much it matters stays with the historian.
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 2/4.
Presenting historical accounts in terms of individuals or social
The software now makes the first pass at historical accounts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Present historical accounts in terms of individuals or social, ethnic, political, economic, or geographic groupings.” (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 assemble the narrative, but deciding how to frame people and groups fairly is an interpretive call historians make.
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.
Determining which topics
The software now makes the first pass at topics, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Determine which topics to research, or pursue research topics specified by clients or employers.” (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: mistakes that are cheap to catch.
The rating behind it: AI can suggest gaps worth studying, though choosing what deserves years of work depends on judgment and funders.
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 1/4 · how much data exists 2/4.
Staying human
12 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.
Gathering historical data from sources
The ratings behind this row put historical data well outside what today's tools can do on their own.
importance 4 · CoreSource: “Gather historical data from sources such as archives, court records, diaries, news files, and photographs, as well as from books, pamphlets, and periodicals.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: AI searches digitized sources quickly, but much archive material is not online and has to be handled in person.
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 0/4 · how much data exists 2/4.
Conducting historical research and publishing or presenting findings and theories
The ratings behind this row put historical research well outside what today's tools can do on their own.
importance 4 · CoreSource: “Conduct historical research, and publish or present findings and theories.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drafting help is real, but the underlying research and the claims made in public remain the historian’s responsibility.
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 1/4 · how much data exists 3/4.
Conserving and preserving manuscripts, records and other artifacts
This work happens in the physical world: manuscripts, records and other artifacts, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Conserve and preserve manuscripts, records, and other artifacts.” (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: Conserving fragile manuscripts and artifacts is careful handwork on the physical object itself.
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.
Show the other 11 tasks
Researching the history of a particular country or region
changing shapeThe software now makes the first pass at the history of a particular country, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Research the history of a particular country or region, or of a specific time period.” (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: Software summarizes published history well, but original scholarship means going beyond what has already been written.
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.
Collecting detailed information on individuals for use in biographies
changing shapeThe software now makes the first pass at detailed information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Collect detailed information on individuals for use in biographies.” (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: Assembling biographical detail from records and published sources is search and summary work AI does quickly.
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.
Conducting historical research as a basis for the identification
staying humanThe ratings behind this row put historical research well outside what today's tools can do on their own.
importance 4 · CoreSource: “Conduct historical research as a basis for the identification, conservation, and reconstruction of historic places and materials.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Research for restoring a historic place leans on local records and site visits that are rarely available digitally.
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 0/4 · how much data exists 2/4.
Preparing publications and exhibits or reviewing those prepared by others, to ensure their historical accuracy
staying humanThe ratings behind this row put publications well outside what today's tools can do on their own.
importance 4 · CoreSource: “Prepare publications and exhibits, or review those prepared by others, to ensure their historical accuracy.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Checking accuracy is partly research software helps with, though exhibits are physical and mistakes carry the institution’s name.
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 0/4 · how much data exists 2/4.
Researching and preparing manuscripts in support of public programming and the development of exhibits at historic sites
staying humanThe ratings behind this row put manuscripts well outside what today's tools can do on their own.
importance 4 · CoreSource: “Research and prepare manuscripts in support of public programming and the development of exhibits at historic sites, museums, libraries, and archives.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI drafts exhibit text quickly, though the underlying research and the fit with real objects still need the historian.
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 1/4 · how much data exists 3/4.
Recommending actions related to historical art
staying humanThe ratings behind this row put actions well outside what today's tools can do on their own.
importance 4 · CoreSource: “Recommend actions related to historical art, such as which items to add to a collection or which items to display in an exhibit.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Software can shortlist items, but collection and display decisions rest on curatorial judgment and knowing the physical objects.
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 1/4 · how much data exists 2/4.
Advising or consulting with individuals and institutions regarding issues
staying humanThe value here is that a specific person handles individuals and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Advise or consult with individuals and institutions regarding issues such as the historical authenticity of materials or the customs of a specific historical period.” (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: Advice on authenticity depends on examining specific materials and on the client trusting the expert giving it.
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.
Teaching and conducting research in colleges
staying humanThis work happens in the physical world: research, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Teach and conduct research in colleges, universities, museums, and other research agencies and schools.” (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: Teaching means being with students in a class, even though AI can help build the material.
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.
Coordinating activities of workers engaged in cataloging and filing materials
staying humanThis work happens in the physical world: activities of workers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Coordinate activities of workers engaged in cataloging and filing materials.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Coordinating cataloging staff means allocating hands-on work and handling day-to-day issues with people on site.
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 2/4 · how much data exists 2/4.
Speaking to various groups, organizations and clubs to promote the aims and activities of historical societies
staying humanThis work happens in the physical world: various groups, organizations and clubs, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Speak to various groups, organizations, and clubs to promote the aims and activities of historical societies.” (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: Talking to clubs and community groups works because a real person is there answering questions and building interest.
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.
Interviewing people to gather information about historical events and to record oral histories
staying humanThis work happens in the physical world: people, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Interview people to gather information about historical events and to record oral histories.” (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: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Oral history interviews depend on someone earning a person’s trust in the room before they share difficult memories.
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 1/4.
What this job pays, and how many people do it
- Median pay
- $76,750a 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
- 3,450in 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: historical development in, a record out. The rows above are exactly that shape: organizing information for publication and for other means of dissemination and tracing historical development in a particular field. What it cannot do is be there in the room, and that is still where manuscripts, records and other artifacts get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: conserving and preserving manuscripts, records and other artifacts is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 15% of this job's task weight sits in rows the software is already learning, 27% in rows that change shape rather than disappear, and 58% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Organizing information for publication and for other means of dissemination is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to historical development, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether manuscripts, records and other artifacts are in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near organizing data and analyzing and interpreting its authenticity and relative significance. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to historians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was social science research assistants: only about 4% of its durable work is work you already do, it is under the same pressure this job is and it pays 19.2% less. Your own job splits about 15/85: 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 “gather historical data from sources”, 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.
Social Science Research Assistants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already conduct historical research, and publish or present findings and theories, and their equivalent is to present research findings to groups of people. 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. I will not move you off one melting floe onto another: 72% 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: $61,990 against your $76,750, 19.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Sociologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already conduct historical research, and publish or present findings and theories, and their equivalent is to present research findings at professional meetings. 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.
Political Scientists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present historical accounts in terms of individuals or social, ethnic, political, economic, or…, and their equivalent is to forecast political, economic, and social trends. 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: 15% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like conserving and preserving manuscripts, records and other artifacts arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Social and humanities scientists is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Social and humanities scientists and Business and related research professionals. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for historians, 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 15% 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 historians. 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 historians 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 Historians?
- Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Historians (United States, SOC 19-3093), 15% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 40 out of 100 (range 35–47, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Historians” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Translate or request translation of reference materials” (83/100, very high); “Trace historical development in a particular field, such as social, cultural, political, or diplomatic history” (75/100, high); “Organize information for publication and for other means of dissemination, such as via storage media or the Internet” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Historians” stay human?
- About 58% 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: “Conserve and preserve manuscripts, records, and other artifacts” (0/100, minimal); “Interview people to gather information about historical events and to record oral histories” (7/100, minimal); “Speak to various groups, organizations, and clubs to promote the aims and activities of historical societies” (9/100, 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 “Historians” do about AI?
- Start from the ledger rather than the headline: 15% of this job's weighted core work is exposed, and roughly 58% 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 Historians 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.
- 2 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.