Will AI replace File Clerks?

Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for File Clerks (United States, SOC 43-4071), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 43 out of 100 (range 38–49, 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 “File Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Input data, such as file numbers, new or updated information, or document information codes into computer systems to support document and information retrieval” (93/100, very high); “Design forms related to filing systems” (75/100, high); “Track materials removed from files to ensure that borrowed files are returned” (69/100, 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 “File Clerks” stay human?

About 47% 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: “Retrieve documents stored in microfilm or microfiche and place them in viewers for reading” (0/100, minimal); “Operate mechanized files that rotate to bring needed records to a particular location” (0/100, minimal); “Place materials into storage receptacles, such as file cabinets, boxes, bins, or drawers, according to classification and identification information” (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 “File Clerks” do about AI?

Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 47% 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 File 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 19 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.

US dataswitch to UK

File Clerks

performing general office activities, finding, retrieving and adding new material to file records or create new records. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: inputting data, such as file numbers is work AI now does quickly and cheaply, and placing materials into storage receptacles 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: 37%
changing shape: 16%
staying human: 47%

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

Whole-job exposure score 43 out of 100 (38–49 allowing for uncertainty): partial exposure, across 19 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 file 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 2026-08-05. Read the full method.

Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.

Your week, as this page understands it

File correspondence, cards, invoices, receipts, and other records in alphabetical or numerical order or according to the filing system used. Locate and remove material from file when requested. The job title says “file clerks”. The real job is the part underneath: placing materials into storage receptacles. 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 file clerks is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is placing materials into storage receptacles, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 37%
changing shape: 16%
staying human: 47%

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 2026-08-05. 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

7 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.

Scanning or reading incoming materials to determine how and where they
This is reading one thing and writing another: materials in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Scan or read incoming materials to determine how and where they should be classified or filed.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: Reading incoming documents and deciding where they belong is a judgment computers now make accurately for standard material.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Sorting or classifying information
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 · Core
Source: “Sort or classify information according to guidelines, such as content, purpose, user criteria, or chronological, alphabetical, or numerical order.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: Deciding the right order or category for information is exactly what software already does reliably and fast.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Answering questions about records or files
This is reading one thing and writing another: questions in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Answer questions about records or files.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 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: Answering questions from indexed records is something a search assistant handles well, with only brief back-and-forth with the asker.
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.
Keeping records of materials
This is reading one thing and writing another: records of materials in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep records of materials filed or removed, using logbooks or computers and generate computerized reports.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping a log of what was filed and generating reports from it is standard automated record-keeping.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

3 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.

Adding new material to file records or create new records
The software now makes the first pass at new material, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Add new material to file records or create new records as necessary.” (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: Creating and updating records from incoming documents is largely software work, though some paper still has to be handled by hand.
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.
Assigning and recording or stamping identification numbers or codes to index materials for filing
The software now makes the first pass at identification numbers, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Assign and record or stamp identification numbers or codes to index materials for filing.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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: Working out the index number is easy for software, but stamping and labeling the physical item still needs a 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 0/4 · how much data exists 3/4.
Completing general financial activities, such as processing accounts payable, reviewing invoices, collecting cash payments or issuing receipts
The software now makes the first pass at general financial activities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Complete general financial activities, such as processing accounts payable, reviewing invoices, collecting cash payments, or issuing receipts.” (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: Processing invoices and payables is well-automated bookkeeping; taking cash and handing over receipts still needs someone present.
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

9 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.

Performing general office activities, such as typing, answering telephones, operating office machines, processing mail or securing confidential materials
This work happens in the physical world: general office activities, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform general office activities, such as typing, answering telephones, operating office machines, processing mail, or securing confidential materials.” (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; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: This mixes screen work with phones, mail and machines, so roughly half of it still needs someone in the office.
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 1/4 · how much data exists 3/4.
Finding, retrieving
This work happens in the physical world: copies of information, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Find, retrieve, and make copies of information from files in response to requests and deliver information to authorized users.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Finding the right information is easy for software, but copying and handing over physical documents still needs a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Placing materials into storage receptacles
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Place materials into storage receptacles, such as file cabinets, boxes, bins, or drawers, according to classification and identification information.” (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: Putting paper into cabinets, boxes and drawers is a physical job that needs hands in the room.
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 2/4.

Show the other 9 tasks

Inputting data, such as file numbers
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Input data, such as file numbers, new or updated information, or document information codes into computer systems to support document and information retrieval.” (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: Typing file numbers and codes into a system is data entry software now does faster and more accurately.
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.
Designing forms related to filing systems
shifting to AI
This is reading one thing and writing another: forms in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Design forms related to filing systems.” (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: Drafting a form layout is straightforward for software, though it still needs checking against how the office actually works.
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.
Tracking materials removed from files to ensure that borrowed files
shifting to AI
This is reading one thing and writing another: materials in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Track materials removed from files to ensure that borrowed files are returned.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: A checkout log with automatic reminders does this job well; only occasionally must someone physically confirm a file came back.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Modifying or improving filing systems or implementing new filing systems
staying human
The ratings behind this row put systems well outside what today's tools can do on their own.
importance 3 · Core
Source: “Modify or improve filing systems or implement new filing systems.” (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: Software can propose a filing structure, but making it fit how a particular office really works still takes a 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 1/4 · how much data exists 3/4.
Eliminating outdated or unnecessary materials
staying human
This work happens in the physical world: outdated, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Eliminate outdated or unnecessary materials, destroying them or transferring them to inactive storage, according to file maintenance guidelines or legal requirements.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Deciding what is past its retention date is rule-based and easy to automate; shredding or moving boxes is not.
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 0/4 · how much data exists 3/4.
Gathering materials to be filed from departments or employees
staying human
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Gather materials to be filed from departments or employees.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Collecting paper from desks around the building needs someone walking to those desks, which software cannot do.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing periodic inspections of materials or files to ensure correct placement
staying human
This work happens in the physical world: periodic inspections of materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Perform periodic inspections of materials or files to ensure correct placement, legibility, or proper condition.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking that paper files are in the right place and still readable means physically going through the cabinets.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Operating mechanized files that rotate to bring needed records to a particular location
staying human
This work happens in the physical world: mechanized files, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Operate mechanized files that rotate to bring needed records to a particular location.” (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: Operating a rotating file machine to bring records forward is hands-on work at the machine 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 2/4.
Retrieving documents stored in microfilm or microfiche and placing them in viewers for reading
staying human
This work happens in the physical world: documents, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Retrieve documents stored in microfilm or microfiche and place them in viewers for reading.” (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: Fetching microfilm and loading it into a viewer is physical handling of the film and the machine.
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 2/4.

What this job pays, and how many people do it

Median pay: $43,600a 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: 73,440in 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: new material in, a record out. The rows above are exactly that shape: inputting data, such as file numbers and scanning or reading incoming materials to determine how and where they. What it cannot do is be there in the room, and that is still where materials get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Your week is splitting in two, and which half fills it is the whole question. Inputting data, such as file numbers is going; placing materials into storage receptacles is not.

So, given all that: 37% of this job's task weight sits in rows the software is already learning, 16% in rows that change shape rather than disappear, and 47% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (placing materials into storage receptacles) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles placing materials into storage receptacles, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to file clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical transcriptionists: only about 5% of its durable work is work you already do and it is under the same pressure this job is. Your own job splits about 37/63: 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 “perform general office activities”, 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.

Medical Transcriptionists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already perform general office activities, and their equivalent is to perform a variety of clerical and office tasks. 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: 79% of its own task list already scores in the top exposure band (71/100 in this release), so the same software is eating it.
Look at that job’s page anyway →
Bookkeeping, Accounting, and Auditing Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already perform general office activities, and their equivalent is to perform general office duties. 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: that job is projected to shrink 5.8% between 2024 and 2034 (BLS Employment Projections), and 71% of its own task list already scores in the top exposure band (75/100 in this release), so the same software is eating it.
Look at that job’s page anyway →
Court, Municipal, and License Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer questions about records or files, and their equivalent is to answer questions or provide advice to the public regarding licensing policies, procedures, or…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 58% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it.
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: 37% of its task weight, across 19 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Elementary administration occupations n.e.c. 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 Elementary administration occupations n.e.c.. 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
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for file 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 37% of the work on this page is already inside what they can do.

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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 file 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 file 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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Collab365 launches new communities where the need is real. If one for file clerks existed, with researched problems, courses and people in the same boat, would you want in?

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This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 File Clerks?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for File Clerks (United States, SOC 43-4071), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 43 out of 100 (range 38–49, 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 “File Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Input data, such as file numbers, new or updated information, or document information codes into computer systems to support document and information retrieval” (93/100, very high); “Design forms related to filing systems” (75/100, high); “Track materials removed from files to ensure that borrowed files are returned” (69/100, 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 “File Clerks” stay human?: About 47% 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: “Retrieve documents stored in microfilm or microfiche and place them in viewers for reading” (0/100, minimal); “Operate mechanized files that rotate to bring needed records to a particular location” (0/100, minimal); “Place materials into storage receptacles, such as file cabinets, boxes, bins, or drawers, according to classification and identification information” (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 “File Clerks” do about AI?: Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 47% 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 File 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 19 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.

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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.