UK dataswitch to US
Data entry administrators
entering data from source documents into specific data fields or onto tapes or disks using keyboards or scanners, digitising paper documents by scanning and uploading them to a computer system and identifying and correcting data inaccuracies or mistakes. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: compiling and sorting data before entry. The tasks, though, are not you.
It would be a lie to soften that, and another lie to promise a rebuilt version of this job inside this job.
So the hope here is what you already carry: the judgment you bring to paper documents is real, and the moves below are built from it. The first step is down this page.
Your week, as this page understands it
Data entry administrators enter a variety of information into databases using various software packages and assist colleagues in retrieving information. The job title says “data entry administrators”. The real job is the part underneath: digitising paper documents by scanning and uploading them to a computer system. 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 data entry administrators is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is digitising paper documents by scanning and uploading them to a computer system, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 78%
- changing shape
- 9%
- staying human
- 12%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 75 out of 100 (70–80 allowing for uncertainty): high exposure, across 23 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 data entry administrators is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.
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.
- 9 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
18 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.
Entering data from source documents into specific data fields or onto tapes or disks using keyboards or scanners
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 95 · 4152/00Source: “Enter data from source documents into specific data fields or onto tapes or disks using keyboards or scanners.” (UK 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: Keying or scanning data from source documents is the most automatable part of records work.
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 4/4.
Compiling and sorting data before entry
This is reading one thing and writing another: data before entry in, a record out. That is the shape today's tools are built for.
importance 90 · 4152/00Source: “Compile and sort data before entry.” (UK 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: Gathering and sorting data into order is exactly what computers were built to do.
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.
Verifying the accuracy and validity of data entered in databases
This is reading one thing and writing another: the accuracy in, a record out. That is the shape today's tools are built for.
importance 90 · 4152/00Source: “Verify the accuracy and validity of data entered in databases, correcting any errors.” (UK 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: Checking entries against rules and fixing errors is routine automated validation.
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.
Checking the quality of the data
This is reading one thing and writing another: the quality of the data in, a record out. That is the shape today's tools are built for.
importance 85 · 4152/00Source: “Check the quality of the data collected.” (UK 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: Checking collected data for quality problems is systematic, rule-based work these tools do 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.
Updating online databases
This is reading one thing and writing another: online databases in, a record out. That is the shape today's tools are built for.
importance 85 · 4152/00Source: “Update online databases.” (UK 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: Updating records in an online database is routine data entry that software already does automatically.
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
2 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.
Adhering to data protection and confidentiality regulations
The software now makes the first pass at data protection, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 90 · 4152/00Source: “Adhere to data protection and confidentiality regulations.” (UK 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: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Data protection rules are a duty a person is held to; software can check and evidence compliance but cannot carry the responsibility.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Organising paperwork to maintain accurate records
The software now makes the first pass at paperwork, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 70 · 4152/00Source: “Organise paperwork to maintain accurate records.” (UK 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: Keeping records tidy is largely a filing-system job, but paper documents still need someone in the office to handle them.
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.
Staying human
3 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.
Digitising paper documents by scanning and uploading them to a computer system
This work happens in the physical world: paper documents, in a real place. Software cannot follow it there.
importance 85 · 4152/00Source: “Digitise paper documents by scanning and uploading them to a computer system.” (UK task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Someone still has to feed the paper into the scanner, even though the uploading and filing is automatic.
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.
Storing completed documents in appropriate locations
This work happens in the physical world: completed documents, in a real place. Software cannot follow it there.
importance 70 · 4152/00Source: “Store completed documents in appropriate locations.” (UK 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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Digital filing is automatic, but paper documents still have to be carried to the right cabinet.
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 0/4 · how much data exists 3/4.
Collaborating with people both internally and externally at all levels with a view to creating value from data
The value here is that a specific person handles people and stands behind it. That is earned, not computed.
importance 50 · 4152/00Source: “Collaborate with people both internally and externally at all levels with a view to creating value from data.” (UK task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 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: Building working relationships across an organisation depends on people talking to each other, not on software.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Show the other 13 tasks
Creating spreadsheets for data organisation
shifting to AIThis is reading one thing and writing another: spreadsheets in, a record out. That is the shape today's tools are built for.
importance 75 · 4152/00Source: “Create spreadsheets for data organisation.” (UK 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: Building a spreadsheet to organise data is a well-documented task software does instantly.
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.
Comparing data with source documents to detect errors
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 80 · 4152/00Source: “Compare data with source documents to detect errors.” (UK 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: Matching entered data against the original document is a straightforward comparison computers do faster and more consistently.
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.
Identifying and correcting data inaccuracies or mistakes
shifting to AIThis is reading one thing and writing another: data inaccuracies in, a record out. That is the shape today's tools are built for.
importance 80 · 4152/00Source: “Identify and correct data inaccuracies or mistakes.” (UK 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: Finding and correcting data errors is checking work software does faster and more consistently than people.
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.
Recording customer details into a database to maintain accurate records
shifting to AIThis is reading one thing and writing another: customer details in, a record out. That is the shape today's tools are built for.
importance 80 · 4152/00Source: “Record customer details into a database to maintain accurate records.” (UK 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: Keying customer details into a database is routine work that software now fills in automatically.
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.
Reentering data in verification format to ensure accuracy
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 75 · 4152/00Source: “Re-enter data in verification format to ensure accuracy.” (UK 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: Double-keying to catch mistakes is a checking step software performs automatically.
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.
Collating statistical data
shifting to AIThis is reading one thing and writing another: statistical data in, a record out. That is the shape today's tools are built for.
importance 70 · 4152/00Source: “Collate statistical data.” (UK 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 numbers together into one set is standard data handling for software.
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.
Maintaining work logs to record daily activities and progress
shifting to AIThis is reading one thing and writing another: work logs in, a record out. That is the shape today's tools are built for.
importance 50 · 4152/00Source: “Maintain work logs to record daily activities and progress.” (UK 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: Recording what was done each day is simple logging that software can capture automatically as work happens.
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.
Presenting data for review and analysis
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 50 · 4152/00Source: “Present data for review and analysis using mediums such as tables, charts, and graphs.” (UK 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: Turning data into tables, charts and graphs is standard, automatic output.
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.
Communicating work results through written reports or oral presentations
shifting to AIThis is reading one thing and writing another: work results in, a record out. That is the shape today's tools are built for.
importance 60 · 4152/00Source: “Communicate work results through written reports or oral presentations.” (UK task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Written reports are produced very well by these tools, though delivering them aloud remains human.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Reading source documents, cancelled cheques, sales reports or bills to identify data for entry
shifting to AIThis is reading one thing and writing another: source documents, cancelled cheques, sales reports or bills in, a record out. That is the shape today's tools are built for.
importance 85 · 4152/00Source: “Read source documents such as cancelled cheques, sales reports, or bills to identify data for entry.” (UK 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: Pulling figures out of cheques, reports and bills is document reading that software now does very accurately.
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.
Securelying store, manage and share all test and related data in compliance with regulations
shifting to AIThis is reading one thing and writing another: store, manage and share all test in, a record out. That is the shape today's tools are built for.
importance 60 · 4152/00Source: “Securely store, manage, and share all test and related data in compliance with regulations.” (UK task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 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: Storing and sharing data under set rules is system work, though someone in the organisation still owns the legal responsibility for it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reporting unresolved data entry errors to supervisors
shifting to AIThis is reading one thing and writing another: unresolved data entry errors in, a record out. That is the shape today's tools are built for.
importance 70 · 4152/00Source: “Report unresolved data entry errors to supervisors.” (UK 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: Flagging errors and drafting the message is easy to automate, though a supervisor still needs telling.
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.
Sourcing data securely from identified trusted sources
shifting to AIThis is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 50 · 4152/00Source: “Source data securely from identified trusted sources.” (UK 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: Pulling data from approved sources is a routine, rule-based job, though someone must decide which sources count.
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.
What this job pays, and how many people do it
- Median pay
- £28,300a year, before tax, the middle of the range, so half earn more and half earn less.ashe-t14, 2025 · ASHE 2025 provisional (reference April 2025)Provisional, because the ONS revises this figure in the autumn.
How we know this
Source: ashe-t14
Reference period: ASHE 2025 provisional (reference April 2025)
Rounding: Shown to the nearest £100. The exact published figure is in the downloadable dataset. We do not render pounds the survey cannot support.
- People doing this job
- 22,900in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
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: data before entry in, a record out. The rows above are exactly that shape: compiling and sorting data before entry and entering data from source documents into specific data fields or onto tapes or disks using keyboards or scanners. What it cannot do is be there in the room, and that is still where paper documents 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
The exposed part of your job is the biggest part, and I am not going to dress that up: compiling and sorting data before entry is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 78% of this job's task weight sits in rows the software is already learning, 9% in rows that change shape rather than disappear, and 12% in rows it is nowhere near. That is the position, measured across 23 scored tasks. It is not a forecast about you.
What you have that the software does not is digitising paper documents by scanning and uploading them to a computer system, 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 data before entry 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 data before entry, 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 compiling and sorting data before entry” 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 digitising paper documents by scanning and uploading them to a computer system 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, spend an hour with National Careers Service. It is free and government-funded, 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 UK occupations to data entry administrators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was typists and related keyboard occupations: only about 14% of its durable work is work you already do, it is under the same pressure this job is and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 78% 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. “adhere to data protection and confidentiality regulations” 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 412 UK 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.
Typists and related keyboard occupations
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “store completed documents in appropriate locations”. Across the whole of both lists that adds up to about 14% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. I will not move you off one melting floe onto another: 75% of its own task list already scores in the top exposure band (72/100 in this release), so the same software is eating it. And it is a narrow door: about 5,100 of those jobs against 22,900 of yours (ONS via Nomis), 22% as many seats.
Elementary administration occupations n.e.c.
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “store completed documents in appropriate locations”. Across the whole of both lists that adds up to about 3% of the work in that job the software is not taking.
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.
Office managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already organise paperwork to maintain accurate records, and their equivalent is to organise and store paperwork, documents and computer-based information. 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: 78% of its task weight, across 23 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: digitising paper documents by scanning and uploading them to a computer system 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 Kingdom figures
The nearest United States equivalent is Data Entry Keyers. It is a close match rather than an identical one: the two countries draw the boundary of the job in slightly different places.
Switch to the United States page →close match
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.
In England:
A free hour with a government-funded careers adviser is worth more than another evening of reading. In England that's the National Careers Service.
Free, government-funded
In England:
National Careers Service - Find a course
Search what's actually running near you before you spend anything.
Free to search; individual courses vary
In England:
Free courses for jobs (Level 3 qualifications)
A free Level 3 qualification you already qualify for beats a paid course you don't need.
Free for eligible adults
In England:
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No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for data entry administrators, and we are not going to point you at the nearest one and call it a fit.
The working behind that
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 78% 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 data entry administrators. 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 data entry administrators launches. Nothing else.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Data entry administrators?
- Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Data entry administrators (United Kingdom, SOC 4152), 78% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 75 out of 100 (range 70–80, 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 “Data entry administrators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Create spreadsheets for data organisation” (100/100, very high); “Compile and sort data before entry” (93/100, very high); “Verify the accuracy and validity of data entered in databases, correcting any errors” (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 “Data entry administrators” stay human?
- About 12% 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: “Digitise paper documents by scanning and uploading them to a computer system” (10/100, minimal); “Collaborate with people both internally and externally at all levels with a view to creating value from data” (23/100, low); “Store completed documents in appropriate locations” (38/100, 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 “Data entry administrators” do about AI?
- Start from the ledger rather than the headline: 78% of this job's weighted core work is exposed, and roughly 12% 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 Data entry administrators 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 23 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
About the data on this page
- Provisional, because the ONS revises this figure in the autumn.
- This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
- 9 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
- gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
- Task weights
- gaisi-index (relatedness)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- ashe-t14 (ASHE 2025 provisional (reference April 2025))nomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))
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.