Will AI replace Payroll and Timekeeping Clerks?

Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Payroll and Timekeeping Clerks (United States, SOC 43-3051), 93% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 72 out of 100 (range 66–78, 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 “Payroll and Timekeeping Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records” (93/100, very high); “Compile statistical reports, statements, and summaries related to pay and benefits accounts, and submit them to appropriate departments” (93/100, very high); “Keep track of leave time, such as vacation, personal, and sick leave, for employees” (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 “Payroll and Timekeeping Clerks” stay human?

About 6% 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: “Distribute and collect timecards each pay period” (10/100, minimal); “Coordinate special programs, such as United Way campaigns, that involve payroll deductions” (57/100, partial); “Train employees on organizations' timekeeping systems” (64/100, high). 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 “Payroll and Timekeeping Clerks” do about AI?

Start from the ledger rather than the headline: 93% of this job's weighted core work is exposed, and roughly 6% 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 Payroll and Timekeeping Clerks calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Payroll and Timekeeping Clerks

verifying attendance, distributing and collecting timecards each pay period and recording employee information, such as exemptions, transfers and resignations. 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: keeping track of leave time. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 93%
changing shape: 2%
staying human: 6%

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

Whole-job exposure score 72 out of 100 (66–78 allowing for uncertainty): high 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 payroll and timekeeping clerks is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

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 timecards each pay period is real, and the moves below are built from it. The first step is down this page.

Your week, as this page understands it

Compile and record employee time and payroll data. May compute employees' time worked, production, and commission. May compute and post wages and deductions, or prepare paychecks. The job title says “payroll” or “timekeeping clerks”: officially one job, two names. The real job is the part underneath: distributing and collecting timecards each pay period. 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 payroll and timekeeping clerks is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is distributing and collecting timecards each pay period, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 93%
changing shape: 2%
staying human: 6%

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

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
1 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

19 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.

Verifying attendance, hours worked and pay adjustments and posting information onto designated records
This is reading one thing and writing another: attendance, hours worked and pay adjustments in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Verify attendance, hours worked, and pay adjustments, and post information onto designated records.” (O*NET 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: Matching hours and pay adjustments against records is routine computer work, though chasing missing or disputed entries still needs a person.
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.
Keeping track of leave time
This is reading one thing and writing another: track of leave time in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep track of leave time, such as vacation, personal, and sick leave, for employees.” (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: Leave balances build up by fixed rules and are already tracked automatically by timekeeping systems.
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 employee information, such as exemptions, transfers and resignations, to maintain and update payroll records
This is reading one thing and writing another: employee information in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping employee records such as exemptions, transfers and resignations up to date is straightforward database work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Issuing and recording adjustments to pay related to previous errors or retroactive increases
This is reading one thing and writing another: adjustments in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Issue and record adjustments to pay related to previous errors or retroactive increases.” (O*NET 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: Working out a back-pay correction needs someone to establish what went wrong, though the recalculation itself is automatic.
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.
Reviewing time sheets, work charts, wage computation and other information to detect and reconcile payroll discrepancies
This is reading one thing and writing another: time sheets, work charts in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Review time sheets, work charts, wage computation, and other information to detect and reconcile payroll discrepancies.” (O*NET 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: Software flags most payroll discrepancies, but sorting out why hours are wrong usually means talking to supervisors.
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.
Computing wages and deductions and entering data into computers
This is reading one thing and writing another: wages in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Compute wages and deductions, and enter data into computers.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Wage and deduction math follows published tax tables and fixed rules, which computers apply faster and more accurately than people.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Processing and issuing employee paychecks and statements of earnings and deductions
This is reading one thing and writing another: employee paychecks in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Process and issue employee paychecks and statements of earnings and deductions.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Producing pay runs and earnings statements is standard software work; only handing out printed checks needs someone on site.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Compiling employee time, production and payroll data from time sheets and other records
This is reading one thing and writing another: employee time, production and payroll data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compile employee time, production, and payroll data from time sheets and other records.” (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: Pulling time and pay data together is easy when it is digital, but paper sheets and odd sources still need handling.
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

1 task

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.

Coordinating special programs, such as United Way campaigns, that involve payroll deductions
The software now makes the first pass at special programs, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Coordinate special programs, such as United Way campaigns, that involve payroll deductions.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Running a payroll-deduction charity campaign is mostly admin, but it depends on local relationships and workplace goodwill.
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 2/4.

Staying human

1 task

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.

Distributing and collecting timecards each pay period
This work happens in the physical world: timecards each pay period, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Distribute and collect timecards each pay period.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Handing out and collecting physical timecards means walking round the workplace.
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.

Show the other 11 tasks

Compiling statistical reports, statements and summaries related to pay and benefits accounts and submitting them to appropriate departments
shifting to AI
This is reading one thing and writing another: statistical reports, statements and summaries in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compile statistical reports, statements, and summaries related to pay and benefits accounts, and submit them to appropriate departments.” (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: Pay and benefits summaries are generated straight from payroll data, which reporting tools do routinely.
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.
Keeping informed about changes in tax and deduction laws that apply to the payroll process
shifting to AI
This is reading one thing and writing another: informed in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep informed about changes in tax and deduction laws that apply to the payroll process.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tax and deduction law is published in full, so AI can track and summarize changes, though deciding what applies needs judgment.
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.
Preparing and balancing period-end reports
shifting to AI
This is reading one thing and writing another: period-end reports in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare and balance period-end reports, and reconcile issued payrolls to bank statements.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Period-end reports and bank reconciliations follow fixed rules against digital records, which is exactly what accounting software already does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting verifications of employment
shifting to AI
This is reading one thing and writing another: verifications of employment in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Conduct verifications of employment.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Employment verifications are a standard letter drawn from payroll records, and are already largely automated.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Balancing cash and payroll accounts
shifting to AI
This is reading one thing and writing another: cash in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Balance cash and payroll accounts.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Balancing cash and payroll accounts is rule-based checking of digital records, the classic strength of accounting software.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Completing time sheets showing employees' arrival and departure times
shifting to AI
This is reading one thing and writing another: time sheets showing employees' arrival in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Complete time sheets showing employees' arrival and departure times.” (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: Filling in arrival and departure times is simple recording work, though the clerk depends on clock data or supervisor input.
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.
Preparing and filing payroll tax returns
shifting to AI
This is reading one thing and writing another: payroll tax returns in, a record out. That is the shape today's tools are built for.
importance 5 · Supplemental
Source: “Prepare and file payroll tax returns.” (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; someone qualified has to answer for it.
The rating behind it: Payroll tax returns are produced from fixed forms and figures, but a responsible officer has to stand behind them.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Completing, verifying and processing forms and documentation for administration of benefits
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: “Complete, verify, and process forms and documentation for administration of benefits, such as pension plans, and unemployment and medical insurance.” (O*NET 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: Benefits forms vary by provider and often arrive on paper, so drafts still need checking by a person.
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.
Processing paperwork for new employees and entering employee information into the payroll system
shifting to AI
This is reading one thing and writing another: paperwork in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Process paperwork for new employees and enter employee information into the payroll system.” (O*NET 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: Entering new starter details is straightforward data work, but checking identity documents in person is still a legal requirement.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Providing information to employees and managers on payroll matters
shifting to AI
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: “Provide information to employees and managers on payroll matters, tax issues, benefit plans, and collective agreement provisions.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Most payroll and benefits questions have documented answers, but employees often want a person to explain their own pay.
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.
Training employees on organizations' timekeeping systems
shifting to AI
This is reading one thing and writing another: employees in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Train employees on organizations' timekeeping systems.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Training on a timekeeping system can be delivered by guides and videos, though some staff still want a live walkthrough.
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: $58,260a 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: 153,140in 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: track of leave time in, a record out. The rows above are exactly that shape: keeping track of leave time and verifying attendance, hours worked and pay adjustments and posting information onto designated records. What it cannot do is be there in the room, and that is still where timecards each pay period gets done. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

The exposed part of your job is the biggest part, and I am not going to dress that up: keeping track of leave time is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 93% of this job's task weight sits in rows the software is already learning, 2% in rows that change shape rather than disappear, and 6% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.

What you have that the software does not is distributing and collecting timecards each pay period, 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 track of leave time 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 track of leave time, 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 keeping track of leave time” 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 distributing and collecting timecards each pay period you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

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

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

I checked the 12 nearest US occupations to payroll and timekeeping clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was compensation, benefits, and job analysis specialists: only about 4% of its durable work is work you already do. I am not going to pretend that is comfortable news: 93% 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. “distribute and collect timecards each pay period” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Compensation, Benefits, and Job Analysis Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide information to employees and managers on payroll matters, tax issues, benefit plans…, and their equivalent is to advise managers and employees on state and federal employment regulations, collective agreements, benefit…. 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.
Look at that job’s page anyway →
Tax Preparers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already keep informed about changes in tax and deduction laws that apply to the…, and their equivalent is to use all appropriate adjustments, deductions, and credits to keep clients' taxes to a…. 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: 56% of its own task list already scores in the top exposure band (65/100 in this release), so the same software is eating it.
Look at that job’s page anyway →
Human Resources Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already process paperwork for new employees and enter employee information into the payroll system, and their equivalent is to hire employees and process hiring-related paperwork. 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.
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: 93% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: distributing and collecting timecards each pay period is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Book-keepers, payroll managers and wages clerks 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 Book-keepers, payroll managers and wages clerks. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Two honest options, and no deadline on either

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.

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A nearby route

There's no Space built for payroll clerks yet.

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.

What a Space actually is, in full

The closest match is Power Automate Builders, a community for non-developers building the approvals, reminders and handoffs that keep working once real people use them. It overlaps with the part of your job that is growing: the timesheet chasing and approval routing around each pay run. It does not cover payroll legislation or your payroll system. If that overlap isn't you, the free route below covers the same ground.

Try Power Automate Builders free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.

Noted, and thank you. We’ll email you if a Space for payroll clerks launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

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 Payroll and Timekeeping Clerks?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Payroll and Timekeeping Clerks (United States, SOC 43-3051), 93% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 72 out of 100 (range 66–78, 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 “Payroll and Timekeeping Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records” (93/100, very high); “Compile statistical reports, statements, and summaries related to pay and benefits accounts, and submit them to appropriate departments” (93/100, very high); “Keep track of leave time, such as vacation, personal, and sick leave, for employees” (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 “Payroll and Timekeeping Clerks” stay human?: About 6% 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: “Distribute and collect timecards each pay period” (10/100, minimal); “Coordinate special programs, such as United Way campaigns, that involve payroll deductions” (57/100, partial); “Train employees on organizations' timekeeping systems” (64/100, high). 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 “Payroll and Timekeeping Clerks” do about AI?: Start from the ledger rather than the headline: 93% of this job's weighted core work is exposed, and roughly 6% 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 Payroll and Timekeeping Clerks calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 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 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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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}
}

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.