Will AI replace Billing and Posting Clerks?

Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Billing and Posting Clerks (United States, SOC 43-3021), 70% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 64 out of 100 (range 59–69, 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 “Billing and Posting Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Post stop-payment notices to prevent payment of protested checks” (93/100, very high); “Prepare itemized statements, bills, or invoices and record amounts due for items purchased or services rendered” (93/100, very high); “Operate typing, adding, calculating, or billing machines” (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 “Billing and Posting Clerks” stay human?

About 20% 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: “Load machines with statements, cancelled checks, or envelopes to prepare statements for distribution to customers or stuff envelopes by hand” (0/100, minimal); “Weigh envelopes containing statements to determine correct postage and affix postage, using stamps or metering equipment” (0/100, minimal); “Fix minor problems, such as equipment jams, and notify repair personnel of major equipment problems” (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 “Billing and Posting Clerks” do about AI?

Start from the ledger rather than the headline: 70% of this job's weighted core work is exposed, and roughly 20% 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 Billing and Posting 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 28 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

Billing and Posting Clerks

verifying accuracy of billing data and revising any errors, tracking accumulated hours and dollar amounts charged to each client job to calculate client fees for professional services and contacting customers to obtain or relay account information. 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: operating typing, adding, calculating or billing machines. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 70%
changing shape: 10%
staying human: 20%

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

Whole-job exposure score 64 out of 100 (59–69 allowing for uncertainty): high exposure, across 28 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 billing and posting 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; fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

Compile, compute, and record billing, accounting, statistical, and other numerical data for billing purposes. Prepare billing invoices for services rendered or for delivery or shipment of goods. The job title says “billing” or “posting clerks”: officially one job, two names. The real job is the part underneath: fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems. 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 billing and posting clerks is not one task. It is 28 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 70%
changing shape: 10%
staying human: 20%

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.

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.

Operating typing, adding, calculating or billing machines
This is reading one thing and writing another: machines in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Operate typing, adding, calculating, or billing machines.” (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, adding and billing machines have largely become software that produces the same output without anyone keying it in.
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 accuracy of billing data and revising any errors
This is reading one thing and writing another: accuracy of billing data in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Verify accuracy of billing data and revise any errors.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking billing figures against the underlying data is screen work software handles well, with odd cases still needing a person.
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.
Keeping records of invoices and supporting documents
This is reading one thing and writing another: records of invoices in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep records of invoices and support documents.” (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: Filing and indexing invoices and supporting paperwork is document handling that record systems do without manual sorting.
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 signatures and required information on checks
This is reading one thing and writing another: signatures in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Verify signatures and required information on checks.” (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: Cheque images and details are checked on screen, and automatic checking clears most of them, leaving unclear ones to a person.
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

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.

Resolving discrepancies in accounting records
The software now makes the first pass at discrepancies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Resolve discrepancies in accounting records.” (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: Software can spot and explain most mismatches, though some answers come from asking colleagues.
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 1/4 · how much data exists 3/4.
Reviewing compiled data on operating costs and revenues to set rates
The software now makes the first pass at compiled data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Review compiled data on operating costs and revenues to set rates.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software can summarise the cost and revenue figures, but choosing what to charge is a business judgement someone has to own.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Returning checks to customers or retrieving checks returned to customers in error
The software now makes the first pass at checks, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Return checks to customers or retrieve checks returned to customers in error, adjusting accounts and answering inquiries about errors as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Adjusting the account and explaining the error is screen work, though returning the actual cheque means posting or handing it over.
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

6 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.

Routing statements for mailing or over-the-counter delivery to customers
This work happens in the physical world: statements, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Route statements for mailing or over-the-counter delivery to customers.” (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: Deciding where each statement goes is quick screen work, but posting or handing them over still needs someone at the counter.
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.
Fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems
This work happens in the physical world: minor problems, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Fix minor problems, such as equipment jams, and notify repair personnel of major equipment problems.” (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: Clearing a jam means opening the machine and using your hands.
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.
Monitoring equipment to ensure proper operation
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor equipment to ensure proper operation.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Watching printing and inserting equipment run means standing by the machine, so only the record-keeping part transfers easily.
The five ratings: output a model can produce 2/4 · 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.

Show the other 18 tasks

Posting stop-payment notices to prevent payment of protested checks
shifting to AI
This is reading one thing and writing another: stop-payment notices in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Post stop-payment notices to prevent payment of protested checks.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Adding a stop-payment note is a simple rule-based update to a banking record that systems already make 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.
Preparing itemized statements, bills or invoices and recording amounts due for items purchased or services
shifting to AI
This is reading one thing and writing another: itemized statements in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare itemized statements, bills, or invoices and record amounts due for items purchased or services rendered.” (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: Invoices and statements are produced from stored order and price data, something billing systems already do from end to end.
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.
Tracking accumulated hours and dollar amounts charged to each client job to calculate client fees for professional services
shifting to AI
This is reading one thing and writing another: accumulated hours in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Track accumulated hours and dollar amounts charged to each client job to calculate client fees for professional services, such as legal or accounting services.” (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: Time and billing systems already add up recorded hours and apply the agreed rates without anyone doing the sums.
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.
Computing credit terms, discounts, shipment charges or rates for goods or services to complete billing documents
shifting to AI
This is reading one thing and writing another: credit terms, discounts, shipment charges or rates in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compute credit terms, discounts, shipment charges, or rates for goods or services to complete billing documents.” (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: Working out discounts, freight charges and rates from set schedules is calculation that billing software performs reliably.
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.
Creating billing documents, shipping labels, credit memorandums or crediting forms
shifting to AI
This is reading one thing and writing another: documents, shipping labels, credit memorandums or crediting forms in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Create billing documents, shipping labels, credit memorandums, or credit forms.” (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: Billing documents, shipping labels and credit notes are standard forms generated straight from order data.
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.
Taking orders for imprinted checks
shifting to AI
This is reading one thing and writing another: orders in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Take orders for imprinted checks.” (O*NET 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: A cheque order is a short standard form, and customers increasingly place it themselves through online banking.
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.
Performing bookkeeping work
shifting to AI
This is reading one thing and writing another: work in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Perform bookkeeping work, including posting data or keeping other records concerning costs of goods or services or the shipment of goods.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Posting costs and shipment entries is screen work, though deciding where unusual items belong still needs someone to check.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing documents, such as purchase orders, sales tickets, charge slips or hospital records
shifting to AI
This is reading one thing and writing another: documents in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Review documents, such as purchase orders, sales tickets, charge slips, or hospital records, to compute fees or charges due.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading purchase orders or charge slips and working out the amount due is document reading and arithmetic that software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Comparing previously prepared bank statements with canceled checks and reconciling discrepancies
shifting to AI
This is reading one thing and writing another: prepared bank statements in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compare previously prepared bank statements with canceled checks and reconcile discrepancies.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing statements with cleared cheques and explaining the gaps is matching work that reconciliation software already performs.
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.
Updating manuals when rates, rules or regulations
shifting to AI
This is reading one thing and writing another: manuals in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Update manuals when rates, rules, or regulations are amended.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Updating a manual when a rate or rule changes is editing text against a known source, which AI does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Compiling reports of cost factors
shifting to AI
This is reading one thing and writing another: reports of cost factors in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Compile reports of cost factors, such as labor, production, storage, and equipment.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling labour, storage and equipment costs into a report is data assembly that reporting tools do from the same records.
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.
Contacting customers to obtain or relay account information
shifting to AI
This is reading one thing and writing another: customers in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Contact customers to obtain or relay account information.” (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: Chasing account details by phone or email is routine contact that automated messages and voice systems now handle for most customers.
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.
Consulting sources, such as rate books, manuals or insurance company representatives
shifting to AI
This is reading one thing and writing another: sources in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Consult sources, such as rate books, manuals, or insurance company representatives, to determine specific charges or information such as rules, regulations, or government tax and tariff information.” (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: Looking up rates, rules and tariff information in manuals and online sources is research AI tools do quickly.
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.
Answering inquiries regarding rates, routing or procedures
shifting to AI
This is reading one thing and writing another: inquiries regarding rates, routing or procedures in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Answer inquiries regarding rates, routing, or procedures.” (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: Questions about rates, routing and procedures have documented answers, and automated chat and phone systems already handle most.
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.
Performing general administrative tasks, such as answering telephones, scheduling appointments and ordering supplies or equipment
shifting to AI
This is reading one thing and writing another: general administrative tasks in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Perform general administrative tasks, such as answering telephones, scheduling appointments, and ordering supplies or equipment.” (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: Phone answering, diary booking and supply ordering are routine office jobs that scheduling and assistant software already cover.
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.
Encoding and cancelling checks, using bank machines
staying human
This work happens in the physical world: checks, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Encode and cancel checks, using bank machines.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (15–23 allowing for uncertainty): minimal exposure, high 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: Encoding and cancelling cheques means feeding paper through a machine, so most of the job stays at that machine.
The five ratings: output a model can produce 3/4 · 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.
Weighing envelopes containing statements to determine correct postage and affix postage
staying human
This work happens in the physical world: envelopes containing statements, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Weigh envelopes containing statements to determine correct postage and affix postage, using stamps or metering equipment.” (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: Weighing envelopes and sticking postage on them is done by hand at the mailing bench.
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.
Loading machines with statements, cancelled checks or envelopes to prepare statements for distribution to customers or stuff envelopes
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Load machines with statements, cancelled checks, or envelopes to prepare statements for distribution to customers or stuff envelopes by hand.” (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: Loading machines and stuffing envelopes is hands-on work at the mailing equipment.
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: $48,500a 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: 404,060in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: accuracy of billing data in, a record out. The rows above are exactly that shape: operating typing, adding, calculating or billing machines and verifying accuracy of billing data and revising any errors. What it cannot do is be there in the room, and that is still where minor problems 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: operating typing, adding, calculating or billing machines is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 70% of this job's task weight sits in rows the software is already learning, 10% in rows that change shape rather than disappear, and 20% in rows it is nowhere near. That is the position, measured across 28 scored tasks. It is not a forecast about you.

What you have that the software does not is fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems, 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 accuracy of billing data 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 accuracy of billing data, 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 operating typing, adding, calculating or billing machines” 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 fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems 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 billing and posting clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was tellers: only about 5% of its durable work is work you already do and it pays 11.3% less. I am not going to pretend that is comfortable news: 70% 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. “resolve discrepancies in accounting records” 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.

Tellers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already verify signatures and required information on checks, and their equivalent is to cash checks and pay out money after verifying that signatures are correct, that…. 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. It is a pay cut, in those words: $43,030 against your $48,500, 11.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Credit Analysts
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact customers to obtain or relay account information, and their equivalent is to contact customers to collect payments on delinquent accounts. 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: 78% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it. And it is a narrow door: about 64,390 of those jobs against 404,060 of yours (OEWS May 2025), 16% as many seats.
Look at that job’s page anyway →
Shipping, Receiving, and Inventory Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh envelopes containing statements to determine correct postage and affix postage, using stamps…, and their equivalent is to pack, seal, label, or affix postage to prepare materials for shipping, using hand…. 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: 70% of its task weight, across 28 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: fixing minor problems, such as equipment jams and notifying repair personnel of major equipment problems 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.

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
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

A nearby route

There's no Space built for billing 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 routing and chasing around invoices - approvals, reminders, exceptions - rather than the keying-in. 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 billing 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 Billing and Posting Clerks?: Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Billing and Posting Clerks (United States, SOC 43-3021), 70% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 64 out of 100 (range 59–69, 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 “Billing and Posting Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Post stop-payment notices to prevent payment of protested checks” (93/100, very high); “Prepare itemized statements, bills, or invoices and record amounts due for items purchased or services rendered” (93/100, very high); “Operate typing, adding, calculating, or billing machines” (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 “Billing and Posting Clerks” stay human?: About 20% 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: “Load machines with statements, cancelled checks, or envelopes to prepare statements for distribution to customers or stuff envelopes by hand” (0/100, minimal); “Weigh envelopes containing statements to determine correct postage and affix postage, using stamps or metering equipment” (0/100, minimal); “Fix minor problems, such as equipment jams, and notify repair personnel of major equipment problems” (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 “Billing and Posting Clerks” do about AI?: Start from the ledger rather than the headline: 70% of this job's weighted core work is exposed, and roughly 20% 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 Billing and Posting 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 28 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.
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-with-imputed)
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.