Will AI replace Order Clerks?

Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Order Clerks (United States, SOC 43-4151), 68% 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 67–77, 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 “Order Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Verify customer and order information for correctness, checking it against previously obtained information as necessary” (93/100, very high); “Check inventory records to determine availability of requested merchandise” (93/100, very high); “Review orders for completeness according to reporting procedures and forward incomplete orders for further processing” (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 “Order Clerks” stay human?

About 13% 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: “Collect payment for merchandise, record transactions, and send items, such as checks or money orders for further processing” (24/100, low); “Inspect outgoing work for compliance with customers' specifications” (29/100, low); “Attempt to sell additional merchandise or services to prospective or current customers by telephone or through visits” (30/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Order Clerks” do about AI?

Start from the ledger rather than the headline: 68% of this job's weighted core work is exposed, and roughly 13% 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 Order Clerks calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Order Clerks

reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing, checking inventory records to determine availability of requested merchandise and verifying customer and ordering 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: reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 68%
changing shape: 19%
staying human: 13%

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

Whole-job exposure score 72 out of 100 (67–77 allowing for uncertainty): high exposure, across 19 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for order 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; inspecting outgoing work for compliance with customers' specifications is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Receive and process incoming orders for materials, merchandise, classified ads, or services such as repairs, installations, or rental of facilities. Generally receives orders via mail, phone, fax, or other electronic means. Duties include informing customers of receipt, prices, shipping dates, and delays; preparing contracts; and handling complaints. The job title says “order clerks”. The real job is the part underneath: inspecting outgoing work for compliance with customers' specifications. 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 order clerks is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting outgoing work for compliance with customers' specifications, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 68%
changing shape: 19%
staying human: 13%

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.

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

13 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.

Obtaining customers' names, addresses and billing information, product numbers and specifications of items to be purchased and entering this information on order forms
This is reading one thing and writing another: customers' names, addresses and billing information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Obtain customers' names, addresses, and billing information, product numbers, and specifications of items to be purchased, and enter this information on order forms.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Capturing customer and product details onto an order form is standard data entry that software already automates.
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.
Reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing
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 · Core
Source: “Review orders for completeness according to reporting procedures and forward incomplete orders for further processing.” (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: Checking an order is complete against set rules and routing the rest is straightforward system 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.
Verifying customer and ordering information
This is reading one thing and writing another: customer in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Verify customer and order information for correctness, checking it against previously obtained information as necessary.” (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: Cross-checking order details against information already held is exactly the kind of checking software does best.
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.
Informing customers by mail or telephone of order information
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: “Inform customers by mail or telephone of order information, such as unit prices, shipping dates, and any anticipated delays.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Sending customers prices, dates and delay notices is templated messaging that systems already handle 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 1/4 · 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.

Receiving and responding to customer complaints
The software now makes the first pass at customer complaints, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Receive and respond to customer complaints.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Complaint replies can be drafted accurately, though an upset customer often wants a person taking responsibility.
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 2/4 · how much data exists 3/4.
Recommending merchandise or services that will meet customers' needs
The software now makes the first pass at merchandise, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Recommend merchandise or services that will meet customers' needs.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Recommendation engines suggest suitable products well, but customers often want a person to confirm the choice.
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 2/4 · how much data exists 3/4.
Conferring with production, sales, shipping, warehouse or common carrier personnel to expedite or trace shipments
The software now makes the first pass at production, sales, shipping, warehouse or common carrier personnel, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Confer with production, sales, shipping, warehouse, or common carrier personnel to expedite or trace shipments.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Chasing a shipment can be automated, but untangling a stuck delivery often depends on knowing who to call.
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 2/4 · how much data exists 3/4.

Staying human

3 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.

Inspecting outgoing work for compliance with customers' specifications
This work happens in the physical world: work, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect outgoing work for compliance with customers' specifications.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 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: Checking outgoing work against a customer's specification usually means looking at the actual goods before they ship.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Attempting to sell additional merchandise or services to prospective or current customers by telephone or through visits
The value here is that a specific person handles sell additional merchandise and stands behind it. That is earned, not computed.
importance 4 · Supplemental
Source: “Attempt to sell additional merchandise or services to prospective or current customers by telephone or through visits.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Scripted upselling exists, but persuading someone to add to their order still works best from a person they know.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Collecting payment for merchandise, record transactions and send items
This work happens in the physical world: payment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Collect payment for merchandise, record transactions, and send items, such as checks or money orders for further processing.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Recording transactions is easy, but handling checks and money orders means physically dealing with them.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Show the other 9 tasks

Checking inventory records to determine availability of requested merchandise
shifting to AI
This is reading one thing and writing another: inventory records in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Check inventory records to determine availability of requested merchandise.” (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: Looking up stock availability in inventory records is a simple system query.
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 total charges for merchandise or services and shipping charges
shifting to AI
This is reading one thing and writing another: total charges in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compute total charges for merchandise or services and shipping charges.” (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: Totalling merchandise and shipping charges is arithmetic against published rate tables.
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.
Adjusting inventory records to reflect product movement
shifting to AI
This is reading one thing and writing another: inventory records in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Adjust inventory records to reflect product movement.” (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: Updating inventory records to match product movement is standard 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.
Notifying departments when supplies of specific items are low
shifting to AI
This is reading one thing and writing another: departments in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Notify departments when supplies of specific items are low, or when orders would deplete available supplies.” (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: Flagging low stock or orders that would exhaust supplies is a simple automatic threshold check.
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.
Calculating and compiling order-related statistics
shifting to AI
This is reading one thing and writing another: order-related statistics in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Calculate and compile order-related statistics, and prepare reports for management.” (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: Compiling order statistics and management reports is standard reporting from data the system already holds.
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 invoices, shipping documents and contracts
shifting to AI
This is reading one thing and writing another: invoices, shipping documents and contracts in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare invoices, shipping documents, and contracts.” (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: Producing invoices, shipping documents and standard contracts from templates is well within routine software and AI drafting.
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.
Directing specified departments or units to prepare and ship orders to designated locations
shifting to AI
This is reading one thing and writing another: specified departments in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Direct specified departments or units to prepare and ship orders to designated locations.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Instructing departments to prepare and ship an order is routine workflow messaging.
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.
Recommending type of packing or labeling needed on order
shifting to AI
This is reading one thing and writing another: type in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Recommend type of packing or labeling needed on order.” (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: Choosing packing and labeling follows documented rules for the product and destination.
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.
Filing copies of orders
shifting to AI
This is reading one thing and writing another: copies of orders in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “File copies of orders received, or post orders on records.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Posting orders to records is a system task, though paper copies still need filing by hand in some offices.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $46,170a 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: 75,200in 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: customers' names, addresses and billing information in, a record out. The rows above are exactly that shape: reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing and obtaining customers' names, addresses and billing information. What it cannot do is be there in the room, and that is still where work 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: reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 68% of this job's task weight sits in rows the software is already learning, 19% in rows that change shape rather than disappear, and 13% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.

What you have that the software does not is inspecting outgoing work for compliance with customers' specifications, 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 customers' names, addresses and billing information 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 customers' names, addresses and billing information, 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 reviewing orders for completeness according to reporting procedures and forwarding incomplete orders for further processing” 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 inspecting outgoing work for compliance with customers' specifications 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 order clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was counter and rental clerks: only about 9% of its durable work is work you already do and it pays 10.5% less. I am not going to pretend that is comfortable news: 68% 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. “receive and respond to customer complaints” 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.

Counter and Rental Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already recommend merchandise or services that will meet customers' needs, and their equivalent is to inspect and adjust rental items to meet needs of customer. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $41,300 against your $46,170, 10.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Retail Salespersons
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already recommend merchandise or services that will meet customers' needs, and their equivalent is to recommend, select, and help locate or obtain merchandise based on customer needs and…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $35,410 against your $46,170, 23.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Door-to-Door Sales Workers, News and Street Vendors, and Related Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already recommend merchandise or services that will meet customers' needs, and their equivalent is to persuade customers to purchase merchandise or services. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $41,380 against your $46,170, 10.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 2,760 of those jobs against 75,200 of yours (OEWS May 2025), 4% as many seats.
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: 68% of its task weight, across 19 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: inspecting outgoing work for compliance with customers' specifications 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 Sales administrators 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 Sales administrators. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
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

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for order clerks, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 68% of the work on this page is already inside what they can do.

Try The AI Authority free →

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

The AI Authority is a general community about working with AI, not a course for order clerks. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for order 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 Space for order clerks yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

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

Your email

We use your email address for one thing: to tell you if a Space for order clerks launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

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

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Order Clerks?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Order Clerks (United States, SOC 43-4151), 68% 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 67–77, 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 “Order Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Verify customer and order information for correctness, checking it against previously obtained information as necessary” (93/100, very high); “Check inventory records to determine availability of requested merchandise” (93/100, very high); “Review orders for completeness according to reporting procedures and forward incomplete orders for further processing” (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 “Order Clerks” stay human?: About 13% 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: “Collect payment for merchandise, record transactions, and send items, such as checks or money orders for further processing” (24/100, low); “Inspect outgoing work for compliance with customers' specifications” (29/100, low); “Attempt to sell additional merchandise or services to prospective or current customers by telephone or through visits” (30/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Order Clerks” do about AI?: Start from the ledger rather than the headline: 68% of this job's weighted core work is exposed, and roughly 13% 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 Order Clerks calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
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.

How we score a job Download this release Look up another job

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.