Will AI replace Production, Planning, and Expediting Clerks?

Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Production, Planning, and Expediting Clerks (United States, SOC 43-5061), 61% 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 “Production, Planning, and Expediting Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Calculate figures, such as required amounts of labor or materials, manufacturing costs, or wages, using pricing schedules, adding machines, calculators, or c…” (93/100, very high); “Compile information, such as production rates and progress, materials inventories, materials used, or customer information, so that status reports can be com…” (93/100, very high); “Compile and prepare documentation related to production sequences, transportation, personnel schedules, or purchase, maintenance, or repair orders” (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 “Production, Planning, and Expediting Clerks” stay human?

About 14% 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: “Confer with department supervisors or other personnel to assess progress and discuss needed changes” (26/100, low); “Examine documents, materials, or products and monitor work processes to assess completeness, accuracy, and conformance to standards and specifications” (38/100, low); “Arrange for delivery, assembly, or distribution of supplies or parts to expedite flow of materials and meet production schedules” (48/100, partial). 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 “Production, Planning, and Expediting Clerks” do about AI?

Start from the ledger rather than the headline: 61% of this job's weighted core work is exposed, and roughly 14% 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 Production, Planning, and Expediting 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 17 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

Production, Planning, and Expediting Clerks

distributing production schedules or work orders to departments, requisitioning and maintaining inventories of materials or supplies necessary to meet production demands and conferring with department supervisors or other personnel to assess progress and discuss needed changes. If that's your week, this page is about your job.

The honest answer

Most tasks in this job are the kind AI has learned to do: compiling information, such as production rates and progress, materials inventories, materials used or customer information. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 61%
changing shape: 25%
staying human: 14%

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 17 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 production, planning, and expediting 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; conferring with department supervisors or other personnel to assess progress and discuss needed changes is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Coordinate and expedite the flow of work and materials within or between departments of an establishment according to production schedule. Duties include reviewing and distributing production, work, and shipment schedules; conferring with department supervisors to determine progress of work and completion dates; and compiling reports on progress of work, inventory levels, costs, and production problems. The job title says “production”, “planning” or “expediting clerks”: officially one job, several names. The real job is the part underneath: conferring with department supervisors or other personnel to assess progress and discuss needed changes. 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 production, planning, and expediting clerks is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with department supervisors or other personnel to assess progress and discuss needed changes, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 61%
changing shape: 25%
staying human: 14%

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

11 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.

Distributing production schedules or work orders to departments
This is reading one thing and writing another: production schedules in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Distribute production schedules or work orders to departments.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Schedules and work orders are sent out from planning systems that already route them to the right departments.
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.
Reviewing documents, such as production schedules, work orders or staffing tables
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 · Core
Source: “Review documents, such as production schedules, work orders, or staffing tables, to determine personnel or materials requirements or material priorities.” (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 schedules and staffing tables to work out what is needed is document analysis and arithmetic.
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.
Requisitioning and maintaining inventories of materials or supplies necessary to meet production demands
This is reading one thing and writing another: inventories of materials in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Requisition and maintain inventories of materials or supplies necessary to meet production demands.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Stock systems already work out what to reorder and raise the requisition, though counting physical stock needs someone present.
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.
Compiling information, such as production rates and progress, materials inventories, materials used or customer information
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compile information, such as production rates and progress, materials inventories, materials used, or customer information, so that status reports can be completed.” (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: Pulling production, inventory and customer figures together for status reports is exactly what reporting systems do.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

4 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.

Arranging for delivery, assembly or distribution of supplies or parts to expedite flow of materials and meeting production schedules
The software now makes the first pass at delivery, assembly or distribution of supplies or parts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Arrange for delivery, assembly, or distribution of supplies or parts to expedite flow of materials and meet production schedules.” (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: Arranging deliveries is mostly messages and system entries, but chasing parts around a site involves being there.
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.
Revising production schedules when required due to design changes
The software now makes the first pass at production schedules, 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: “Revise production schedules when required due to design changes, labor or material shortages, backlogs, or other interruptions, collaborating with management, marketing, sales, production, or engineering.” (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: Rescheduling is calculation software does well, though the changes have to be agreed with the people affected.
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 establishment personnel, vendors or customers to coordinate production or shipping activities and to resolve complaints or eliminate delays
The software now makes the first pass at establishment personnel, vendors or customers, 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: “Confer with establishment personnel, vendors, or customers to coordinate production or shipping activities and to resolve complaints or eliminate delays.” (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: Routine chasing and updates can be automated, but resolving a complaint or delay often needs a persuasive conversation.
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.
Establishing and preparing product construction directions and locations and information on required tools
The software now makes the first pass at product construction directions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Establish and prepare product construction directions and locations and information on required tools, materials, equipment, numbers of workers needed, and cost projections.” (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: Build instructions and resource lists follow documented patterns, though someone who knows the site checks they work.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

2 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.

Conferring with department supervisors or other personnel to assess progress and discuss needed changes
The value here is that a specific person handles department supervisors and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Confer with department supervisors or other personnel to assess progress and discuss needed changes.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (22–30 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Progress meetings on the shop floor depend on what supervisors say face to face rather than on written figures.
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 2/4.
Examining documents, materials or products and monitoring work processes to assess completeness, accuracy and conformance to standards and specifications
This work happens in the physical world: documents, materials or products and monitoring work processes, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Examine documents, materials, or products and monitor work processes to assess completeness, accuracy, and conformance to standards and specifications.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Checking documents is screen work, but inspecting materials and watching the process happen means being on the floor.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 7 tasks

Calculating figures, such as required amounts of labor or materials, manufacturing costs or wages
shifting to AI
This is reading one thing and writing another: figures in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Calculate figures, such as required amounts of labor or materials, manufacturing costs, or wages, using pricing schedules, adding machines, calculators, or computers.” (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 labour, material and cost figures from set schedules is calculation software does 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.
Compiling and preparing documentation related to production sequences
shifting to AI
This is reading one thing and writing another: documentation in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compile and prepare documentation related to production sequences, transportation, personnel schedules, or purchase, maintenance, or repair orders.” (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: Production, transport and order documentation is assembled from existing records into standard formats.
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.
Contacting suppliers to verify shipment details
shifting to AI
This is reading one thing and writing another: suppliers in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Contact suppliers to verify shipment details.” (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: Shipment details are confirmed automatically through tracking systems and routine messages to suppliers.
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.
Planning production commitments or timetables for business units
shifting to AI
This is reading one thing and writing another: production commitments in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Plan production commitments or timetables for business units, specific programs, or jobs, using sales forecasts.” (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: Turning sales forecasts into production timetables is planning arithmetic with the data already in company systems.
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.
Recording production data, including volume produced, consumption of raw materials or quality control measures
shifting to AI
This is reading one thing and writing another: production data, including volume produced in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Record production data, including volume produced, consumption of raw materials, or quality control measures.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Production volumes and material use are increasingly logged straight from the machines rather than written down.
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.
Maintaining files, such as maintenance records, bills of lading or cost reports
shifting to AI
This is reading one thing and writing another: files in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Maintain files, such as maintenance records, bills of lading, or cost reports.” (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: Maintaining records and cost files is document management systems handle, though some paperwork is still physical.
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.
Providing documentation and information to account
shifting to AI
This is reading one thing and writing another: documentation in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Provide documentation and information to account for delays, difficulties, or changes to cost estimates.” (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: Explaining a delay or a cost change in writing is straightforward once the underlying figures are available.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $59,650a 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: 390,160in 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: production schedules in, a record out. The rows above are exactly that shape: compiling information and distributing production schedules or work orders to departments. What it cannot do is be trusted in person, which is what department supervisors run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

The exposed part of your job is the biggest part, and I am not going to dress that up: compiling information, such as production rates and progress, materials inventories, materials used or customer information is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 61% of this job's task weight sits in rows the software is already learning, 25% in rows that change shape rather than disappear, and 14% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.

What you have that the software does not is conferring with department supervisors or other personnel to assess progress and discuss needed changes, 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 production schedules 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 production schedules, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is compiling information, such as production rates and progress, materials inventories, materials used or customer information” 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 conferring with department supervisors or other personnel to assess progress and discuss needed changes 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 production, planning, and expediting clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was machine feeders and offbearers: only about 4% of its durable work is work you already do, it pays 30.9% less and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 61% 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. “confer with department supervisors or other personnel to assess progress and discuss…” 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.

Machine Feeders and Offbearers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine documents, materials, or products and monitor work processes to assess completeness, accuracy…, and their equivalent is to inspect materials and products for defects, and to ensure conformance to specifications. 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. It is a pay cut, in those words: $41,220 against your $59,650, 30.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 42,330 of those jobs against 390,160 of yours (OEWS May 2025), 11% as many seats.
Look at that job’s page anyway →
Inspectors, Testers, Sorters, Samplers, and Weighers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine documents, materials, or products and monitor work processes to assess completeness, accuracy…, and their equivalent is to monitor production operations or equipment to ensure conformance to specifications, making necessary process…. 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. It is a pay cut, in those words: $48,570 against your $59,650, 18.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Helpers--Production Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine documents, materials, or products and monitor work processes to assess completeness, accuracy…, and their equivalent is to examine products to verify conformance to quality standards. 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. It is a pay cut, in those words: $39,070 against your $59,650, 34.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 61% of its task weight, across 17 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: conferring with department supervisors or other personnel to assess progress and discuss needed changes 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.

If you run a team doing this job

If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are compiling information, such as production rates and progress, materials inventories, materials used or customer information, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Records clerks and assistants 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

The other groups this work is counted across:

Stock control clerks and assistants

In UK official statistics this job is counted as Records clerks and assistants and Stock control clerks and assistants. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

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

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

The working behind that

The automation Space was considered and declined - the scheduling systems this job runs on are not the Microsoft 365 stack it teaches.

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 61% 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 production, planning and expediting 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 production, planning and expediting clerks launches. Nothing else.

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No Space for production, planning and expediting 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 production, planning and expediting 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 production, planning and expediting 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 Production, Planning, and Expediting Clerks?: Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Production, Planning, and Expediting Clerks (United States, SOC 43-5061), 61% 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 “Production, Planning, and Expediting Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Calculate figures, such as required amounts of labor or materials, manufacturing costs, or wages, using pricing schedules, adding machines, calculators, or c…” (93/100, very high); “Compile information, such as production rates and progress, materials inventories, materials used, or customer information, so that status reports can be com…” (93/100, very high); “Compile and prepare documentation related to production sequences, transportation, personnel schedules, or purchase, maintenance, or repair orders” (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 “Production, Planning, and Expediting Clerks” stay human?: About 14% 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: “Confer with department supervisors or other personnel to assess progress and discuss needed changes” (26/100, low); “Examine documents, materials, or products and monitor work processes to assess completeness, accuracy, and conformance to standards and specifications” (38/100, low); “Arrange for delivery, assembly, or distribution of supplies or parts to expedite flow of materials and meet production schedules” (48/100, partial). 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 “Production, Planning, and Expediting Clerks” do about AI?: Start from the ledger rather than the headline: 61% of this job's weighted core work is exposed, and roughly 14% 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 Production, Planning, and Expediting 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 17 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.

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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.