Will AI replace Purchasing Managers?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Purchasing Managers (United States, SOC 11-3061), 61% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 56 out of 100 (range 51–61, band: partial). 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 “Purchasing Managers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Maintain records of goods ordered and received” (93/100, very high); “Prepare reports regarding market conditions and merchandise costs” (83/100, very high); “Prepare and process requisitions and purchase orders for supplies and equipment” (81/100, 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 “Purchasing Managers” stay human?

About 34% 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: “Direct and coordinate activities of personnel engaged in buying, selling, and distributing materials, equipment, machinery, and supplies” (13/100, minimal); “Represent companies in negotiating contracts and formulating policies with suppliers” (13/100, minimal); “Interview and hire staff, and oversee staff training” (13/100, 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 “Purchasing Managers” do about AI?

Start from the ledger rather than the headline: 61% of this job's weighted core work is exposed, and roughly 34% 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 Purchasing Managers 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 18 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

Purchasing Managers

developing and implementing purchasing and contract management instructions, administering online purchasing systems and controlling purchasing department budgets. 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: maintaining records of goods. 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: 5%
staying human: 34%

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

Whole-job exposure score 56 out of 100 (51–61 allowing for uncertainty): partial exposure, across 18 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 purchasing managers 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; directing and coordinating activities of personnel engaged in buying is what this work rebuilds around. The plan below starts there.

Your week, as this page understands it

Plan, direct, or coordinate the activities of buyers, purchasing officers, and related workers involved in purchasing materials, products, and services. Includes wholesale or retail trade merchandising managers and procurement managers. The job title says “purchasing managers”. The real job is the part underneath: directing and coordinating activities of personnel engaged in buying. 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 purchasing managers is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is directing and coordinating activities of personnel engaged in buying, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 61%
changing shape: 5%
staying human: 34%

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.

3 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

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.

Developing and implementing purchasing and contract management instructions
This is reading one thing and writing another: contract management instructions in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Develop and implement purchasing and contract management instructions, policies, and procedures.” (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: Purchasing policies follow well-documented patterns software drafts well, though getting staff to adopt them needs a manager.
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.
Controlling purchasing department budgets
This is reading one thing and writing another: department budgets in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Control purchasing department budgets.” (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: Controlling a department budget is spreadsheet and records work software does reliably.
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.
Developing cost reduction strategies and savings plans
This is reading one thing and writing another: cost reduction strategies in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop cost reduction strategies and savings plans.” (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: Cost reduction plans come from spend data and documented sourcing methods, which software analyzes 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.
Analyzing market and delivery systems to assess present and future material availability
This is reading one thing and writing another: market in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze market and delivery systems to assess present and future material availability.” (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: Analyzing markets and delivery systems for material availability is data and research work 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.
Preparing bid awards requiring board approval
This is reading one thing and writing another: bid awards requiring board approval in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare bid awards requiring board approval.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Bid award papers are structured documents built from tender data, which software assembles reliably for the board to approve.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

1 task

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Participating in the development of specifications
The software now makes the first pass at the development of specifications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Participate in the development of specifications for equipment, products, or substitute materials.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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 draft specifications from documented requirements, but agreeing them with engineers and users takes discussion.
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 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.

Locating vendors of materials
The value here is that a specific person handles vendors of materials and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Locate vendors of materials, equipment or supplies, and interview them to determine product availability and terms of sales.” (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: Finding vendors is easy for software, but interviewing them about availability and terms is a live commercial conversation.
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.
Directing and coordinating activities of personnel engaged in buying
The value here is that a specific person handles activities of personnel and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Direct and coordinate activities of personnel engaged in buying, selling, and distributing materials, equipment, machinery, and supplies.” (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: the value is that a specific person does it.
The rating behind it: Directing buying and distribution staff depends on daily working relationships with a team.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Interviewing and hiring staff and overseeing staff training
The value here is that a specific person handles staff and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Interview and hire staff, and oversee staff training.” (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: the value is that a specific person does it.
The rating behind it: Hiring and training staff depends on judging people in person and building a working relationship.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Resolving vendor or contractor grievances and claims against suppliers
The value here is that a specific person handles vendor and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Resolve vendor or contractor grievances and claims against suppliers.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Settling a grievance or claim with a supplier depends on a commercial relationship both sides want to keep.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Show the other 8 tasks

Maintaining records of goods
shifting to AI
This is reading one thing and writing another: records of goods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain records of goods ordered and received.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping records of goods ordered and received is routine system work software does accurately.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing reports regarding market conditions and merchandise costs
shifting to AI
This is reading one thing and writing another: reports regarding market conditions in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Prepare reports regarding market conditions and merchandise costs.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Market condition and cost reports are built from published and internal data, which software compiles 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 4/4.
Preparing and processing requisitions and purchasing orders for supplies and equipment
shifting to AI
This is reading one thing and writing another: requisitions in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare and process requisitions and purchase orders for supplies and equipment.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Raising purchase orders follows a set process, with a manager still approving the spend.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing purchase order claims and contracts for conformance to company policy
shifting to AI
This is reading one thing and writing another: purchase order claims in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review purchase order claims and contracts for conformance to company policy.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking purchase orders and contracts against company policy is exactly the document-comparison work software does very well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Administering online purchasing systems
shifting to AI
This is reading one thing and writing another: online purchasing systems in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Administer online purchasing systems.” (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: Running an online purchasing system is computer administration software handles efficiently.
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, evaluating and approving specifications for issuing and awarding bids
shifting to AI
This is reading one thing and writing another: specifications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review, evaluate, and approve specifications for issuing and awarding bids.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reviewing and evaluating bid specifications is structured document work, with a manager approving the final call.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Arranging for disposal of surplus materials
staying human
The ratings behind this row put disposal of surplus materials well outside what today's tools can do on their own.
importance 3 · Core
Source: “Arrange for disposal of surplus materials.” (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: mistakes that are cheap to catch.
The rating behind it: Software can find disposal routes and paperwork, but arranging removal involves suppliers and physical materials.
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 1/4 · how much data exists 2/4.
Representing companies in negotiating contracts and formulating policies with suppliers
staying human
The value here is that a specific person handles companies and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Represent companies in negotiating contracts and formulating policies with suppliers.” (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: the value is that a specific person does it.
The rating behind it: Negotiating contracts for a company depends on trust and give-and-take between people across a table.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $148,080a 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: 84,320in 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: records of goods in, a record out. The rows above are exactly that shape: maintaining records of goods and developing and implementing purchasing and contract management instructions. What it cannot do is be trusted in person, which is what activities of personnel 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: maintaining records of goods 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, 5% in rows that change shape rather than disappear, and 34% in rows it is nowhere near. That is the position, measured across 18 scored tasks. It is not a forecast about you.

What you have that the software does not is directing and coordinating activities of personnel engaged in buying, 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 records of goods 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 records of goods, 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 maintaining records of goods” 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 directing and coordinating activities of personnel engaged in buying 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 purchasing managers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was computer and information systems managers: only about 8% of its durable work is work you already do. Your own job splits about 61/39: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “locate vendors of materials, equipment or supplies, and interview them to determine…”, and let the exposed end go.

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.

Computer and Information Systems Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already interview and hire staff, and oversee staff training, and their equivalent is to recruit, hire, train and supervise staff, or participate in staffing decisions. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Administrative Services Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already interview and hire staff, and oversee staff training, and their equivalent is to supervise administrative staff and provide training and orientation to new staff. 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: $114,130 against your $148,080, 22.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Marketing Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already interview and hire staff, and oversee staff training, and their equivalent is to direct the hiring, training, or performance evaluations of marketing or sales staff and…. 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: 61% of its task weight, across 18 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: directing and coordinating activities of personnel engaged in buying 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 maintaining records of goods, 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 Property, housing and estate managers 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:

In UK official statistics this job is counted as Property, housing and estate managers, Office managers and Functional managers and directors n.e.c.. 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

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

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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 approval and request routing behind purchasing, not the negotiating or the supplier strategy. If that overlap isn't you, the free route below covers the same ground.

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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.

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Questions people ask about this job

Will AI replace Purchasing Managers?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Purchasing Managers (United States, SOC 11-3061), 61% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 56 out of 100 (range 51–61, band: partial). 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 “Purchasing Managers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Maintain records of goods ordered and received” (93/100, very high); “Prepare reports regarding market conditions and merchandise costs” (83/100, very high); “Prepare and process requisitions and purchase orders for supplies and equipment” (81/100, 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 “Purchasing Managers” stay human?: About 34% 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: “Direct and coordinate activities of personnel engaged in buying, selling, and distributing materials, equipment, machinery, and supplies” (13/100, minimal); “Represent companies in negotiating contracts and formulating policies with suppliers” (13/100, minimal); “Interview and hire staff, and oversee staff training” (13/100, 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 “Purchasing Managers” do about AI?: Start from the ledger rather than the headline: 61% of this job's weighted core work is exposed, and roughly 34% 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 Purchasing Managers 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 18 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.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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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.