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Buyers and Purchasing Agents

buying merchandise or commodities for resale to wholesale or retail consumers, evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes and reviewing orders to determine product types and quantities required to meet demand. 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: evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; inspecting merchandise or products to determine quality is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

BLS publishes one employment and wage estimate for this SOC 2018 broad group rather than for the detailed occupations inside it, so it is reported here as a single occupation covering three detailed occupations: Buyers and Purchasing Agents, Farm Products, Wholesale and Retail Buyers, Except Farm Products and Purchasing Agents, Except Wholesale, Retail, and Farm Products. The task statements recorded against this occupation are the union of those detailed occupations' official O*NET tasks, kept grouped by detailed occupation so the weighting stays honest. The job title says “buyers” or “purchasing agents”: officially one job, two names. The real job is the part underneath: inspecting merchandise or products to determine quality. 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 buyers and purchasing agents is not one task. It is 5 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting merchandise or products to determine quality, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
57%
changing shape
27%
staying human
16%

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

Whole-job exposure score 56 out of 100 (49–63 allowing for uncertainty): partial exposure, across 5 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 buyers and purchasing agents 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.

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.

Shifting to AI

3 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.

  • Evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes

    This is reading one thing and writing another: contract performance in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Evaluate and monitor contract performance to ensure compliance with contractual obligations and to determine need for changes.” (O*NET task statement)
    How this row was scored

    Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Monitoring whether suppliers are meeting contract terms is comparison work software does, with buyers acting on it.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Reviewing orders to determine product types and quantities required to meet demand

    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 to determine product types and quantities required to meet demand.” (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: Reading orders to work out what and how much is needed is data analysis software does well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Comparing transportation options to determine the most energy-efficient options

    This is reading one thing and writing another: transportation options in, a record out. That is the shape today's tools are built for.

    importance 3 · Supplemental
    Source: “Compare transportation options to determine the most energy-efficient options.” (O*NET task statement)
    How this row was scored

    Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Comparing transport options on cost and emissions is a documented calculation with public data.

    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.

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.

  • Buying merchandise or commodities for resale to wholesale or retail consumers

    The software now makes the first pass at merchandise, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 5 · Core
    Source: “Buy merchandise or commodities for resale to wholesale or retail consumers.” (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: Choosing what to buy for resale is analysis of sales data, though suppliers are often met in person.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

1 task

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

  • Inspecting merchandise or products to determine quality

    This work happens in the physical world: merchandise, in a real place. Software cannot follow it there.

    importance 3 · Core
    Source: “Inspect merchandise or products to determine quality, value, or yield.” (O*NET task statement)
    How this row was scored

    Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Judging quality or value by inspecting goods means handling and looking at the actual items.

    The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay
$77,710a 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
491,430in 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: contract performance in, a record out. The rows above are exactly that shape: evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes and reviewing orders to determine product types and quantities required to meet demand. What it cannot do is be there in the room, and that is still where merchandise 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: evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 57% of this job's task weight sits in rows the software is already learning, 27% in rows that change shape rather than disappear, and 16% in rows it is nowhere near. That is the position, measured across 5 scored tasks. It is not a forecast about you.

What you have that the software does not is inspecting merchandise or products to determine quality, 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 contract performance 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 contract performance, 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 evaluating and monitoring contract performance to ensure compliance with contractual obligations and to determine need for changes” 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 merchandise or products to determine quality 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 buyers and purchasing agents (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was securities, commodities, and financial services sales agents: only about 3% of its durable work is work you already do and it is under the same pressure this job is. Your own job splits about 57/43: 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 “buy merchandise or commodities for resale to wholesale or retail consumers”, 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.

  • Securities, Commodities, and Financial Services Sales Agents

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already compare transportation options to determine the most energy-efficient options, and their equivalent is to discuss financial options with clients and keep them informed about transactions. 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. I will not move you off one melting floe onto another: 56% of its own task list already scores in the top exposure band (62/100 in this release), so the same software is eating it.

    Look at that job’s page anyway →

  • Retail Salespersons

    Why it looked obvious: It came up as a near neighbour on the overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.

    Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. It is a pay cut, in those words: $35,410 against your $77,710, 54.4% 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 overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.

    Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. It is a pay cut, in those words: $41,380 against your $77,710, 46.8% 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 491,430 of yours (OEWS May 2025), 1% 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: 57% of its task weight, across 5 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 merchandise or products to determine quality 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 Buyers and procurement officers 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 Buyers and procurement officers. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Two honest options, and no deadline on either

Free, and complete

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

A nearby route

There's no Space built for buyers and purchasing agents yet.

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

What a Space actually is, in full

The closest match is Power Automate Builders, a community for non-developers building the approvals, reminders and handoffs that keep working once real people use them. It overlaps with the part of your job that is growing: the purchase-order and approval routing, and the supplier data behind it. It does not cover negotiation or category strategy. If that overlap isn't you, the free route below covers the same ground.

Try Power Automate Builders free →

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

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

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

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

We use your email address for one thing: to tell you if a Space for buyers and purchasing agents 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 Buyers and Purchasing Agents?
Not as a job, but it is already doing parts of the work. Across the 5 official task statements scored for Buyers and Purchasing Agents (United States, SOC 13-1020), 57% 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 49–63, 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 “Buyers and Purchasing Agents” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Compare transportation options to determine the most energy-efficient options” (83/100, very high); “Review orders to determine product types and quantities required to meet demand” (75/100, high); “Evaluate and monitor contract performance to ensure compliance with contractual obligations and to determine need for changes” (66/100, 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 “Buyers and Purchasing Agents” stay human?
About 16% 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: “Inspect merchandise or products to determine quality, value, or yield” (8/100, minimal); “Buy merchandise or commodities for resale to wholesale or retail consumers” (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 “Buyers and Purchasing Agents” do about AI?
Start from the ledger rather than the headline: 57% of this job's weighted core work is exposed, and roughly 16% 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 Buyers and Purchasing Agents 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 5 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

  • The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
  • 46 of this occupation's 51 published task statements are not yet scored and are omitted from this release. Every exposure figure on this page is computed from the 5 that are, so treat it as a partial reading of the job rather than a complete one.
  • 5 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.

How we score a jobDownload this releaseLook 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 . 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.