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Elementary storage supervisors

keeping a record of stored equipment items, maintaining records of purchases and sales transactions and setting up merchandise displays to optimise storage and product accessibility. 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 purchases and sales transactions. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; setting up merchandise displays to optimise storage and product accessibility is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Elementary storage supervisors oversee operations and directly supervise and coordinate the activities of those working in warehouses, docks and other storage facilities. The job title says “elementary storage supervisors”. The real job is the part underneath: setting up merchandise displays to optimise storage and product accessibility. 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 elementary storage supervisors 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 setting up merchandise displays to optimise storage and product accessibility, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
62%
changing shape
0%
staying human
38%

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

Whole-job exposure score 52 out of 100 (48–57 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 elementary storage supervisors 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.

  • Keeping a record of stored equipment items

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

    importance 70 · 9251/00
    Source: “Keep a record of stored equipment items.” (UK 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: Keeping the equipment record is simple data work, though someone still counts what is on the racking.

    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 records of purchases and sales transactions

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

    importance 60 · 9251/00
    Source: “Maintain records of purchases and sales transactions.” (UK 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 purchase and sales records is routine bookkeeping that software already does end to end.

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

  • Taking inventory orders over the phone for storage and logistics services

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

    importance 50 · 9251/00
    Source: “Take inventory orders over the phone for storage and logistics services.” (UK 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: Taking orders by phone follows a set script and system, which software handles 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 1/4 · how much data exists 3/4.

Changing shape

0 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

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.

  • Setting up merchandise displays to optimise storage and product accessibility

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

    importance 60 · 9251/00
    Source: “Set up merchandise displays to optimise storage and product accessibility.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

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

    The rating behind it: Building a display means moving stock by hand.

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

  • Preparing inventory for purchase or rental

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

    importance 50 · 9251/00
    Source: “Prepare inventory for purchase or rental.” (UK task statement)
    How this row was scored

    Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.

    The rating behind it: Listing and pricing is easy, but preparing the actual items is physical work.

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

What this job pays, and how many people do it

Median pay
£31,000a year, before tax, the middle of the range, so half earn more and half earn less.ashe-t14, 2025 · ASHE 2025 provisional (reference April 2025)Provisional, because the ONS revises this figure in the autumn.
How we know this

Source: ashe-t14

Reference period: ASHE 2025 provisional (reference April 2025)

Rounding: Shown to the nearest £100. The exact published figure is in the downloadable dataset. We do not render pounds the survey cannot support.

People doing this job
30,000in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.

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 purchases in, a record out. The rows above are exactly that shape: maintaining records of purchases and sales transactions and keeping a record of stored equipment items. What it cannot do is be there in the room, and that is still where merchandise displays get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

The exposed part of your job is the biggest part, and I am not going to dress that up: maintaining records of purchases and sales transactions is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 62% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 38% 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 setting up merchandise displays to optimise storage and product accessibility, 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 purchases 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 purchases, 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 purchases and sales transactions” 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 setting up merchandise displays to optimise storage and product accessibility 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, spend an hour with National Careers Service. It is free and government-funded, 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 UK occupations to elementary storage supervisors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was market and street traders and assistants: only about 5% of its durable work is work you already do. Your own job splits about 62/38: 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 “set up merchandise displays to optimise storage and product accessibility”, and let the exposed end go.

How that was checked: this job was compared against all 412 UK 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.

  • Market and street traders and assistants

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already set up merchandise displays to optimise storage and product accessibility, and their equivalent is to set up and display sample merchandise at parties or stands. 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. This release publishes no median pay for that job, so I cannot show you what the move costs or pays. I do not recommend a move I cannot price.

    Look at that job’s page anyway →

  • Shelf fillers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already set up merchandise displays to optimise storage and product accessibility, and their equivalent is to organise merchandise displays to ensure product availability and accessibility. 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: £24,896 against your £31,010, 19.7% less (ASHE Table 14.7a, 2025 provisional (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Vehicle and parts salespersons and advisers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare inventory for purchase or rental, and their equivalent is to prepare vehicles for purchase or rental by customers. 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: 62% 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: setting up merchandise displays to optimise storage and product accessibility 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 purchases and sales transactions, 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 Kingdom figures

The United States splits this work across more than one official group, of which Aircraft Cargo Handling Supervisors is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United States page →partial match

The other groups this work is counted across:

In US official statistics this job is counted as Aircraft Cargo Handling Supervisors, Laborers and Freight, Stock, and Material Movers, Hand and Tank Car, Truck, and Ship Loaders. 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.

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

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

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 62% 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 elementary storage supervisors. 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 elementary storage supervisors 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 elementary storage supervisors 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 elementary storage supervisors 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 elementary storage supervisors 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 Elementary storage supervisors?
Not as a job, but it is already doing parts of the work. Across the 5 official task statements scored for Elementary storage supervisors (United Kingdom, SOC 9251), 62% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 52 out of 100 (range 48–57, 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 “Elementary storage supervisors” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Maintain records of purchases and sales transactions” (93/100, very high); “Keep a record of stored equipment items” (69/100, high); “Take inventory orders over the phone for storage and logistics services” (64/100, 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 “Elementary storage supervisors” stay human?
About 38% 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: “Set up merchandise displays to optimise storage and product accessibility” (0/100, minimal); “Prepare inventory for purchase or rental” (29/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Elementary storage supervisors” do about AI?
Start from the ledger rather than the headline: 62% of this job's weighted core work is exposed, and roughly 38% 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 Elementary storage supervisors 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

  • Provisional, because the ONS revises this figure in the autumn.
  • This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
  • The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
  • 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
  • Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Task statements
gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
Task weights
gaisi-index (relatedness)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment
ashe-t14 (ASHE 2025 provisional (reference April 2025))nomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))

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 . 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.