Futureproof
Menu

US dataswitch to UK

Receptionists and Information Clerks

operating telephone switchboard, transmiting information or documents and hearing and resolving complaints from customers or the public. 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: providing information about establishment, such as location of departments or offices. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; greeting persons entering establishment is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

Answer inquiries and provide information to the general public, customers, visitors, and other interested parties regarding activities conducted at establishment and location of departments, offices, and employees within the organization. The job title says “receptionists” or “information clerks”: officially one job, two names. The real job is the part underneath: greeting persons entering establishment, determine nature and purpose of visit and directing or escorting them to specific destinations. 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 receptionists and information clerks 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 greeting persons entering establishment, determine nature and purpose of visit and directing or escorting them to specific destinations, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
63%
changing shape
3%
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 receptionists and information clerks is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

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

12 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.

  • Operating telephone switchboard

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

    importance 5 · Core
    Source: “Operate telephone switchboard to answer, screen, or forward calls, providing information, taking messages, or scheduling appointments.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.

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

    The rating behind it: Automated and AI phone systems already answer, route and take messages; a person handles the calls that do not fit.

    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.

  • Transmiting information or documents

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

    importance 4 · Core
    Source: “Transmit information or documents to customers, using computer, mail, or facsimile machine.” (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: Sending documents electronically is fully automatable; only physical post still needs someone to handle it.

    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 4/4.

  • Filing and maintaining records

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

    importance 4 · Core
    Source: “File and maintain records.” (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: Digital record-keeping is largely automatic; the remaining paper filing needs hands at the cabinet.

    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 4/4.

  • Providing information about establishment, such as location of departments or offices

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

    importance 4 · Core
    Source: “Provide information about establishment, such as location of departments or offices, employees within the organization, or services provided.” (O*NET task statement)
    How this row was scored

    Exposure score: 85 out of 100 (81–89 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: This is answering well-documented questions about the organisation, which chatbots and online directories already do reliably.

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

  • Scheduling appointments and maintaining and updating appointment calendars

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

    importance 4 · Core
    Source: “Schedule appointments and maintain and update appointment calendars.” (O*NET task statement)
    How this row was scored

    Exposure score: 85 out of 100 (81–89 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: Online booking and calendar tools handle scheduling from start to finish, which is why much is already automatic.

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

  • Calculating and quoting rates

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

    importance 4 · Supplemental
    Source: “Calculate and quote rates for tours, stocks, insurance policies, or other products or services.” (O*NET task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

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

    The rating behind it: Quotes come from documented rate tables and pricing rules, so systems calculate them with staff checking unusual cases.

    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 1/4 · how much data exists 3/4.

Staying human

5 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.

  • Greeting persons entering establishment

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

    importance 5 · Core
    Source: “Greet persons entering establishment, determine nature and purpose of visit, and direct or escort them to specific destinations.” (O*NET task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, medium confidence.

    Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.

    The rating behind it: Welcoming people through the door and walking them to where they need to go means being physically present.

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

  • Hearing and resolving complaints from customers or the public

    The value here is that a specific person handles complaints and stands behind it. That is earned, not computed.

    importance 4 · Core
    Source: “Hear and resolve complaints from customers or the public.” (O*NET task statement)
    How this row was scored

    Exposure score: 24 out of 100 (20–28 allowing for uncertainty): low exposure, high 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: Standard replies draft themselves, but an upset customer usually wants a person who listens and takes ownership.

    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 3/4 · how much data exists 3/4.

  • Collecting, sorting, distributing or preparing mail, messages or courier deliveries

    This work happens in the physical world: mail, messages or courier deliveries, in a real place. Software cannot follow it there.

    importance 4 · Core
    Source: “Collect, sort, distribute, or prepare mail, messages, or courier deliveries.” (O*NET task statement)
    How this row was scored

    Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.

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

    The rating behind it: Sorting and handing out physical post and parcels means picking things up and moving them around the office.

    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 3/4.

  • Receiving payment and recording receipts for services

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

    importance 5 · Core
    Source: “Receive payment and record receipts for services.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.

    The rating behind it: Card and online payment systems record most transactions automatically, though taking payment at a counter needs someone there.

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

Show the other 8 tasks

  • Keeping a current record of staff members' whereabouts and availability

    shifting to AI

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

    importance 4 · Supplemental
    Source: “Keep a current record of staff members' whereabouts and availability.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (86–100 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: Shared calendars and status tools track availability automatically, though informal comings and goings still get noticed by people.

    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.

  • Performing administrative support tasks, such as proofreading

    shifting to AI

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

    importance 4 · Core
    Source: “Perform administrative support tasks, such as proofreading, transcribing handwritten information, or operating calculators or computers to work with pay records, invoices, balance sheets, or other documents.” (O*NET task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Proofreading, transcription and working with invoices and pay records are core strengths of current office software and AI.

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

  • Processing and preparing memos, correspondence, travel vouchers or other documents

    shifting to AI

    This is reading one thing and writing another: memos, correspondence, travel vouchers or other documents in, a record out. That is the shape today's tools are built for.

    importance 3 · Supplemental
    Source: “Process and prepare memos, correspondence, travel vouchers, or other documents.” (O*NET task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Memos, letters and expense forms follow templates and existing data, which office AI produces to a ready-to-check standard.

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

  • Taking orders for merchandise or materials and send them to the proper departments

    shifting to AI

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

    importance 3 · Supplemental
    Source: “Take orders for merchandise or materials and send them to the proper departments to be filled.” (O*NET task statement)
    How this row was scored

    Exposure score: 85 out of 100 (78–92 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: Capturing an order and routing it to the right department is standard ordering-system work with clear rules.

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

  • Enrolling individuals to participate in programs and notify them of their acceptance

    shifting to AI

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

    importance 3 · Supplemental
    Source: “Enroll individuals to participate in programs and notify them of their acceptance.” (O*NET task statement)
    How this row was scored

    Exposure score: 85 out of 100 (81–89 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: Enrolment and acceptance notices are rule-based steps that online systems already run automatically 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 1/4 · how much data exists 4/4.

  • Scheduling space or equipment for special programs and preparing lists of participants

    shifting to AI

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

    importance 4 · Supplemental
    Source: “Schedule space or equipment for special programs and prepare lists of participants.” (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: Room and equipment booking plus participant lists are standard system tasks, though clashes sometimes need someone to resolve.

    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.

  • Analyzing data to determine answers to questions from customers or members of the public

    shifting to AI

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

    importance 4 · Supplemental
    Source: “Analyze data to determine answers to questions from customers or members of the public.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.

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

    The rating behind it: Looking up and combining information to answer a question is exactly what search and AI assistants do 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.

  • Performing duties, such as taking care of plants or straightening magazines to maintain lobby or reception area

    staying human

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

    importance 3 · Core
    Source: “Perform duties, such as taking care of plants or straightening magazines to maintain lobby or reception area.” (O*NET 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: Tidying the reception area and looking after plants is hands-on work in the room.

    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.

What this job pays, and how many people do it

Median pay
$38,010a 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
910,180in 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: telephone switchboard in, a record out. The rows above are exactly that shape: providing information about establishment and operating telephone switchboard. What it cannot do is be there in the room, and that is still where persons entering establishment 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: providing information about establishment is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 63% of this job's task weight sits in rows the software is already learning, 3% 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 greeting persons entering establishment, 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 telephone switchboard 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 telephone switchboard, 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 providing information about establishment, such as location of departments or offices” 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 greeting persons entering establishment 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 receptionists and information clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical secretaries and administrative assistants: only about 14% of its durable work is work you already do. Your own job splits about 63/37: 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 “greet persons entering establishment, determine nature and purpose of visit, and direct…”, 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.

  • Medical Secretaries and Administrative Assistants

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet persons entering establishment, determine nature and purpose of visit, and direct or…, and their equivalent is to greet visitors, ascertain purpose of visit, and direct them to appropriate staff. Across both published task lists that is about 14% of the durable work in that job.

    Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.

    Look at that job’s page anyway →

  • Medical Transcriptionists

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already schedule appointments and maintain and update appointment calendars, and their equivalent is to receive patients, schedule appointments, and maintain patient records. Across both published task lists that is about 11% of the durable work in that job.

    Why I am not recommending it: You would be starting most of it from nothing: about 11% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. I will not move you off one melting floe onto another: 79% of its own task list already scores in the top exposure band (71/100 in this release), so the same software is eating it. And it is a narrow door: about 41,550 of those jobs against 910,180 of yours (OEWS May 2025), 5% as many seats.

    Look at that job’s page anyway →

  • Order Clerks

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already hear and resolve complaints from customers or the public, and their equivalent is to receive and respond to customer complaints. Across both published task lists that is about 6% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 68% of its own task list already scores in the top exposure band (72/100 in this release), so the same software is eating it. And it is a narrow door: about 75,200 of those jobs against 910,180 of yours (OEWS May 2025), 8% 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: 63% 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: greeting persons entering establishment 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 Receptionists 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 Receptionists, Customer service occupations n.e.c. and Call and contact centre occupations. 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.

A nearby route

There's no Space built for receptionists 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 Microsoft 365 Productivity Workers, a community for coordinators, administrators and team leads who run their working day in Outlook, Teams, meetings, files and task lists. It overlaps with the part of your job that is growing: keeping calendars, rooms, visitors and messages straight for other people, in the tools your employer already pays for. If that overlap isn't you, the free route below covers the same ground.

Try Microsoft 365 Productivity Workers 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 receptionists 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 receptionists yet. Should there be one?

Collab365 launches new communities where the need is real. If one for receptionists 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 receptionists 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 Receptionists and Information Clerks?
Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Receptionists and Information Clerks (United States, SOC 43-4171), 63% 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 “Receptionists and Information Clerks” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Keep a current record of staff members' whereabouts and availability” (93/100, very high); “Perform administrative support tasks, such as proofreading, transcribing handwritten information, or operating calculators or computers to work with pay reco…” (88/100, very high); “Process and prepare memos, correspondence, travel vouchers, or other documents” (88/100, 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 “Receptionists and Information Clerks” 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: “Perform duties, such as taking care of plants or straightening magazines to maintain lobby or reception area” (0/100, minimal); “Greet persons entering establishment, determine nature and purpose of visit, and direct or escort them to specific destinations” (0/100, minimal); “Collect, sort, distribute, or prepare mail, messages, or courier deliveries” (10/100, 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 “Receptionists and Information Clerks” do about AI?
Start from the ledger rather than the headline: 63% 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 Receptionists and Information Clerks calculated?
Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 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.
  • 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.