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Customer Service Representatives

conferring with customers by telephone or in person to provide information about products or services, resolving customers' service or billing complaints by performing activities and determining charges for services. 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: keeping records of customer interactions or transactions. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; conferring with customers by telephone or in person to provide information about products or services is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Interact with customers to provide basic or scripted information in response to routine inquiries about products and services. May handle and resolve general complaints. Excludes individuals whose duties are primarily installation, sales, repair, and technical support. The job title says “customer service representatives”. The real job is the part underneath: conferring with customers by telephone or in person to provide information about products or services. 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 customer service representatives is not one task. It is 13 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with customers by telephone or in person to provide information about products or services, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
66%
changing shape
22%
staying human
12%

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

Whole-job exposure score 70 out of 100 (65–76 allowing for uncertainty): high exposure, across 13 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 customer service representatives 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

8 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 records of customer interactions or transactions

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

    importance 5 · Core
    Source: “Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Writing up what a customer asked and what was done is note-taking straight into the system.

    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.

  • Checking to ensure that appropriate changes were made to resolve customers' problems

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

    importance 4 · Core
    Source: “Check to ensure that appropriate changes were made to resolve customers' problems.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.

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

    The rating behind it: Checking that a change actually went through means comparing records, 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 0/4 · how much data exists 3/4.

  • Contacting customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments

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

    importance 4 · Core
    Source: “Contact customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments.” (O*NET task statement)
    How this row was scored

    Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high confidence.

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

    The rating behind it: Letting a customer know an outcome is a written or spoken message that templates and automated contact already handle.

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

  • Referring unresolved customer grievances to designated departments for further investigation

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

    importance 4 · Core
    Source: “Refer unresolved customer grievances to designated departments for further investigation.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Passing an unresolved complaint to the right department is routing work based on what the complaint says.

    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.

  • Completing contract forms, prepare change of address records or issuing service discontinuance orders, using computers

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

    importance 4 · Core
    Source: “Complete contract forms, prepare change of address records, or issue service discontinuance orders, using computers.” (O*NET task statement)
    How this row was scored

    Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Filling in contract forms, address changes and service-stop orders on a computer is structured form work.

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

Changing shape

4 tasks

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

  • Resolving customers' service or billing complaints by performing activities

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

    importance 4 · Supplemental
    Source: “Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills.” (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: Refunds and bill adjustments are mostly system steps, though swapping physical goods can involve a store.

    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.

  • Recommending improvements in products, packaging, shipping, service or billing methods and procedures to prevent future problems

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

    importance 3 · Supplemental
    Source: “Recommend improvements in products, packaging, shipping, service, or billing methods and procedures to prevent future problems.” (O*NET task statement)
    How this row was scored

    Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.

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

    The rating behind it: Suggesting product, packaging or billing improvements needs judgment about the business that only partly shows up in complaint data.

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

  • Soliciting sales of new or additional services or products

    The software now makes the first pass at sales of new or additional services or products, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.

    importance 4 · Supplemental
    Source: “Solicit sales of new or additional services or products.” (O*NET task statement)
    How this row was scored

    Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Offering extra products takes some persuasion, but the pitch itself is scripted and well practiced.

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

  • Comparing disputed merchandise with original requisitions and information from invoices and preparing invoices for returned goods

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

    importance 4 · Supplemental
    Source: “Compare disputed merchandise with original requisitions and information from invoices and prepare invoices for returned goods.” (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: Matching disputed goods to requisitions and invoices and raising a return invoice is paperwork comparison.

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

Staying human

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.

  • Conferring with customers by telephone or in person to provide information about products or services

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

    importance 5 · Core
    Source: “Confer with customers by telephone or in person to provide information about products or services, take or enter orders, cancel accounts, or obtain details of complaints.” (O*NET task statement)
    How this row was scored

    Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Talking a customer through products, orders and complaints is well covered by information tools, but people often want a person on the line.

    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 2/4 · how much data exists 3/4.

Show the other 3 tasks

  • Determining charges for services

    shifting to AI

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

    importance 4 · Core
    Source: “Determine charges for services requested, collect deposits or payments, or arrange for billing.” (O*NET task statement)
    How this row was scored

    Exposure score: 79 out of 100 (72–86 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: Working out charges, taking payment details and setting up billing is calculation and system entry.

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

  • Obtaining and examining all relevant information to assess validity of complaints and to determine possible causes

    shifting to AI

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

    importance 4 · Supplemental
    Source: “Obtain and examine all relevant information to assess validity of complaints and to determine possible causes, such as extreme weather conditions that could increase utility bills.” (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: Gathering and weighing the facts behind a complaint is analysis of records, though local causes can be hard to pin down.

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

  • Reviewing insurance policy terms to determine whether a particular loss is covered by insurance

    shifting to AI

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

    importance 4 · Supplemental
    Source: “Review insurance policy terms to determine whether a particular loss is covered by insurance.” (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: Reading policy wording against a specific loss is document work, though the coverage call usually sits with a qualified person.

    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.

What this job pays, and how many people do it

Median pay
$44,770a 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
2,595,750in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: records of customer interactions in, a record out. The rows above are exactly that shape: keeping records of customer interactions or transactions and checking to ensure that appropriate changes were made to resolve customers' problems. What it cannot do is be trusted in person, which is what customers run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

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

So, given all that: 66% of this job's task weight sits in rows the software is already learning, 22% in rows that change shape rather than disappear, and 12% in rows it is nowhere near. That is the position, measured across 13 scored tasks. It is not a forecast about you.

What you have that the software does not is conferring with customers by telephone or in person to provide information about products or services, 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 customer interactions 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 customer interactions, 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 keeping records of customer interactions or 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 conferring with customers by telephone or in person to provide information about products or services 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

Now the roads out, with the honest bill on each one. One route survived the checks. The rest are below with the reason they did not.

  • Customer Service RepresentativesParalegals and Legal Assistants

    a year or morematched on shared tasks

    The hardest calls you handle - the ambiguous complaint, the is-this-covered question, the case you had to untangle - are a small version of what paralegals do all day.

    Digging through the facts to work out who's actually right is the job you already do; paralegals do it with a case file instead of a caller.

    The work the two jobs share

    • You already do

      “Obtain and examine all relevant information to assess validity of complaints and to determine possible causes, such as extreme weather conditions that could increase utility bills.” (importance 66 in your job)

      They do

      “Investigate facts and law of cases and search pertinent sources, such as public records and internet sources, to determine causes of action and to prepare cases.”

    • You already do

      “Review insurance policy terms to determine whether a particular loss is covered by insurance.” (importance 76 in your job)

      They do

      “Prepare, edit, or review legal documents, including legislation, briefs, pleadings, appeals, wills, contracts, and real estate closing statements.”

    • You already do

      “Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.” (importance 88 in your job)

      They do

      “Request, review, and summarize relevant records for the cases.”

    What you would not already have: Court procedure - 'File pleadings with court clerks', 'Prepare for trial by performing tasks such as organizing exhibits' - is entirely new work.

    The honest bill

    Pay: $62,890 against your $44,770 (OEWS May 2025 (both)).

    • The entry ticket: Typical entry-level education moves from 'High school diploma or equivalent' to 'Associate's degree' (BLS EP, both). A degree gate, so banded year-plus and named on the card. The card copy may say 'months to a couple of years' in prose; the data field stays conservative.
    • How many seats there are: FIRES. 392,880 paralegal jobs against 2,595,750 customer service jobs is 15.1%, under the 25% floor. The card must carry both counts: a real door, not a wide one.
    • What you live on meanwhile: Study alongside work; free workforce-board help with training funding via us.careeronestop.ajc.
    • Is the target job itself holding up: Passes on growth, but barely: paralegals +0.2% 2024-2034 (BLS EP) - flat, and the card says so. Exposure leg.
    • Direction caveat: The reading-and-summarising end of legal work is itself being automated. The honest aim is the dispute and judgement end, not document processing - and the card says that rather than leaving the reader to find out.

    How long: An associate's degree part-time, typically a year or more alongside work.

    See what AI is doing to paralegals and legal assistants

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.

  • Bookkeeping, Accounting, and Auditing Clerks

    Why it looked obvious: Highest raw task overlap in the slice for customer service reps, via the billing and records clusters: 'Determine charges for services requested, collect deposits or payments, or arrange for billing' and 'Resolve customers' service or billing complaints...' against 'Calculate, prepare, and issue bills, invoices, account statements...'

    Why I am not recommending it: It fails on growth: bookkeeping clerks are projected at -5.8% 2024-2034 (BLS EP) against customer service's -5.5%. This is the naive overlap answer and it is the wrong answer - it moves someone off one shrinking job onto a faster-shrinking one.

    Look at that job’s page anyway →

  • Marketing Managers

    Why it looked obvious: One genuine shared task: 'Recommend improvements in products, packaging, shipping, service, or billing methods and procedures to prevent future problems' against 'Recommend modifications to products, packaging, production processes...'

    Why I am not recommending it: One shared task is not a route. Coverage of a marketing manager's durable work - strategy, pricing, budgets, staff management - by a customer service rep's task set is very low. It fails hard at 3.73x. It fails on the Bachelor's degree gate. It fails at 15.2%.

    Look at that job’s page anyway →

  • First-Line Supervisors of Retail Sales Workers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact customers to respond to inquiries or to notify them of claim investigation…, and their equivalent is to provide customer service by greeting and assisting customers and responding to customer inquiries…. 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.

    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: 66% of its task weight, across 13 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.

  • “It’s too late for me to become something else”

    You are not starting from zero, and the page shows why: conferring with customers by telephone or in person to provide information about products or services 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 Call and contact centre occupations 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 Call and contact centre occupations. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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 customer service rep, and we are not going to point you at the nearest one and call it a fit.

The working behind that
RE-REVIEWED in routes.v3 against The AI Authority's actual published Problems, and the decline STANDS - now on content evidence rather than inference. All 19 of that Space's Problems are written for the person who briefs, reviews and delegates: "I can't hand off AI work without it falling apart", "My team sends me AI work that looks nothing like each other", "I spend hours fixing AI drafts that miss what leadership actually wants". Not one is written for the person answering the queue. The growing part of this job is the contact the bot could not finish, and no Space teaches it. Support and contact-centre work remains the single largest state-(c) block by employment (2.6M in the US alone) and the strongest Space-launch candidate the demand loop can measure.

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 66% 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 customer service rep. 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.

Nearby moves

The jobs above, as pages you can read the same way as this one. Your job shares its core work with these. That is what the match is, and it is all it is.

Noted, and thank you. We’ll email you if a Space for customer service and support work 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 customer service and support work 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 customer service and support work 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 customer service and support work 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 Customer Service Representatives?
Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Customer Service Representatives (United States, SOC 43-4051), 66% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 70 out of 100 (range 65–76, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Customer Service Representatives” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken” (93/100, very high); “Refer unresolved customer grievances to designated departments for further investigation” (93/100, very high); “Complete contract forms, prepare change of address records, or issue service discontinuance orders, using computers” (81/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Customer Service Representatives” stay human?
About 12% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Confer with customers by telephone or in person to provide information about products or services, take or enter orders, cancel accounts, or obtain details o…” (39/100, low); “Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills” (48/100, partial); “Solicit sales of new or additional services or products” (53/100, 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 “Customer Service Representatives” do about AI?
Start from the ledger rather than the headline: 66% of this job's weighted core work is exposed, and roughly 12% 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 Customer Service Representatives 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 13 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.
  • 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
onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights
onet-db (im-rt)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment
bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published . 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.