Telemarketers
In short: today's best AI models could do about 56% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 44% of the time, stays with people.
Solicit donations or orders for goods or services over the telephone.
58,430 people do this job in the US. $35,450 a year.
Reviewed on October 18, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on21 h
- A person checks AI's work1 h
- Desk work AI can't do yet0 h
- Stays with people18 h
Telemarketers, now: of a 40-hour week, AI could take on 21 hours, 1 hours become checking its work, 0 hours are desk work it can't do yet, and 18 hours stay with people.
The largest pieces: Maintain contact records (about 10 h); Build call lists (about 4 h); Adjust the sales script (about 4 h).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 19%
- 37%
- 44%
- 0%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 56% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 7%. With Claude Mythos Preview (early), measured in April 2026, 55%. Today's estimate: 56%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.49% went to this job's tasks in May 2026, the 50th largest of the 447 jobs that showed up at all; 3 of its 12 tasks showed up. Within each of Anthropic's methods, never across a change: 0.057% to 0.041% (released February and March 2025); 0.021% to 0.058% (August 2025 to February 2026); 0.51% to 0.49% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.057% | 251 of 708 | 4 of 12 |
| Released March 2025 | 0.041% | 302 of 703 | 2 of 12 |
| The week of Aug 4, 2025 (released September 2025) | 0.021% | 363 of 655 | 2 of 12 |
| The week of Nov 13, 2025 (released January 2026) | 0.029% | 328 of 672 | 3 of 12 |
| The week of Feb 5, 2026 (released March 2026) | 0.058% | 230 of 677 | 4 of 12 |
| April 2026 (released June 2026) | 0.51% | 49 of 434 | 3 of 12 |
| May 2026 (released June 2026) | 0.49% | 50 of 447 | 3 of 12 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
44% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Explain products and prices, the conversation, 9%
- Deliver scripted sales talks, the conversation, 8%
- Answer inbound sales calls, the conversation, 6%
- Call prospects to sell, the conversation, 6%
- Take orders over the phone, the conversation, 5%
- Adjust the sales script, the conversation, 4%
What becomes checking the machine
4% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 52% is desk work that is small if wrong, which can be handed over with lighter checks.
- Take orders over the phone, costly if wrong, 2%
- Call prospects to sell, costly if wrong, 1%
What to learn
- The conversations. About 44% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Running AI inside the business systems. About 37% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Briefing a model and checking its drafts. About 19% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 12 tasks from .
| Task | ||||
|---|---|---|---|---|
| Maintain records of contacts, accounts, and orders.Keeping the contact and order records up to date in the system.Done hourly or more by most who do it. | 24% | 25 min13 min to 50 min | Systems 100% | 100%Half by now |
| Adjust sales scripts to better target the needs and interests of specific individuals.Rewriting the script for a type of customer, with what the calls have taught.Done hourly or more by most who do it. | 14% | 38 min17 min to 1.3 h | Documents 70%, conversation 30% | 70%Desk part half by now |
| Obtain names and telephone numbers of potential customers from sources such as telephone directories, magazine reply cards, and lists purchased from other organizations.Pulling names and numbers from directories and bought lists into the call list.Done hourly or more by most who do it. | 11% | 38 min13 min to 1.7 h | Documents 60%, systems 40% | 100%Half by now |
| Explain products or services and prices, and answer questions from customers.Answering the customer's questions on the call.Done hourly or more by most who do it. AI observed on this task. | 9% | 8 min4 min to 17 min | Conversation 100% | Stays with people |
| Obtain customer information such as name, address, and payment method, and enter orders into computers.Taking the details and payment method on the call and keying the order.Done hourly or more by most who do it. AI observed on this task. | 8% | 8 min4 min to 17 min | Conversation 70%, systems 30% | 30%Desk part half by now |
| Deliver prepared sales talks, reading from scripts that describe products or services, to persuade potential customers to purchase a product or service or to make a donation.Reading the script to the person on the line.Done hourly or more by most who do it. | 8% | 8 min4 min to 17 min | Conversation 100% | Stays with people |
| Contact businesses or private individuals by telephone to solicit sales for goods or services, or to request donations for charitable causes.Cold calls down the list, with the outcome keyed in.Done hourly or more by most who do it. AI observed on this task. | 7% | 8 min4 min to 21 min | Conversation 80%, systems 20% | 20%Desk part half by now |
| Answer telephone calls from potential customers who have been solicited through advertisements.Live calls from people responding to advertisements.Done hourly or more by most who do it. | 6% | 8 min4 min to 17 min | Conversation 100% | Stays with people |
| Telephone or write letters to respond to correspondence from customers or to follow up initial sales contacts.Calls and letters back to customers after the first contact.Done hourly or more by most who do it. | 6% | 10 min4 min to 25 min | Conversation 60%, documents 40% | 40%Desk part half by now |
| Record names, addresses, purchases, and reactions of prospects contacted.Notes keyed into the contact record after each call.Done hourly or more by most who do it. | 4% | 4 min2 min to 8 min | Systems 100% | 100%Half by now |
| Conduct client or market surveys to obtain information about potential customers.Asking the survey questions on the call and keying the answers.Done more than monthly by most who do it. | 3% | 25 min13 min to 50 min | Conversation 70%, systems 30% | 30%Desk part half by now |
| Schedule appointments for sales representatives to meet with prospective customers or for customers to attend sales presentations.Agreeing a time on the call and booking it in the calendar.Done daily by most who do it. AI observed on this task. | 0.8% | 8 min4 min to 17 min | Conversation 60%, systems 40% | 40%Desk part half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel59% now, 60% by 2028
- Customer Service Representatives64% now, 64% by 2028
- Advertising Sales Agents56% now, 61% by 2028
- Counter and Rental Clerks23% now, 23% by 2028
- Receptionists and Information Clerks60% now, 60% by 2028
- New Accounts Clerks61% now, 61% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Telemarketers is within reach of AI now?
- An estimated 56% of the working time is within reach of AI models now and 56% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Telemarketers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Maintain contact records (100% of the task's time now); Build call lists (100% of the task's time now); and Adjust the sales script (70% of the task's time now).
- Which tasks of Telemarketers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Explain products and prices; Deliver scripted sales talks; and Answer inbound sales calls. Together such tasks are about 23% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Telemarketers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the conversations; running AI inside the business systems; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 44% of the working time stays with people, 4% becomes checking a model's work and 52% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Telemarketers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.49% went to this job's tasks, the 50th largest share of the 447 jobs that showed up; 3 of its 12 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 29%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Telemarketers?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 12 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 18, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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