Parts Salespersons
In short: today's best AI models could do about 18% 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 82% of the time, stays with people.
Sell spare and replacement parts and equipment in repair shop or parts store.
270,070 people do this job in the US. $38,630 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on6 h
- A person checks AI's work1 h
- Desk work AI can't do yet0 h
- Stays with people33 h
Parts Salespersons, now: of a 40-hour week, AI could take on 6 hours, 1 hours become checking its work, 0 hours are desk work it can't do yet, and 33 hours stay with people.
The largest pieces: Manage shipments (about 2 h); Look up part numbers and prices (about 2 h); Prepare sales slips (about 1 h, a person checks).
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.
- 1%
- 18%
- 6%
- 18%
- 57%
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 18% 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, 6%. With Claude Mythos Preview (early), measured in April 2026, 18%. Today's estimate: 18%.
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, 0.085% went to this job's tasks in May 2026, the 186th largest of the 447 jobs that showed up at all; 2 of its 19 tasks showed up. Within each of Anthropic's methods, never across a change: 0.047% to 0.033% (released February and March 2025); 0.0059% to 0.019% (August 2025 to February 2026); 0.068% to 0.085% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.047% | 286 of 708 | 4 of 19 |
| Released March 2025 | 0.033% | 343 of 703 | 3 of 19 |
| The week of Aug 4, 2025 (released September 2025) | 0.0059% | 515 of 655 | 2 of 19 |
| The week of Nov 13, 2025 (released January 2026) | 0.016% | 416 of 672 | 4 of 19 |
| The week of Feb 5, 2026 (released March 2026) | 0.019% | 406 of 677 | 4 of 19 |
| April 2026 (released June 2026) | 0.068% | 207 of 434 | 2 of 19 |
| May 2026 (released June 2026) | 0.085% | 186 of 447 | 2 of 19 |
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 55% of this job's working time as physical work. For 54% of the working time, a robot has been shown doing most of the task somewhere: 50% in a purpose-built cell, 4% in a structured facility. 0.58 of 3, the 287th highest of the 923 jobs.
- Not physical 45%
- No robot shown 1%
- In a purpose-built cell 50%
- In a structured facility 4%
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
82% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Mark and shelve parts, the hands-on work, 10%
- Clean the counter and stockroom, the hands-on work, 9%
- Repair parts and equipment, the hands-on work, 9%
- Label parts and keep inventory, the hands-on work, 7%
- Pick up and deliver parts, the hands-on work, 6%
- Work out which part is needed, the in-person work, 6%
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 15% is desk work that is small if wrong, which can be handed over with lighter checks.
- Prepare sales slips, costly if wrong, 3%
- Handle returns, costly if wrong, 0.6%
What to learn
- The hands-on craft. About 57% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 24% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Running AI inside the business systems. About 18% 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.
- 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 . 19 tasks from .
| Task | ||||
|---|---|---|---|---|
| Mark and store parts in stockrooms, according to prearranged systems.Stocking the shelves by hand.Done daily by most who do it. Robots: in a purpose-built cell, Amazon Robotics Stow system (Vulcan Stow) (deployed). Anthropic's rating. | 10% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Maintain and clean work and inventory areas.Cleaning by hand.Done daily by most who do it. Robots: in a purpose-built cell, Pudu CC1 (deployed). Anthropic's rating. | 9% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
| Repair parts or equipment.Hands-on repair.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 9% | 1.3 h17 min to 5 h | Hands-on 100% | Stays with people |
| Locate and label parts, and maintain inventory of stock.Handling and labeling stock, with the counts entered.Done daily by most who do it. Robots: in a purpose-built cell, Exotec Skypod ASRS (deployed). Anthropic's rating. | 8% | 25 min8 min to 1.3 h | Hands-on 80%, systems 20% | 20%Desk part half by now |
| Fill customer orders from stock, and place orders when requested items are out of stock.Pulling the part and ordering what is missing.Done hourly or more by most who do it. Robots: in a purpose-built cell, KNAPP Pick-it-Easy Robot (AI piece-picking robot at an ASRS goods-to-robot station) (deployed). Anthropic's rating. | 7% | 13 min4 min to 33 min | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Receive and fill telephone orders for parts.Taking the order by phone, keying it, and pulling the part.Done hourly or more by most who do it. Robots: in a purpose-built cell, Brightpick Autopicker 2.0 / Intuition (deployed). Anthropic's rating. | 6% | 10 min4 min to 25 min | Conversation 60%, systems 20%, hands-on 20% | 20%Desk part half by now |
| Pick up and deliver parts.Driving the parts run.Done daily by most who do it. Robots: in a structured facility, Moxi (Diligent Robotics) (deployed). Anthropic's rating. | 6% | 38 min17 min to 1.3 h | Hands-on 100% | Stays with people |
| Determine replacement parts required, according to inspections of old parts, customer requests, or customers' descriptions of malfunctions.Looking at the old part and hearing the customer's description.Done hourly or more by most who do it. | 6% | 13 min4 min to 33 min | In person 60%, hands-on 40% | Stays with people |
| Demonstrate equipment to customers, and explain functioning of equipment.Showing the customer at the counter.Done daily by most who do it. Robots: in a purpose-built cell, Figure 01 (with OpenAI vision-language model) (demonstrated). Anthropic's rating. | 5% | 25 min8 min to 50 min | In person 100% | Stays with people |
| Manage shipments by researching shipping methods or costs and tracking packages.Comparing shipping options and tracking in the system.Done daily by most who do it. | 4% | 21 min8 min to 38 min | Systems 100% | 100%Half by now |
| Assist customers, such as responding to customer complaints and updating them about back-ordered parts.Calls and counter conversations.Done daily by most who do it. | 4% | 13 min4 min to 25 min | Conversation 60%, in person 40% | Stays with people |
| Read catalogs, microfiche viewers, or computer displays to determine replacement part stock numbers and prices.Searching the catalog system.Done hourly or more by most who do it. | 4% | 7 min3 min to 21 min | Systems 100% | 100%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.
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 Parts Salespersons is within reach of AI now?
- An estimated 18% of the working time is within reach of AI models now and 18% 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 Parts Salespersons 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: Manage shipments (100% of the task's time now); Look up part numbers and prices (100% of the task's time now); and Prepare sales slips (100% of the task's time now).
- Which tasks of Parts Salespersons stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Mark and shelve parts; Clean the counter and stockroom; and Repair parts and equipment. Together such tasks are about 61% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Parts Salespersons 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 hands-on craft; the conversations; and dealing with people outside the organization. By the end of 2028 on the long-run pace at four in five, about 82% of the working time stays with people, 4% becomes checking a model's work and 15% 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 Parts Salespersons?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.085% went to this job's tasks, the 186th largest share of the 447 jobs that showed up; 2 of its 19 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. 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 Parts Salespersons?
- Anthropic's robot exposure rating (September 2026) finds 55% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 54% of the working time, 4% in a structured facility. Its index is 0.58 of 3. One example: Moxi (Diligent Robotics) for "pick up and deliver parts". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 19 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 September 30, 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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