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Singulariki

Driver/Sales Workers

Occupation · SOC 53-3031.00

Drive truck or other vehicle over established routes or within an established territory and sell or deliver goods, such as food products, including restaurant take-out items, or pick up or deliver items such as commercial laundry. May also take orders, collect payment, or stock merchandise at point of delivery.

Also called: Driver · Driver Salesman · Route Driver · Route Salesman · Delivery Man · Pizza Delivery Driver · Route Delivery Driver · Route Sales Driver · Route Sales Representative · Sales Route Driver · Automotive Parts Delivery Driver (Auto Parts Delivery Driver) · Bakery Deliverer

Job family: Transportation and Material Moving Occupations

Take this to your AI
Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-53-3031-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

Use as a copilot

Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.

  • Call on prospective customers to explain company services or to solicit new business. · 0.3%
See collaboration patterns →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Call on prospective customers to explain company services or to solicit new business. · 97.0% need a human
  • Listen to and resolve customers' complaints regarding products or services. · 96.6% need a human
See the boundary tasks →

29th-percentile task overlap — yet about 51,300 openings a year (+8.8% projected, BLS), and observed AI use leans 4640% copilot, not hand-off (AEI) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 28th -0.7
LLM task exposure, γ (OpenAI / Eloundou) Moderate 44th 0.5
AI assistant applicability (Microsoft) Low 18th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.3), and including AI-powered software (γ 0.5). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 1.0 · 97th percentile among occupations · High

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Listen to and resolve customers' complaints regarding products or services. 1.0%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Growing fast · +8.8% by 2034
Projected annual openings 51,300
Employment 2024 → 2034 451,500 → 491,300

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

28% mean task exposure (2025)
51st percentile of 427 placed occupations
+4 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Car, Taxi and Van Drivers · 8322 28% Gradient 1

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Working with AI in this job

How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.

Augmentation vs. automation 46.4% working with AI · 8.3% handed to AI
Most common way people use AI here none ·
Typical AI autonomy 3.5 / 5 · higher = AI acts more independently
Used for work (vs. personal / coursework) 32.6%

What people delegate to AI

The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.

Task How Usage
Listen to and resolve customers' complaints regarding products or services. none 1.5%
Call on prospective customers to explain company services or to solicit new business. Iteration 0.3%

Where a human is still needed

Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.

Call on prospective customers to explain company services or to solicit new business. 97.0%
Listen to and resolve customers' complaints regarding products or services. 96.6%

What people most often hand AI here

Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.

  • Help me listen to and resolve customers' complaints regarding products or services.

    From: Listen to and resolve customers' complaints regarding products or services. · 1.5% of measured AI use · none

  • Help me call on prospective customers to explain company services or to solicit new business.

    From: Call on prospective customers to explain company services or to solicit new business. · 0.3% of measured AI use · task iteration

Tasks

All 12 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Customer and Personal Service 4.2
Food Production 3.6
English Language 3.5
Transportation 3.5
Public Safety and Security 3.2
Administration and Management 3.0
Sales and Marketing 3.0
Mathematics 2.9
Production and Processing 2.9

Essential skills

Active Listening 3.8
Speaking 3.6
Critical Thinking 3.1
Reading Comprehension 3.0
Monitoring 2.9

Abilities

Oral Comprehension 3.8
Oral Expression 3.8
Near Vision 3.3
Problem Sensitivity 3.1
Speech Recognition 3.1
Speech Clarity 3.1
Written Comprehension 3.0
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Information Ordering 3.0
Category Flexibility 3.0
Arm-Hand Steadiness 3.0
Finger Dexterity 3.0
Control Precision 3.0
Static Strength 3.0
Trunk Strength 3.0
Far Vision 3.0
Written Expression 2.9
Perceptual Speed 2.9
Selective Attention 2.9

Transferable skills

Service Orientation 3.1
Social Perceptiveness 3.0
Complex Problem Solving 3.0
Time Management 3.0
Persuasion 2.9
Judgment and Decision Making 2.9

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
bMobile Technology Route Manager Project management software
bMobile Technology Sales Project management software
Computer Directions Route Sales Tracker Inventory management software
GEOCOMtms A.Maze Planning Map creation software
IBM Domino Communications server software
MobiTech Systems Route Sales Trakker Data base user interface and query software
Regulussoft Route Accounting Data base user interface and query software
Route planning software Map creation software
Soft Essentials Vending Essentials Data base user interface and query software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Outdoors, Exposed to All Weather Conditions 5.0
Time Pressure 4.9
Exposed to Very Hot or Cold Temperatures 4.8
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.6
Face-to-Face Discussions with Individuals and Within Teams 4.6
Importance of Repeating Same Tasks 4.4
Contact With Others 4.4
Deal With External Customers or the Public in General 4.3
Spend Time Making Repetitive Motions 4.2
Importance of Being Exact or Accurate 4.2
Spend Time Bending or Twisting Your Body 4.2
In an Enclosed Vehicle or Operate Enclosed Equipment 4.1
Determine Tasks, Priorities and Goals 4.0
Spend Time Walking or Running 3.8
Telephone Conversations 3.8
Exposed to Extremely Bright or Inadequate Lighting Conditions 3.7
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 3.7
Impact of Decisions on Co-workers or Company Results 3.6
Spend Time Standing 3.6
Frequency of Decision Making 3.6
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 3.5
Exposed to Contaminants 3.5
Freedom to Make Decisions 3.4
Exposed to Cramped Work Space, Awkward Positions 3.4
Spend Time Keeping or Regaining Balance 3.4
Dealing With Unpleasant, Angry, or Discourteous People 3.4
Spend Time Kneeling, Crouching, Stooping, or Crawling 3.3
Health and Safety of Other Workers 3.3
Work With or Contribute to a Work Group or Team 3.3
Indoors, Environmentally Controlled 3.2
Consequence of Error 3.1
Written Letters and Memos 3.1
Level of Competition 3.0
Public Speaking 2.9
Physical Proximity 2.9
Spend Time Sitting 2.9
Conflict Situations 2.9
Indoors, Not Environmentally Controlled 2.8
Exposed to Minor Burns, Cuts, Bites, or Stings 2.6
Coordinate or Lead Others in Accomplishing Work Activities 2.5

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

Post-Secondary Certificate 0.4%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Interest areas

Transportation/Machine Operation 6.1
Sales 4.4
Physical/Manual Labor 3.6
Personal Service 2.1
Mechanics/Electronics 2.0
Management/Administration 1.7
Culinary Art 1.7
Marketing/Advertising 1.6
Accounting 1.6

Career interests (Holland / RIASEC)

Realistic 5.3
Conventional 4.7
Enterprising 3.7
Social 2.4

Work styles

Dependability 2.5
Social Orientation 1.9
Perseverance 1.8

Wages & employment

U.S. · annual wages (BLS OEWS)

$22k10th$29k25th$37kMedian$48k75th$60k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
452k2024491k2034 (proj.)+8.8% · Growing fast
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $21,760
25th percentile $29,120
Median (50th) $37,130
75th percentile $47,590
90th percentile $59,730
People employed 417,420

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Accommodation and Food Services · Sector 183,980 $29,850
Wholesale Trade · Sector 99,460 $46,690
Retail Trade · Sector 44,350 $36,010
Full-Service Restaurants · National industry 34,960 $31,010
Transportation and Warehousing · Sector 33,490 $51,860
Other Services (except Public Administration) · Sector 22,400 $46,520
Manufacturing · Sector 15,850 $46,760
Pharmacies and Drug Retailers · National industry 5,410 $32,790
Administrative and Support and Waste Management and Remediation Services · Sector 4,030 $39,510
Real Estate and Rental and Leasing · Sector 3,100 $38,020
Management of Companies and Enterprises · Sector 2,810 $46,470
Information · Sector 2,510 $35,560

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Newspaper Publishers · National industry 8.19× 2,010
Wholesale Trade · Sector 6.09× 99,460
Accommodation and Food Services · Sector 4.77× 183,980
Pharmacies and Drug Retailers · National industry 2.82× 5,410
Full-Service Restaurants · National industry 2.41× 34,960
Other Services (except Public Administration) · Sector 1.87× 22,400
Transportation and Warehousing · Sector 1.67× 33,490
Retail Trade · Sector 1.05× 44,350

Part of the Supply Chain & Transportation career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Driver/Sales Workers sits at the 29th percentile of AI task-overlap and the 7th percentile of median pay, placed here against 11 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Driver/Sales Workers Postal Service Mail Carriers Light Truck Drivers Postal Service Clerks Cashiers Door-to-Door Sales Workers, News and Street Vendors, and Related Workers Counter and Rental Clerks Dispatchers, Except Police, Fire, and Ambulance AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Driver/Sales Workers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 51st percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Driver/Sales Workers show 29th-percentile AI task overlap — and about 51,300 annual U.S. openings

  • Driver/Sales Workers rank in the 29th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 51,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be growing fast (+8.8%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $37,130, across about 417,420 U.S. workers.BLS OEWS (May 2024)
  • Of the AI use actually observed for this work, 46% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Driver/Sales Workers show 29th-percentile AI task overlap — and about 51,300 annual U.S. openings

• Driver/Sales Workers rank in the 29th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 51,300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be growing fast (+8.8%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $37,130, across about 417,420 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 46% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2)

Source: Singulariki — "Driver/Sales Workers". https://singulariki.com/roles/role-53-3031-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Driver/Sales Workers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-53-3031-00

APA

Singulariki. (2026). Driver/Sales Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-53-3031-00

BibTeX 
@misc{singulariki-role-53-3031-00,
  title  = {Driver/Sales Workers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-53-3031-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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