Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Provide customer assistance and information, such as giving directions or handling wheelchairs. · 0.9%
Parking Attendants
Occupation · SOC 53-6021.00
Park vehicles or issue tickets for customers in a parking lot or garage. May park or tend vehicles in environments such as a car dealership or rental car facility. May collect fee.
Also called: Parking Attendant · Parking Lot Attendant · Valet Attendant · Valet Parker · Hiker · Parking Cashier · Parking Ramp Attendant · Valet Parking Attendant · Attendant · Auto Hiker · Auto Lot Attendant (Automotive Lot Attendant) · Auto Parker
Job family: Transportation and Material Moving Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-53-6021-00/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.
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.
- Provide customer assistance and information, such as giving directions or handling wheelchairs. · 97.6% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+3.0% by 2034)
- 18,500 openings/yr
- Moderate AI exposure
- Observed AI use leans automation
- Median pay $34,600/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (74th); today's AI task-overlap is comparatively low (29th)
29th-percentile task overlap — yet about 18,500 openings a year (+3% projected, BLS), and observed AI use leans 4118% 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 | 24th | -0.8 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 23rd | 0.2 | |
| AI assistant applicability (Microsoft) Moderate | 45th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). 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 0.9 · 74th 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.
| Explain and calculate parking charges, collect fees from customers, and respond to customer complaints. | 0.3% | |
| Provide customer assistance and information, such as giving directions or handling wheelchairs. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.0% by 2034 |
| Projected annual openings | 18,500 |
| Employment 2024 → 2034 | 135,700 → 139,800 |
“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.
Parking Attendants sits at the 55th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
29% mean task exposure (2025)
55th percentile of 427 placed occupations
−9 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Elementary Workers Not Elsewhere Classified · 9629 | 29% | Minimal |
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 | 41.2% working with AI · 42.4% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 35.3% |
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 |
|---|---|---|
| Provide customer assistance and information, such as giving directions or handling wheelchairs. | Directive | 0.9% |
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.
| Provide customer assistance and information, such as giving directions or handling wheelchairs. | 97.6% |
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 provide customer assistance and information, such as giving directions or handling wheelchairs.From: Provide customer assistance and information, such as giving directions or handling wheelchairs. · 0.9% of measured AI use · directive
Tasks
All 18 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Take numbered tags from customers, locate vehicles, and deliver vehicles, or provide customers with instructions for locating vehicles. Core · importance 4.5
- Inspect vehicles to detect any damage. Core · importance 4.5
- Greet customers and open their car doors. Core · importance 4.5
- Issue ticket stubs or place numbered tags on windshields, log tags or attach tag to customers' keys, and give customers matching tags for locating parked vehicles. Core · importance 4.4
- Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips. Core · importance 4.1
- Patrol parking areas to prevent vehicle damage and vehicle or property thefts. Supplemental · importance 4.0
- Explain and calculate parking charges, collect fees from customers, and respond to customer complaints. Core · importance 4.0
- Park and retrieve automobiles for customers in parking lots, storage garages, or new car lots. Core · importance 4.0
- Provide customer assistance and information, such as giving directions or handling wheelchairs. Core · importance 4.0
- Keep parking areas clean and orderly to ensure that space usage is maximized. Core · importance 3.8
- Direct motorists to parking areas or parking spaces, using hand signals or flashlights as necessary. Supplemental · importance 3.8
- Escort customers to their vehicles to ensure their safety. Supplemental · importance 3.6
- Perform maintenance on cars in storage to protect tires, batteries, or exteriors from deterioration. Supplemental · importance 3.4
- Lift, position, and remove barricades to open or close parking areas. Supplemental · importance 3.3
- Call emergency responders or the proper authorities and provide motorist assistance, such as giving directions or helping jump start a stalled vehicle. Core · importance 3.3
- Perform personnel activities, such as supervising or scheduling employees. Supplemental
- Review motorists' identification before allowing them to enter parking facilities. Supplemental
- Service vehicles with gas, oil, and water. Supplemental
Work activities
- Apply identification labels or tags.
- Balance receipts.
- Prepare cash for deposit or disbursement.
- Collect fares or payment from customers.
- Provide transportation information to passengers or customers.
- Assist customers to ensure comfort or safety.
- Request emergency personnel.
- Clean facilities or work areas.
- Direct vehicle traffic.
- Monitor surroundings to detect potential hazards.
- Drive passenger vehicles.
- Move materials, equipment, or supplies.
- Inspect motor vehicles.
- Assist passengers during vehicle boarding.
- Maintain vehicles in good working condition.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Essential skills
| Speaking | 3.5 | |
| Active Listening | 3.3 | |
| Critical Thinking | 2.9 | |
| Writing | 2.8 | |
| Mathematics | 2.8 | |
| Monitoring | 2.8 |
Abilities
| Far Vision | 3.5 | |
| Oral Comprehension | 3.4 | |
| Oral Expression | 3.4 | |
| Near Vision | 3.4 | |
| Speech Recognition | 3.4 | |
| Problem Sensitivity | 3.3 | |
| Speech Clarity | 3.3 | |
| Spatial Orientation | 3.1 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Information Ordering | 3.0 | |
| Selective Attention | 3.0 | |
| Control Precision | 3.0 | |
| Multilimb Coordination | 3.0 | |
| Depth Perception | 3.0 | |
| Perceptual Speed | 2.9 | |
| Visualization | 2.9 | |
| Manual Dexterity | 2.9 | |
| Reaction Time | 2.9 | |
| Trunk Strength | 2.9 | |
| Extent Flexibility | 2.9 | |
| Visual Color Discrimination | 2.9 | |
| Flexibility of Closure | 2.8 | |
| Arm-Hand Steadiness | 2.8 | |
| Stamina | 2.8 | |
| Gross Body Coordination | 2.8 | |
| Night Vision | 2.8 |
Knowledge
| Customer and Personal Service | 3.5 | |
| English Language | 3.1 | |
| Transportation | 3.0 |
Transferable skills
| Service Orientation | 3.4 | |
| Social Perceptiveness | 3.0 | |
| Coordination | 2.8 | |
| Judgment and Decision Making | 2.8 |
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 |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| CorePark Valet | Point of sale POS software | |
| Email software | Electronic mail software | |
| Payment processing software | Point of sale POS software | |
| SMS Valet | Point of sale POS 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.
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
- No formal educational credential · 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.
| High School Diploma | 60.8% | |
| Less than a High School Diploma | 38.3% | |
| Bachelor's Degree | 1.0% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 5.6 | |
| Conventional | 5.1 | |
| Enterprising | 3.1 | |
| Social | 3.0 |
Interest areas
| Transportation/Machine Operation | 4.0 | |
| Physical/Manual Labor | 3.2 | |
| Personal Service | 2.9 | |
| Sales | 1.6 | |
| Protective Service | 1.6 | |
| Accounting | 1.5 |
Work styles
| Dependability | 2.0 | |
| Cooperation | 1.7 | |
| Integrity | 1.5 | |
| Attention to Detail | 1.4 | |
| Optimism | 1.4 | |
| Social Orientation | 1.3 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $24,960 |
| 25th percentile | $29,590 |
| Median (50th) | $34,600 |
| 75th percentile | $37,230 |
| 90th percentile | $43,840 |
| People employed | 134,650 |
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 |
|---|---|---|
| Other Services (except Public Administration) · Sector | 73,190 | $34,960 |
| Retail Trade · Sector | 22,340 | $34,120 |
| Arts, Entertainment, and Recreation · Sector | 7,580 | $33,990 |
| Accommodation and Food Services · Sector | 7,150 | $33,420 |
| Real Estate and Rental and Leasing · Sector | 3,940 | $36,020 |
| Transportation and Warehousing · Sector | 2,600 | $34,700 |
| Casino Hotels · National industry | 2,580 | $32,640 |
| Wholesale Trade · Sector | 2,060 | $31,680 |
| Health Care and Social Assistance · Sector | 2,060 | $37,610 |
| Educational Services · Sector | 1,680 | $37,110 |
| Full-Service Restaurants · National industry | 690 | $34,960 |
| Management of Companies and Enterprises · Sector | 230 | $33,560 |
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 |
|---|---|---|
| Other Services (except Public Administration) · Sector | 18.93× | 73,190 |
| Casino Hotels · National industry | 8.77× | 2,580 |
| Arts, Entertainment, and Recreation · Sector | 3.29× | 7,580 |
| Real Estate and Rental and Leasing · Sector | 1.91× | 3,940 |
| Retail Trade · Sector | 1.64× | 22,340 |
| Accommodation and Food Services · Sector | 0.58× | 7,150 |
| Transportation and Warehousing · Sector | 0.4× | 2,600 |
| Wholesale Trade · Sector | 0.39× | 2,060 |
Related occupations
Part of the Supply Chain & Transportation career cluster.
- Shuttle Drivers and Chauffeurs · 53-3053.00 Compare →
- Parking Enforcement Workers · 33-3041.00 Compare →
- Baggage Porters and Bellhops · 39-6011.00 Compare →
- Taxi Drivers · 53-3054.00 Compare →
- Counter and Rental Clerks · 41-2021.00 Compare →
- Passenger Attendants · 53-6061.00 Compare →
- Concierges · 39-6012.00 Compare →
- Locker Room, Coatroom, and Dressing Room Attendants · 39-3093.00 Compare →
- Bus Drivers, Transit and Intercity · 53-3052.00 Compare →
- Amusement and Recreation Attendants · 39-3091.00 Compare →
- Light Truck Drivers · 53-3033.00 Compare →
- Dispatchers, Except Police, Fire, and Ambulance · 43-5032.00 Compare →
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 Parking Attendants — 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.
- No formal educational credential · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 55th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Parking Attendants show 29th-percentile AI task overlap — and about 18,500 annual U.S. openings
- Parking Attendants 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 18,500 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 about average (+3%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $34,600, across about 134,650 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 41% 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
Parking Attendants show 29th-percentile AI task overlap — and about 18,500 annual U.S. openings • Parking Attendants 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 18,500 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 about average (+3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $34,600, across about 134,650 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 41% 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 — "Parking Attendants". https://singulariki.com/roles/role-53-6021-00 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.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Parking Attendants." 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-53-6021-00
APA
Singulariki. (2026). Parking Attendants. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-53-6021-00
BibTeX
@misc{singulariki-role-53-6021-00,
title = {Parking Attendants},
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-53-6021-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.