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Landscaping and Groundskeeping Workers

Occupation · SOC 37-3011.00

Landscape or maintain grounds of property using hand or power tools or equipment. Workers typically perform a variety of tasks, which may include any combination of the following: sod laying, mowing, trimming, planting, watering, fertilizing, digging, raking, sprinkler installation, and installation of mortarless segmental concrete masonry wall units.

Also called: Grounds Maintenance Worker · Grounds Worker · Groundskeeper · Outside Maintenance Worker · Gardener · Greenskeeper · Grounds Person · Grounds Specialist · Landscape Specialist · Landscape Technician · Athletic Field Custodian · Bonsai Tender

Job family: Building and Grounds Cleaning and Maintenance Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-37-3011-00/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.

  • Advise customers on plant selection or care. · 1.2%
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.

  • Advise customers on plant selection or care. · 98.3% need a human
See the boundary tasks →

2nd-percentile task overlap — yet about 158,200 openings a year (+3.6% projected, BLS), and observed AI use leans 5470% 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 1st -1.8
LLM task exposure, γ (OpenAI / Eloundou) Low 7th 0.0
AI assistant applicability (Microsoft) Low 8th 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). 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 · 89th 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.

Advise customers on plant selection or care. 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 · +3.6% by 2034
Projected annual openings 158,200
Employment 2024 → 2034 1,192,500 → 1,235,000

“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.

12% mean task exposure (2025)
6th percentile of 427 placed occupations
+2 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Garden and Horticultural Labourers · 9214 12% Not exposed

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 54.7% working with AI · 32.5% handed to AI
Most common way people use AI here Learning · you ask AI to explain or teach
Typical AI autonomy 3.0 / 5 · higher = AI acts more independently

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
Advise customers on plant selection or care. Learning 1.2%

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.

Advise customers on plant selection or care. 98.3%

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 advise customers on plant selection or care.

    From: Advise customers on plant selection or care. · 1.2% of measured AI use · learning

Tasks

All 27 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Emerging tasks

Newer responsibilities O*NET has flagged as growing for this occupation.

  • Move furniture.

Work activities

Knowledge, skills & abilities

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

Abilities

Multilimb Coordination 3.6
Manual Dexterity 3.4
Trunk Strength 3.3
Arm-Hand Steadiness 3.1
Control Precision 3.1
Static Strength 3.1
Extent Flexibility 3.1
Problem Sensitivity 3.0
Visualization 3.0
Stamina 3.0
Near Vision 3.0
Oral Comprehension 2.9
Information Ordering 2.9
Selective Attention 2.9
Far Vision 2.9
Speech Recognition 2.9
Speech Clarity 2.9
Oral Expression 2.8
Deductive Reasoning 2.8
Inductive Reasoning 2.8
Finger Dexterity 2.8
Dynamic Strength 2.8
Gross Body Coordination 2.8
Flexibility of Closure 2.6
Rate Control 2.6
Depth Perception 2.6

Knowledge

English Language 3.4
Customer and Personal Service 3.2
Chemistry 3.0
Mechanical 2.9
Public Safety and Security 2.8
Administration and Management 2.7
Biology 2.6

Transferable skills

Operation and Control 3.1
Coordination 2.8
Operations Monitoring 2.8
Time Management 2.6

Essential skills

Speaking 2.9
Critical Thinking 2.9
Active Listening 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
Facebook Web page creation and editing software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
IBM Notes Electronic mail 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
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.7
Exposed to Very Hot or Cold Temperatures 4.7
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.5
Exposed to Hazardous Equipment 4.5
Spend Time Standing 4.3
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.3
Exposed to Contaminants 4.2
Physical Proximity 4.0
Spend Time Walking or Running 4.0
Face-to-Face Discussions with Individuals and Within Teams 4.0
Work With or Contribute to a Work Group or Team 4.0
In an Open Vehicle or Operating Equipment 3.9
Pace Determined by Speed of Equipment 3.9
Exposed to Minor Burns, Cuts, Bites, or Stings 3.5
Health and Safety of Other Workers 3.3
Dealing With Unpleasant, Angry, or Discourteous People 3.3
Time Pressure 3.3
In an Enclosed Vehicle or Operate Enclosed Equipment 3.2
Conflict Situations 3.2
Contact With Others 3.2
Importance of Being Exact or Accurate 3.2
Spend Time Making Repetitive Motions 3.2
Impact of Decisions on Co-workers or Company Results 3.2
Determine Tasks, Priorities and Goals 3.2
Work Outcomes and Results of Other Workers 3.2
Spend Time Bending or Twisting Your Body 3.0
Consequence of Error 2.9
Coordinate or Lead Others in Accomplishing Work Activities 2.9
Level of Competition 2.8
Frequency of Decision Making 2.7
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.7
Exposed to Cramped Work Space, Awkward Positions 2.7
Freedom to Make Decisions 2.5
Degree of Automation 2.5
Telephone Conversations 2.4
Exposed to Whole Body Vibration 2.4
Importance of Repeating Same Tasks 2.3
Deal With External Customers or the Public in General 2.3
Exposed to Hazardous Conditions 2.1

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.

What to study: Agriculture, Agriculture Operations, and Related Sciences , Parks, Recreation, Leisure, Fitness, and Kinesiology . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

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

Less than a High School Diploma 39.1%
Post-Secondary Certificate 31.2%
Bachelor's Degree 18.3%
High School Diploma 8.2%
Associate's Degree (or other 2-year degree) 3.1%

Interests & work styles

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

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.4
Artistic 1.9
Enterprising 1.8
Investigative 1.6
Social 1.4

Interest areas

Physical/Manual Labor 6.6
Nature/Outdoors 6.4
Agriculture 3.9
Transportation/Machine Operation 3.8
Construction/Woodwork 1.9
Mechanics/Electronics 1.6
Applied Arts and Design 1.6
Life Science 1.3

Work styles

Dependability 2.1
Attention to Detail 1.3

Wages & employment

U.S. · annual wages (BLS OEWS)

$30k10th$35k25th$38kMedian$46k75th$54k90th
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.
1.19M20241.24M2034 (proj.)+3.6% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $29,990
25th percentile $35,250
Median (50th) $38,090
75th percentile $45,870
90th percentile $53,900
People employed 943,430

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
Administrative and Support and Waste Management and Remediation Services · Sector 597,250 $38,380
Landscaping Services · National industry 571,490 $38,480
Arts, Entertainment, and Recreation · Sector 101,840 $35,570
Educational Services · Sector 30,700 $42,230
Real Estate and Rental and Leasing · Sector 28,650 $38,050
Other Services (except Public Administration) · Sector 25,730 $38,070
Accommodation and Food Services · Sector 16,670 $36,160
Construction · Sector 14,410 $41,600
Temporary Help Services · National industry 12,050 $35,020
Professional, Scientific, and Technical Services · Sector 10,770 $40,520
Health Care and Social Assistance · Sector 9,290 $37,140
Retail Trade · Sector 9,000 $37,380

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
Landscaping Services · National industry 102.06× 571,490
Administrative and Support and Waste Management and Remediation Services · Sector 10.81× 597,250
Arts, Entertainment, and Recreation · Sector 6.3× 101,840
Real Estate and Rental and Leasing · Sector 1.98× 28,650
Fitness and Recreational Sports Centers · National industry 1.23× 4,730
Exterminating and Pest Control Services · National industry 1.1× 1,000
Other Services (except Public Administration) · Sector 0.95× 25,730
Temporary Help Services · National industry 0.74× 12,050

Part of the Hospitality, Events, & Tourism career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Landscaping and Groundskeeping Workers sits at the 2nd percentile of AI task-overlap and the 9th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Landscaping and Groundskeeping Workers Highway Maintenance Workers Construction Laborers Fallers Agricultural Equipment Operators Farmworkers and Laborers, Crop, Nursery, and Greenhouse Operating Engineers and Other Construction Equipment Operators First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers 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 Landscaping and Groundskeeping 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 6th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Landscaping and Groundskeeping Workers show 2nd-percentile AI task overlap — and about 158,200 annual U.S. openings

  • Landscaping and Groundskeeping Workers rank in the 2nd 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 158,200 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 (+3.6%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $38,090, across about 943,430 U.S. workers.BLS OEWS (May 2024)
  • Of the AI use actually observed for this work, 55% 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
Landscaping and Groundskeeping Workers show 2nd-percentile AI task overlap — and about 158,200 annual U.S. openings

• Landscaping and Groundskeeping Workers rank in the 2nd 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 158,200 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 (+3.6%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $38,090, across about 943,430 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 55% 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 — "Landscaping and Groundskeeping Workers". https://singulariki.com/roles/role-37-3011-00
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. "Landscaping and Groundskeeping 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-37-3011-00

APA

Singulariki. (2026). Landscaping and Groundskeeping Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-37-3011-00

BibTeX 
@misc{singulariki-role-37-3011-00,
  title  = {Landscaping and Groundskeeping 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-37-3011-00}
}

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

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