Share and intensity of work current AI systems can materially affect.
SOC 37-3011
Landscaping and Groundskeeping Workers AI displacement risk
Robotic mowers handle flat residential lawns, but pruning, planting, irrigation work, and terrain-variable grounds care resist automation. Equipment operation, plant knowledge, and site judgment keep landscaping crews busy in a tight labor market.
Likely potential for exposed tasks to move to software after workflow integration.
Automation is task-specific — mowing first, everything else later. Slopes, obstacles, plantings, and customer-specific requests keep most of this work manual for the foreseeable future.
Distribution
Where Landscaping and Groundskeeping Workers sits across 620 tracked roles
Displacement pressure 22 — higher than 29% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
19 O*NET task statements matched to SOC 37-3011. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $39,150 (May 2025, US national). The latest BLS row matched SOC 37-3011.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Landscaping and Groundskeeping Workers
SOC 37-3011 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 22/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Landscaping and Groundskeeping Workers
The current evidence import matched 19 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks19
SOC37-3011
- Core task / ID 9589
Mow or edge lawns, using power mowers or edgers.
- Core task / ID 20761
Operate vehicles or powered equipment, such as mowers, tractors, twin-axle vehicles, snow blowers, chainsaws, electric clippers, sod cutters, or pruning saws.
- Core task / ID 9592
Use hand tools, such as shovels, rakes, pruning saws, saws, hedge or brush trimmers, or axes.
- Core task / ID 9593
Prune or trim trees, shrubs, or hedges, using shears, pruners, or chain saws.
- Core task / ID 9594
Gather and remove litter.
- Core task / ID 9599
Trim or pick flowers and clean flower beds.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Mow and edge lawns
Exposure 32, automation 24%, augmentation 12%.
O*NET evidence: Mow or edge lawns, using power mowers or edgers. (ID 9589)
Prune trees and shrubs
Exposure 18, automation 8%, augmentation 18%.
O*NET evidence: Prune or trim trees, shrubs, or hedges, using shears, pruners, or chain saws. (ID 9593)
Plant and maintain gardens
Exposure 16, automation 6%, augmentation 20%.
O*NET evidence: Plant seeds, bulbs, foliage, flowering plants, grass, ground covers, trees, or shrubs, ... (ID 9602)
Maintain irrigation systems
Exposure 28, automation 10%, augmentation 38%.
TaskExposureAutomationAugmentation
Mow and edge lawns3224%12%
Prune trees and shrubs188%18%
Plant and maintain gardens166%20%
Maintain irrigation systems2810%38%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowLandscape Crew Leader
Training horizon: 1-3 months. Skill overlap 78. Wage preservation signal 122.
- Own daily crew routing
- Track job completion quality
- Train new crew members
credentialed transition
LowIrrigation Technician
Training horizon: 3-9 months. Skill overlap 58. Wage preservation signal 128.
- Learn irrigation system design
- Practice controller programming
- Document water-use audits
Comparison guides
Compare the next move before you commit
1-3 months
Landscaping and Groundskeeping Workers to Landscape Crew Leader
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Landscaping and Groundskeeping Workers into Landscape Crew Leader.
3-9 months
Landscaping and Groundskeeping Workers to Irrigation Technician
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Landscaping and Groundskeeping Workers into Irrigation Technician.
What the AI risk score means for Landscaping and Groundskeeping Workers
The displacement pressure score for Landscaping and Groundskeeping Workers is 22. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Mow and edge lawns carries 24% automation pressure, while Maintain irrigation systems carries 38% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $39,150 (May 2025, US national). Employment context: Large outdoor physical workforce. Typical education: No formal educational credential.
Wage vulnerability is 68, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Mowing robots take narrow tasks
- Labor demand exceeds supply
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Landscaping and Groundskeeping Workers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Equipment operation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Plant knowledge
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Irrigation basics
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Physical stamina
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Landscape Crew Leader, such as own daily crew routing.
- By 90 days, compare internal openings and external postings for Landscape Crew Leader or Irrigation Technician and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Landscaping and Groundskeeping Workers
Will AI replace Landscaping and Groundskeeping Workers?
Robotic mowers handle flat residential lawns, but pruning, planting, irrigation work, and terrain-variable grounds care resist automation. Equipment operation, plant knowledge, and site judgment keep landscaping crews busy in a tight labor market. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Landscaping and Groundskeeping Workers work are most exposed to AI?
Mow and edge lawns and Maintain irrigation systems show the strongest automation pressure in this model. Maintain irrigation systems and Plant and maintain gardens are better treated as AI-augmented work.
What should Landscaping and Groundskeeping Workers learn next?
Start with Equipment operation, Plant knowledge, Irrigation basics. The most practical adjacent paths in this model are Landscape Crew Leader and Irrigation Technician.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics