Share and intensity of work current AI systems can materially affect.
SOC 47-4051
Highway Maintenance Workers AI displacement risk
Patching pavement, clearing drainage, plowing snow, and setting work zones are physical tasks along live roadways. Route optimization and fleet telematics assist planning; the flagging, shoveling, and equipment work stay manual.
Likely potential for exposed tasks to move to software after workflow integration.
Work-zone automation (arrow boards, attenuator trucks) improves safety without replacing crews. Infrastructure funding keeps this public workforce stable, and the tasks are too varied for narrow machines.
Distribution
Where Highway Maintenance Workers sits across 620 tracked roles
Displacement pressure 16 — higher than 14% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. 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 47-4051. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $50,260 (May 2025, US national). The latest BLS row matched SOC 47-4051.
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 Highway Maintenance Workers
SOC 47-4051 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 16/100 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 Highway Maintenance 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
SOC47-4051
- Core task / ID 4872
Set out signs and cones around work areas to divert traffic.
- Core task / ID 4871
Flag motorists to warn them of obstacles or repair work ahead.
- Core task / ID 18783
Perform preventative maintenance on vehicles and heavy equipment.
- Core task / ID 4875
Drive trucks to transport crews and equipment to work sites.
- Core task / ID 4878
Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools.
- Core task / ID 4880
Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures.
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
Repair pavement and road surfaces
Exposure 20, automation 11%, augmentation 24%.
O*NET evidence: Apply oil to road surfaces, using sprayers. (ID 4886)
Set up work zones and flag traffic
Exposure 18, automation 9%, augmentation 26%.
Operate maintenance equipment
Exposure 26, automation 15%, augmentation 36%.
O*NET evidence: Perform preventative maintenance on vehicles and heavy equipment. (ID 18783)
Inspect roads and drainage structures
Exposure 30, automation 13%, augmentation 48%.
O*NET evidence: Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures. (ID 4876)
TaskExposureAutomationAugmentation
Repair pavement and road surfaces2011%24%
Set up work zones and flag traffic189%26%
Operate maintenance equipment2615%36%
Inspect roads and drainage structures3013%48%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowHighway Maintenance Supervisor
Training horizon: 2-4 months. Skill overlap 78. Wage preservation signal 122.
- Own crew assignments
- Track maintenance metrics
- Manage work-zone safety programs
adjacent role
LowHeavy Equipment Operator
Training horizon: 2-6 months. Skill overlap 66. Wage preservation signal 122.
- Get equipment certifications
- Log supervised operating hours
- Study grade control systems
Comparison guides
Compare the next move before you commit
2-4 months
Highway Maintenance Workers to Highway Maintenance Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Highway Maintenance Workers into Highway Maintenance Supervisor.
2-6 months
Highway Maintenance Workers to Heavy Equipment Operator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Highway Maintenance Workers into Heavy Equipment Operator.
What the AI risk score means for Highway Maintenance Workers
The displacement pressure score for Highway Maintenance Workers is 16. 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. Operate maintenance equipment carries 15% automation pressure, while Inspect roads and drainage structures carries 48% 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: $50,260 (May 2025, US national). Employment context: Public road maintenance workforce with infrastructure funding. Typical education: High school diploma or equivalent; CDL often required.
Wage vulnerability is 58, 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.
- Very low displacement pressure
- Infrastructure funding supports crews
- Safety automation protects rather than replaces
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Highway Maintenance 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.
Work-zone safety
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.
Route 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.
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 Highway Maintenance Supervisor, such as own crew assignments.
- By 90 days, compare internal openings and external postings for Highway Maintenance Supervisor or Heavy Equipment Operator and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Highway Maintenance Workers
Will AI replace Highway Maintenance Workers?
Patching pavement, clearing drainage, plowing snow, and setting work zones are physical tasks along live roadways. Route optimization and fleet telematics assist planning; the flagging, shoveling, and equipment work stay manual. 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 Highway Maintenance Workers work are most exposed to AI?
Operate maintenance equipment and Inspect roads and drainage structures show the strongest automation pressure in this model. Inspect roads and drainage structures and Operate maintenance equipment are better treated as AI-augmented work.
What should Highway Maintenance Workers learn next?
Start with Equipment operation, Work-zone safety, Physical stamina. The most practical adjacent paths in this model are Highway Maintenance Supervisor and Heavy Equipment Operator.
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
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*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