AI exposure: Highway Maintenance Workers
Maintain highways, municipal and rural roads, airport runways, and rights-of-way. Duties include patching broken or eroded pavement and repairing guard rails, highway markers, and snow fences. May also mow or clear brush from along road, or plow snow from roadway.
Reading this score
computedHighway Maintenance Workers is among the least exposed occupations measured, at 1.2% of weighted task load, rank 911 of 923. 96.8% of this job is work current AI systems cannot produce at all. That is not a statement about skill or value. It is a statement about what these systems can and cannot do.
What holds the line here is embodiment. Across this occupation's 19 tasks it averages 3.00 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Drive trucks to transport crews and equipment to work sites, at 8.3%. The least is Blend compounds to form adhesive mixtures used for marker installation, at 0.0%. A gap of 8.3% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within construction and extraction occupations, this one is less exposed than the median of 5.1% across the group's 61 roles, with 54 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.
What would move this score. Of 19 tasks, 0 are currently banded exposed, 0 assisted and 19 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 0.12 | 0-4 |
| Embodiment | 3.00 | 0-3 |
| Presence | 0.59 | 0-3 |
| Accountability | 0.68 | 0-3 |
| Context | 0.92 | 0-3 |
| Verification cost | 0.96 | 0-3 |
Task by task
19 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Drive trucks to transport crews and equipment to work sites. | 8.3% | 16.7% | 75.0% | 4.27 | untouched |
| Inspect markers to verify accurate installation. | 8.3% | 16.7% | 75.0% | 3.35 | untouched |
| Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures. | 4.4% | 3.9% | 91.7% | 4.04 | untouched |
| Set out signs and cones around work areas to divert traffic. | 0.0% | 0.0% | 100.0% | 4.50 | untouched |
| Flag motorists to warn them of obstacles or repair work ahead. | 0.0% | 0.0% | 100.0% | 4.37 | untouched |
| Perform preventative maintenance on vehicles and heavy equipment. | 0.0% | 0.0% | 100.0% | 4.32 | untouched |
| Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools. | 0.0% | 0.0% | 100.0% | 4.24 | untouched |
| Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures. | 0.0% | 0.0% | 100.0% | 4.19 | untouched |
| Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt and sand. | 0.0% | 0.0% | 100.0% | 4.14 | untouched |
| Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders. | 0.0% | 0.0% | 100.0% | 4.14 | untouched |
| Remove litter and debris from roadways, including debris from rock and mud slides. | 0.0% | 0.0% | 100.0% | 4.03 | untouched |
| Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement. | 0.0% | 0.0% | 100.0% | 4.02 | untouched |
| Perform roadside landscaping work, such as clearing weeds and brush, and planting and trimming trees. | 0.0% | 0.0% | 100.0% | 3.59 | untouched |
| Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents. | 0.0% | 0.0% | 100.0% | 3.81 | untouched |
| Measure and mark locations for installation of markers, using tape, string, or chalk. | 0.0% | 0.0% | 100.0% | 3.70 | untouched |
| Paint traffic control lines and place pavement traffic messages, by hand or using machines. | 0.0% | 0.0% | 100.0% | 3.60 | untouched |
| Apply oil to road surfaces, using sprayers. | 0.0% | 0.0% | 100.0% | 3.51 | untouched |
| Place and remove snow fences used to prevent the accumulation of drifting snow on highways. | 0.0% | 0.0% | 100.0% | 3.34 | untouched |
| Blend compounds to form adhesive mixtures used for marker installation. | 0.0% | 0.0% | 100.0% | 2.87 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.