AI exposure: Maintenance Workers, Machinery
Lubricate machinery, change parts, or perform other routine machinery maintenance.
Reading this score
computedAt 15.5% of weighted task load, Maintenance Workers, Machinery sits at the 24th percentile, below the point where a job's centre of gravity has moved. 75.7% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.
What holds the line here is embodiment. Across this occupation's 18 tasks it averages 2.59 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 Record production, repair, and machine maintenance information, at 73.3%. The least is Replace or repair metal, wood, leather, glass, or other lining in machines, or in equipment..., at 0.0%. A gap of 73.3% 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 installation, maintenance and repair occupations, this one is more exposed than most. The median across the 50 roles in the group is 12.8%, and only 17 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 18 tasks, 5 are currently banded exposed, 0 assisted and 13 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 | 0.94 | 0-4 |
| Embodiment | 2.59 | 0-3 |
| Presence | 0.36 | 0-3 |
| Accountability | 0.62 | 0-3 |
| Context | 1.06 | 0-3 |
| Verification cost | 0.99 | 0-3 |
Task by task
18 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Record production, repair, and machine maintenance information. | 73.3% | 26.7% | 0.0% | 4.10 | exposed |
| Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished. | 52.5% | 22.5% | 25.0% | 3.80 | exposed |
| Read work orders and specifications to determine machines and equipment requiring repair or maintenance. | 50.0% | 25.0% | 25.0% | 4.00 | exposed |
| Collaborate with other workers to repair or move machines, machine parts, or equipment. | 26.7% | 23.3% | 50.0% | 4.01 | exposed |
| Start machines and observe mechanical operation to determine efficiency and to detect problems. | 26.7% | 23.3% | 50.0% | 3.93 | exposed |
| Inspect or test damaged machine parts, and mark defective areas or advise supervisors of repair needs. | 17.0% | 16.3% | 66.7% | 3.98 | untouched |
| Set up and operate machines, and adjust controls to regulate operations. | 15.0% | 10.0% | 75.0% | 4.03 | untouched |
| Measure, mix, prepare, and test chemical solutions used to clean or repair machinery and equipment. | 7.5% | 5.0% | 87.5% | 3.38 | untouched |
| Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists. | 0.0% | 0.0% | 100.0% | 4.14 | untouched |
| Reassemble machines after the completion of repair or maintenance work. | 0.0% | 0.0% | 100.0% | 4.12 | untouched |
| Lubricate or apply adhesives or other materials to machines, machine parts, or other equipment according to specified procedures. | 0.0% | 0.0% | 100.0% | 4.04 | untouched |
| Install, replace, or change machine parts and attachments, according to production specifications. | 0.0% | 0.0% | 100.0% | 4.03 | untouched |
| Transport machine parts, tools, equipment, and other material between work areas and storage, using cranes, hoists, or dollies. | 0.0% | 0.0% | 100.0% | 3.84 | untouched |
| Collect and discard worn machine parts and other refuse to maintain machinery and work areas. | 0.0% | 0.0% | 100.0% | 3.83 | untouched |
| Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment. | 0.0% | 0.0% | 100.0% | 3.78 | untouched |
| Replace, empty, or replenish machine and equipment containers such as gas tanks or boxes. | 0.0% | 0.0% | 100.0% | 3.65 | untouched |
| Clean machines and machine parts, using cleaning solvents, cloths, air guns, hoses, vacuums, or other equipment. | 0.0% | 0.0% | 100.0% | 3.63 | untouched |
| Replace or repair metal, wood, leather, glass, or other lining in machines, or in equipment compartments or containers. | 0.0% | 0.0% | 100.0% | 3.48 | 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.