How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Maintain and guard stations in bridges to check waterways for boat traffic.72
- Check that bridges are clear of vehicles and pedestrians prior to opening.72
- Record names, types, and destinations of vessels passing through bridge openings or locks, and numbers of trains or vehicles crossing bridges.71
- Stop automobile and pedestrian traffic on bridges, and lower automobile gates prior to moving bridges.71
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Move levers to activate traffic signals, navigation lights, and alarms.01
- Perform maintenance duties, such as sweeping, painting, and yard work to keep facilities clean and in order.02
- Clean and lubricate equipment, and make minor repairs and adjustments.03
- Observe position and progress of vessels to ensure best use of lock spaces or bridge opening spaces.04
- Direct movements of vessels in locks or bridge areas, using signals, telecommunication equipment, or loudspeakers.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Hoist and Winch Operators
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Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
