Bridge and Lock Tenders
AI is not doing this work today.
This occupation's activities barely register in measured AI usage. Generative AI is not doing this work today. The pressures that matter here are more likely economic or robotic than linguistic.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Operate and tend bridges, canal locks, and lighthouses to permit marine passage on inland waterways, near shores, and at danger points in waterway passages. May supervise such operations. Includes drawbridge operators, lock operators, and slip bridge operators.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Operate transportation equipment or vehicles. | 21.5% | Not observed 0.00 |
| Monitor operations to ensure adequate performance. | 19.5% | Not observed 0.00 |
| Maintain operational records. | 11.4% | Not observed 0.00 |
| Direct vehicle traffic. | 11.0% | Not observed 0.00 |
| Notify others of emergencies or problems. | 7.2% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 5.5% | Not observed 0.00 |
| Monitor traffic conditions. | 5.5% | Not observed 0.00 |
| Monitor safety or security of work areas, facilities, or properties. | 4.5% | Not observed 0.00 |
| Operate pumping systems or equipment. | 4.1% | Not observed 0.00 |
| Schedule operational activities. | 3.9% | Not observed 0.00 |
| Prepare reports of operational or procedural activities. | 3.9% | Not observed 0.00 |
| Tend watercraft. | 2.2% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
Low exposure is an asset. Spend it deliberately.
Next 30 days
- Don't buy the panic. This occupation's activities barely appear in AI usage data. Your near-term exposure is other people's headlines, not your tasks.
- Skim the gains anyway. Even here, an assistant helps with the thin admin layer: Operate transportation equipment or vehicles is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Operate transportation equipment or vehicles are measured as fully outside current AI. Mastery there is compounding, defensible value.
- Mentor the exposed. Colleagues in adjacent office-heavy roles are the ones facing redesign; understanding their tools makes you a better coordinator and leader.
The longer game
- Watch the actual frontier for this work: usually physical automation, demographics or regulation rather than language models. This dataset intentionally measures only generative AI.
- Revisit after major data updates. If this page's vintage changes and your band moves, the plan moves with it.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.