Sailors and Marine Oilers
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
Stand watch to look for obstructions in path of vessel, measure water depth, turn wheel on bridge, or use emergency equipment as directed by captain, mate, or pilot. Break out, rig, overhaul, and store cargo-handling gear, stationary rigging, and running gear. Perform a variety of maintenance tasks to preserve the painted surface of the ship and to maintain line and ship equipment. Must hold government-issued certification and tankerman certification when working aboard liquid-carrying vessels. Includes able seamen and ordinary seamen.
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 |
|---|---|---|
| Maintain vehicles in working condition. | 18.6% | Not observed 0.00 |
| Tend watercraft. | 12.8% | Not observed 0.00 |
| Inspect commercial, industrial, or production systems or equipment. | 11.3% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 10.0% | Not observed 0.00 |
| Monitor safety or security of work areas, facilities, or properties. | 7.4% | Not observed 0.00 |
| Maintain operational records. | 6.8% | Not observed 0.00 |
| Load products, materials, or equipment for transportation or further processing. | 5.9% | Not observed 0.00 |
| Operate pumping systems or equipment. | 5.7% | Not observed 0.00 |
| Connect components or supply lines to equipment or tools. | 5.7% | High overlap 0.66 |
| Maintain current knowledge in area of expertise. | 5.6% | High overlap 0.61 |
| Monitor equipment operation. | 5.4% | Not observed 0.00 |
| Set up equipment. | 5.0% | High overlap 0.75 |
"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.
What drives this score: the biggest AI-overlapped activity in this role is connect components or supply lines to equipment or tools; the most important activity AI is not observed doing is maintain vehicles in working condition. The plan below is built around exactly that split.
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: Connect components or supply lines to equipment or tools is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Maintain vehicles in working condition 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.