Water transportation workers: AI exposure and career outlook
Water transportation workers (SOC 53-5000) sit at the 8th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 5% of tasks are already automated and 15% are being reshaped rather than replaced — both modelled figures, not direct measurements. Insulated group: little measured AI exposure so far. No urgency here, though the tools are still worth having in your hands. In Canada the role maps to NOC 72602 (Deck officers, water transport), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 53-5000
- Canada NOC 2021
- 72602
- TEER level
- Not available
- COPS outlook
- Balance
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 6% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 4 detailed roles. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). Occupation-group average of 4 detailed roles. |
| Academic AI exposure | 29th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 4 detailed roles. |
| Automation-pattern usage | Not available | Not covered by Anthropic's collaboration-mode dataset. |
| Augmentation-pattern usage | Not available | Not covered by Anthropic's collaboration-mode dataset. |
| Estimated task automation (modelled) | 5% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 15% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
This skill domain has minimal AI substitution risk. AI is primarily augmenting — not replacing — professionals here.
In Canada: NOC 72602
Water transportation workers map to NOC 72602 — Deck officers, water transport. ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). TEER is the second digit of the NOC code, so it is only as certain as the code is. Several correspondence candidates were available here and we took the closest by title, so we do not report a TEER level for it.
What this job actually involves
These are the O*NET task statements for Water transportation workers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 87, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 6% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 53-5000 is a BLS occupation group. These statements are pooled across the 4 detailed occupations in it, so a given task may apply to only some of them.
Operating Vehicles, Mechanized Devices, or Equipment · 4 tasks
Operate engine throttles and steering mechanisms to guide boats on desired courses.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 4 occupations in this group
Steer and operate vessels, using radios, depth finders, radars, lights, buoys, or lighthouses.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 4 occupations in this group
Dock or undock vessels, sometimes maneuvering through narrow spaces, such as locks.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 4 occupations in this group
Start engines to propel ships, and regulate engines and power transmissions to control speeds of ships, according to directions from captains or bridge computers.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 4 occupations in this group
Monitoring Processes, Materials, or Surroundings · 2 tasks
Monitor engine, machinery, or equipment indicators when vessels are underway, and report abnormalities to appropriate shipboard staff.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 4 occupations in this group
Monitor and test operations of engines or other equipment so that malfunctions and their causes can be identified.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 4 occupations in this group
Guiding, Directing, and Motivating Subordinates · 2 tasks
Prevent ships under navigational control from engaging in unsafe operations.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 4 occupations in this group
Serve as a vessel's docking master upon arrival at a port or at a berth.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 4 occupations in this group
Getting Information · 1 task
Consult maps, charts, weather reports, or navigation equipment to determine and direct ship movements.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 4 occupations in this group
Making Decisions and Solving Problems · 1 task
Direct courses and speeds of ships, based on specialized knowledge of local winds, weather, water depths, tides, currents, and hazards.
Core task · Importance 5.0/5 (O*NET) · reported by 1 of 4 occupations in this group
Controlling Machines and Processes · 1 task
Operate ship-to-shore radios to exchange information needed for ship operations.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 4 occupations in this group
Monitoring and Controlling Resources · 1 task
Monitor the availability, use, or condition of lifesaving equipment or pollution preventatives to ensure that international regulations are followed.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 4 occupations in this group
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Water transportation workers as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
With low AI exposure, the focus for this role is on adopting productivity tools. Leverage AI for scheduling, administrative tasks, and analytics to double operational throughput.
Physical requirements and manual dexterity provide strong natural insulation from AI substitution. AI is primarily a peripheral tool for diagnostics.
Obtain technical certifications in advanced diagnostic tools and smart systems relevant to water transportation workers to prepare for future technological integrations.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the safe band, not an occupation-specific research finding about water transportation workers.
Sources for this occupation
Only the sources that hold data for water transportation workers are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
Related occupations
Other roles in Management & Ops.
This is the picture for the role. What about your skills?
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