Ships' Engineers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Ships' Engineers (ISCO-08 3151) score an average of 0.23 on a 0–1 exposure scale — more exposed than about 42% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Ordering fuel and other engine-room department stores and maintaining record of operations;”

Scores 0.31 on the 2025 scale. The task of ordering fuel and maintaining records for engine-room operations involves a combination of administratively driven tasks and elements of logistical planning. Given this, Generative AI can aid significantly in data management, procedural assistance, and providing analytical insights to optimize ordering processes. This aligns with the capabilities shown in tasks such as managing orders in wholesale, which received a score of 0.45, and storing goods at fuel stations with a score of 0.29, both requiring logistical organization and compliance with procedures. However, the physical verification, decision-making based on unforeseen circumstances, and safety compliance require human oversight and intervention. Tasks involving hands-on physical actions and high context-specific human decision-making, like engine operation tasks with a score of 0.265, suggest the limits of current AI capabilities in fully automating this task. Given that this operation is likely conducted in a high-income country like Poland with good access to technology infrastructure, the score is adjusted to 0.275 to reflect AI's supportive role without displacing the human element wholly necessary for context-specific and physical task execution.

Moving fastest, 2023 → 2025

“Inspecting and conducting maintenance and emergency repairs to engines, machinery and auxiliary equipment;”

Model capability on this task changed by +0.07 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3151, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Airfield Operations Specialists
• Ship Engineers

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Ships' Engineers sit at the 42nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Ships' Engineers rank in the 42nd percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Ordering fuel and other engine-room department stores and maintaining record of operations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Ships' Engineers". https://singulariki.com/gradient/3151-ships-engineers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.