Aquaculture and Fisheries Production Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 13 task statements that define Aquaculture and Fisheries Production Managers (ISCO-08 1312) score an average of 0.22 on a 0–1 exposure scale — more exposed than about 39% 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 13 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Establishing and managing budgets, monitoring production output and costs, recording information such as fisheries management practices, and preparing financial and operational reports;”

Scores 0.41 on the 2025 scale. The task of establishing and managing budgets, monitoring production output and costs, recording information such as fisheries management practices, and preparing financial and operational reports, involves a combination of strategic decision-making, data handling, and routine documentation. Generative AI shows potential in automating parts of this task that involve data analysis, trend recognition, and structured report generation—areas where it excels due to its capabilities in processing and generating text-based outputs. However, the need for human oversight in strategic decision-making, real-time adaptation to complex scenarios, and ensuring compliance with specific industry regulations limits the full automation potential. Compared to related tasks, such as maintaining breeding and economic documentation for farmers and handling production processes in agriculture, which have scores ranging from 0.27 to 0.295, the task at hand is slightly more complex due to its managerial components. This aligns it more closely with tasks such as participating in budget development (scores around 0.36 to 0.40). Additionally, tasks involving supervisory roles and strategic oversight in similar clusters received adjusted scores that reflect a partial automation potential, recognizing the blend of technical assistance from AI and the indispensable human element. Given these factors, a score of 0.37 is justified as it captures the significant contribution AI can make in managing routine and data-centric aspects of the task, balanced with the ongoing requirement for human expertise in strategic and managerial decisions within the context of Poland's technological infrastructure and industry standards.

Moving fastest, 2023 → 2025

“Overseeing the selection, training and performance of aquaculture or fishery workers and contractors.”

Model capability on this task changed by +0.20 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 1312, 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.

• Farmers, Ranchers, and Other Agricultural Managers

In context

Part of the 1 - Managers major group. Return to the full gradient to see how the whole group sits.

Aquaculture and Fisheries Production Managers sit at the 39th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Aquaculture and Fisheries Production Managers rank in the 39th 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 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Establishing and managing budgets, monitoring production output and costs, recording information such as fisheries management practices, and preparing financial and operational reports;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Aquaculture and Fisheries Production Managers". https://singulariki.com/gradient/1312-aquaculture-and-fisheries-production-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.