Crane, hoist and related plant operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Crane, hoist and related plant operators (ISCO-08 8343) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 25% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Operating and monitoring ski-lifts and similar equipment;”

Scores 0.23 on the 2025 scale. The task of operating and monitoring ski-lifts involves real-time decision-making, situational awareness, and an understanding of safety and technical protocols, akin to tasks such as operating cable railway traffic and monitoring the operation of complex machinery. These tasks involve controlling equipment where human oversight, physical presence, and the ability to respond to dynamic and safety-critical situations are essential. While Generative AI could potentially assist by monitoring sensor data or predicting maintenance needs, the core function of actively operating and overseeing the safety of ski-lifts is heavily reliant on human judgment and interaction. This responsibility parallels tasks like monitoring individual machine mechanisms, which have been assigned adjusted scores around 0.25, reflecting limited potential for full automation. Given the sophisticated technology and high internet access in Poland, there is potential for AI to augment but not replace human operators, justifying an adjusted automation score of 0.25.

Moving fastest, 2023 → 2025

“Operating and monitoring stationary or mobile cranes by raising and lowering jibs and booms, to lift, move, position or place equipment and materials;”

Model capability on this task changed by +0.11 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 8343, 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.

• Amusement and Recreation Attendants
• Crane and Tower Operators
• Bridge and Lock Tenders
• Hoist and Winch Operators

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Crane, hoist and related plant operators sit at the 25th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Crane, hoist and related plant operators rank in the 25th 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 rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Operating and monitoring ski-lifts and similar equipment;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Crane, hoist and related plant operators". https://singulariki.com/gradient/8343-crane-hoist-and-related-plant-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.