Lifting Truck Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Lifting Truck Operators (ISCO-08 8344) score an average of 0.20 on a 0–1 exposure scale — more exposed than about 33% 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

“Keeping records of work undertaken and breakdowns of vehicles.”

Scores 0.38 on the 2025 scale. The task of "Keeping records of work undertaken and breakdowns of vehicles" involves structured data entry, documentation, and potentially preliminary analysis tasks, areas where Generative AI can significantly contribute by automating repetitive data entry, standardizing reports, and flagging anomalies or patterns in data. This task aligns with several tasks provided in the context, notably "Maintaining required records of process progress" (Adjusted Score: 0.45) where AI-driven automation can streamline record-keeping and documentation processes. Additionally, it shares similarities with tasks such as "Maintaining necessary documentation" (Adjusted Score: 0.45) and "Analyzing and verifying entered information and data" (Adjusted Score: 0.72), reflecting the ability of AI to assist in data management yet acknowledging the need for human oversight for accuracy and decision-making in nuanced contexts. Given the task's structured nature and performed in a high-tech environment like Poland, an adjusted score of 0.405 balances the potential benefits of AI automation with the need for human intervention to ensure context-specific accuracy and integrity, especially in handling exceptional or context-dependent vehicle issues.

Moving fastest, 2023 → 2025

“Inspecting equipment to identify wear and damage;”

Model capability on this task changed by +0.05 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 8344, 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.

• Industrial Truck and Tractor 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.

Lifting Truck Operators sit at the 33rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Lifting Truck Operators rank in the 33rd 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 0.12 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Keeping records of work undertaken and breakdowns of vehicles.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Lifting Truck Operators". https://singulariki.com/gradient/8344-lifting-truck-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.