Garden and Horticultural Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Garden and Horticultural Labourers (ISCO-08 9214) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 6% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Harvesting and packaging plants for sale and transport;”

Scores 0.15 on the 2025 scale. The task of "Harvesting and packaging plants for sale and transport" involves considerable physical labor and manual dexterity, similar to tasks like "Packaging fruits and delivering them to collection points and sales points" and "Preparing and packing raw materials for sale," which have adjusted scores of 0.15 and 0.12, respectively. These tasks involve manual handling, sorting, and packaging, aspects that are beyond current Generative AI capabilities. While AI could assist with peripheral tasks such as optimizing transport logistics or scheduling, the core activities require human involvement due to the need for physical interaction with the plants. Given that this task is performed in a high-income country like Poland, technological infrastructure could support limited AI involvement in logistical planning and monitoring, but such support does not significantly enhance automation potential for the hands-on components. Consequently, an adjusted score of 0.145 reflects this limited role for AI and the predominant reliance on human labor, in line with similar tasks in the provided context.

Moving fastest, 2023 → 2025

“Harvesting and packaging plants for sale and transport;”

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 9214, 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.

• Landscaping and Groundskeeping Workers
• Farmworkers and Laborers, Crop, Nursery, and Greenhouse
• Grounds Maintenance Workers, All Other

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Garden and Horticultural Labourers sit at the 6th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Garden and Horticultural Labourers rank in the 6th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Harvesting and packaging plants for sale and transport;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Garden and Horticultural Labourers". https://singulariki.com/gradient/9214-garden-and-horticultural-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.