Occupation details
International Labour Organization (ILO) Working Paper 140

Drivers of Animal-drawn Vehicles and Machinery in the age of AI: impact evidence and adaptation options

ISCO-08 9332 · Thai source label: คนงานขับเคลื่อนยานพาหนะและเครื่องจักรต่าง ๆ ที่ลากจูงโดยสัตว์

This score estimates where generative AI may assist with or perform parts of tasks. It does not predict that a job will disappear.
1.3AI / 10

Use the score to choose where to start adapting

Not Exposed

Compare the score with the work you actually do. Separate repetitive work, work that needs review, and work that depends on human judgement.

Source ID: SRC-2026-07-14-ilo-wp140

What the ILO evidence measures

Potential for AI assistance or task performanceAI 1.3/10
Variation across task-level scores0.08 on a 1-point scale
Occupation codeISCO-08 9332
AI exposure groupNot Exposed
Score sourceILO Working Paper 140
AI 1.3/10Task-level potential
0.08 points1-point scale
Not ExposedILO classification
9 dutiesNSO classification evidence
8 task examplesNo O*NET skill rows; see ESCO context below

Tasks in this occupation

O*NET statements are enrichment from US roles connected through the official BLS and O*NET crosswalk chain. Confirm that an example fits your local work before using it.

O*NET Core Task

Apply de-icing fluid to aircraft from baskets lifted by truck-mounted cranes.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Change aircraft oil, coolant, or other fluids.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Clean aircraft interiors by picking up waste, wiping down windows, or vacuuming.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Climb ladders to reach aircraft surfaces to be cleaned.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Complete forms describing tasks completed.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

De-grease aircraft exteriors.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Empty aircraft lavatory systems or refill them with sanitizer fluid.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

O*NET Core Task

Guide aircraft to designated areas using hand signals, batons, or other methods.

Use this source statement to identify repetitive work, review points, and work that still depends on human judgement.

Skills used in this occupation

O*NET importance ratings provide a sourced starting point for deciding which existing capabilities to strengthen.

O*NET data status

No O*NET skill rows exist for the mapped codes in the captured release

This does not mean the occupation has no skills. Roongan uses the ESCO skill-group context below and labels its mapping scope instead of inventing occupation-specific ratings.

ESCO skill-group context

These shares describe how ESCO skill records for the occupation group are distributed across broad categories. They are not personal capability scores or mandatory skills for every job.

ESCO Skill Group

assisting and caring

34.6% of the published ESCO matrix row

ESCO Skill Group

handling and moving

34.6% of the published ESCO matrix row

ESCO Skill Group

communication, collaboration and creativity

11.5% of the published ESCO matrix row

ESCO Skill Group

management skills

11.5% of the published ESCO matrix row

Build a plan from the task you choose

The Planner does not display invented capability percentages. You select a task, baseline the current method, run a safe bounded experiment, and keep evidence before deciding whether to continue.

30 days

Select one task, map its process, and measure time, quality, and rework across at least three examples.

60 days

Test one bounded step with synthetic or de-identified data, then log errors, human checks, and limitations.

90 days

Use the reviewed method in a real cycle with the process owner's approval, or stop if the result is not good enough.

Relevant courses

Recommendations match reviewed course categories to ISCO-08 and mapped ESCO skill-group records. Occupation-title keywords are not used as sole evidence. Before enrolling, recheck the syllabus, language, schedule, cost, and current access with the provider.

Find more courses
No verified course currently matches this occupation's specific skill evidence as of 12 August 2026. The foundation below applies across occupations and is not occupation-specific.
Cross-occupation foundation

AI Fluency

ความคล่องตัวของ AI

Why it is recommended
A cross-occupation foundation, not a course specific to Drivers of Animal-drawn Vehicles and Machinery: use it to practise AI literacy, prompting, output checks, and responsible use.
Practice output
Choose one Drivers of Animal-drawn Vehicles and Machinery duty, use synthetic data to create a prompt, verify three outputs against a source, and record the errors.
Study time
7 modules
Cost and access
Microsoft Learn content is free and can be consumed without signing in; a free profile is needed to save progress or achievements.
View details with the provider
Data and method

This page uses scores from International Labour Organization (ILO) Working Paper 140, official Thai duties from NSO, mapped O*NET enrichment, and ESCO skill-group context with an explicit mapping scope. Roongan does not fill evidence gaps with generated claims.

This service uses the ESCO classification of the European Commission.

Roongan · Source ID: SRC-2026-07-14-ilo-wp140 · This page uses traceable occupation evidence.