1.5AI / 10
Use the score to choose where to start adapting
Not ExposedCompare 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-wp140International Labour Organization (ILO) Working Paper 140
Fur and Leather Preparing Machine Operators in the age of AI: impact evidence and adaptation options
ISCO-08 8155 · Thai source label: ผู้ควบคุมเครื่องจักรจัดเตรียมขนสัตว์และหนังฟอก
This score estimates where generative AI may assist with or perform parts of tasks. It does not predict that a job will disappear.
What the ILO evidence measures
Occupation codeISCO-08 8155
AI exposure groupNot Exposed
Score sourceILO Working Paper 140
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.
No O*NET task rows exist for the mapped codes in the captured release
Roongan still has official Thai ISCO-08 duty evidence for this occupation. The Planner asks you to confirm a real task instead of inventing an English task statement.
Skills used in this occupation
O*NET importance ratings provide a sourced starting point for deciding which existing capabilities to strengthen.
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.
communication, collaboration and creativity
49.3% of the published ESCO matrix row
assisting and caring
14.1% of the published ESCO matrix row
handling and moving
11.3% of the published ESCO matrix row
information skills
9.9% 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.
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.
AI Fluency
ความคล่องตัวของ AI
- Why it is recommended
- A cross-occupation foundation, not a course specific to Fur and Leather Preparing Machine Operators: use it to practise AI literacy, prompting, output checks, and responsible use.
- Practice output
- Choose one Fur and Leather Preparing Machine Operators 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.
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.