Region
AI Job Risk in Wales
Wales scores 56.3/100 for AI job risk, ranking #8 out of 12 uk regions. That puts it in the bottom third for AI exposure — the local economy has relatively fewer jobs in AI-vulnerable occupations compared to other areas.
Ranked #8 of 12 uk regions
Rank
#8
less exposed than most
Total Workforce
1 M
jobs analysed
High-Risk Jobs
59 K
6% of workforce
Out of
12
uk regions
Most at Risk
Jobs in Wales most likely to be automated or transformed by AI
Customer service occupations n.e.c.
18 K employed
Customer service managers
5.1 K employed
Graphic and multimedia designers
3 K employed
Housing officers
6.5 K employed
Data analysts
5.5 K employed
Specialist nurses
2.5 K employed
Medical secretaries
4.6 K employed
Receptionists
5.7 K employed
Sales administrators
3 K employed
Specialist medical practitioners
5.2 K employed
Safest from AI
Jobs in Wales least likely to be affected by AI
Children's nurses
2.6 K employed
Laboratory technicians
4.9 K employed
IT operations technicians
4.5 K employed
Clergy
2.6 K employed
Nursing auxiliaries and assistants
17 K employed
Caretakers
1.9 K employed
Paramedics
2.2 K employed
Delivery drivers and couriers
8 K employed
Bus and coach drivers
4.4 K employed
Fork-lift truck drivers
2.3 K employed
What this means
The exposure score (56.3/100) measures how much of Wales's workforce is in jobs that AI can automate or significantly change. It's not a prediction that jobs will disappear — it's a measure of how exposed the local economy is to AI-driven change.
How it works: We score 289 occupations from 0 (AI has little impact) to 100 (AI can do most of the job) using 10 research sources. We then match these scores to real employment data for Wales, weighting by how many people actually work in each role.
High-risk jobs (score 60+) include roles like customer service, data entry, and bookkeeping where AI can already handle most tasks. Low-risk jobs (score under 30) include trades, healthcare, and social work where physical presence or human judgement is essential.
Full methodology · Data covers 37% of occupation categories in this area