Metro Area
AI Job Risk in Milwaukee-Waukesha-West Allis, WI
Milwaukee-Waukesha-West Allis, WI scores 53.8/100 for AI job risk, ranking #70 out of 396 us metro areas. That puts it in the top third for AI exposure — a relatively large share of local jobs are in occupations that AI can automate or significantly change.
Ranked #70 of 396 us metro areas
Rank
#70
more exposed than most
Total Workforce
870 K
jobs analysed
High-Risk Jobs
29 K
3% of workforce
Out of
396
us metro areas
Most at Risk
Jobs in Milwaukee-Waukesha-West Allis, WI most likely to be automated or transformed by AI
Data Entry Keyer
720 employed
Telemarketer
150 employed
Transcriptionist
240 employed
Customer Service Representative
17 K employed
Tax Preparer
250 employed
Bookkeeper
8.7 K employed
Computer Programmer
680 employed
Copywriter
210 employed
Translator
470 employed
Payroll Clerk
810 employed
Safest from AI
Jobs in Milwaukee-Waukesha-West Allis, WI least likely to be affected by AI
Tree Trimmer
340 employed
Roofer
890 employed
Landscaper
5 K employed
Grounds Maintenance Worker
Lifeguard
790 employed
Zookeeper
1.5 K employed
Massage Therapist
560 employed
Tile Setter
110 employed
Janitor
12 K employed
Farm Worker
160 employed
What this means
The exposure score (53.8/100) measures how much of Milwaukee-Waukesha-West Allis, WI'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 Milwaukee-Waukesha-West Allis, WI, 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 35% of occupation categories in this area