Metro Area
AI Job Risk in Orlando-Kissimmee-Sanford, FL
Orlando-Kissimmee-Sanford, FL scores 52.9/100 for AI job risk, ranking #111 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 #111 of 396 us metro areas
Rank
#111
more exposed than most
Total Workforce
1.5 M
jobs analysed
High-Risk Jobs
54 K
4% of workforce
Out of
396
us metro areas
Most at Risk
Jobs in Orlando-Kissimmee-Sanford, FL most likely to be automated or transformed by AI
Data Entry Keyer
1.3 K employed
Telemarketer
2.7 K employed
Transcriptionist
490 employed
Bookkeeper
13 K employed
Customer Service Representative
34 K employed
Tax Preparer
550 employed
Copywriter
330 employed
Computer Programmer
680 employed
Translator
350 employed
Software Engineer
360 employed
Safest from AI
Jobs in Orlando-Kissimmee-Sanford, FL least likely to be affected by AI
Tree Trimmer
340 employed
Zookeeper
2.2 K employed
Grounds Maintenance Worker
Lifeguard
2.9 K employed
Roofer
3.6 K employed
Landscaper
11 K employed
Tile Setter
740 employed
Janitor
21 K employed
Massage Therapist
1.4 K employed
Pest Control Technician
1.5 K employed
What this means
The exposure score (52.9/100) measures how much of Orlando-Kissimmee-Sanford, FL'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 Orlando-Kissimmee-Sanford, FL, 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