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State

AI Job Risk in Kansas

Kansas scores 53.5/100 for AI job risk, ranking #21 out of 54 us states. That puts it around the middle β€” a moderate mix of AI-vulnerable and AI-resistant jobs in the local economy.

53.5 /100
High Risk

Ranked #21 of 54 us states

53.5
Low risk High risk

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Rank

#21

around the middle

Total Workforce

1.5 M

jobs analysed

High-Risk Jobs

57 K

4% of workforce

Out of

54

us states

Most at Risk

Jobs in Kansas most likely to be automated or transformed by AI

1

Data Entry Keyer

1.1 K employed

95
2

Telemarketer

170 employed

95
3

Transcriptionist

550 employed

92
4

Bookkeeper

21 K employed

90
5

Customer Service Representative

32 K employed

90
6

Tax Preparer

560 employed

90
7

Copywriter

110 employed

88
8

Computer Programmer

510 employed

88
9

Proofreader

190 employed

88
10

Translator

480 employed

86

Safest from AI

Jobs in Kansas least likely to be affected by AI

1

Tree Trimmer

550 employed

8
2

Roofer

810 employed

10
3

Zookeeper

3.2 K employed

10
4

Landscaper

8.1 K employed

10
5

Lifeguard

1.3 K employed

10
6

Glazier

370 employed

12
7

Janitor

18 K employed

12
8

Wind Turbine Technician

380 employed

12
9

Pest Control Technician

830 employed

12
10

Tile Setter

310 employed

12

What this means

The exposure score (53.5/100) measures how much of Kansas's workforce is in jobs that AI can automate or significantly change. It's not a prediction that jobs will disappear β€” 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 Kansas, 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 33% of occupation categories in this area

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