Jacksonville

๐Ÿ“ North CarolinaยทPop. 112,702ยท52,970 employedยทRanked #41 of 393 metros
76

High Risk

AI Risk Score

โš ๏ธ

76/100

#41 of 393 ยท +6 vs avg

Workers Vulnerable

๐ŸŽฏ

18,360

34.7% of workforce

Average Wage

๐Ÿ’ฐ

$49K

$-11K vs national

Tech Employment

๐Ÿ’ป

1.2%

National avg: 2.0%

Service Employment

๐Ÿช

39.8%

National avg: 31.3%

WARN Notices (2025)

๐Ÿ“‹

0

Layoff filings

๐Ÿ’ก Jacksonville has an AI risk score of 76/100 with 34.7% of workers in vulnerable roles โ€” led by Food Service & Hospitality. Average wages of $49K are below the national metro average. See North Carolina overview โ†’

AI Risk Analysis

The Jacksonville metropolitan area receives an AI displacement risk score of 76 out of 100, placing it at rank #41 among 393 US metros. This is 6 points above the national metro average of 70, reflecting moderately elevated risk. An estimated 18,360 workers โ€” 34.7% of the workforce โ€” hold positions in occupations highly susceptible to automation. This vulnerability rate exceeds the national average of 29.5% by 5.2 percentage points.

The primary driver of risk in Jacksonville is the concentration of employment in Food Service & Hospitality, an industry where routine tasks, data processing, and customer interactions are increasingly being handled by AI systems. Among the most at-risk occupations in the area are Cooks, Fast Food, Cashiers, Retail Salespersons, and Fast Food and Counter Workers โ€” roles where advances in natural language processing, computer vision, and robotic process automation are already reducing demand. The metro's heavy service sector concentration (39.8% vs 31.3% nationally) amplifies vulnerability, as customer-facing and back-office roles are prime targets for AI automation.

Below-average wages ($49K vs $60K nationally) may slow automation adoption due to lower ROI on AI investment, but also leave workers with fewer resources for career transitions. Workers in this metro should consider developing complementary AI skills, exploring transition paths to lower-risk occupations, and leveraging local workforce development resources.

Automation Vulnerability

18,360

workers at risk (34.7%)

0%34.7%33%+

Top At-Risk Occupations

* Estimated local employment based on metro's share of national workforce. Actual distribution may vary.

Industry Breakdown

Top at-risk industry: Food Service & Hospitality

Tech Sector1.2%

National avg: 2.0%

Service Sector39.8%

National avg: 31.3% โš ๏ธ High concentration โ€” elevated AI risk

Comparison to National Average

Risk Score

+6

vs 70 national avg

Average Wage

$-11K

vs $60K national avg

Vulnerable Workers

+5.2%

vs 29.5% national avg

National Economic Context

Latest national labor market indicators from FRED (Federal Reserve Economic Data)

Unemployment Rate

4.4%

2026-02

Labor Participation

62%

2026-02

Weekly UI Claims

214,000,000

2026-02

Job Openings

6.9M

2026-01

๐Ÿ“Š Methodology

Metro area AI risk scores are calculated using a composite model that weighs multiple factors: occupational automation probability (based on Frey & Osborne methodology and updated GenAI exposure scores), industry concentration risk, local employment mix, wage levels, and historical WARN Act layoff notices.

Scores range from 0 (lowest risk) to 100 (highest risk) and represent relative vulnerability compared to other US metro areas. Individual occupation risk scores within the metro are estimated by applying the metro's employment share to national occupation-level data. Data sources include BLS Occupational Employment and Wage Statistics, Census Bureau population estimates, and state WARN Act filings.