Miami-Fort Lauderdale-West Palm Beach

๐Ÿ“ FloridaยทPop. 5,919,872ยท2,782,340 employedยทRanked #122 of 393 metros
72

High Risk

AI Risk Score

โš ๏ธ

72/100

#122 of 393 ยท +2 vs avg

Workers Vulnerable

๐ŸŽฏ

886,070

31.8% of workforce

Average Wage

๐Ÿ’ฐ

$66K

+$6K vs national

Tech Employment

๐Ÿ’ป

2.4%

National avg: 2.0%

Service Employment

๐Ÿช

36.4%

National avg: 31.3%

WARN Notices (2025)

๐Ÿ“‹

0

Layoff filings

๐Ÿ’ก Miami-Fort Lauderdale-West Palm Beach has an AI risk score of 72/100 with 31.8% of workers in vulnerable roles โ€” led by Retail. Average wages of $66K are above the national metro average. See Florida overview โ†’

AI Risk Analysis

The Miami-Fort Lauderdale-West Palm Beach metropolitan area receives an AI displacement risk score of 72 out of 100, placing it at rank #122 among 393 US metros. This is 2 points above the national metro average of 70, reflecting moderately elevated risk. An estimated 886,070 workers โ€” 31.8% of the workforce โ€” hold positions in occupations highly susceptible to automation.

The primary driver of risk in Miami-Fort Lauderdale-West Palm Beach is the concentration of employment in Retail, 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 Retail Salespersons, Cashiers, Office Clerks, General, and Customer Service Representatives 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 (36.4% vs 31.3% nationally) amplifies vulnerability, as customer-facing and back-office roles are prime targets for AI automation.

Higher-than-average wages ($66K vs $60K nationally) may provide workers more resources to invest in reskilling, but also create stronger economic incentives for employers to automate. 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

886,070

workers at risk (31.8%)

0%31.8%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: Retail

Tech Sector2.4%

National avg: 2.0%

Service Sector36.4%

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

โš ๏ธ Retail Trade โ€” Highest Risk Industry

National risk score: 56/100 ยท 26,703,360 employed nationally ยท Projected -2.1% job decline ยท Advanced AI adoption stage

Comparison to National Average

Risk Score

+2

vs 70 national avg

Average Wage

+$6K

vs $60K national avg

Vulnerable Workers

+2.3%

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.