Pensacola-Ferry Pass-Brent

๐Ÿ“ FloridaยทPop. 401,872ยท188,880 employedยทRanked #134 of 393 metros
72

High Risk

AI Risk Score

โš ๏ธ

72/100

#134 of 393 ยท +2 vs avg

Workers Vulnerable

๐ŸŽฏ

60,590

32.1% of workforce

Average Wage

๐Ÿ’ฐ

$56K

$-3K vs national

Tech Employment

๐Ÿ’ป

2.4%

National avg: 2.0%

Service Employment

๐Ÿช

36.1%

National avg: 31.3%

WARN Notices (2025)

๐Ÿ“‹

0

Layoff filings

๐Ÿ’ก Pensacola-Ferry Pass-Brent has an AI risk score of 72/100 with 32.1% of workers in vulnerable roles โ€” led by Food Service & Hospitality. Average wages of $56K are below the national metro average. See Florida overview โ†’

AI Risk Analysis

The Pensacola-Ferry Pass-Brent metropolitan area receives an AI displacement risk score of 72 out of 100, placing it at rank #134 among 393 US metros. This is 2 points above the national metro average of 70, reflecting moderately elevated risk. An estimated 60,590 workers โ€” 32.1% of the workforce โ€” hold positions in occupations highly susceptible to automation.

The primary driver of risk in Pensacola-Ferry Pass-Brent 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 Fast Food and Counter Workers, Retail Salespersons, Cashiers, and Office Clerks, General 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 (36.1% vs 31.3% nationally) amplifies vulnerability, as customer-facing and back-office roles are prime targets for AI automation.

Average wages in the metro ($56K) are close to the national average, creating neither a strong pull toward nor away from automation investment. 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

60,590

workers at risk (32.1%)

0%32.1%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 Sector2.4%

National avg: 2.0%

Service Sector36.1%

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

Comparison to National Average

Risk Score

+2

vs 70 national avg

Average Wage

$-3K

vs $60K national avg

Vulnerable Workers

+2.6%

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.