As of August 2026, Urban and Regional Planners has an AI-exposure score of 56/100 (Elevated exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Urban and Regional Planners

56/100
Elevated exposure
LowModerateElevatedHighVery High

More exposed than 50% of the roles we track. Median pay ~US$89,320. About 3,400 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Urban and Regional Planners?

No exposure score can predict whether AI will replace this role. The 56/100 score means our current model estimates elevated task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role is in the Elevated exposure band, but its assessed task mix is not automation-heavy. The study found the decline concentrated where AI was more likely to automate rather than augment work, so the headline figure should not be applied directly.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Develop comprehensive plans and programs for use of land and physical facilities of jurisdictions, such as towns, cities, counties, and metropolitan areas.

Common titles
Community Development PlannerPlannerPlanning ConsultantPlanning TechnicianCity PlannerCommunity Planner
O*NET job-zone preparation
Job Zone 5 · Extensive Preparation Needed Most of these occupations require graduate school. For example, they may require a master's degree, and some require a Ph.D., M.D., or J.D. (law degree). Extensive skill, knowledge, and experience are needed for these occupations. Many require more than five years of experience.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
Judgment and Decision MakingSpeakingCritical ThinkingReading ComprehensionSystems AnalysisWriting
Work context
Indoor controlled settingFrequent contact with othersDecision latitude

Source: O*NET 29.1 exact occupation - Urban and Regional Planners, SOC 19-3051.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Urban and Regional Planners tasks, by AI exposure

Automatable

No automatable tasks identified for this role - all 20 of its individually-assessed tasks read as durable.

Augmentable

No augmentable tasks identified for this role - all 20 of its individually-assessed tasks read as durable.

Durable
  • Mediate community disputes or assist in developing alternative plans or recommendations for programs or projects.
  • Investigate property availability for purposes of development.
18 more durable tasks locked in the report.

We analyzed all 20 Urban and Regional Planners tasks - 20 durable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

No evidence-backed lower-exposure match appears in this O*NET adjacency set. The adjacent paths below remain useful comparisons; the strongest resilience moves are task-level.

Transportation Planners
80% skills overlap; 4 points higher - similar exposure within the 5-point model resolution; Elevated band; ~US$101,110
View path
60
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.

Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Urban and Regional Planners - median pay by US state (BLS OEWS, USD)

California: US$109,610New York: US$89,630Texas: US$82,830Florida: US$80,720

Median annual wage, in USD. US national: US$89,320. More states are being added.

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