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Comparison · dataset August 2026

Policy Analyst vs Urban Planner: which is more exposed to AI?

Policy Analyst carries 11 points more AI exposure than Urban Planner.

Policy Analyst sits at 59% time-weighted AI exposure against 48% for Urban Planner, an 11-point gap driven by the 52% of policy analyst work time that current models can already substitute outright. Urban Planner holds a larger human-critical core — 58% of the role's time sits in work like "community engagement and public meetings" that models score poorly on. The two sit in different families — Science & Research and Architecture & Engineering — so any move between them is a career change, not a lateral step.

11PP GAP

Seven dimensions, side by side.

METRICPOLICY ANALYSTURBAN PLANNERDELTA
AI exposure59%48%11pp gap
Resilience score56/10072/10016pt gap
Substitutable work time52%30%Fully automatable today
Human-critical work time20%58%Models score poorly here
Median salary$76k$82k$6k apart
10-year growth5%4%Policy Analyst
US workforce130k42kBLS OEWS
Task level

What actually creates the gap.

Policy Analysts spend 52% of their time-weighted week on tasks a current model can produce end-to-end, against 30% for Urban Planners. The single largest contributor is "summarize legislation and research", graded at 88% and worth 14% of the role's time. That one task accounts for more of the gap than any difference in seniority, tooling, or industry.

Policy Analyst
MOST EXPOSED TASKS
  • Summarize legislation and research88% · 14% time
  • Draft policy briefs and memos86% · 18% time
  • Compile comparative policy scans82% · 12% time
  • Track legislative developments80% · 8% time
HUMAN-CRITICAL CORE
  • Testify and defend recommendations12% · 2% time
  • Negotiate stakeholder positions14% · 4% time
  • Brief decision-makers in person16% · 6% time
Urban Planner
MOST EXPOSED TASKS
  • Analyse land use and demographic data84% · 16% time
  • Write planning reports and environmental assessments76% · 14% time
HUMAN-CRITICAL CORE
  • Community engagement and public meetings12% · 22% time
  • Interagency and political coordination14% · 12% time
  • Policy development and zoning decisions18% · 18% time
What transfers

Both roles lean on cognitive, judgement, procedural — that is the part of your experience that travels intact. The real divide is creative: Urban Planners score 64 there against 40 for Policy Analysts, a 24-point spread. That is the gap you would actually have to close.

DIMENSIONPOLICY ANALYSTURBAN PLANNER
Creative4064
Manual224
Social5678
Switching between them
ModerateDIFFICULTY

Urban Planner appears in our dataset as a mapped adjacent career for Policy Analysts: the move lowers exposure by 11 points, landing at 48%. Switch difficulty reads moderate — capability profiles are 17 points apart on average.

Score your own exposure in 8 questions →

Common questions.

Is Policy Analyst or Urban Planner more at risk from AI?

Policy Analyst. It scores 59% time-weighted AI exposure against 48% for Urban Planner — an 11-point gap. 52% of policy analyst work time is already fully substitutable by current models, versus 30% for Urban Planners.

Which pays more, Policy Analyst or Urban Planner?

Urban Planner, by roughly $6k at the median ($82k versus $76k). Note that the higher-paying role here is also the less AI-exposed one, which matters if you are weighing pay against durability.

Can a policy analyst switch to being a urban planner?

Urban Planner appears in our dataset as a mapped adjacent career for Policy Analysts: the move lowers exposure by 11 points, landing at 48%. Switch difficulty reads moderate — capability profiles are 17 points apart on average.

Which role is growing faster, Policy Analyst or Urban Planner?

Policy Analyst, at 5% projected ten-year growth versus 4% — a 1-point difference. Growth and AI exposure are separate signals: a role can grow in headcount while the content of the work is substantially rewritten.

Methodology
Scores are time-weighted across each role's canonical O*NET tasks, graded against current frontier-model capability. How we score.