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Policy Analyst

Based on 30 assessments

38% Moderate risk

Average realistic automation risk across all Policy Analyst profiles in the dataset.

Raw potential
72%
Realistic risk
38%
Research benchmark ?
66%

Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context. Research benchmark: Eloundou et al. (2023)

Distribution across 30 profiles. Middle half of Policy Analysts score between 34% and 42%.

0% 50% 100%
p10 · 30%
45% · p90
On-screen work 71%

Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.

In-person + screen 12%

Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.

Computer + action 0%

Computer input, real-world output — needs someone to act on it, not just software.

Fully in-person 17%

No computer required. Furthest from automation — the strongest human advantage.

3 synthetic profiles for a Policy Analyst, ordered by automation exposure. Tab between them to see how task mix drives the score difference.

Task Time Type Exposure
Write policy briefs, reports, and formal recommendations for senior officials or elected representatives
deep expertise
40% DD 14%
Monitor policy implementation outcomes and prepare impact assessments or evaluation reports
16% AD 26%
Conduct data analysis using spreadsheets, databases, and statistical tools to support policy arguments with evidence and trends
13% DD 47%
Research and review existing legislation, regulations, and policy documents to understand current frameworks and identify gaps
10% DD 66%
Draft legislative language, regulatory text, or implementation guidelines for new or modified policies
deep expertise social element
10% DD 35%
Coordinate with other government agencies or external partners to align on policy direction and resolve conflicts
deep expertise
5% AA 6%
Attend stakeholder meetings, interviews, and consultations to gather input from affected groups and build consensus
3% AA 5%

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