Mapping the Rise of AI Across State Government

A public benchmark by Isaac Seiler measuring generative AI adoption, training, governance, pilots, transparency, and preparedness across U.S. states and territories.

Project Snapshot

Project type: RESEARCH
Year: 2025
Primary topics: generative AI, state government, AI governance, public policy
Search focus: AI state government index, public-sector generative AI adoption, state AI governance, training, pilots, sandboxes, and transparency.

Mapping the Rise of AI Across State Government work preview

Key Proof Points

Built a 15-criteria GenAI Preparedness Score from publicly verifiable evidence.
Benchmarked all U.S. states and territories on AI governance and adoption signals.
Published state-by-state analysis with recommendations for lawmakers.

Overview

This work, published with the Council of State Governments, translates the GenAI hype cycle into a measurable picture of how state and territory governments are actually responding.

I built a public-information benchmark that scores all states and territories on concrete adoption signals including employee guidance, training, sandboxes, pilots, governance structures, and transparency, then paired the index with a policy roadmap for what leaders should do next.

The headline result was stark: most states scored below 50/100, and only a small handful cleared 80, suggesting the U.S. state landscape is early, uneven, and often opaque.

What I Built

GenAI Preparedness Score: a 15-criteria scoring framework grounded in publicly verifiable evidence.
Composite scoring: a weighted model combining preparedness with an efficiency adjustment.
State-by-state analysis and rankings with concise synopses explaining each placement.
A practitioner roadmap tied directly to observed policy, training, pilot, and transparency gaps.

Final Products

Related Isaac Seiler Sections

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