CivicAI Readiness Blueprint

CivicAI Readiness Blueprint is a decision-support dashboard that helps a community understand how ready it is for AI, and what a responsible first step looks like. Aqeela Urooj and Talal Khawaja built it for the USAII Global AI Hackathon 2026 on Devpost, and it was named a finalist.

Problem

Community organisations are asked to "adopt AI" without a common way to judge readiness. A city may have strong infrastructure but little data governance. A nonprofit coalition may have motivated staff but no budget for training. Without a structured view, the loudest use case tends to win, and the pilot fails on a gap nobody measured. We wanted something a council or coalition could fill in during one meeting, which would give them a defensible starting point rather than a verdict.

How it works

The user describes the community: its name, region and type, whether a city, a nonprofit coalition, a workforce agency, an education or healthcare network, or a local government. They then rate ten areas: internet access, AI literacy, digital infrastructure, data governance, workforce readiness, education, healthcare, government and nonprofit readiness, and budget and staff capacity.

Deterministic code in the browser turns those inputs into an overall score and a readiness band (Emerging, Developing, Ready or Advanced), with a confidence level and the reason for it. It lists the weakest gaps, marked as critical, needing attention or to monitor, each tied to a priority action such as launching role-based AI literacy sessions or setting up a lightweight data-governance process. Next it compares three scenarios: invest in AI literacy now, invest in infrastructure first, or delay adoption for three years. Each is compared on impact range, risk, difficulty, cost and effort. Finally it lays out a three-phase roadmap. Phase 1 covers awareness, policy basics and data review. Phase 2 covers training, narrow pilots and a governance process. Phase 3 covers scaling only what shows measurable benefit, equity, safety and reliability. Recharts draws the score views.

An optional AI policy insight sends the scores, gaps, scenarios and roadmap summary to a server-side route that calls the OpenAI Responses API with a strict JSON schema. The reply contains an executive insight, top risks, a recommended scenario with reasoning, a human-review plan, data limitations and next actions.

Key decisions and trade-offs

• Scoring doesn't depend on the model. The numbers, gaps, scenarios and roadmap come from transparent local logic. If there's no API key or the call fails, the app says the AI insight is unavailable and everything else keeps working.
• Advisory, not authoritative. The AI output is labelled as advisory policy insight, and the design keeps human review, transparency, privacy, accessibility and appeal in the first phase of the roadmap.
• Ranges over false precision. Scenarios show impact as ranges with a confidence rating, not single-point predictions.

The public repository's README is still the framework default, so this write-up comes from the source code and the hackathon record rather than from project documentation.

Cities, nonprofit coalitions, workforce agencies and health or education networks are under pressure to adopt AI, but few have a clear view of their readiness across access, literacy, infrastructure, data governance and staff capacity, or of which first step is safe.

A community profile is scored across ten readiness areas by transparent, deterministic code. The dashboard shows an overall band with confidence, the weakest gaps, priority actions, a comparison of three adoption scenarios and a phased 0–24-month roadmap. An optional server-side OpenAI call adds an advisory policy insight in a strict JSON schema. When it isn't available, the local scoring still works.

• Finalist — USAII Global AI Hackathon 2026