AI Readiness Audit — Smart Cities

Score how ready Smart Cities is to deploy practical AI — and see the highest-ROI use cases for your sector.

Where AI pays off in Smart Cities

AI for adaptive traffic signal control and congestion routing, with measurable commute-time and emissions reductions per corridor.
Computer vision for infrastructure inspection (potholes, bridge condition, streetlight outages) using municipal vehicles as mobile sensors.
Predictive analytics on water and energy networks to flag leaks, outages, and grid imbalances before they become outages.
AI-assisted constituent services — chat and voice assistants for permits, 311 requests, and benefits navigation in multiple languages.

Section 1 of 6 · Strategy & Use Cases0/18 answered

Strategy & Use Cases

Whether AI is pointed at a real, measurable business problem.

1.How clearly have you identified where AI should help?

2.Are the target outcomes measurable?

3.Is there executive sponsorship and budget?

Score process clarity, data readiness, team adoption, and guardrails before investing in AI.

Sample output

67/100 → pilot with control, not full rollout

Assess KPI clarity, reporting, tool alignment, ownership, and automation maturity.

Sample output

52/100 → foundation gaps in metrics and handoffs

Quantify the annual cost and people-weeks lost to repetitive manual work — and the automation payback.

Compare the multi-year total cost of SaaS subscriptions against a custom build — with a clear build, buy, or hybrid recommendation.

Score how mature your operations are across metrics, handoffs, tooling, ownership, and automation — and pinpoint the weakest link to fix first.

Score how much spreadsheet and SaaS sprawl is slowing your team — and where a purpose-built internal tool would pay off.