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We often talk about smart cities as data hubs. But AI is turning that data into action. In the latest wave, cities are using AI not just for sensors or dashboards, but for predictive, real-time urban management.
Take Buenos Aires: its WhatsApp bot Boti began as a COVID info tool. Now it handles parking infractions, alerts, event reporting, and even image analysis. That’s civic interaction served via chat.
In New York, a startup is using crowdsourced dashcam footage to inspect crosswalk paint integrity. The AI model flags areas needing re-striping and tracks wear over time.
Cities like Singapore and Shanghai are advancing digital twins, 3D, dynamic models of their urban systems. They simulate impacts of new infrastructure, test flood scenarios, or visualize mobility changes, all before construction.
These AI functions aren’t just for show. Decision-makers use them to prioritize street repairs, optimize trees and public space investments, monitor air quality patterns, and manage crowd flows. AI brings the possibility of pre-emptive action.
But it’s not all upside. Challenges loom: data privacy, algorithmic bias, infrastructure gaps (cities that don’t yet have real-time sensors), and public trust. Will residents accept that their phone reports get used for policing or traffic control? How transparent are the models? How do you audit misfires?
Still, for cities trying to stretch tight budgets, AI offers leverage. Automate the mundane so planners can focus on vision. Use small pilots to test. Blend human oversight. Be clear in how data is collected and used. And always leave room for correction.
For you, think of AI as a co-pilot, not autopilot. Let it crunch the routine. You guide toward equity, justice, and resilience.