Perceiving the present
Toronto is growing quickly. Its metro area population surged by 500,000 residents and with 40 percent of the greater Toronto population (7 million) speaking⁵ languages other than English or French, it is one of the world's most multicultural cities. Rapid growth, along with swiftly changing cultures and perspectives, is adding more complexity to the issues facing Toronto and many other large cities today. This is where AI could become so important - providing cutting- edge solutions to help us identify and understand the emerging needs of our rapidly growing and diversifying urban populations. The City of Toronto, for example, used AI to help process 25,000 comments from residents in just days, helping city leaders accelerate and deepen their understanding of the community they serve. In the UK, a local council recently asked residents for feedback⁶ on plans to build over 20,000 new homes. To adequately process all of the submitted comments, they would have needed to read the equivalent of Leo Tolstoy’s War and Peace — twice. So the council turned to AI to read and process the feedback, saving them weeks of work. Earlier this year in Bowling Green, Kentucky, officials wanted more community engagement⁷ than they
typically see at conventional forums, such as town halls. So they leveraged AI to spark more conversations online, resulting in an almost 80-fold increase in community input. In Singapore⁸, the government is utilising an AI-powered chatbot on WhatsApp and Telegram to assist residents in reporting complaints about litter and damaged facilities within their communities. Handling approximately 30,000 files a month, it automatically extracts key details and then sends them to the relevant government department, saving up to two working days per case. This is just the beginning, as the scope and scale of AI solutions continue to expand. For example, some researchers came up with an idea to analyse social media for emotional posts⁹ in different neighbourhoods, effectively mapping the mood of a city and highlighting areas that may need more attention from city planners. Others are leveraging AI to track the loss of greenery 10 . AI is enabling planners and decision-makers to unlock new insights about their communities and become more responsive to the needs and preferences of residents. With better understanding of these needs, city planners can be more confident in applying solutions that are sustainable, cost-effective and well-received by the residents now and in the years to come.
Unlocking the past One way of achieving that is by uncovering lessons from the past that can be applied now. City computer servers are full of stagnant, unharnessed historical data that AI tools could tap to generate insights about the present and the future. Toronto 11 has the minutes of council meetings stretching back to the 1800s, while New York has a photo of every city building 12 from the 1930s. Much of this data remains un-digitised. AI tools can be used to extract information from this ocean of unstructured data to reveal trends and support (or dispute) research being conducted today. Whether you want to find out how many people were cycling to work 10 years ago or predict how many may be cycling to work 10 years from now, it’s all becoming possible. Predicting the future Cities around the world are littered with examples of projects that launched with high hopes but later sank due to bad planning and misjudged community support. Using everything from old maps to new surveys, as well as social media posts, traffic data and more, AI can harness and analyse information at scale. Understanding what makes a city tick today enables leaders to know how to build a better city tomorrow. Consider your weekly garbage pickup — it hasn’t changed much over the years. However, with each truck making about 1,000 stops a day on average, even a small improvement in planning 13 could add up to significant savings. By using AI to identify who plans to opt out of service or go on holiday, approximately 20 percent of a truck’s route could be adjusted, saving money for the city and speeding up service for the customers who remain. From planning 14 walkable neighbourhoods to predicting crime 15 on the New York subway, AI is increasingly being used to look over the horizon to forecast how both problems and solutions may evolve over time.
Data security and privacy Using AI to process large amounts of data naturally raises questions about privacy. What information is available and what is not? How do we use sensitive data while ensuring it is secure? Imagine you receive a postcard 16 in the mail that details the contents of your garbage and tells you how to sort it differently. Would you find this helpful or intrusive? Now, consider areas like healthcare or finance. Privacy becomes much more critical, potentially affecting your insurance coverage or job prospects. Fortunately, concerns about data security and privacy are being taken seriously. Whether it’s government oversight, such as the European Union’s EU AI Act 17 , or measures from AI companies themselves (see OpenAI’s Privacy Policy) 18 , institutions are recognising that progress in AI must go hand-in-hand with the strict protection of personal data and privacy. Such guardrails need to be continually built as AI and its uses progress. Your city is talking – are you listening? Cities have always been talking to leaders — the problem is that it’s been tough to hear what they were saying above the noise and the sheer volume of signals. The cities whose leaders can harness AI while retaining citizen trust will be better equipped to understand their communities, predict problems and capitalise on opportunities that may have been previously hidden or unclear. The tools are already here or being developed. The question is, which cities will use them to take the lead?
Seven ways AI is already changing cities
Toronto 25,000 comments from residents processed in days
UK council 20,000 homes. AI read “War and Peace” equivalent twice
Singapore 30,000+ complaints handled per month via AI chatbot, 2 days saved per case
Mood mapping AI scans social posts to chart emotional geography
Urban canopy loss tracking AI monitors greenery reduction
Predict user behavior for infrastructure/systems planning. 20% route adjustment in delivery routes
Information repository Structure data, reveal trends, support research
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