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AI can reshape urban governance by improving planning, service delivery, and citizen engagement, but only when its use is anchored in ethical safeguards, strong institutions, and inclusive, data-driven decision-making
Innovation has always been a double-edged force, and its power lies in its own paradox. This year’s Sveriges Riksbank Prize in Economic Sciences in memory of Alfred Nobel has, once again, drawn our attention to the question of innovation and long-run growth. The laureates argued that innovation, much like Joseph Schumpeter’s concept of creative destruction, drives progress, while also disrupting existing systems. In the context of Artificial Intelligence (AI), we are witnessing intense sectoral transformation and a changing competitive landscape. Two key tensions emerge here: on the one hand, AI can automate many routine tasks, raising concerns about job security and livelihoods. On the other hand, it can augment human capability, helping us to design faster, work smarter, and build new opportunities.
For city governance, integrating AI into urban design and planning opens up a world of possibilities. Existing technologies and smart city activities generate a significant amount of data and information about complex urban processes, which AI can help analyse, enabling data-driven decision-making and preparing forecasting models. However, such technological change alone cannot lead to economic growth unless it is embedded in supporting institutions. Alongside the benefits of automation and efficiency, regulatory challenges such as service delivery biases, privacy, legal, and ethical issues must also be considered.
For city governance, integrating AI into urban design and planning opens up a world of possibilities. Existing technologies and smart city activities generate a significant amount of data and information about complex urban processes, which AI can help analyse, enabling data-driven decision-making and preparing forecasting models.
The question before us, therefore, is how the cities can ensure that AI not just augments efficiency but also fosters democratic governance, promotes transparency, and earns public trust. Citizen engagement and trust building begin with transparency about the nature of data cities collect and for what purpose. It helps create a tangible connection between citizens and data, making data a civic asset rather than a surveillance instrument.
Considering these, some of the domains for early AI deployment in cities are:
Many urban water utilities across the world have been using Machine Learning (ML) for demand predictions and leakage identification. But with smart sensor technology, drone and satellite imagery, and SCADA time series data, AI can now foster data-driven scenario mapping, impact assessment, planning, and decision-making. Urban water systems are becoming increasingly more complex as cities reach farther distances to source water, which is then treated and supplied throughout the city through a complex web of pipes. Mumbai, for example, sources its water from as far as 100 to 175 km. In this entire process, leakages and non-revenue water (NRW) are a huge concern, where sensor-enabled AI-based monitoring systems could be helpful. As per an Asian Development Bank report, Asian cities, on average, have 35 percent NRW and can reach much higher levels. An AI-based water leak detection system can collect and manage leakage sounds while generating a model through a mobile application, which can pinpoint leaking pipes to the operators. Amsterdam, for example, has implemented a smart water metering project that uses AI anomaly detection algorithms to identify unusual consumption patterns, leaks and other issues. Similarly, AI can also be used for assessing and monitoring water quality in cities. Integrating AI with spectroscopic sensor systems can help detect contamination and pollution, creating efficient water quality monitoring systems that are crucial for ensuring safe access to water and maintaining public health in cities.
But with smart sensor technology, drone and satellite imagery, and SCADA time series data, AI can now foster data-driven scenario mapping, impact assessment, planning, and decision-making.
AI can help with Adaptive Traffic Control Systems (ATCS) to optimise traffic flow and reduce congestion. It uses real-time data from sensors, cameras, and other sources to track traffic volume, speed, and occupancy, adjusting traffic signal timings to suit changing traffic conditions. Bengaluru has already implemented ATCS at forty-one junctions, reducing the need for manual traffic management. AI can also run Automatic Number Plate Recognition (ANPR) and red-light violation detection, along with other violation detections, including helmetless riding, three riders on a two-wheeler, and not wearing a seat belt. The Delhi Transport Infrastructure Development Corporation is in the process of deploying this system featuring AI-powered cameras capable of detecting nineteen different street violations. This model can be scaled up in other cities as well.
AI can play an important role in transforming energy systems through the use of smart grids and demand forecasting. Smart grids can leverage AI to balance supply and demand in real-time and detect faults or outages, leading to optimised distribution and consumption, reduced wastage, and improved overall system efficiency. For example, the Grid4EU is an innovative AI-based smart grid project that optimises grid operations. It also integrates renewable energy sources across Europe.
AI can revolutionise municipal waste management through effective waste collection, processing, and classification. AI-powered intelligent garbage bins, classification robots, predictive models, and wireless detection systems can help monitor waste bins, predict waste collection needs, and enhance the performance of waste processing facilities. AI algorithms, combined with real-time traffic data maps, can indicate the shortest routes for waste collection vehicles, optimising fuel consumption, reducing emissions, and saving time. The Bhopal Municipal Corporation, for example, has implemented an AI-based waste management system using GPS and sensors on waste collection trucks to collect data on route efficiency, waste load, and fuel consumption. This system has already increased collection efficiency in the city by 30 percent. In Pune, an AI-based waste sorting system has achieved a 95 percent accuracy rate in sorting plastics, metals, and organic waste. Moreover, AI can also help prepare waste collection schedules based on real-time data and changing conditions.
AI algorithms, combined with real-time traffic data maps, can indicate the shortest routes for waste collection vehicles, optimising fuel consumption, reducing emissions, and saving time.
Active public participation is key to aligning technological progress and development initiatives with the needs of residents, promoting inclusion, trust, and transparency within the community. AI can be leveraged to bridge the gap between communities and administrations, making urban governance more participatory and equitable. For example, Chandigarh Smart City has already launched an AI-driven chatbot called BIRBAL to engage with citizens and provide information on services provided by the Union Territory administration and the Municipal Corporation. It serves as a one-stop platform for grievance redressal and information dissemination on citizen services, public transport services, online payment services, and others. Similarly, Barcelona’s AI-driven public forums app, decidim.barcelona, is a digital and democratic platform for citizen participation. Through this initiative, citizens are given a voice to design and improve the participatory process, and contribute proposals which are then debated and, in some cases, even translated into legislation.
Active public participation is key to aligning technological progress and development initiatives with the needs of residents, promoting inclusion, trust, and transparency within the community. AI can be leveraged to bridge the gap between communities and administrations, making urban governance more participatory and equitable.
While there are numerous possibilities for AI in city governance, it is essential to recognise that AI systems are only as reliable as the data on which they are trained. Therefore, one must be mindful of data inaccuracies or gaps in municipal datasets, as they can lead to misleading results. Secondly, although AI can aid some functions of urban governance, the core questions of equity, access, prioritisation of stakeholder interests, and accountability still rely on human decision-making. And third, the deployment of AI solutions in city governance must be cognizant of the water footprint of AI and ensure a net positive scenario for both people and the planet.
AI offers powerful levers to improve mobility, utilities, security, housing, and other citizen services in Indian cities. However, cities require much more than just technical tools to harness that potential. A planned approach with metrics, enhanced institutional capacity, and ethical guardrails is imperative for engaging the citizens, protecting their rights and scaling with care.
Soma Sarkar is an Associate Fellow with the Urban Studies Programme at the Observer Research Foundation.
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Soma Sarkar is an Associate Fellow with ORF’s Urban Studies Programme. Her research interests span the intersections of environment and development, urban studies, water governance, Water, ...
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