The last decade has established foundational groundwork. National urban initiatives such as the Atal Mission for Rejuvenation and Urban Transformation, Swachh Bharat Mission, Pradhan Mantri Awas Yojana, and the Smart Cities Mission have focused on urban development and enhanced service delivery. The Smart Cities Mission (2015-25) has implemented over 8,000 projects valued at Rs. 1.64 lakh crore ($18.2 billion), including integrated command centers, smart roads, and digital classrooms. Still, critiques point out issues such as its top-down structure, the creation of exclusive zones, and limited municipal ownership. The takeaway is clear: without governance capacity, technology risks widening existing divides. As India readies itself for the next stage of urban development and rejuvenation, AI must be utilized in ways that reinforce local institutions and foster citizen trust.
India stands in a distinctive position to spearhead this transition. Over 56% of metropolitan adults in India are already using generative AI tools, the highest proportion in the Asia Pacific region. Bengaluru accounts for nearly a quarter of India’s digital workforce, establishing it as one of the world’s leading
AI talent hubs. Coupled with open data platforms and a dynamic ecosystem, India possesses the components necessary for intelligent governance. The challenge lies in aligning these national strengths with the practical realities of municipal administration, where fragmented data sets and limited capacities frequently hinder innovation.
Global insights highlight that technology alone isn’t enough; governance capacity is crucial. Singapore’s Smart Nation initiative was developed over decades, starting with civil service computerization in the 1980s, progressing through the IT 2000 plan in 1992, and culminating in the Smart Nation initiative in 2014. Its effectiveness stems from long-term investments in data infrastructure, open data, and cross-government integration. In 2023, Singapore advanced its AI strategy, focusing on continuously enhancing public service delivery. Barcelona’s civic data commons have redefined urban data as a public asset, granting residents control over their data. New York City’s AI Action Plan (2023) introduced algorithm registries, bias audits, and an Office of Algorithmic Data Integrity to ensure accountability in public sector applications. Most recently, the Global Observatory for Urban Artificial Intelligence, GOUAI (2025), initiated by the Barcelona Centre for International Affairs in collaboration with the cities of Barcelona, Amsterdam, and London in partnership with UN-Habitat, promotes ethical urban AI practices and applications globally. These examples underscore the significance of institutional design, transparency, and citizen trust—principles that India must integrate as it propels AI throughout its cities.
For India, the first step is to establish a long-term urban data infrastructure. For instance, urban transport data remains fragmented across metro rail corporations, municipal bus services, and traffic police departments, with minimal interoperability—a significant barrier to AI-enabled mobility solutions. India must invest in robust, integrated, and independently verifiable urban data systems, akin to Singapore’s extensive efforts. Without this foundation, AI could lead to inefficiency, bias, and fragmentation. A sustained commitment to data quality and interoperability will form the cornerstone of intelligent urban governance.
Second, India should adapt the IndiaAI Mission for cities. Approved in 2024 with a Rs. 10,300 crore ($1.14 billion) investment, the IndiaAI Mission focuses on national compute infrastructure, datasets, and startup funding. However, the greatest impact of AI will be realized in cities, where governance directly interacts with citizens’ lives. Tailoring IndiaAI for urban transformation would yield greater benefits for both sectors. Designating IndiaAI–Urban as a key focus would enable the curation of AI tools for municipal utilization, promoting vetted models for traffic management, waste collection, grievance resolution, and citizen service delivery.
Third, India should initiate an Urban AI Challenge. Startups should be tasked with managing civic assets, urban flood risk notifications, public safety, and multilingual citizen services. Successful projects could receive joint IndiaAI–MoHUA funding and be required to open-source their models, facilitating replication across cities. This approach would not only speed up adoption but also democratize access to AI solutions, establishing predictable procurement pathways for innovators while embedding transparency in the system.
Fourth, India should compile a repository of city-specific use cases to disseminate proven solutions nationwide. A national repository, aligned with initiatives like GOUAI, would enable municipalities to learn from each other and adopt ethical AI practices more swiftly. This would accelerate knowledge sharing, minimize redundancy, and ensure that smaller cities benefit from innovations developed in larger metropolitan areas.
The strategic alignment of AI and urban rejuvenation is evident. For AI, cities serve as practical testbeds with measurable outcomes. For cities, AI provides analytical depth and adaptability centered on citizens. For startups, predictable procurement pathways emerge. For citizens, transparency and fairness are institutionalized. India’s forthcoming urban transformation will focus not on more sensors but on smarter integration between missions, adaptive intelligence, and citizen trust. If India embraces this moment, it could set a global standard for inclusive and intelligent urban governance, showcasing how the world’s fastest urbanizing democracy utilizes AI for the public good.
Bhawna Prakash is Adjunct Fellow (non-resident) and former Senior Fellow with the Chair on India and Emerging Asia Economics at the Centre for Strategic and International Studies (CSIS) in Washington D.C.
