Artificial Intelligence is rapidly moving beyond experimentation to become a powerful enabler of governance, public service delivery, and informed decision-making. At the 3rd National Digital Innovation Summit 2026, a high-level panel on “AI-Powered Governance & Predictive Public Services” brought together senior government officials and technology leaders to discuss how AI is being applied across healthcare, agriculture, urban development, welfare delivery, citizen services, and digital infrastructure. The discussion highlighted the need to move from reactive governance to predictive, data-driven, and citizen-centric administration.
The conversation explored how governments can harness fragmented datasets, digital infrastructure, and emerging AI capabilities to anticipate citizen needs, optimize resources, and improve essential service delivery. From predictive healthcare and smarter urban planning to connected telecom networks, data intelligence platforms, and secure AI deployments, the panel highlighted technology’s growing role in building more responsive and efficient governance systems. A recurring theme throughout the discussion was the need to balance innovation with data security, privacy, interoperability, and public trust.
Shri Sameer Verma, Managing Director, Uttar Pradesh Development Systems Corporation Ltd.
With its large population, diverse datasets and multiple government departments, Uttar Pradesh presents a significant landscape for developing and scaling AI use cases. Shri Sameer Verma outlined the state’s proposed UP AI Mission, which takes a multi-layered approach covering AI tools, research and development, certification of AI solutions and their potential commercialisation. The initiative brings together government departments, industry, academia and practitioners to create an ecosystem capable of developing and deploying AI solutions at scale.
The approach also places data and cybersecurity at the centre of digital governance. AI applications can support both citizen-facing services and internal government decision-making, provided departments are able to securely share and utilise their datasets. Uttar Pradesh is consequently working on strengthening its state data centre ecosystem, UP SWAN, data lake capabilities, GPU and computing infrastructure, alongside private-sector participation. The larger objective is to create an interconnected digital ecosystem in which government data can support real-time decision-making without compromising confidentiality, integrity and availability.
Vikas Garg, Principal Secretary, Department of Urban Development & Housing, Government of Punjab
Large-scale urban development involves managing extensive datasets covering land ownership, acquisition, land pooling, plot requirements and infrastructure planning. Vikas Garg described how Punjab is using digital platforms connected with state land records to streamline these processes. Once land is identified for development, digital systems can bring together ownership information and other relevant records, creating a stronger information base for planning and development decisions.
AI can take this further by analysing historical patterns and helping predict requirements for different plot sizes, land uses and infrastructure facilities. Such data-driven planning can also improve the spatial distribution of schools, healthcare facilities, police stations, sports infrastructure, sewage treatment plants and other essential services. By reducing unnecessary distances between residential areas and public facilities, AI-enabled planning can contribute to lower congestion, better land utilisation and improved accessibility. The approach reflects a transition from simply expanding cities to designing integrated urban ecosystems around citizen needs.
Har Sahay Meena, Principal Secretary and Project Director, Water Resources Department, Government of Tamil Nadu
The intersection of agriculture, water management and climate resilience is creating an important space for technology-led intervention. Har Sahay Meena’s perspective brought these interconnected areas into the discussion, particularly the need to improve agricultural productivity while using increasingly valuable water resources more efficiently. Technology can provide better visibility into resource availability, agricultural conditions and changing environmental patterns.
Applications such as precision farming, sprinkler irrigation, water reuse and recycling can contribute to more efficient resource utilisation. In the context of increasing climate variability and environmental risks, data-driven systems can also support early identification of potential challenges and enable more informed resource allocation. The combination of AI, agricultural technology and water-management systems therefore offers an opportunity to strengthen both productivity and long-term environmental resilience.
Anand Khare, Director General Telecom, Department of Telecommunications, Ministry of Communications, Government of India
The expansion of AI-enabled governance depends fundamentally on the availability of reliable digital connectivity. Anand Khare positioned telecom infrastructure as the underlying foundation for data governance, cloud platforms, data centres, Artificial Intelligence and other digital services. Three interconnected requirements define this ecosystem: connectivity, computing efficiency and trust. While India has achieved substantial telecom coverage, bringing remaining underserved areas into the digital ecosystem and ensuring meaningful connectivity remain important priorities.
The telecom sector itself is increasingly using AI for network planning, optimisation and traffic management, allowing infrastructure to respond more efficiently to changing patterns of data consumption. Security is equally important as information moves across networks between different systems and locations. Network-level security measures, audits and continuous monitoring are therefore critical to protecting the infrastructure that supports digital public services. A secure and reliable telecom layer ultimately determines how effectively citizens and government institutions can access and use AI-enabled services.
Madhvi Mishra, Director, Department of IT & e-Governance, Government of Jharkhand and Director, Jharkhand Space Application Centre
Jharkhand is developing its AI ecosystem around the idea of using government data to enable earlier intervention and more informed decision-making. Madhvi Mishra outlined the proposed Chief Minister’s Data Intelligence Platform (CM-DIP), which seeks to connect departmental datasets and address the problem of information remaining confined within individual government departments. Bringing these datasets together can provide decision-makers with a more comprehensive picture of emerging issues and enable action before they develop into larger challenges.
The state’s proposed dedicated AI policy is being developed around sectors including governance, education, healthcare, mining and the environment, with inputs from industry, technology experts and academia. AI also has significant potential in Jharkhand’s agriculture and horticulture sectors, where data can support better planning and productivity. Another important application is the Health and Nutrition Vigilance System, which uses information to identify children and vulnerable groups at risk of malnutrition or anaemia at an early stage. Such applications demonstrate how AI can become part of routine governance processes, supporting prevention and early intervention rather than only responding after a problem emerges.
Swastik Chakraborty, Vice President – Technology, Netweb Technologies India Ltd
One of the key challenges in public-sector AI adoption is converting large volumes of government data into usable intelligence for frontline services. Swastik Chakraborty discussed AI-enabled healthcare solutions designed for public health centres, where limited resources and uneven access to specialised medical expertise can affect service delivery. AI-based systems can collect patient information through non-invasive methods and use conversational interfaces to support preliminary assessment, while generating confidence scores that can help doctors prioritize cases.
Scaling such solutions requires an underlying technology architecture capable of supporting multiple departments and growing user volumes. AI infrastructure can begin with focused applications and gradually expand across healthcare, education and other citizen services. Indigenous hardware and software capabilities can further support secure deployments within government environments. The combination of computing infrastructure, AI platforms and domain-specific applications can allow states and districts to develop solutions that are scalable, locally deployable and aligned with citizen needs.
Sameep Mehta, Distinguished Engineer, IBM Software India
Government AI systems often need to work with data distributed across multiple departments, platforms, and ownership structures. Sameep Mehta discussed an approach where data can remain with its respective owners while still being accessed through secure connectors and governed mechanisms. This allows different datasets to contribute to a unified intelligence layer without requiring organisations to unnecessarily relocate or duplicate sensitive information.
The governance of AI also extends to the questions being asked of models and the information produced in response. AI guardrails can control inappropriate queries and prevent outputs that conflict with organisational policies, privacy requirements or security considerations. This framework forms part of a broader concept of sovereign AI, where organisations retain control over their data, models and insights. For public-sector applications, building these controls directly into the AI lifecycle can create systems where security, governance and trust are inherent components rather than additional layers added later.
Key Takeaways
- Predictive governance: AI can help governments identify risks and citizen needs before they become critical.
- Data integration: Breaking departmental silos is essential for effective AI-driven decision-making.
- Citizen-centric services: AI must ultimately translate into faster, more accessible, and responsive public services.
- Smarter urban planning: Data and AI can optimise land use, infrastructure placement, and access to essential services.
- Healthcare and welfare: Predictive systems can help identify vulnerable populations and enable earlier intervention.
- Agriculture and water: AI can support precision farming, efficient irrigation, resource management and climate resilience.
- Digital infrastructure: Connectivity, computing capacity and secure telecom networks are fundamental to scaling AI.
- Responsible AI: Data security, privacy, governance and AI guardrails must be integrated into deployments from the outset.
- Scalable and sovereign technology: States need AI infrastructure that can grow with adoption while retaining control over data and systems.
- Collaboration: Government, industry and academia must work together to develop and scale meaningful AI use cases.
Conclusion: From Digital Governance to Predictive Governance
The panel discussion made it clear that the next phase of digital transformation will be defined not simply by the adoption of Artificial Intelligence, but by how intelligently governments use it to anticipate needs and solve real-world challenges. Across urban planning, healthcare, agriculture, nutrition, telecom and citizen services, AI is emerging as a tool to make governance more responsive, efficient and evidence-driven.
At the same time, the discussion reinforced that technology alone cannot transform governance. Secure infrastructure, integrated data, responsible AI, institutional capacity and public trust must evolve alongside innovation. The emerging vision is therefore not of governments simply becoming more digital, but of governments becoming more predictive, proactive and citizen-centric.
Ultimately, AI-powered governance will succeed when technology moves beyond dashboards and applications to create tangible improvements in people’s everyday lives, helping governments anticipate problems, allocate resources better and deliver the right service to the right citizen at the right time.