India stands at a one-of-a-kind threshold. Unlike traditional tech discourse, the upcoming AI Impact Summit 2026 is built on a well-rounded framework of seven interconnected chakras, which range from human capital to social empowerment. While computing capacity and data are vital, they are merely the starting point. The true test of inclusion and trust will occur only when artificial intelligence (AI) solutions hit the ground on scale. This is the same test that defined Aadhaar and the Unified Payments Interface (UPI). Now, success hinges on getting the fundamental components right and deliberately designing applications that translate the principles of people, planet, and progress into tangible global action.
Aadhaar and UPI did not succeed because they were technically perfect. Both systems faced early setbacks and public skepticism. They proved that large-scale digital systems could earn legitimacy if they improve everyday outcomes. They must also survive failure through strong governance. Aadhaar reduced leakages in welfare delivery and enhanced people’s experience in regular activities, such as banking and telecom. UPI removed friction in payments. People learned to trust these systems because they failed predictably and could be corrected without permanent exclusion.
India’s AI systems will face the same test but with higher stakes. Traditional digital systems are deterministic. The system denies access if a name, demographic details, or some other parameter does not match exactly. Much of the digital exclusion in India emerged when rigid rules collided with messy lives at the point of transaction. AI changes this dynamic because it is probabilistic. It can assess likelihood and context rather than demand perfect matches. This capability allows AI to function as an exception management layer. In theory, this makes AI a powerful tool to reduce exclusion.
This potential remains conditional on localised relevance and strong governance. Systems trained primarily on data from the Global North often struggle with local contexts and languages. In sectors, such as agriculture, a poorly translated advisory can lead to harmful instructions. For example, a system that translates content from English to Hindi might tell a farmer to “bury” a seed rather than “sow.”
Such errors quickly break fragile trust because a farmer knows that seeds are never buried. In a local context, burial may carry an altogether different meaning associated with finality rather than growth. Users at the margins view technology through the lens of rational risk management. In their world, a nonsensical instruction is a signal of total system unreliability rather than a minor glitch. When the stakes involve livelihoods and food security, even a single such alien output can cause users to abandon the technology permanently in favor of human intermediaries they know.
India’s readiness must be measured by institutional capacity, not infrastructure alone. Currently, the global community, especially the Global South, lacks sector-specific regulations, validation, and certification standards for AI solutions in critical areas, such as health, education, agriculture, and finance. This gap is untenable when AI mediates access to food and identity. The Global South risks a new era of digital imperialism without data sovereignty, where a few powers hold all control.
Full automation or intelligence in public systems is Utopian. A human in the loop is essential to inclusion. AI should support decisions and flag anomalies. Final accountability must remain human for at least the next decade for vulnerable populations. AI holds immediate value when it strengthens frontline workers, which includes banking correspondents, frontline health workers, and agricultural extension workers, who already command social trust. When these actors use AI tools, inclusion improves without forcing direct adoption on those least comfortable with it. Trust flows through people before it flows through the machine.
The global significance of this approach cannot be overstated. The UN Governing AI for Humanity report (2024) states that high-income countries are likely to see a 70% acceleration in AI discoveries during the next three years. For the Global South, that figure is only 30%, with a maturity gap that could take 10 years to close. India provides a blueprint to navigate this gap without falling into new forms of digital dependency. Success will not be defined at summits or in benchmarks. It will be decided quietly in clinics, ration shops, and farms. India’s AI moment will be remembered as a breakthrough only if its systems become as dependable as the human-centric processes they seek to support. Inclusion and trust will decide whether this decade is a breakthrough or a missed opportunity.
India has the opportunity to lead the Global South in the responsible and inclusive adoption of AI. The nation has done this before when it built world-class digital public infrastructure (DPI) at home, then made it a global movement by proactively sharing lessons and technology with the world. Will India be able to repeat the story in AI?
This was first published in “Hindustan Times” on 3rd February 2026.
National ID systems have evolved from administrative registries into core public infrastructure that underpins access to welfare, finance, healthcare, and education, as well as a growing range of digital services. During the past decade, artificial intelligence (AI) and data science have shaped how these systems are built and operated. In most countries, adoption of AI in national ID programs has remained narrow, focused on immediate scale challenges, such as biometric deduplication, authentication security, and fraud control. This limited adoption is often due to constraints in institutional capacity, legal and reputational risk, and rigid public procurement models.
These factors lead governments to favor proven, high-certainty AI applications, such as biometric de-duplication, over experimental or adaptive systems. Few national ID systems have deployed AI across the full identity lifecycle at scale. Beyond biometric de-duplication and liveness detection, most systems lack this capability due to governance, institutional, and risk constraints rather than technical feasibility.
While these applications are necessary, they are no longer sufficient. As national ID programs mature, identity systems must respond to new pressures, which include changing population characteristics, rising transaction volumes, evolving threat models, and increasing citizen expectations. For this analysis, we divide countries into three groups based on the maturity or life stage of their ID systems.
Stage 1 countries are currently establishing a national ID system or expanding coverage among populations that remain undocumented or underserved. These countries include Nigeria, Rwanda, and Ethiopia. Stage 2 countries have achieved high enrollment coverage and operate a mature foundational registry, and they include the Philippines, India, Thailand, and Indonesia. Stage 3 countries, such as Singapore and Estonia, operate highly mature national ID systems with deep integration across public and private services.
These stages are not sequential. Most national ID systems span multiple stages simultaneously, as individual components mature at different rates. These components include citizen enrollment coverage, use of digital ID for access to services and programs, integration with law enforcement, and the robustness of technology and scale. The strategic question for governments today is not whether to use AI, but how to align it with the country’s identity system maturity.
Stage 1: Building and saturating a foundational ID system
For countries in stage 1, the core objective is to scale with integrity. National ID authorities must onboard millions of residents and ensure that everyone is enrolled only once, often in contexts with limited infrastructure, weak connectivity, and incomplete population data. Further, a dominant systemic risk is exclusion. Individuals cannot enroll or repeatedly fail biometric or demographic quality checks, which results in denial or delay of downstream services.
Current application 1: Automated biometric identification systems
The technological backbone of ID systems is the automated biometric identification system (ABIS). It uses machine learning (ML) to compare fingerprints, facial images, and iris scans across population-scale datasets. ABIS helps establish an individual’s identity under two functions. One is identification, which searches for the submitted biometrics in the entire database, often referred to as a 1:N match. The other is verification, where it tries to match the submitted biometrics against the single record of the same person in the database, also called a 1:1 match.
India’s Aadhaar program illustrates the centrality of ABIS. The Unique Identification Authority of India (UIDAI) governs Aadhaar. It requiresmultimodal biometric capabilities to process millions of data packets daily for over a billion people with a minimal error rate. This multimodal approach ensures inclusiveness through alternative biometrics if a particular one cannot be captured or registered. For example, the use of iris or face if hands are amputated or fingerprints worn away by manual labor. However, population-scale ABIS increases sensitivity to capture quality, thresholds, adjudication, and exception handling. These are areas that can drive exclusion if poorly governed.
Current application 2: Optical character recognition with natural language processing (NLP) to extract physical information
Foundational ID systems often stall before they reach maturity, not due to lack of vision, but due to enrollment challenges. Governments must first enroll every eligible resident to unlock downstream value, but this task remains complex. Authorities must reach remote populations, capture accurate demographic and biometric data, navigate language barriers, and ensure inclusion for groups with limited digital access. In several countries, enrollment still relies on paper forms and assisted registration. Operators manually transcribe details and capture biometrics through shared devices, often under time and capacity constraints.
These conditions create predictable risks, such as limited training, low awareness, and manual data entry. These risks frequently lead to missing or inconsistent information that weakens the integrity of the ID system. One emerging solution is to use optical character recognition (OCR) and NLP to digitize handwritten forms at scale. Ethiopia, for example, is developing an AI-powered solution that reads consent forms and auto-populates digital records, which reduces manual errors and speeds processing across multiple languages. However, such tools must include strong validation checks and well-trained models. Without safeguards, automation can amplify errors, compromise data quality, and unintentionally exclude the very populations these systems seek to serve.
Opportunity 1: Edge AI for biometric quality enhancement
Poor biometric capture is a major source of exclusion. Glare, dirt on sensors, motion blur, or improper positioning often result in low-quality biometric images. Many systems detect these issues only after data reaches the central server, which triggers deferred rejection that forces citizens to repeat enrollment. This increases costs, prolongs onboarding, and raises higher dropout rates.
Edge-based AI models can address this challenge through quality control at the point of capture. On-device deep learning models can assess image quality in real time, verify compliance with international standards, and provide capture coaching for operators and citizens. Advancements in generative adversarial networks (GANs) and diffusion models have theoretically been successful in fingerprint enhancement and reflection removal from iris scans, among other applications. The national ID system authorities can enable this approach when they enforce device standards and support local quality checks. Edge AI transmits biometric data only after it meets quality thresholds, and deep learning models enhance image quality at capture. This combination could reduce re-enrollment, lower backend processing costs, and improve privacy through minimization of unnecessary data transmission.
Though the performance of these techniques depends on field conditions, such as camera quality, lighting, and device constraints, such emerging technologies offer potentially viable solutions for biometrics capture. Further, the deployment of edge AI in enrollment and authentication workflows would also require certified devices, controlled model and version rollouts, and audit logs to support accountability and dispute resolution.
Edge AI-enabled enrollment devices entail higher upfront costs, but they can materially reduce downstream expenditures. This reduction occurs as such devices lower re-enrollment rates, manual adjudication, and grievance handling, particularly in remote or high-error contexts. Authorities must limit this edge intelligence to capture quality assessment and operator guidance, preserve raw biometric data, and log all transformations to avoid compromise of evidentiary integrity.
Opportunity 2: Geospatial intelligence for outreach planning
A persistent weakness in early-stage ID programs lies in the enrollment outreach plan. Traditional approaches rely on administrative boundaries, electoral rolls, or census data that may be outdated by several years. Informal settlements, migratory populations, and sparsely populated rural areas are often undercounted or entirely missed, which leads to structural exclusion that is difficult to reverse later.
ML-assisted geospatial intelligence offers a way to overcome this limitation. AI models can estimate population distribution at a finer spatial resolution through satellite imagery, settlement extraction algorithms, mobile network coverage data, and road networks datasets. These models can identify exclusion hotspots, predict enrollment demand, and generate optimized routes for mobile enrollment units.
The GRID3 initiative in Nigeria demonstrates the practical value of this approach. GRID3 enabled authorities to plan malaria campaigns in locations where affected people lived with high-resolution satellite imagery and population estimates, rather than where administrative maps suggested.
National ID authorities can adopt similar settlement layers to identify and plan enrollment campaigns in remote or rapidly growing peri-urban areas, which ensures that coverage targets translate into real inclusion. Further, the use of open-source datasets, such as Meta’s AFD datasets, for population estimation enables quick development and reduced friction in consent boundaries.
Stage 2: Driving usage and safeguarding integrity
For countries in stage 2, the strategic focus shifts from onboarding to usage. As national IDs increasingly function as trust anchors for welfare delivery, financial inclusion, telecommunications, and digital services, risk shifts from exclusion to abuse at scale. Such abuse includes spoofing, coercion, and insider fraud, which could potentially erode trust. As transaction volumes grow, systems must remain reliable, secure, and interoperable.
Current application: Liveness detection and anti-spoofing
As authentication expands to mobile and remote channels, most Stage 2 countries deploy liveness detection algorithms. They focus on facial recognition to counter presentation attacks, such as photographs, masks, or deepfake videos. These techniques have become a baseline requirement for secure digital transactions. However, liveness detection algorithms often introduce false positives and accessibility challenges, especially for the elderly.
Opportunity 1: Graph neural networks (GNNs) for fraud detection
Traditional fraud detection relies on linear rules, such as transaction frequency thresholds or static blacklists. While effective against simple abuse, these approaches struggle to detect coordinated fraud that involves networks of identities, devices, or operators.
GNNs shift the analytical focus from individual transactions to relational structures. GNNs can detect structural anomalies that indicate coordinated misuse without the inspection of personal attributes by modeling anonymized identity graphs, such as device-to-user or operator-to-location networks. Graph or ML risk scores can trigger review or step-up verification within rights-sensitive ID systems to improve governance.
Graph-based fraud detection is proven at scale in the financial sector, most notably by PayPal, where graph models identify coordinated fraud rings that evade rule-based systems. Governments can adopt this proven approach for national ID systems. However, constraints around data availability, legal mandates, technical capacity, and governance safeguards challenge practical deployment. Further, the introduction of ML-based approaches should be integrated with the transparency and appeal mechanisms that are essential and integral for a national ID program.
Opportunity 2: Multilingual name normalization and match algorithms
In linguistically diverse societies, name variation is a pervasive but underappreciated source of exclusion. Differences in transliteration, spellings, and naming conventions often cause legitimate users to fail database-matching checks, even when their identities are valid. These failures generate manual exceptions and delay service delivery.
Transformer-based transliteration models and phonetic embeddings can normalize names across scripts and languages, which produce culturally aware canonical representations. Match scores allow systems to distinguish between likely variants of the same name and true mismatches, which enable fuzzy matching without loss of accuracy. Thus, the normalization of names can improve interoperability across civil registration, banking, and welfare systems, which reduces exception handling and does not force citizens to conform to a single representation of identity.
AI4Bharat’s IndicTrans model illustrates this capability through high-quality transliteration across Indian languages. The Indian judiciary already uses this model in initiatives, such as the Supreme Court Vidhik Anuvaad Software (SUVAS). Integration of these models into identity systems could improve interoperability across civil registries, banks, and welfare platforms, which reduces manual adjudication and improves user experience.
Stage 3: Enhancement of citizen experience and system intelligence
For countries with highly mature national ID programs, the binding constraint shifts to public trust and legitimacy, as citizens expect reliability, transparency, and fast resolution when failures occur.
Current application: Reactive analytics
Most advanced systems rely on dashboards that track failures, throughput, and performance indicators. Key signals include rising authentication failure rates in specific cohorts, growing exception volumes, and grievance backlogs. These interventions are typically reactive, triggered after a transaction fails or a grievance is lodged.
Opportunity 1: Identity lifecycle forecast
Biometric attributes degrade over time due to aging, occupational wear, and environmental factors. Documents expire, and demographic attributes change. Today, these issues often surface only when a citizen attempts a transaction and is denied service.
Survival analysis and time-series models can forecast when specific cohorts are likely to experience biometric or credential failure. Through analysis of historical authentication logs and update patterns, systems can prompt proactive updates before failures occur. This shifts identity management from a fail-and-fix approach to a proactive, predict-and-prevent model.
Opportunity 2: AI-powered grievance redressal
As ID systems grow more complex, citizens struggle to navigate static FAQs and overloaded call centers. Conversational AI systems based on LLMs can provide context-aware support in local languages, which explains errors, guides next steps, and resolves routine issues instantly.
For instance, Ethiopia deployed a local-language AI chatbot to support citizen interaction with their national ID system. The chatbot delivers accurate, multilingual responses across major digital platforms, which reduces response times, manual workload, and misinformation. In the context of national ID systems, LLMs with retrieval-grounded responses can prevent policy hallucination. Clear escalation to human agents for high-stakes cases and full audit logs can significantly improve grievance resolution times and citizen trust.
Conclusion
The future of national ID systems lies not in the deployment of more AI, but in the right intelligence based on the stage of maturity. Stage 1 systems must prioritize inclusion and data quality. Stage 2 systems must focus on interoperability and integrity. Stage 3 systems must emphasize resilience, anticipation, and citizen experience. These stages are not standalone technology upgrades. Authorities must sequence them with legal frameworks, institutional capacity, and operational readiness. When deployed prematurely or without adequate governance capacity, AI can amplify exclusion risks and erode trust in identification systems. The risk is especially high when systems use ID to determine eligibility or access to essential services.
With mature and aligned AI and data science, governments can transform national ID systems from static registries into intelligent public infrastructure. This infrastructure can maintain itself, remain accurate, inclusive, and trusted over time.
Leadership quotes on India’s 2026 Union Budget announcement:
Abhishek Anand, Senior Partner – Banking Financial Services and Insurance, on “The Daily Jagran”
“Refinancing ease for NBFC-MFIs is important for micro enterprise growth and the ultimate growth of the Viksit Bharat goal ahead.”
Vikash Sinha, Associate Partner – Climate Change and Sustainability, on “India CSR”
“Climate adaptation needs dedicated local provisions and said the allocation of INR 1.4 lakh crore under the 16th Finance Commission is commendable and strengthens fiscal federalism. Also, climate resilience requires dedicated provisions for locally led adaptation mechanisms that can crowd in private innovation and investment.”
Puneet Khanduja, Associate Partner – Health and Nutrition, on “Money control”
“Such announcements create a strong platform for systemic reforms. However, their impact will depend on effective state-level implementation, regulatory alignment, and integrated models that translate policy intent into actionable institutional expansion.”
Rajnish Kumar, Senior Manager – Agriculture and Food System, on “Nuffoods Spectrum”
“The Union Budget 2026–27 signals a decisive shift toward productivity-led agricultural growth. The emphasis on integrated fisheries and livestock value chains, development of 500 reservoirs and Amrit Sarovar lakes, and AI-enabled productivity through Bharat Vistaar with AgriStack–ICAR integration aligns well with the Economic Survey’s diagnosis. The real test will be rapid execution and demonstrable gains in productivity and farm incomes.”
Ayushi Mishra, Senior Manager – Communities and Livelihoods, on “Business world”
“Diversification must extend beyond crops to livelihoods. Aligning community livelihoods with climate-resilient agriculture could transform sustainability into a scalable economic strategy. Integrating self-help groups into formal value chains and promoting green jobs could strengthen rural resilience while aligning growth with India’s climate commitments.”
Rajarshi Dutta Barua, Associate Partner – Banking Financial Services and Insurance, on “CNBC”
“The ₹10,000 crore SME Growth Fund is a decisive intervention to create future champions by providing equity support and risk capital to promising MSMEs. It addresses critical equity gaps that prevent small enterprises from scaling globally and can be strengthened through faster settlements on platforms like TReDS and expanded credit guarantee coverage for first-time women and marginalised entrepreneurs.”
As the Union Budget for 2026–27 approaches, agriculture has been articulated as the first engine of development. According to the Periodic Labour Force Survey (2023–24), agriculture and allied activities employ 46.1 per cent of India’s workforce, underscoring the sector’s centrality to livelihoods, food security, and rural demand. The question before policymakers, however, is not one of intent or aggregate spending, but of whether budgetary choices are aligned with the sector’s structural needs.
Budgetary allocations to the Department of Agriculture and Farmers’ Welfare (DA&FW) have expanded significantly in nominal terms, from ₹21,933 crore in FY 2013–14 to ₹1.27 lakh crore in the Budget Estimates for FY 2025–26. Agriculture-related spending also flows through multiple ministries, covering irrigation, renewable energy, fertilisers, rural employment, and research. This reflects sustained fiscal attention and an increasingly whole-of-government approach.
However, relative prioritisation tells a different story. DA&FW’s share in the total Central Plan outlay has declined steadily, from 3.53 per cent in 2021–22 to 2.51 per cent in 2025–26. Actual expenditure has frequently undershot budget estimates, particularly during fiscally constrained years. While allocations remain large, agriculture’s relative weight within the expanding public expenditure framework is diminishing.
Persistent productivity challenge
This trend is significant because Indian agriculture continues to face a persistent productivity challenge. Despite employing nearly half the workforce, the sector contributes less than one-fifth of GDP and exhibits lower growth than the rest of the economy. Incremental increases in outlays, without changes in spending composition, are unlikely to alter this imbalance.
A review of recent budgets shows a continued concentration of resources in income support, input subsidies, and risk mitigation schemes. PM-KISAN, for instance, has improved income predictability and strengthened direct state–farmer linkages through digital transfers. The expansion of Aadhaar-linked delivery systems and the rollout of AgriStack represent genuine improvements in targeting, transparency, and administrative capacity. These initiatives have strengthened the welfare architecture and merit acknowledgement.
But welfare efficiency is not the same as productivity enhancement. The binding constraints in Indian agriculture lie in weak seed systems, inefficient water use, deteriorating soil health, limited extension capacity, post-harvest losses, and poor market integration. Budgetary priorities continue to underweight these productivity-enhancing public goods relative to recurring expenditures.
Complementing with sustained budgetary support
Seed systems provide a useful illustration. Yield stagnation across several crops reflects slow varietal turnover, uneven quality assurance, and limited diffusion of improved genetics. The draft Seed Bill recognises this institutional gap by proposing reforms to certification, quality control, and innovation incentives. However, regulatory reform must be complemented by sustained budgetary support for agricultural research, adaptive trials, extension networks, and farmer adoption to generate measurable gains.
Risk mitigation schemes show a similar pattern. Crop insurance and credit-linked support absorb substantial resources but remain constrained by delayed settlements, uneven coverage, and weak alignment with actual production risks. Without complementary investments in irrigation, climate-resilient practices, and localised extension, these schemes function primarily as ex-post compensation rather than ex-ante resilience-building tools.
Input subsidies, including fertiliser support, further illustrate the tension. While digital monitoring has improved transparency, distorted price signals continue to encourage inefficient input use and impose high fiscal costs. Policy discourse increasingly recognises the need for rationalisation, but progress remains incremental. Reform must be sequenced and linked to productivity, soil health, and income outcomes, not treated as a narrow fiscal correction.
Public investment gaps
Public investment gaps are most evident in post-harvest infrastructure, storage, processing, logistics, and value-chain integration. Persistent post-harvest losses, particularly in horticulture, livestock, and fisheries, continue to erode farm incomes. Instruments such as the Agriculture Infrastructure Fund have demonstrated demand, but their effectiveness depends on last-mile execution and stronger integration with farmer producer organisations and markets.
The broader lesson is clear. India has built an extensive safety net for agriculture, but a weak ladder for growth. Digital public infrastructure such as AgriStack offers an opportunity to rebalance this approach, enabling differentiated support, outcome-linked incentives, and accountability. But technology cannot substitute for strategic prioritisation.
If agriculture is truly to serve as India’s engine of development, the Budget must signal a shift, from income support to income generation, from fragmented schemes to coherent value-chain strategies, and from managing distress to enabling productivity-led growth. This does not require dramatically higher spending. It requires sharper choices. The foundations are in place. What is needed now is strategic follow-through.
This was first published in “The Hindu” on 31st January 2026.
As the Union Budget 2026 approaches, the national discourse is increasingly anchored in the vision of Viksit Bharat 2047. For the health sector, this ambition now requires a clear shift from a primary focus on infrastructure expansion to the creation of a technology-enabled, patient-centric health ecosystem.
Budget 2026 must be framed as a health systems budget, not a healthcare spending budget. The required shift is from fragmented funding to predictable financing, from service expansion to system resilience, from treatment dominance to prevention and early detection, from technology pilots to national digital infrastructure, and from cost containment to innovation-led competitiveness.
India’s recent public health gains are significant. The elimination of trachoma in 2024 and the decline in the total fertility rate to 1.9 reflect decades of sustained investment and reform. The next phase of transformation must build on this foundation by enabling a second revolution: the large-scale integration of digital public infrastructure and artificial intelligence into public health delivery.
The digital mandate: universalising health data systems
A core expectation from Budget 2026 is accelerated saturation and deepening of the Ayushman Bharat Digital Mission. While over 84 crore Ayushman Bharat Health Accounts have been created, the true value of ABDM lies in interoperability rather than registration alone. Budgetary support is required to integrate vertical programmes—from maternal and child health to tuberculosis and non-communicable diseases—into a unified digital stack.
Interoperable patient records are essential to ensure that medical histories move seamlessly across levels of care, from rural sub-centres to district hospitals and tertiary facilities. At the same time, the success of eSanjeevani, which has enabled more than 44 crore teleconsultations, demonstrates both demand and feasibility. The next step is to expand specialist tele-services, including tele-radiology and AI-assisted diagnostics, to reduce diagnostic delays in underserved regions.
Artificial intelligence: fromlogisticsto life-saving care
Artificial intelligence is emerging as a critical enabler of efficiency and responsiveness within the health system. Budget 2026 offers an opportunity to support pilots and scale initiatives that embed AI into core public health functions.
One priority area is supply chain management. AI-based demand forecasting within drug and vaccine distribution systems can reduce stock-outs and wastage, improving service continuity and planning. Another important frontier is drone-enabled logistics. Scaling drone-based delivery of vaccines, diagnostics, and emergency medicines to remote and hilly regions is no longer experimental; it is essential for achieving health equity in hard-to-reach geographies.
Addressing the triple burden: NCDs and cancer care
India’s epidemiological transition has made the rising burden of non-communicable diseases a pressing fiscal and policy concern. Budget allocations must reflect this shift, particularly for cancer and chronic disease care.
Decentralized cancer treatment should be prioritised. Operationalising day care cancer centres in district hospitals would significantly reduce travel burdens and treatment disruptions for patients requiring chemotherapy. In parallel, preventive care must be strengthened. Expanding population-level screening beyond diabetes and hypertension to include conditions such as chronic kidney disease, chronic obstructive pulmonary disease, and fatty liver disease, especially among younger populations, will be critical to preventing long-term health and productivity losses.
Building a climate-resilient health system
Climate change is increasingly influencing disease patterns and service disruptions. Budget 2026 must respond by investing in climate-resilient and energy-efficient health facilities. Integrating climate risk assessments into existing maternal and child health programmes will be essential to strengthen preparedness for heat stress, vector-borne diseases, and climate-related health shocks.
Fragmented funding to predictable financing
This begins with shifting from scheme-by-scheme allocations to multi-year, predictable financing that states and providers can plan around. It also requires pooling and aligning funds across the Center and states, and across vertical programs, to reduce duplication and close last-mile gaps. Predictable financing should be tied to measurable outcomes—coverage, quality, and continuity of care—rather than only to inputs and infrastructure. Strengthening primary care and public health functions through stable operating budgets will be as important as capital expenditure. Finally, timely fund flows and streamlined procurement will determine whether digital systems, diagnostics, and frontline services scale reliably.
Conclusions
India’s health achievements to date are substantial. Maternal mortality has declined sharply since 1990, and out-of-pocket expenditure as a share of total health spending has fallen steadily over the past decade. Yet, reaching the global top tier of health systems will require a strategic pivot.
Budget 2026 must move beyond incremental change and invest in a whole-of-society approach that places digital public infrastructure, artificial intelligence, and preventive care at the centre of health policy. The opportunity now is to transition decisively from an illness-centric system to a wellness-led, digitally empowered healthcare future.
This was first published in “ET edge insights” on 30th January 2026.
This white paper explores data sharing as a key pillar of digital public infrastructure, which operationalizes interoperability in practice. It presents a broad conceptualization of data sharing systems that combines technology solutions, policies, and governance. These systems highlight trust and data protection by design as core features. This paper explores how these systems can improve public service delivery based on case studies from Brazil, Cambodia, Mauritius, and Uganda. Further, the paper outlines diverse models, use cases, challenges, and enabling factors. It also discusses emerging efforts to build open data ecosystems to drive AI innovation in public service delivery. This paper concludes with recommendations for governments and ecosystem stakeholders to scale data sharing systems for more accessible and efficient public services in LMICs.
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