Budget 2026 must shift India’s AI push from announcements to outcomes, experts say

As India approaches Budget 2026, policymakers face growing pressure to convert AI ambition into execution. Experts across policy, technology, and industry say the government must move beyond headline commitments toward measurable outcomes across compute access, startup enablement, sovereign model strategy, governance deployment, and digital infrastructure.

A recurring consensus is that India risks over-investing in symbolic AI milestones while under-investing in real-world deployment, compute democratization, power and data-center readiness, and regulatory capacity.

DPDP Enforcement and the Push to Operationalise the Data Protection Board

One of the Budget’s near-term priorities is expected to be operationalising the Data Protection Board under the Digital Personal Data Protection (DPDP) Act.

Aparajita Bharti, Founding Partner at The Quantum Hub, noted that last year’s allocation of INR 5 crore for the Board focused largely on revenue expenditure and digital office setup.

“In the last budget, INR 5 crore was allocated for the setting up of the Data Protection Board, of which INR 4.5 crore were revenue expenditure, and the outcome-output framework set a target of achieving 75 percent of tasks related to setting up the Digital Office,” Bharti said.

She expects a higher allocation in Budget 2026 as the government accelerates DPDP enforcement.

“With expedited guidelines for DPDP implementation expected, operationalising the Board will be a priority,” she added.

Experts argue that staffing, enforcement capacity, digital case-handling systems, and institutional readiness will determine whether India’s privacy regime becomes functional or merely symbolic.

IndiaAI Mission: From Commitments to Concrete Results

While the IndiaAI Mission has secured billions in commitments, analysts say Budget 2026 should focus on outputs rather than inputs.

Sourya Banerjee, Associate Director at Jajabor Brand Consultancy, said performance must be measured through real-world impact.

“We should stop looking at commitments and inputs and start looking at outputs. Unless we are getting the correct outputs, no amount of input is worth it,” Banerjee said.

He suggested success metrics should include compute access for startups and researchers, the existence of usable Indian-language models, and public-sector AI adoption that improves governance efficiency.

GV Krishnamurthy, Principal at AiNxtGen, warned that India remains overly focused on frontier model optics instead of deployment impact.

“There is a glamour bias toward ‘we built our own GPT-class model’. For India, the real win is thousands of AI-enabled workflows quietly raising productivity and inclusion,” he said.

Budget Must Treat AI Like Infrastructure and Back India-Specific Models

Kartik Mehta, Chief Business Officer and Head of Asia at Channel Factory, argued that Budget 2026 should elevate AI and digital infrastructure to the same priority level as roads, power, and physical infrastructure.

“The budget should focus on the need of the hour, which is India-specific AI models. AI and digital-led initiatives should be given equal or similar importance to roads and infrastructure, as the next few years will lay a strong base for India in AI,” Mehta said.

He said government policy should push AI use cases aligned with local languages, governance needs, and India-specific consumption patterns.

“The government should use AI model use cases, align with local languages and governance requirements, to build stronger infrastructure,” he added.

Mehta also called for targeted tax benefits, startup incentives, and smoother early-stage investment norms.

“I am looking to see what tax cuts and benefits are announced to boost AI tech startups in India. India needs faster, process-driven norms that allow easy early-stage investment in the AI sector,” he said.

While acknowledging the IndiaAI Mission and the previously announced INR 10,000 crore Fund of Funds for deep tech startups, Mehta said the ecosystem still lacks long-term backing.

“We need better tax clarity, ease of capital investment, and sustained funding. India should invest in sovereign LLMs in Indian languages to strengthen the Atmanirbhar Bharat vision,” he said.

Compute Subsidies and Access: Prioritising Startups Over Incumbents

A key policy question ahead of Budget 2026 is whether compute subsidies should prioritize startups, academic researchers, and public-interest initiatives rather than large incumbents.

Banerjee argued subsidies should lower entry barriers and avoid reinforcing market concentration.

“If subsidies disproportionately benefit large incumbents, the policy will reinforce existing concentration rather than build a competitive ecosystem. Public money should prioritize broad access,” he said.

Dhruv Garg, Partner at the Indian Governance and Policy Project (IGAP), said large firms already have capital and vendor access.

“Startups, academic researchers, and public-interest initiatives face higher barriers. Prioritizing these groups strengthens the long-term innovation base,” Garg said.

GV Krishnamurthy recommended a tiered subsidy structure.

“The right design is generous credits for accredited academic and open-source projects, targeted subsidies for Indian IP-creating startups, and mostly market-rate access for large incumbents so they don’t crowd out others,” he said.

Infrastructure Bottlenecks: Power, Data Centers, and Institutional Capacity

Experts say India’s AI constraints extend beyond compute and funding into power supply, cooling, land availability, data-center readiness, and government procurement capacity.

Dhruv Garg highlighted institutional modernization as a critical bottleneck.

“Many AI projects fail because data are fragmented, legacy systems are difficult to connect, and government processes are not designed for digital tools,” he said.

GV Krishnamurthy warned that power and data-center capacity could become the next major constraint.“For startups today, affordable compute and usable data are binding constraints. In the next three to five years, the bottleneck becomes power and data-center capacity,” he said.

Dr. Srinivas Padmanabhuni, CTO of AIEnsured, emphasized energy and cooling as major risks. “Air cooling requirements for AI data centers are high because of heat conversion. Power is the biggest constraint for AI data center capacity in India,” he said.

Sovereign AI vs Global Partnerships: A Strategic Balance

Experts broadly agree that India should not frame sovereign AI as a binary choice against global partnerships.

Banerjee said sovereignty matters most in governance, Indian languages, and sensitive data. “For language, governance, and public-sector use cases, India needs models built around Indian data and realities,” he said.

For general-purpose intelligence, global partnerships remain practical. “The goal is to trade with the best from the world and create what is needed only for us,” Banerjee added.

Garg described the objective as strategic autonomy rather than isolation.

“India needs domestic capability where trust and accountability matter, while leveraging global partnerships for speed and scale,” he said.

Risk of Dependence on US-Based AI Infrastructure

Several experts warned of long-term dependency risks if India relies heavily on foreign AI platforms for governance, media, and public services.

Banerjee said dependence arises when workflows, data pipelines, and institutional memory become embedded in external platforms. “The issue is less about model nationality and more about who controls the system architecture,” he said.

Garg added that dependency includes legal jurisdiction, supply chains, and model governance norms.

Dr. Padmanabhuni flagged data sovereignty and geopolitical risks, including potential technology access restrictions.

Open-Source AI as Digital Public Infrastructure

Experts also backed treating open-source AI models, datasets, and evaluation tools as public digital goods.

Garg said shared foundational AI resources align with India’s digital public infrastructure strategy. “Open AI resources lower entry barriers, reduce vendor lock-in, and enable private innovation,” he said.

Banerjee described open models as competitive infrastructure that strengthens ecosystem resilience.

Deployment Remains India’s Weakest AI Link

Multiple experts said India is under-investing in real-world AI deployment. Dr. Padmanabhuni noted that scalable, end-to-end implementations remain rare and insufficiently adapted to Indian linguistic and fairness contexts.

Krishnamurthy argued Budget 2026 should rebalance spending away from frontier narratives toward practical AI adoption in governance, MSMEs, healthcare, logistics, and agriculture.

“For India, the real win is AI quietly improving productivity at scale, not just building headline models,” he said.

Budget 2026: A Shift From Vision to Execution

As Budget 2026 approaches, experts converge on a single message: India must pivot from ambition to execution.

Key expectations include increased funding for DPDP enforcement, compute subsidies for startups and researchers, investment in power and data-center readiness, support for India-specific and sovereign AI models, open-source public AI assets, and policies that accelerate real-world AI deployment.

With the IndiaAI Mission already funded, Budget 2026 may determine whether India builds a resilient, inclusive AI ecosystem or remains dependent on foreign infrastructure, fragmented deployments, and symbolic technological sovereignty.