Authored by Sumit Dubey, Partner and Head of Asia, YAVA
India’s AI ambitions are beginning to acquire physical weight. Models that once lived mainly in research labs and cloud demonstrations are moving into banking, healthcare, manufacturing, government and public services. As AI becomes part of real operations, its performance increasingly depends on the infrastructure beneath it staying available, secure and connected to the systems around it.
Building the infrastructure around compute
The scale-up is already visible. In February 2026, the Indian government announced that it would add 20,000 GPUs to the more than 38,000 already onboarded through the IndiaAI Mission’s common compute facility. The aim is to give start-ups, researchers, academic institutions and government organisations affordable access to high-end computing capacity.
That push is being matched by efforts to strengthen India’s domestic semiconductor industry. At SEMICON India 2026 in September, Prime Minister Narendra Modi launched new commercial semiconductor production lines. Five manufacturing plants are now in commercial production, with further investment planned across design, manufacturing, packaging, R&D and talent.
That is an important foundation, but compute sits within a much wider technology environment. Production AI also relies on electricity, cooling, networks, storage, cloud architecture, identity, cybersecurity, backup and recovery. Each dependency must keep working as workloads grow and AI becomes embedded in services people and businesses rely on.
India’s data centre expansion shows how quickly those requirements are increasing. According to Cushman & Wakefield’s India Data Centre H1 2026 Update, the country’s top seven data centre markets added 178 MW of capacity in the first half of 2026, taking total operational capacity to 1,789 MW. A further 3,860 MW is under construction or planned for delivery by 2030, with AI deployments, hyperscale cloud and enterprise digital transformation driving demand.
Power is likely to become one of the clearest pressure points. The International Energy Agency found that global electricity consumption from data centres increased by around 17% in 2025, while consumption from AI-focused data centres grew even faster.
In India, that additional demand will arrive as electricity consumption is already expected to grow strongly through the end of the decade, making power availability a central part of AI infrastructure planning.
New compute capacity will need reliable grid connections, sufficient generation, effective cooling and resilience built in from the outset. As AI moves into more critical operations, the infrastructure supporting it will need to be as dependable as the systems it is expected to improve.
Engineering AI into existing systems
A second infrastructure challenge sits inside the organisations adopting AI.
Most Indian enterprises will introduce AI into technology estates built over many years. Those environments contain applications, databases, cloud services and networks, as well as operational technology in industrial settings. Many of those systems cannot simply be replaced. AI must connect with them without weakening security, fragmenting data further or interrupting live operations.
For engineering teams, that makes systems integration a core part of AI deployment. An AI service may need information from several applications, data platforms and physical assets before its output becomes useful. That creates three immediate engineering requirements:
Data must move securely across systems: Identity, access controls and governance need to remain consistent as information passes between applications, platforms and environments.
Integration needs to remain reliable under real operating conditions: APIs and data pipelines must cope with changing workloads, outages and dependencies without becoming another point of failure.
Existing systems must continue to run: Legacy technology cannot simply be switched off, making phased integration and continuity essential.
We saw this first-hand at YAVA when a client asked us to design a system that would let an AI service draw on data spread across legacy applications, cloud platforms and operational systems without interrupting live operations. The model was the simplest part; most of the engineering went into secure data movement, resilient integration and continuity.
These challenges receive less attention than model performance, but they often determine whether an AI project survives beyond the pilot stage.
Managing data, security and operational dependencies
Successful integration also depends on the quality and accessibility of the underlying data. India is already developing national infrastructure around this requirement through AIKosh, IndiaAI’s platform for AI-ready datasets, models, toolkits and development resources.
Inside an enterprise, however, useful AI still depends on data that is accurate, governed, accessible and understood in context. Healthcare, finance, government and critical infrastructure add further requirements around provenance, permissions and accountability.
Security and sovereignty also become infrastructure considerations as AI moves deeper into important operations. Leaders need to understand where sensitive information is processed, which external services their systems depend on, how access is controlled and how operations recover when a connection, platform or supplier becomes unavailable.
A single AI-enabled service may rely on a cloud environment, data centre, telecoms network, model provider, identity service and several internal applications. A failure anywhere across that chain can affect the service that users eventually see.
Building for operational scale
India has an opportunity to address these questions while its AI ecosystem is expanding rapidly. It already has significant software expertise, strong digital foundations, growing compute capacity and policy momentum. The engineering environment around AI now needs to develop at the same pace.
An infrastructure plan for AI should connect compute capacity with power, connectivity, data, cybersecurity, integration and operational support. It should also recognise that deploying AI into a bank, factory, airport or government department is fundamentally different from demonstrating what a model can do in isolation.
India’s next phase of AI leadership will be shaped by how successfully intelligent systems make that transition. The strongest measure of progress will be AI that works reliably inside the organisations, infrastructure and public services that increasingly depend on it.
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