As India’s surging data demand reshapes the digital landscape, success will depend on designing AI‑ready data centres as critical infrastructure for smarter factories, notes Robert HK Demann.
Artificial intelligence (AI) is not only about software; it is also equally about infrastructure. Just as steel plants, refineries, and other industrial systems are engineered to run reliably and produce output at scale, AI factories are the digital equivalent: engineered environments that repeatedly convert data into intelligence.
An AI factory is not a single graphics processing unit (GPU), just as a factory is not a single machine. It is the coordinated system of compute, data, power, heating, cooling, and automation that brings all of this together and orchestrates it in a meaningful way. As India’s data demand accelerates, success will increasingly depend on designing AI‑ready data centres as critical infrastructure: resilient, efficient, and scalable.
Data and Demand Advantage
India generates roughly 20 per cent of the world’s data, yet less than 3 per cent of it is hosted in India. An easy way to picture this: if ten trucks are transporting data, two are filled in India, but hardly any of them ‘park’ here to be processed and turned into value. With Unified Payments Interface (UPI), massive telecom adoption and fast digitisation across manufacturing, energy, agriculture, and healthcare, the opportunity is to retain and leverage more of that data locally at scale.
AI is already delivering measurable outcomes: optimising building operations by nearly 30 per cent, reducing breakdowns in heavy industry via predictive maintenance by up to 70 per cent, and improving data centre cooling efficiency, often by 50 per cent or more in leading deployments. Applied locally, this is not an incremental change; it can become a structural advantage, preventing two‑thirds of unplanned stoppages in industrial plants or materially reducing energy consumption in major facilities.
Projections indicate India’s data centre compute capacity could grow to nearly 8 GW by 2030, comparable to the electricity demand of a city like Delhi on a hot summer day. At that scale, AI factories are no longer ‘just IT’ infrastructure. They become grid‑relevant infrastructure, influencing stability and requiring tighter coordination with generation, transmission, and planning.
From MW to GW
AI factories are expected to scale faster than traditional cloud data centres, often cited as an approximately 4x growth. The industry is also shifting from megawatts to gigawatts. Where traditional sites used to be nearly 10-40 MW per building, and more recently nearly 80 MW, AI‑driven facilities are already reaching nearly 150 MW per building, and campuses can reach gigawatt scale. This changes everything, from how utilities forecast load to how sites are engineered for resilience.
The shift starts inside the rack. Power density is moving from a few kilowatts per rack to tens of kilowatts—and increasingly beyond—compressing enormous energy into a small footprint. With this level of complexity and consequence, design cannot rely on trial and error. Digital simulation and digital twins help validate architectures before buildout, answering ‘what if’ questions and reducing the risk of expensive mistakes, much like how metro systems are modelled before they are constructed.
Cooling becomes equally pivotal. With higher densities, thermal excursions escalate faster: what used to allow minutes to respond can shrink to seconds, making manual intervention insufficient. This is accelerating the move from air cooling to liquid cooling, typically via direct‑to‑chip or immersion approaches. Success depends on tightly synchronising power and cooling with intelligent automation and controls.
Power, Cooling, and Silicon
The underlying driver is silicon.
Processor power has rapidly increased—from roughly 200 W to nearly 3,000 W in a short time—forcing changes in how equipment is powered and cooled. Power delivery is shifting toward direct current (DC) architectures, instead of conventional alternating current (AC) distribution, while cooling is shifting from air‑cooled racks to liquid‑cooled designs. These are not optional upgrades; physics is making the infrastructure redesign unavoidable.
AI factories will also be heterogeneous environments, mixing low, mid, and high‑density compute, including central processing units (CPUs), GPUs, and accelerators, with different cooling needs and refresh cycles. Think of it like a rail network running freight, passenger, and express trains on the same tracks: the operator who orchestrates the mix best—capacity, scheduling, and constraints—will perform best. Digital twins play a key role in optimising these trade‑offs over time.
New Operating Metrics
AI factories change what ‘performance’ means. Beyond uptime, operators will be measured on learning speed and AI output, often expressed as tokens per second. In the same way a refinery measures barrels per day, an AI factory measures how efficiently it can produce tokens. This drives a new focus on energy per token (watts per token) and on end‑to‑end efficiency across chips, power systems, cooling, and automation.
Because energy demand is so great—powering compute and removing heat—operators who can reduce consumption or reuse waste heat gain a durable advantage. Efficiency becomes a competitiveness lever, not just a sustainability goal, especially as campuses scale and interact directly with grid constraints.
Delivering these outcomes is not a single‑player effort. AI factories require coordinated ecosystems across silicon, infrastructure, cooling technologies,
software, and operations. The teams that bring together the right partners to integrate their solutions into repeatable, industrial‑grade designs will be best positioned to scale responsibly and reliably.
Grid‑Ready Infrastructure
The transition to AI factories is a transition to foundational, industrial‑grade infrastructure. It requires designing for higher densities, faster thermal dynamics, and new performance metrics, while planning at the campus scale and coordinating closely with the grid. Digital twins, advanced automation, liquid cooling, and modern power architectures are key enablers of this shift.
Technology companies will need to partner with customers and the broader ecosystem to help move from traditional colocation models to AI factories, so India can build resilient, efficient, future‑ready digital foundations that turn data into intelligence at scale.
Robert HK Demann, Executive Vice President and Head of Smart Infrastructure, Siemens Ltd, brings over two decades of leadership in global management, building new ventures and transforming businesses across industries.