AI Data Centres Are Reshaping Business Economics

Understanding the shift from traditional cloud to AI-powered compute - and what Corporates must prepare for in the decade ahead

Executive Summary : Why AI Data Centres Matter Now
India is entering a decisive AI infrastructure acceleration phase, fundamentally reshaping compute capacity, data-hosting strategies, and digital cost economics. Recent moves place India not just as a cloud market, but as a rising global hub for AI infrastructure.

Key developments:

• AdaniConneX + Google: A 1 GW+ AI and hyperscale DC park announced in Visakhapatnam, one of India’s largest AI campuses. 

• Hyperscale expansions in Navi Mumbai by major players: Equinix, AWS, NTT, Yotta, and Web Werks-Iron Mountain, all commissioning new AI-ready GPU zones. 

• Growing density in southern & western India: Chennai, Hyderabad, Bengaluru, Delhi-NCR, Noida emerging as AI compute corridors. 

• A major recent entrant: TCS, via its newly formed unit Hyper Vault, has pledged significant investments to build AI-ready data centres in India: 

• TCS + investment partner TPG together will invest up to ₹18,000 crore (≈ US$2b equity + debt) over the next few years to build gigawatt-scale capacity. 

• Hyper Vault aims for at least 1 GW of AI-ready capacity in India over the next 5-7 years. 

• A 2024 study by Tata Tele Business Services (TTBS) and CMR found that around 50% of Indian SMEs are already leveraging cloud for business expansion, and 52% rely on public-cloud services, confirming that MSMEs are well into their cloud-adoption journey and accelerating further.

• From a cost-economics and transformation standpoint, this means MSMEs should first consolidate their workloads on traditional public cloud, optimise for scale and cost, and only then evaluate which AI-driven workloads merit migration to AI Data Centres. This phased approach ensures financial prudence, compliance readiness, and maximum ROI as AI capabilities mature.

These developments underscore a structural shift: India is not just upgrading cloud capacity, it is building the backbone for AI. Given this backdrop, Corporates must understand what changes for them and adapt their digital strategy accordingly.

Why Corporates Must Now Care about "Where Their Data Lives"

Corporates have long adopted cloud services Gmail, Tally / Zoho, SaaS-based HR / CRM, GST, e-commerce, ERP, file storage without worrying where the data is hosted.

But in 2025-26, that assumption no longer holds.

• The new law, Digital Personal Data Protection Act, 2023 (DPDP), raises expectations around data residency, storage location, and compliance for sensitive sectors such as finance, healthcare, e-commerce, retail, education, and more.

• As AI-enabled services become mainstream, global vendors are increasingly deploying Indian customer data on Indian AI-ready infrastructure.

This shift impacts Corporates because:

• Cloud pricing models may evolve with rising AI compute costs

• AI-enabled features (analytics, automation, ML) will run on India-hosted AI DCs, improving latency, compliance, and performance

• Clients or regulators may demand data-residency or compliance certifications

In short, location of data, compute, and inference now matters more than ever.

Why Corporates Must Now Care about "Where Their Data Lives"

Many Corporates assume "cloud is cloud." But in reality, infrastructure has diverged sharply.

Traditional Data Centres

• Designed for CPU-based workloads: ERP, CRM, HRMS, email, basic applications

• Typical rack power: 3-10 kW

• Cooling: Standard air cooling

• Cost: ~US$5-7 million per MW

These support legacy SaaS and standard applications, and are still ideal for everyday business needs.

AI Data Centres (AI DCs)

• Built for high-performance AI workloads: deep learning, GPU / TPU clusters, real-time analytics, machine learning, GenAI

• Typical rack power: 30-80+ kW

• Cooling: Advanced liquid / immersion cooling, high-density racks

• Cost: ~US$10-20 million per MW, several times traditional DC

AI DCs deliver massive compute power, extremely high performance, and GPU-optimized infrastructure, making them essential for modern AI workloads.

This underlines why the new investments (like Hyper Vault) matter, AI applications simply cannot run efficiently on legacy cloud infrastructure at scale.

What Corporates Currently Run on Cloud, and What Should Shift to AI DC-Enabled Services

Workloads Suited to Traditional Cloud (No AI DC Needed)

• Email and collaboration tools

• Accounting / finance, GST, invoicing

• HR, payroll, leave management

• Basic CRM and sales tracking

• File / document management

• E-commerce storefronts and inventory management

• ERP, POS systems, order processing

• Basic BI dashboards and analytics

These workloads are CPU-lean, predictable, and don’t need high-density GPU compute, traditional cloud remains ideal.

Workloads That Will Benefit from AI DC–Enabled Services :

Few use cases:

Workload	Traditional Setup	AI-Enhanced Future
Sales & CRM	Rule-based scoring	AI-driven lead scoring, propensity prediction, churn prediction
Marketing	Human-generated content	GenAI-powered campaigns, copywriting, creatives
Inventory & Demand Planning	Fixed reorder logic	AI-driven demand forecasting, dynamic replenishment
Manufacturing / Machine Monitoring	Periodic manual checks	Predictive maintenance, anomaly detection using ML
Customer Support	Call centres / tickets	AI chatbots, voice bots, 24×7 multilingual support
Training & Learning	Static internal modules	AI-personalised learning paths, chatbots, generative content

These capabilities require GPU/TPU clusters, high compute, and real-time inference, which only AI DCs can reliably support at scale and cost efficiency.

What Corporates Must Evaluate Before Adopting AI-Enabled SaaS

Even if Corporates do not own AI DCs themselves, they must assess vendors carefully as cost structures and compliance begin to matter more.

• Compute Type: CPU vs GPU, ask vendors about additional compute charges

• Billing Model: Monthly subscription, per inference, per GPU-hour, per document or per 1,000 predictions, choose accordingly, especially if usage is seasonal

• Data Hosting Region: Confirm whether data and AI inference are India-hosted, this affects latency, compliance under DPDP, cost, and data control

• Data & Model Ownership: If you supply data to train the AI, ask whether you own the trained model. Prefer SaaS vendors that offer dedicated or hybrid models when data is proprietary.

• Vendor Lock-In & Portability: Ensure you can export data, model weights, and have API portability or exit clauses, migrating large AI workloads is difficult.

Why "Burst Compute" Is a Big Advantage for Corporates

Many Corporates work on seasonal or cyclical business models - retail surges, harvest seasons, logistical peaks, batch manufacturing, tourism.

Instead of investing permanently in expensive GPU infrastructure, Corporates can opt for burst compute, paying only when needed. Example use cases:

• A retailer forecasts demand only before festive seasons, use AI compute for 2-3 months only

• A manufacturer runs quality-control AI monthly or quarterly, not continuously

• A logistics firm uses route optimisation during busy months only, and sticks to basic optimization otherwise

This model enables access to cutting-edge AI on flexible costs, without upfront capital or long-term commitments.

Why Data & Trained Models Are Strategic Assets

Corporates generate valuable data - sales, production logs, customers, suppliers, inventory, workflows. With AI:

• Data becomes actionable insight

• A trained model becomes proprietary intellectual property, encapsulating business-specific logic

• Retraining periodically is cheaper than new model development

• Ownership ensures vendor independence, data sovereignty, and better long-term value

In short, data + model = a competitive moat.

The Bottom Line for Corporates: The Next Decade Belongs to AI-Native Businesses

India’s AI Data Centre boom is more than a technology trend, it is an economic inflection point. Corporates no longer need to understand cooling systems or rack densities, but they must grasp:

• How cloud pricing will evolve with AI

• Why SaaS offerings will begin to rely heavily on AI DCs

• Why data location matters for compliance and performance

• How AI-enabled workflows will redefine operations, competitiveness, and cost economics

Early adoption can yield 10-30% productivity and efficiency gains, better customer experiences, and scalable intelligent operations. Delay could mean higher costs, data lock-in, and fragmented systems.

Your AI future depends on where your data - and compute - live. Indian AI Data Centres are now the foundation for that future.

We Invite Your Perspective

We welcome you to share your experiences, questions, and viewpoints. Join the conversation on LinkedIn or reach out directly to discuss how these infrastructure developments can support your growth roadmap.

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