Infrastructure

AI-Ready GCCs in India: The Infrastructure Decisions That Will Define the Next Five Years

The Next GCC Will Be Designed Around Intelligence, Not Just Headcount India's GCC landscape is moving rapidly toward AI-led enterprise capability. EY's GCC research reported sign...

Data Confiance • • 5 min read
AI-Ready GCCs in India: The Infrastructure Decisions That Will Define the Next Five Years
In brief: The Next GCC Will Be Designed Around Intelligence, Not Just Headcount India's GCC landscape is moving rapidly toward AI-led enterprise capability. EY's GCC research reported significant investment momentum around agent...

In this article

    The Next GCC Will Be Designed Around Intelligence, Not Just Headcount

    India's GCC landscape is moving rapidly toward AI-led enterprise capability.

    EY's GCC research reported significant investment momentum around agentic AI, while PwC describes Indian capability centres as increasingly supporting AI research, analytics, cybersecurity and enterprise-wide transformation. EY

    This changes infrastructure planning.

    A workplace designed only around laptops, email and standard business applications may not be sufficient for the next generation of GCC requirements.

    AI Changes the Network

    AI workloads can create significant movement of data between users, cloud platforms, storage environments and compute resources.

    Network architecture therefore needs to consider higher bandwidth, lower latency, resilient connectivity and visibility.

    AI Changes Compute

    Some GCCs will consume AI entirely from cloud platforms.

    Others may require on-premises GPU infrastructure, private AI environments or hybrid models.

    Power, cooling, rack density, high-speed networking and storage architecture then become strategic design considerations.

    AI Changes Data Governance

    AI depends on data.

    That makes questions around identity, classification, access, privacy, retention and data movement even more important.

    AI readiness should therefore develop alongside data-security readiness rather than independently.

    AI Changes Cybersecurity

    AI creates opportunities for both defenders and attackers.

    GCCs need architecture capable of protecting identities, endpoints, workloads, APIs, cloud resources and sensitive data while maintaining appropriate monitoring.

    AI Changes the Workplace

    AI-enabled collaboration, smart meeting environments, advanced development workloads and distributed global teams place greater demands on digital workplace infrastructure.

    The modern GCC therefore connects network, cloud, security, AV, endpoints and support.

    India's GCC Expansion Is Also Moving Beyond Traditional Hubs

    The ecosystem is broadening. A September 2026 EY-CII report, for example, identified Coimbatore as already hosting more than 75 GCCs and potentially approaching 200 by 2032—illustrating the growth potential of emerging GCC locations beyond the traditional largest markets. EY

    This creates an additional requirement: technology architecture needs to remain consistent even when capability centres expand across multiple Indian cities.

    Infrastructure Is a Strategic GCC Decision

    A global enterprise can change applications relatively quickly.

    Replacing poorly planned physical and core infrastructure is harder.

    GCC leaders should therefore design technology foundations around what the centre could become—not simply what it is at launch.

    CTA

    Build the infrastructure your GCC will need tomorrow—not only the infrastructure it needs today.

    Speak with Data Confiance about AI-ready networking, data centre infrastructure, cybersecurity, cloud, data protection and digital workplace architecture.

    Data Confiance perspective: The strongest enterprise technology outcome comes from connecting architecture, implementation, testing, documentation and lifecycle support—not treating the product purchase as the finish line.
    Frequently asked questions

    Questions technology leaders ask

    Does an AI-ready GCC always need an on-premises GPU cluster?

    No. The right model may be cloud-first, on-premises, workstation-based or hybrid depending on workloads, data sensitivity, economics, latency and governance.

    What network changes should be considered for AI workloads?

    Potential considerations include higher bandwidth, lower-latency paths, resilient uplinks, fibre capacity, east-west traffic, cloud connectivity, segmentation and observability.

    Why is data governance part of AI infrastructure?

    AI quality and risk depend heavily on the data used. Access, classification, privacy, retention and authorised use need to be controlled alongside compute and model infrastructure.

    How can Data Confiance support AI-ready GCC planning?

    Data Confiance can bring together network, data-centre infrastructure, cloud, cybersecurity, data protection, workplace and lifecycle operations into one coordinated readiness roadmap.

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