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Across India’s Global Capability Centers (GCCs), artificial intelligence (AI) has moved well beyond experimentation. Dedicated AI Centers of Excellence (CoE) are becoming increasingly common; enterprise pilots are delivering measurable results, and leadership teams are now under increasing pressure to translate early success into enterprise-wide value.
The novelty has worn off, and GenAI is no longer on trial. The conversation is now moving towards a harder question of ‘how do you scale it reliably across the enterprise?’
The roadmap is tricky. Models that perform well in controlled environments often struggle once they encounter enterprise realities of fragmented infrastructure, hybrid environments, regulatory obligations, and business-critical workloads. As deployments grow, the bottleneck shifts from AI capability to enterprise readiness.
The Missing Layer Between AI and Enterprise Scale
India’s GCC ecosystem has made remarkable progress in enterprise AI adoption. According to the Zinnov-NASSCOM India GCC Landscape Report 2026, AI penetration has increased significantly, and more than 250 GCCs now operate dedicated AI CoEs. These investments have ensured that these organizations are building AI, not just deploying it. But building a functional technology and operating it at scale are fundamentally different challenges.
While an AI CoE is designed to explore possibilities, the expectation from Enterprise AI is to deliver predictability. Even well-established AI CoEs can struggle when high-latency networks, fragmented cloud environments, and disconnected data pipelines begin affecting production workloads. This matters because enterprise deployments are required to meet business expectations for performance, resilience, security, and compliance simultaneously.
These constraints often remain invisible during pilots but emerge quickly as enterprises begin scaling AI workloads. For GCCs responsible for global engineering, customer platforms, or product development, these inefficiencies directly affect delivery speed, operational costs, and ultimately business outcomes.
The cost implications extend beyond AI itself. When the majority of technology spending remains tied to maintaining fragmented environments, organizations struggle to redirect investment toward innovation. AI then becomes another workload competing for constrained operational budgets instead of a capability that transforms how the business operates.
Most often, the missing layer is an integrated digital foundation. One that brings together high-performance connectivity, cloud and compute environments, trusted data pipelines, and embedded governance into a single operating model. It is this integration that allows enterprises to move from successful AI pilots to reliable, enterprise-scale operations.
What Changes When the Foundation Is Ready
India is home to more than 2,100 GCCs, out of which over 1,200 have AI and machine learning (ML) capabilities, allowing them to drive innovation and focus on scaling business operations. As a result, Indian GCCs are increasingly being entrusted with product engineering, platform development, AI operations, and business-critical decision support instead of just execution.
An integrated digital foundation enables that shift. When connectivity, cloud, compute, cybersecurity, governance, and data operate as a single fabric rather than independent technology layers, AI becomes significantly easier to operationalize across business functions. Instead of repeatedly solving infrastructure, compliance, and integration challenges for every deployment, GCCs can focus on expanding AI into products, customer experiences, and enterprise workflows.
The most effective organizations do not treat AI CoEs and infrastructure as separate investments competing for budget. They develop them together. AI CoEs build organizational capability. Integrated digital foundations make that capability repeatable, governable, and scalable across the enterprise. That combination ultimately determines whether a GCC becomes an execution center for AI projects or a strategic hub for enterprise AI innovation.
Sify helps GCCs move from AI capability to enterprise AI capability. By integrating AI-ready data center infrastructure, cloud, high-performance connectivity, cybersecurity, and managed services into a single operating foundation, we enable enterprises to build, run, and govern AI at scale. The result is a digital foundation that allows GCCs to scale AI confidently, expand their enterprise mandate, and create measurable business value.
To learn more about how Sify can help accelerate your GCC growth, write to us at marketing@sifycorp.com.
FAQs
As AI moves into production, organizations need an integrated operating foundation that combines cloud, compute, networking, trusted data, cybersecurity, and governance. This allows AI workloads to run consistently across global capability centers (GGCs) while meeting enterprise requirements for performance, security, compliance, and resilience.
Enterprise GenAI depends on a combination of AI-ready compute, scalable cloud environments, high-performance connectivity, trusted data pipelines, cybersecurity, and governance. Treating them as a unified operating environment is more effective than managing each technology independently.
An AI Center of Excellence helps organizations build AI capability by developing technical expertise, identifying high-value use cases, establishing governance practices, and accelerating experimentation.
Sify helps GCCs move from AI capacity to operational and scalable enterprise AI capability by integrating AI-ready data centers, cloud, high-performance connectivity, cybersecurity, and managed services into a single digital foundation. This enables GCCs to operationalize AI more efficiently, reduce infrastructure complexity, strengthen governance, and support production-scale AI deployments across distributed enterprise environments.
Sify provides an integrated digital infrastructure designed specifically for enterprise AI. Rather than offering individual technology components, Sify combines AI-ready data centers, cloud, networking, cybersecurity, and managed services into a unified operating foundation. This helps organizations reduce deployment complexity, strengthen governance, and scale AI with greater operational resilience and business confidence.
Murtaza Bhatia is a technology and cybersecurity leader with over two decades of experience across IT infrastructure, cybersecurity, cloud and enterprise technology solutions. As Vice President & Practice Head – Cybersecurity at Sify Technologies, he leads the company’s cybersecurity practice, focusing on helping enterprises strengthen their security posture, simplify security operations and build AI-ready digital environments.
Based in Mumbai, Murtaza is responsible for driving cybersecurity strategy, business growth, solution development and customer engagement across industries. His experience spans enterprise security, data center technologies, cloud, consulting and managed security services, with a strong focus on aligning security frameworks with evolving business and technology requirements.
Before joining Sify, Murtaza held leadership roles at organizations including NTT DATA, NTT Ltd. and Dimension Data, where he led cybersecurity, presales and technology consulting functions across enterprise accounts and industry verticals. His focus areas include cybersecurity simplification, AI-led security operations, enterprise resilience and securing AI ecosystems. He actively engages with enterprises on how AI and cybersecurity can work together to improve operational efficiency while maintaining strong governance and protection frameworks.
