The Rise of Agentic Artificial Intelligence (AI) in Indian GCCs : A Detailed Overview

Unlike traditional AI systems that operate on distinct inputs and respond to deterministic commands, agentic AI is built on the foundation of three pillars: 

• Autonomy
• Contextual awareness, and 
• Multi-step reasoning. 

These systems are composed of AI agents which are modular, goal-driven powerful entities capable of perceiving complex environments, generating strategies, collaborating with other agents, and learning from outcomes.

Agentic AI is not just intelligent; it is self-directed or ‘automated’. It interprets high-level objectives, formulates plans, adapts in real time, and executes across diverse systems without human micromanagement. This capability moves the needle from automation to orchestration, from response to foresight making GCCs in India all the more efficient.

For example, rather than relying on a rules-based system to process invoices, an Agentic AI network can autonomously handle the full procure-to-pay lifecycle. It can monitor contract terms, flag anomalies, negotiate payment schedules, reconcile discrepancies across ledgers, and escalate to the right stakeholder without waiting for human input. It behaves less like a tool and more like a digital colleague with an agency.

India’s GCC ecosystem is uniquely positioned to incubate and scale Agentic AI capabilities at a global level. First, the sheer volume and complexity of workflows handled by Indian centers spanning finance, compliance, risk, customer experience, HR, legal, and supply chain offer a rich substrate for agentic orchestration. These are environments that demand contextual intelligence, continuous adaptation, and fluid decision-making.

Second, Indian GCCs are now deeply embedded in enterprise value chains. They are no longer siloed delivery units but integrated hubs with end-to-end ownership of products, platforms, and processes. This shift from functional to strategic makes them fertile ground for piloting and industrializing agent-based models that span multiple domains.

Third, the talent advantage is decisive. India now hosts the world’s largest pool of AI and data science professionals outside the United States, and this talent is increasingly fluent not just in machine learning, but in systems design, cognitive architecture, and autonomous computing. Many offshore development centers are creating in-house AI agent labs, setting up LLM experimentation pods, and forming multi-disciplinary fusion teams to build agent frameworks that can operate across cloud, edge, and on-premise ecosystems.

Agentic AI marks a defining moment in the evolution of the global GCC model. It represents a shift from process-based outsourcing to capability-based partnering, and now, to intelligence-based autonomy. For CXOs, the implication is clear. This is not just a technology strategy, it is an enterprise strategy. One that requires vision, orchestration, and a willingness to embrace machine agency as a core business capability.

The GCC of tomorrow will not be defined by headcount, cost arbitrage, or even domain expertise. It will be defined by its ability to host, train, and scale intelligent agents that think, act, and evolve. The question for leadership is not whether to adopt Agentic AI—but whether they are ready to build a business that can lead alongside it.