Executive Summary

For the past two decades, India’s Global Capability Centers (GCCs) have transitioned from back-office support to the “innovation engines” of global enterprises. As we move into 2026, business growth consulting in India has shifted from Generative AI—which generates content—to Agentic AI, which executes workflows autonomously.

Unlike traditional automation, Agentic AI features “agents” that can reason, plan multi-step sequences, and use enterprise tools to achieve high-level business outcomes. Consider a BFSI GCC in Bengaluru serving a US bank. Traditional RPA automates invoice processing; GenAI summarizes documents. An agentic AI agent, however, autonomously detects fraud anomalies, cross-references with customer history across siloed systems, queries external APIs for market data, approves micro-loans under $5K, and updates compliance logs—all in minutes, with human oversight only for edge cases. In healthcare GCCs like those in Hyderabad, agents orchestrate patient scheduling, predict no-shows using weather and traffic data, reallocate resources, and notify providers. For CXOs, the promise is clear: 40–60% reduction in process times, error rates dropping to <1%, and talent reallocated to high-value strategy.

However, implementation lags behind hype. It is seen, 70% of GCC pilots fail to scale due to systemic challenges unique to India’s ecosystem—vast scale, diverse verticals, and hybrid global-local operations. The transition is fraught with risks that span the entire value chain—from flawed use-case selection to the failure of reimagining the organizational structure post-deployment.

This white paper examines these critical challenges CXOs face in deploying agentic workflows across the 1,700+ GCCs in India, providing an executive roadmap to bridge the gap between “flashy pilots” and enterprise-scale value. It provides a creative, pragmatic roadmap summarising GCC growth strategies in India for CXOs to drive meaningful productivity and business impact.

1. The Value Chain of Failure: Strategic Missteps in Agentic AI

The journey to an agentic organization often fails before the first line of code is written. CXOs must manage the implementation value chain with surgical precision. 

I. The Due Diligence Deficit: The “Wrong” Use Cases

The most common mistake in Indian GCCs is selecting use cases based on “technical flashiness” rather than “process readiness”. 

• Linear vs. Agentic Tasks: Many GCCs attempt to use Agentic AI for simple, linear processes where standard Robotic Process Automation (RPA) is more cost-effective. 
• The “Context Blindness” Trap: Use cases are often identified in isolation without considering the messy, unstructured nature of global data.
  • Example (Supply Chain GCC): A global logistics GCC in Chennai attempted to deploy an autonomous agent to manage “Supplier Dispute Resolution.” They failed because they did not account for the high level of emotional intelligence and manual “offline” negotiations required in vendor management. The agent could process the data but could not navigate the human relationship nuances, leading to a breakdown in supplier trust.

II. The Integration Paradox: The “Data-to-Action” Gap

Agentic AI is only as good as the tools it can “wield” (APIs, databases, and enterprise software). 

• API Fragmentation: GCCs often manage a patchwork of legacy systems and modern cloud apps. If an agent cannot talk to these systems securely, it becomes a “brain without limbs”.
  • Example (Banking GCC): A financial services GCC tried to build a “Claims Processing Agent.” While the agent could read the claim, it lacked the authorized API access to the core banking ledger to execute the payout. The result was a “broken loop” that required more human intervention than the original manual process. 

2. The Strategic Context: From Tools to Teammates

Traditionally, AI in GCCs has been “assistive”—tools like chatbots and dashboards that required human triggers to function. Agentic AI represents a leap forward because these agents observe, decide, and act independently. 

• Autonomy: The ability to execute multi-step reasoning without step-by-step instructions. 
• Contextual Awareness: Agents that understand complex environments and adapt in real-time. 
• Orchestration: Moving from isolated tasks to end-to-end value stream ownership.

Over 90% of Indian organizations expect to deploy AI agents by the end of 2026, but only 29% have successfully scaled more than 30% of their initial Proofs of Concept (PoCs). For CXOs, the challenge is no longer “Can we build it?” but “How do we trust and scale it?”. 

3. Core Implementation Challenges for the GCC Leader

A. The “Identity Anarchy” and Cybersecurity Gap

In an agentic ecosystem, the traditional perimeter of “who is acting inside my organization” blurs. When an AI agent autonomously accesses a CRM, opens a support ticket, and executes a transaction, it creates a “blast radius” if misconfigured. 

• Challenge: Only 7% of Indian GCCs report having a fully embedded Cyber Centre of Excellence. 
• Example: A financial services GCC deploying an agent for loan processing. If the agent gains unauthorized “identity ambiguity,” it could potentially modify risk parameters or access sensitive customer data without a traceable human-grade control.

B. The Integration Paradox: Legacy vs. Agentic

GCCs often manage fragmented legacy systems and hyper-scale cloud deployments simultaneously. Agentic AI requires seamless inter-connectivity across APIs and SaaS platforms. 

• Challenge: 78% of Indian enterprises struggle with system integration when trying to scale AI beyond pilot stages. 
• Example: A retail GCC attempting to use a supply chain agent to dynamically reroute shipments. If the agent cannot communicate in real-time with an on-premise legacy inventory system, its “autonomous” decisions will be based on stale data, leading to stockouts or logistics failures

C. Data Governance and Sovereign AI Hurdles

Agentic AI thrives on data hoarding to improve its reasoning, which often conflicts with strict data privacy mandates like India’s Digital Personal Data Protection (DPDP) Act of 2023. 

• Challenge: Data governance and security are cited as “very severe” barriers by 64.5% of Indian enterprises. 
• Example: A healthcare GCC using agents to monitor patient vitals. The agent might “over-collect” personal information irrelevant to the task to improve its predictive accuracy, inadvertently violating “data minimization” principles.

D. The Talent and Skills Reset

The rise of agentic AI is re-leveling the workforce. Demand is skyrocketing for niche roles like AI Architects, AIOps Engineers, and AI Ethics Officers, while traditional mid-office roles are waning. 

• Challenge: Niche talent shortages and rising people costs are the top risks for 81% of GCCs. 
• Example: Many GCCs find that while they have “coders,” they lack “orchestrators”—professionals who can design a culture where humans trust and collaborate with AI teammates rather than just using them as tools. 

4. Function-Specific Impact and Challenges

5. The Post-Implementation Vacuum: Structural and Cultural Challenges

The technical “Go-Live” is often the beginning of the real trouble. Many revenue growth strategy consultants opine that organizations fail because they do not envisage the Post-Agentic Workflow.  

I. The Missing “Agent Handler” Role

When an AI agent takes over a 24/7 workflow, the role of mid-level managers changes from “Supervising People” to “Auditing Agents”. 

• Skill Gaps: Existing managers often lack the technical depth to debug an agent’s reasoning logic when it fails. 
• Lack of Accountability: If an agent makes a $1M error in a procurement contract, who is responsible? Most GCC structures do not have an “AI Responsibility Framework” in place.

II. Workflow Atrophy and Lack of Feedback Loops

Autonomous agents require constant “Reinforcement Learning from Human Feedback” (RLHF). 

• The Feedback Chasm: If the GCC organization doesn’t create formal loops where domain experts (Accountants, Engineers, HRBP) monitor and “coach” the agents, the AI’s accuracy will drift over time. 
  • Example (IT Services GCC): An infrastructure GCC deployed self-healing agents to manage server downtimes. However, because they didn’t restructure the team to include “Agent Quality Assurance,” the agents began applying outdated patches, eventually causing a cascading system failure that the human team was no longer “in the flow” to stop quickly. 

Conclusion 

The agentic moment is here. 2026 marks the point where Indian GCCs transition from being global adopters to global creators and exporters of agentic innovation. The winners will not be the ones with the flashiest demos, but those who build a “credibility foundation” of identity, transparency, and trust.

As a CXO, your mandate is to prepare your center with the help of strategic business advisory firms, not just to withstand this disruption, but to lead the global enterprise through it.