Most business leaders are not building their Global Capability Centres (GCCs) from scratch today. Instead, they inherit mature hubs that have spent years refining finance, IT, engineering, and analytics operations. But as AI shifts from experimental pilots to full production, leadership face a critical question: does this traditional operating model still work?  

For most centres, the honest answer is no. Success in the AI era requires a total model redesign, not just minor improvements. 

The shift is already underway 

Enterprise AI has moved past pilots. Nearly half of large organizations now run mature AI use cases in production. Here’s a task level analysis from the EY GCC Pulse Survey 2025 that shows that 24 percent of enterprise tasks can be fully automated today, and another 42 percent can be significantly augmented. That is two thirds of enterprise work that no longer needs to run the old way. 

GCC leaders are responding. 83 percent are testing new GenAI capabilities, and 58 percent are building agent-based systems. The centre of gravity is moving fast, and it is moving toward centres that can perceive context, plan actions, and execute with minimal human handoffs. 

Why the old model runs out of road 

Lean programs, ERP consolidation, and robotic process automation carried GCCs a long way. But these levers typically deliver five to fifteen percent improvement, and most enterprises have already extracted what they can from them. Agentic AI works differently. It does not automate a task in isolation, but orchestrates an entire workflow, gathers data from outcomes, and handles exceptions with its own assessments.  

What an AI-first GCC looks like 

This shift reshapes how work happens within the centre. Instead of following linear, approval-driven processes, teams run event-driven operations where AI agents execute tasks continuously. These agents escalate only the issues that genuinely require a human decision. This allows humans to move away from repetitive execution and focus instead on oversight and complex judgement calls.  

This repositions the GCC itself.   

Four pillars that hold the redesign together 

An AI-first operating model rests on four things working together. 

• Talent: Companies must shift their culture toward oversight and orchestration instead of just technical training. 

  
  

• Platform: Organizations must build a modular, API-first architecture so agents move seamlessly across systems. 

  
  

• Governance: Teams must embed explainability and human controls from day one. 

  
  

• Operating Model: Businesses must redesign workflows around events instead of old approval chains. 

  
  

Neglecting any single pillar weakens the other three. For instance, a strong platform without governance creates risk, while strong governance without redesigned workflows creates bottlenecks. 

Getting the foundation right 

We know AI already sits deep inside your strategy. It is not a side initiative anymore. It is how you plan to run the business at scale in the years ahead. That ambition needs more than internal bandwidth. It needs a talent pool that has already been building agentic systems in production, and a startup ecosystem that moves at the pace AI demands. 

India offers both. A deep bench of AI and engineering talent, and a startup pool building at the edge of what agentic AI can do. Covasant brings this together through CAMS, an enterprise agent platform built to orchestrate, govern, and scale AI agents in production, with the connectors, data readiness, and governance a GCC needs to move fast without moving recklessly.  

Enablr applies that same AI first approach to how centres are set up and run in India, from strategy to talent to daily operations. 

Co-building your AI journey with the right partner on the ground is how you turn strategy into scaled deployment faster. If your GCC is ready to accelerate its AI adoption, this is the conversation worth having now.