The defining question for India’s next generation of GCCs may no longer be how much work they can execute, but how much of the enterprise they can actually shape. As AI reshapes software development, data engineering and decision-making, GCCs are moving into territory once reserved for global headquarters, designing platforms, building AI systems and taking responsibility for technology that sits at the heart of the business.
That raises a more fundamental question. At what point does a centre built to support the enterprise become one of the places where the enterprise itself is being built?
Ramya Parashar, COO, MiQ, sees that transition already underway. “The first chapter was about scale, the second was about operational excellence, and the next decade will be defined by how much of an enterprise’s core technology, its platforms, its architecture, its AI systems, actually gets designed in India rather than just deployed here,” she says.
The end of the delivery centre
For years, the GCC proposition was built around execution. India offered the talent, scale and cost economics to run technology and business operations for global enterprises. But the mandate is changing.
A centre executing a roadmap designed elsewhere operates under a fundamentally different mandate from one that owns architecture, engineering decisions and the evolution of a global platform.
Parashar points to MiQ’s Bengaluru Centre of Excellence as an example. Established in 2012 as a delivery function, it has evolved into an engineering, AI and data science hub supporting systems used across global markets.
“The test for any GCC now isn’t whether it can absorb more workload. It’s whether it’s trusted to own systems that the business cannot afford to get wrong,” she says. “That’s a much higher bar than efficiency ever was.”
That is also changing the basis on which India’s GCCs compete. Cost efficiency may remain part of the equation, but it is no longer sufficient. The greater prize is strategic responsibility, and the ability to take ownership of increasingly complex technology mandates.
AI moves the value upstream
Artificial intelligence could accelerate that shift. Automation will remove portions of repetitive execution, but it also raises the value of the people responsible for the systems underneath it.
“Automation removes the low-value work. It does not remove the need for people who understand the systems underneath it, and that’s where the real value shifts,” Parashar points out.
An AI system still has to be architected, secured, governed and integrated into the enterprise. Someone must also determine when its output can be trusted and when human intervention is required.
That makes AI less a threat to the GCC model than a test of its maturity. The centres that remain focused on execution risk becoming less differentiated. Those that can combine engineering, data science, product thinking and judgement move closer to the core of the enterprise.
At MiQ, that complexity is reflected in Sigma, which operates across more than 600 data feeds and 2.5 petabytes of data through a Databricks-based architecture. Parashar mentions Bengaluru has played a significant role in building its engineering and data infrastructure.
The broader implication is significant. The more critical AI becomes to enterprise operations, the greater the influence of the teams responsible for building and governing it.
When India starts making the decisions
AI is also challenging the traditional geography of decision-making.
Global operating models historically separated product ownership, engineering and execution across different locations. AI development makes those boundaries harder to maintain. Engineering, data science and product teams need to iterate quickly, often in close proximity to the technology and the business problem.
“AI systems don’t work well with slow, centralised, decision-making,” Parashar avers. “They need engineering, data science and product teams iterating in the same room, on the same timeline.” That dynamic could accelerate the transfer of global mandates to India.
At MiQ, Bengaluru teams are involved directly in elements of Sigma’s AI engineering and data architecture rather than simply implementing a roadmap developed elsewhere. It is a subtle but important distinction. The GCC is no longer merely translating strategy into execution. It is increasingly participating in the decisions that shape the technology itself.
The question for global enterprises, therefore, is changing. It is no longer whether India can execute the technology strategy. It is whether India can help define it.
The ROI test
Greater ownership also brings greater scrutiny. AI investment is moving beyond the pilot stage, forcing enterprises to establish harder measures of success.
For Parashar, model sophistication is not an adequate measure of AI maturity. The technology has to move a real business metric, be used consistently, outperform what it replaced and operate reliably across markets while meeting governance requirements.
“Those are the numbers that justify further investment for us, not model sophistication, but measurable, repeatable business impact at scale,” she says.
MiQ says Sigma has powered more than 40,000 campaigns for over 2,300 advertisers since launch. In controlled A/B testing against standard programmatic setups, the company says Sigma campaigns delivered $2.22 in value for every dollar spent, based on incremental reach, conversions and cost efficiency.
The significance of such measurements extends beyond one platform. The AI conversation is moving from whether a model works to whether it delivers a repeatable business outcome at scale.
For GCCs, that distinction matters. Ownership without measurable impact simply creates another layer of complexity. Strategic relevance has to be earned through outcomes.
The trust problem
Capability alone, however, will not determine which GCCs gain greater influence. As Indian centres take responsibility for increasingly consequential AI systems, governance becomes inseparable from technology ownership. Security, responsible AI, model governance and the broader trust architecture around AI will become part of the GCC mandate rather than functions sitting somewhere else in the enterprise.
“The next frontier is governance, not just capability,” Parashar says. “Enterprises are handing GCCs real ownership of AI systems now, which means Indian centres need to lead on the harder, less visible work too.” That may be the real test of the next GCC era.
India’s first advantage was its ability to execute at scale. The second was its ability to deliver operational excellence. The emerging advantage is more difficult to quantify. It is the ability to conceive, build, govern and continuously evolve technology that the global enterprise depends on.
That is when the definition of a GCC begins to change. Perhaps the more interesting question facing India’s GCC ecosystem now is not how far a global enterprise can extend its operations into India, but how much of its future it is prepared to build here.