India’s GCCs are quietly becoming enterprise AI control towers

Most enterprises have seen enough AI pilots to know what is possible. A demo can be built. A use case can be proven. A model can produce something impressive. The harder work begins when AI has to operate every day inside messy systems, real workflows, changing data, cost pressures, exception paths, and business accountability. That is when the conversation moves from experimentation to execution.

This is where India’s GCCs are beginning to matter more. Mature GCCs sit close to the systems, data, platforms, processes, and operating teams that AI must connect with to create value. That proximity makes them increasingly central to the enterprise AI agenda.

The next phase of the AI agenda will favour enterprises that can make AI work in the run: connected to systems, embedded in workflows, measured against outcomes, and owned by the business.

What puts India’s GCCs at the forefront in this context is best understood by looking at the realities of scaling AI inside the enterprise.

Pilots create belief. Production creates value

Pilots build confidence. They are ring-fenced, easy to showcase, and useful for demonstrating promise. But once AI enters production, it becomes a different equation.

Real dependencies show up when systems connect, data holds, exceptions need ownership, costs need to be understood, and business processes need to change. There must also be a way to tell if the business is improving. That is where the centre of gravity shifts.

This is where the gap becomes visible. A Zinnov-ProHance study found that while 92% of Indian GCCs are piloting or scaling AI initiatives, more than 70% still lack structured ROI frameworks to measure impact. In other words, the challenge is no longer whether GCCs can build AI. It is whether they can connect AI to measurable business outcomes.

Increasingly, that shift is placing India’s mature GCCs in a different role. Lowe’s India, for example, spans technology, analytics, and product capabilities, while Lowe’s has deployed Mylow Companion across more than 1,700 stores as an associate-facing AI assistant.

AI does not amplify intelligence. It amplifies context

In most global enterprises, the answer is not the model alone. It is the quality of context around it: customer, systems, organisational, and process context. That determines whether AI produces something useful or simply faster noise.

This is where mature India GCCs have an unusual advantage. They sit at the intersection of platforms, enterprise systems, data engineering, analytics, finance operations, risk workflows, customer journeys, and product operations. That horizontal view helps them connect signals otherwise fragmented across the enterprise.

Walmart offers a useful example. Its India GCCs are part of a global technology engine working across AI, agentic systems, and core infrastructure. Walmart’s broader deployment of AI-powered tools for associates shows these capabilities moving from experimentation into frontline work. The relevance is not AI in isolation, but AI connected to work, operations, and enterprise systems at scale.

The next advantage will come from connected intelligence: AI woven into the operating fabric of the enterprise, close to the systems, workflows, and decisions where value is created.

Connected intelligence also changes the talent question

The romanticised version of the future is all agents and no humans. Enterprise reality will be more selective. AI will not be applied everywhere simply because it can be. In some contexts, automation may create more cost than incremental value; in others, speed and scale will make agents the obvious choice.

For the foreseeable future, advantage will come from knowing where to automate, where to retain human judgment, and where to design the right continuum.

That is where India retains an edge: the relevant depth of multidimensional talent mature GCCs can bring together across engineering quality, product management, experience design, data engineering, enterprise context, and AI-first ways of working.

The human-plus-agent model will not be built by technology teams in isolation. It needs people who understand the workflow, customer, data, platform, exception paths, and business outcome. India GCCs are increasingly one of the few enterprise locations where those capabilities can come together with enough depth to deploy agentic systems at scale.

From activity metrics to CEO metrics

If this role is changing, the way GCCs are measured must change too. Traditional operating metrics such as efficiency, throughput, tickets closed, error rates, and utilisation still matter. But they no longer tell the full story. If GCCs are becoming central to how AI works inside the enterprise, their relevance must be measured against business outcomes, not just operational activity.

That means a stronger line of sight to CEO metrics: growth, cost impact, customer experience, resilience, decision velocity, and P&L-linked outcomes. One of the clearest measures of a GCC’s ambition may now be its proximity to those metrics.

This is not a shift away from operational discipline. It is a shift toward making business accountability more visible. AI accelerates that because once it enters the run, technical execution and commercial consequence become impossible to separate.

The opportunity is real. But it is not automatic

None of this should be mistaken for inevitability. Opportunity is not a guarantee. India’s GCCs may be well placed to help AI move from pilots to production. But that advantage will deepen only if global enterprises make deliberate choices about the role their GCCs should play.

The first choice is ambition. If India’s GCCs are expected to help shape production AI, they cannot be designed only for delivery efficiency. Operating models must give them the mandate, proximity, and decision rights to take on larger roles.

The second is capability. Enterprises will need multidimensional India footprints across product, data, customer experience, operations, and strategy, with senior roles owning product strategy, customer journeys, and business outcomes.

The third is accountability. As AI moves into the run, GCCs will increasingly be measured not just by activity or output, but by ROI-linked business impact.

The move from delivery to capability was the first chapter. The next one will be defined by whether India's GCCs have the ambition, permission, and ownership to turn production AI into measurable enterprise value.

(The author is EVP and MD, Publicis Sapient India. Views are personal.)