Why India’s GCCs Are Moving Beyond Cost Arbitrage

India’s global capability centres (GCCs) have spent years building a reputation for execution. They handled back-office operations, finance, support, and later software engineering. Now, the enterprise software firm Celonis suggests that the next transformation has started: GCCs are transitioning from executing tasks to guiding how work is structured.

At its Process Intelligence Day India 2026 event in Bengaluru, the company made a case for what it calls process intelligence, a layer of operational context that sits between enterprise systems and AI agents. 

If enterprises want AI to move beyond chatbots and productivity tools, they first need systems that understand how their businesses actually function.

“We’re going to figure out how we run by having a digital twin and building up this context,” Dan Brown, Chief Product Officer at Celonis, told AIM. “We will re-engineer and optimise quickly, using agents to do much of it. That's why this context matters.”

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The Missing Layer Between AI and Business

Much of the enterprise AI rush has focused on deploying frontier models into workflows. Yet many firms still struggle to show measurable returns or scale AI systems across functions. 

Brown contended that the core problem is the lack of business context. He points out that although frontier models perform well on public data, enterprise operations require a more comprehensive understanding of the business environment.

He explained that AI systems often fail in enterprise environments because they cannot trace why a workflow breaks across interconnected systems. Hence, Brown emphasised the importance of providing relevant context to support operational assistance. 

Celonis began as a process mining company. It analysed enterprise data to uncover how workflows functioned and where bottlenecks emerged. Over time, it expanded into process intelligence, which Brown described as moving “beyond process discovery” into “optimising, re-engineering, operating” using a richer operational context.

Brown says Celonis has created just about $11 billion worth of value for its customers, arguing that enterprises increasingly want measurable operational outcomes rather than experimentation.

The company says this context becomes more important as enterprises adopt agentic AI. A sales order blocked by supply issues or a stalled production process cannot be fixed by a generic AI system without understanding the chain of events behind it.

Competitors like SAP, ServiceNow, Microsoft, IBM, and Salesforce are also pursuing similar context layers. But Brown says Celonis positions itself differently, building context models on process intelligence and object-centric process graphs, rather than just workflow automation or enterprise copilots.

“One of the ways you can think about this is what we do uniquely is allow agents to answer the question why,” Brown says. “If there is a stockout or there is a blocked sales order, or there is a production order that is stalled, our context model can answer the question why.”

What Beyond Cost?

That shift may matter most in India’s GCC ecosystem.

For years, GCCs focused on operational support and cost efficiency. Yet many have begun taking ownership of engineering, product development, and transformation mandates. Some GCCs are already moving in this direction. 

Ford Business Solutions (FBS) India, for example, started as a shared services operation and has expanded into engineering, platform development, connected vehicles, and AI-led workflows. India teams now influence product direction and own global programmes across Ford’s business.

FBS India, which employs over 12,000 people, now contributes across the engineering, mobility, and connected-vehicles domains. Regardless, many centres still struggle to move beyond support roles. Yet enterprises increasingly want teams in India to own outcomes rather than execute instructions.

Various teams at FBS contribute across connected vehicle systems, cloud-native platforms, and AI-enabled manufacturing. The company also uses AI for software engineering, predictive quality, and enterprise-wide decision-making.

Celonis sees a similar path unfolding across enterprises.

Eugenio Cassiano, Executive Vice President for Corporate Strategy & Innovation at Celonis, says GCCs spent the last decade focused on robotic process automation and task-based efficiencies. The next phase, he argued, will revolve around operational intelligence.

What for India?

For Celonis, India is central to its growth, with over 300 employees—a 250% increase in a year—and plans to expand further in engineering, research, and product roles.

Cassiano went further, calling India a strategic centre for the company. “At the end of the day, we see India as our third headquarters,” he says, adding that the company can now measure the impact of its AI initiatives through an AI Value Control Tower that uses agent mining to trace inputs and understand how they translate into business value.

“My dream will be a totally redesigned GCC model,” Cassiano says. “All GCCs will leverage the context model to make better decisions.”

He argued that enterprises still struggle to link AI projects to measurable business outcomes. While many report productivity gains, proving business value remains harder. Cassiano also cited analyst findings showing firms reported nearly 65% productivity gains but only a 5–6% impact on top-line or bottom-line outcomes, creating demand for systems that can track operational impact.

While that vision may still take time, many enterprises remain in the experimentation phase of AI adoption. Brown argued that process intelligence could eventually make enterprise redesign a continuous exercise rather than a multi-year project.

Yet if the next decade of GCC growth depends less on labour arbitrage and more on operational intelligence, firms like Celonis are betting that India’s biggest export may no longer be services. It may be enterprise decision-making itself.