As AI reshapes enterprise operations, GCCs are increasingly expected to influence strategy rather than simply execute it. Now, that rapid rise of AI is creating another turning point, the one that could redefine how GCCs create value within global enterprises.
The shift comes at a time when India hosts more than 2,000 GCCs employing over two million professionals. New centres continue to be announced across Bengaluru, Hyderabad, Pune and Chennai, but industry conversations have moved beyond expansion plans and hiring numbers. Increasingly, the focus is on strategic influence.
“It will not be so much about scale going forward,” says Krishnaji Desai, Vice President, Software Engineering at Epsilon. “It will be about owning deliverables end-to-end, influencing decisions and operating with strategic intent.”
The end of the execution-only model
For years, many GCCs were primarily responsible for execution. While they played a critical role in global operations, strategic decisions often remained concentrated at headquarters.
That model has steadily changed. As enterprises have become more comfortable distributing product ownership across geographies, Indian centres have gained greater responsibility for product engineering, architecture, data platforms and customer-facing innovation.
According to Desai, the change has been particularly visible over the past five to six years. “What used to be more execution-focused has transformed into greater accountability and ownership,” he says. “We are part of the decisions right from requirements to architecture and all the way to production readiness and final deployment.”
The transition reflects a broader trend across the GCC ecosystem, where centres are increasingly expected to contribute to business outcomes rather than simply execute predefined tasks.
AI changes the equation
If ownership defines the current phase of GCC evolution, AI may define the next one. Generative AI and agentic systems are altering how software is developed, how business processes are automated and how decisions are made. For GCCs, this creates both an opportunity and a challenge.
On one hand, AI lowers barriers to entry in areas that previously required years of specialised expertise. On the other, it raises expectations around innovation and productivity.
“The AI inflection point has given us a lot more leverage,” says Desai. “Areas that traditionally required deeper expertise or complex architectural understanding are becoming more accessible.”
Yet many organisations are still struggling to move beyond experimentation. Across industries, proof-of-concept projects continue to outnumber large-scale deployments.
Desai argues that the issue is often not technology but operationalisation. “One can always take a random shot at using AI tools for productivity,” he says. “But the real value comes when you build reusable skills, reusable frameworks and common best practices that can be scaled across the organisation.”
The challenge for enterprises is no longer whether to adopt AI, but how to integrate it into everyday workflows in a way that produces measurable business outcomes.
Engineers move closer to the business
AI is also reshaping the nature of technology work itself.
Historically, software engineers spent much of their time translating business requirements into code. As AI increasingly automates coding, testing and documentation tasks, the emphasis is shifting toward higher-order problem-solving.
“This is a paradigm change,” says Desai. “The shift is moving upstream. Engineers are becoming more involved in the ‘why’ and ‘what’ instead of focusing only on the ‘how’.”
As a result, skills such as systems thinking, problem framing and business understanding are becoming increasingly important. The expectation is no longer that engineers simply build solutions, but that they help define the problems worth solving.
This evolution is likely to have significant implications for talent development within GCCs, particularly as organisations seek professionals who can combine technical expertise with business context.
Governance becomes a priority
The rapid adoption of AI has also introduced new concerns around governance, security and risk management.
Many organisations are grappling with issues ranging from data privacy to the rise of “shadow AI,” where employees use unsanctioned tools without organisational oversight.
Desai believes governance frameworks must evolve alongside AI adoption. “Security and privacy remain non-negotiable,” he says.
He describes the progression from AI-assisted workflows to AI-augmented operations and, increasingly, agentic systems capable of carrying out tasks with greater autonomy.
That transition is also changing the role of human oversight. “Earlier it was human in the loop. Increasingly it is becoming human on the loop,” says Desai.
The challenge for GCCs will be balancing speed and innovation with the controls needed to ensure reliability, security and compliance.
Influence, not headcount
As the GCC sector enters its next phase, industry leaders increasingly argue that success will be measured less by workforce size and more by strategic relevance.
The centres that stand out are likely to be those that own critical capabilities, participate in global decision-making and demonstrate measurable business impact.
For Indian GCCs, this represents both an opportunity and a test. The talent pool remains a significant advantage, but talent alone may no longer be enough.
The next chapter will depend on whether GCCs can translate technical capability into influence. “The future GCCs are the ones that work in a globally integrated model, participate in co-creation and deliver value with full ownership,” says Desai.
As AI reshapes enterprise operating models, the debate is no longer about whether Indian GCCs can execute. The more important question is whether they can help define the future direction of the organisations they serve.