About 44% of India’s GCCs now own platforms and AI outcomes, shifting from simple execution to strategic innovation. Neeti Sharma, CEO of TeamLease Digital, said, “The GCC story in India is no longer about where work gets done.” These centres now drive transformation, leading to higher salaries for skilled digital professionals.
Synopsis
Global Capability Centres in India are transitioning into AI capability hubs, significantly increasing salaries for digital professionals. The focus of GCCs is shifting from execution to innovation and strategic capabilities. Reports indicate that AI talent in India commands a significant wage premium due to higher demand. As GCCs expand their roles, the need for skilled talent in AI and related fields continues to grow.
Global Capability Centres (GCCs) are steadily moving from delivery centres to AI capability hubs, lifting pay for digital talent in tandem, according to a report by TeamLease Digital, a specialised talent solutions firm. The salary findings are based on a survey of 37,000 technology and digital roles.
The scale of the opportunity is already significant. India has 2,117 active GCCs, employing 2.36 million professionals and contributing $98.4 billion in annual revenue.
What stands out now is a shift in activity in these centres. As much as 44% of GCCs have reached the portfolio hub or transformation stage where they increasingly own platforms, products, AI adoption and business-critical outcomes. AI is the engine behind this shift. India has more than 250,000 AI/ML talent and more than 1,200 AI-enabled GCCs, marking its lead in global AI hiring. Also, 83% of GCCs are scaling GenAI, 70% are following a hybrid AI model and 58% are investing in agentic AI, which shows GCCs are no longer just executing for global enterprises but helping drive their transformation.
“The GCC story in India is no longer about where work gets done,” said Neeti Sharma, CEO, TeamLease Digital. “It is about who shapes what global enterprises build next. As these centres take on the thinking, ownership and accountability behind AI, India is becoming central to how enterprises across the world transform.”
Live Events
“The next phase of GCC growth will be led less by scale of headcount and more by judgment, governance and the ability to turn AI into business outcomes,” said Sharma. “The task for India now is to build talent at that depth, so that GCCs remain not just an execution hub for global enterprises, but a source of their innovation and strategic capability.”
As GCCs take on more ownership, the enabling talent is also getting rewarded. The 2026 salary increment in GCCs is at 10.4%, ahead of the 9.1% India Inc average. AI, ML, cyber and cloud skills carry a 30–40% pay premium because demand outpaces supply.
The AI talent landscape identified by TeamLease Digital showed this premium is spread unevenly across roles. Depth is concentrated in AI-core, roles that build and own intelligent systems, which appears in only two places: the AI family and AI risk governance. Breadth sits in AI-adjacent roles, which apply AI within established workflows and span data, cloud and cybersecurity. No AI-support role appears at premium in a GCC, indicating that the routine layer is being repriced downward rather than upward.
The salary findings showed that this divide begins at entry itself. For early and mid-career roles, at 0–2 years of experience, AI-core roles earn a median fixed pay of ₹10.8 lakh per annum (LPA) for a data scientist) to ₹13.2 LPA for GenAI engineer). AI-adjacent roles earn ₹9.0 LPA (cybersecurity engineer) to ₹10.2 LPA (data engineer), while AI-support roles earn ₹4.8 LPA (data quality analyst) to ₹5.4 LPA (application support engineer). AI-core roles therefore open 2.5–3× above AI-support, and they are also projected to grow faster, at 16–21% against 7–8%.
The findings also pointed to where salary growth is headed in FY28. GenAI, ML and data science remain the highest-return skilling pathways for salary acceleration in GCCs. Skills in LLMs, RAG, and prompt engineering, are projected to grow 20.5% in FY27–28, followed by ML skills such as PyTorch, TensorFlow and model deployment at 18.7%. At senior levels, LLM architecture, RAG at scale, and agentic AI lead at 20.8%, followed by AI architecture, MLOps, GenAI strategy and model governance at 19.6%.