The old narrative that Global Capability Centers (GCCs) are merely cost-effective centers for 'back-office' operations, incapable of delivering strategic and groundbreaking ideas, is now obsolete. In fact, the rise of AI-first GCCs and AI transformation in GCCs is fundamentally rewriting the playbook. GCCs are now adopting and embedding AI into their very core, transforming from being operational executors into dynamic innovation engines that drive enterprise-wide transformation.

And the answer is not ‘chatbots for customer service’. It extends to the complete re-architecture of how a GCC operates, thinks, and creates value.

Referring to NASSCOM’s India GCC landscape report, the GCC market size is poised to reach around $100 billion by 2030, and the headcount is expected to cross 2.5 million. So, what is the role of AI-first approach and AI transformation in this landscape?

According to Sindhu Gangadharan, Chairperson, Nasscom, “Today, AI and automation are the key drivers of transformation, positioning India’s GCCs as the innovation engines for global enterprises. Machine learning (ML) models are helping organizations analyze vast data sets, generate actionable insights, and predict future trends with remarkable precision. AI-powered chatbots, cognitive automation, and hyper-automation have redefined customer service, research, and business operations, significantly improving efficiency and reducing error rates.”

The narrative here is to take that strategic step into building an AI-first ecosystem that’s ready for future challenges. Let’s see how.

A Tale of two GCCs

To illustrate this shift, we can look at two different kinds of GCCs.

First, there is the traditional GCC. Let's call it ‘Efficiency Solutions’. Their core mission is to reduce costs and maintain service level agreements (SLAs). The teams are highly skilled, but their work is largely reactive. If a new finance process needs to be automated, then they build an RPA bot. If a customer support backlog is growing, then they hire more agents and implement a basic chatbot. They are good at what they do, but their impact is measured in cost savings, not new revenue streams. They operate in a silo, often disconnected from the strategic decision-making done at the headquarters.

Now, meet the AI-first GCC, ‘Innovation Nexus’. They have an ‘innovation-first’ approach. A few years ago, their leadership team saw the AI wave coming and made a bold bet. They added a few AI tools and completely restructured their operational strategy. They brought in data scientists, machine learning engineers, and even explored the aspect of ethical AI.

The outcome? Their finance team, instead of just automating invoices, now uses machine learning to predict cash flow and flag potential fraud with an accuracy that no human can match. Their IT team is building predictive maintenance models for infrastructure, mitigating outages before they happen.

Earlier, their team used to have a massive, annual task of reconciling supply chain data across dozens of vendors. It was a month-long, manual nightmare. Practically, a team of 15 analysts in a room would collectively perform this task. After they became an AI-first GCC, they built a custom AI model that does the job in a matter of hours. The analysts now work with the product teams, using that same data to optimize global logistics. With change of perspective and strategy, they turned a back-breaking chore into a strategic advantage.

How to Build an AI-first GCC?

• Operational excellence through intelligent automation: This goes far beyond simple RPA. Intelligent automation leverages AI and machine learning to handle complex, cognitive tasks. It is automation with a brain, essentially, moving from ‘if-then’ rules to systems that can learn, adapt, and improve continuously. For example, a global bank’s GCC uses AI to process loan applications. The system scans for information, it analyzes documents, flags discrepancies, and also assesses the risk profile of the applicant by cross-referencing thousands of data points, all in real time. Now, this is a new capability and a new level of scaling GCC operations.

• Data warehouse to decision-making powerhouse: In the past, GCCs generated a lot of data, but that data often resided in silos. An AI-first GCC treats data as an asset. They use advanced analytics and AI platforms to create a hub for enterprises to leverage that data. The result? They can proactively identify issues, predict maintenance needs, and provide strategic recommendations to their global headquarters, transforming from a data warehouse into a decision-making powerhouse.

• Accelerated innovation and IP creation: AI-transformed GCCs are perceived as creators in this new paradigm. They are building new digital intellectual property and developing cutting-edge products and services. They are shaping up as the R&D labs of the future, developing new AI models, piloting generative AI use cases, and co-authoring patents. They are driving the innovation agenda.

Let’s say a GCC has to build a GenAI-powered internal knowledge base. Initially, they had to build a better search engine for their employees. However, as they developed the search engine, they discovered the true potential was in its ability to process complex information and generate new content on demand. This led them to pivot the project, transforming it from a simple internal utility into a strategic asset. Now, they have built it into a new capability to offer to their own customers, demonstrating how a small, focused task, can mature into an entirely new business line.

Powerhouse With Governance and Culture Shift

Building an AI-first GCC requires a significant investment in talent, a robust data governance framework, and, most importantly, a cultural shift. Empowering teams to experiment and fail, moving from a mindset of risk aversion to one of calculated risk-taking. Investing in your GCC to lead with AI turns it into a future-proof innovation engine.  

Ready to transform your GCC into an AI-first powerhouse? Learn more by registering for our upcoming webinar ‘Key Success Factors in Building and Scaling AI-First GCCs’. This webinar will cover the key factors for building and scaling a high-performing GCC.  

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