AI Revolution in GCCs: Empowering Human Potential

A massive ninety-five percent of CIOs surveyed by Gartner  believe that GenAI has significant potential to improve their organizations. More importantly, the enterprise spending on GenAI is expected to rise by as much as 50%, translating to no less than $200 billion, 

At the forefront of this enablement revolution stand Global Capability Centers, or GCCs, as they are better known. Acting as Center of Excellence (CoE) that bring together subject matter expertise, superlative technology talent and the ideal environment, GCCs create the optimal environment for AI-powered innovation within the enterprise.  A recent ANSR research discovered that over three-fourths of new GCCs are building deep capabilities in emerging technologies such as Gen AI/ML. Of the established GCCs, those that haven’t embraced AI/ML will do so within the next 2-3 years, with 90% of GCC leaders confirming these plans.

This development will transform Indian GCCs into a new breed of innovation hubs. Already leading enterprise AI, GCCs now have Gen AI, which surpasses earlier AI by self-learning from diverse, real-time interactions. The primary advantage of this shift lies in its ability to release GCC teams from routine tasks, empowering them to concentrate on strategic initiatives that advance organizational vision and enhance customer success.

GCCs Spearhead the Transition into Human-AI Collaboration for Greater Efficiency and Creativity

As of today, a large number of  India-based GCCs are ideating and testing PoCs (proof of concept) leveraging Gen AI  across every industry to automate routine tasks – from creating data pipelines and processing invoices, to answering customer complaints and improving fraud detection – with minimal human oversight. 

• In data engineering pipelines, once the initial architecture is automated, AI and generative AI can further automate exception management, reducing the need for frequent human oversight.
• In invoice processing and payables for large organizations, AI can automate fraud detection and flag unusual outliers, reducing the need for regular manual monitoring. In reporting, AI will add value by generating insights tailored to the company and industry.
• In customer service, AI already streamlines complaint routing, assignment, and resolution of basic queries, boosting efficiency and resolution rates. With Gen AI-powered agentic AI, more complex, routine tasks—like handling refunds and scheduling field service—can also be managed, further enhancing service efficiency. When alerted to complex issues that AI cannot handle, agents can step in right away, equipped with AI-enhanced insights for faster, more effective resolutions. This approach not only improves response times for challenging cases but also strengthens customer relationships, increasing the chances of building lasting loyalty.
• Turning to the critical healthcare sector, the India GCC of a German healthcare provider has developed over 40 Gen AI-led innovations. One of them, the ‘AI Pathway Companion’, integrates longitudinal patient data and correlates insights from imaging, pathology, lab, and genetics into a unified data model. Patient-facing teams use it to quickly assess the complex causes of a patient’s complaint and immediately start suitable care plans and treatments. Prior to this collaboration, teams required significantly more time to achieve a comprehensive, multidimensional understanding, often missing critical, patient-specific insights.
• In the BFSI sector, GCCs are already leveraging AI/ML to enhance fraud detection and risk mitigation. This collaboration will be strengthened by a Gen AI layer that can quickly identify suspicious behavior in unstructured scenarios, such as social media interactions. With AI supporting the identification of potential threats, teams will remain focused, reducing fatigue and becoming more adept at recognizing emerging fraudulent activities that require an understanding of human behavior.

Across every sector, GCCs are playing a crucial role in the advancement of human-AI collaboration, enabling stakeholders to maintain direct control over core functions while leveraging AI-powered tools for accelerated, optimized outcomes. At the C-Suite level, management dashboards, including those of GCC leaders are now more powerful with AI, offering a more detailed, holistic overview. 

While AI will play an increasingly central role, decision-making will remain with leaders and managers for the foreseeable future. In product innovation, for example, teams will use Gen AI to analyze customer preferences related to composition, packaging, and pricing. However, experienced product development and marketing experts will continue to make the final call on go-to-market strategies. Similarly, Finance shared service leaders will leverage Gen AI’s capabilities to refine scenario planning, enhancing their confidence in goal-setting. Yet, the final outputs presented to boards and executives will still reflect the deep expertise of finance teams.

Nurturing Talent to Navigate the Transition

This transformative narrative of human-AI collaboration will also have a tangible impact on talent acquisition and retention. While GCCs have shown considerable acumen in identifying, upskilling, and reskilling talent apace with evolving technology and objectives, the AI-shift brings certain specific challenges to the forefront.

1. The fear of obsolescence: A notable concern among employees and the broader talent pool is the fear that their skills and roles may become obsolete. To address this, Indian GCCs must proactively reassure both current employees and external candidates through various channels that comprehensive upskilling and reskilling programs will empower them to harness AI solutions and assume more advanced responsibilities as routine tasks become automated. These programs should focus not only on technical skills but also on fostering the ability to identify opportunities for deeper AI integration, ensuring that teams working alongside AI provide valuable insights to drive the transformation.
2. Compliance with regulatory and ethical standards: The second critical challenge, ensuring the responsible use of AI, requires employees to be trained on how to interact with AI within global regulatory frameworks and the ethical guidelines established by the GCC and parent company. This includes the ability to identify and address any deviations, biases, or inaccuracies that may arise from the models. Regular audits and continuous education will further ensure that AI usage aligns with both ethical standards and regulatory compliance, helping build a culture of accountability and trust.
3. Using feedback to train the model: Lastly, to gain maximum benefit from AI’s self-learning, teams will have to consciously and continuously provide feedback to the models, establishing a collaborative improvement loop. This iterative process will not only  refine the AI model’s accuracy and efficiency, but also enable teams to adapt the models to evolving business needs and challenges. 

A Roadmap

Moving forward, GCCs will need a clear roadmap to implement and scale AI-human collaboration. Here is what it can look like:

• Identify areas where Gen-AI will add maximum value and set clear, actionable objectives: GCCs should try to prioritize impactful use cases such as customer service for a key brand to deliver quick, significant results operationally as well as monetarily. This will help garner buy-in from internal and external stakeholders, effectively smoothing the path for larger, more complex collaborations that will come next.
• Prioritize workforce enablement through upskilling: This involves offering tailored training programs that focus on the specific needs and applications relevant to their roles and the company’s product offerings. Additionally, hands-on workshops and continuous learning opportunities will help employees gain practical experience with Gen AI tools, fostering deeper expertise. 
• Revamp your data infrastructure: To unlock the full potential of AI, particularly generative AI, a robust data infrastructure is needed—one that integrates a broader range of diverse data. Data engineering experts within the GCC must identify new functional and domain-specific requirements, then implement the necessary infrastructure changes to collect data from across the entire ecosystem. This data must be stored securely, prepared efficiently, and made readily accessible to support seamless company-wide human-AI collaboration.
• Create a cross-functional body dedicated to implementing Gen AI: The GCC leadership should be part of a cross-functional body – comprising BU leaders, technology experts, legal and finance personnel, and other essential stakeholders within the GCC and across the organization. This body should be tasked with identifying, building, and implementing organization-wide human-AI collaboration that powers the company’s critical business objectives.

Striding into the Future

As GCCs continue to be the pilots within the cockpit that drives the organization’s AI trajectory, increasing human-AI collaboration will relieve GCC teams from numerous business-as-usual tasks, such as initial optimization iterations and routine enterprise projects focused on execution and quality control.

The focus will shift from determining the optimal combination of technologies for an initiative to selecting those that unlock a competitive advantage. It will no longer be about executing tailored marketing campaigns more effectively, but about crafting strategies that align with the brand and resonate with the target customer. Similarly, the emphasis will move from simply identifying the right vendors and ensuring fulfillment to transforming the supply chain into a genuine profit center.

Human-AI collaboration will significantly accelerate the metamorphosis of the GCC into a digital twin that leverages its strategic and functional expertise and technological advantage to serve as a second headquarters.