A massive ninety-five percent of CIOs surveyed by Gartner believe that Gen AI has significant potential to improve their organisations. More importantly, enterprise spending on Gen AI 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 Centres, or GCCs, as they are better known. As centres of excellence (CoEs) that bring together subject matter expertise, superlative technology talent, and the ideal environment, GCCs create the optimal setting for AI-powered innovation within the enterprise. A recent ANSR study discovered that over three-fourths of new GCCs build deep capabilities in emerging technologies such as Gen AI/ML. Of the established GCCs, those that have not 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 in 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 organisational 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 intervention.
• In invoice processing and payables for large organisations, 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—such as 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.
• In 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. Before 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 behaviour 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 recognising emerging fraudulent activities that require an understanding of human behaviour.

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 such as:

Addressing 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 programmes will empower them to harness AI solutions and assume more advanced responsibilities as routine tasks become automated.

Ensuring compliance with regulatory and ethical standards: 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 companies. 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.

Using feedback to train the model: 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 for successful AI-human collaboration

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

• Identify high-impact areas: Focus on use cases like customer service to show quick, significant results and gain stakeholder support.
• Enable workforce through upskilling: Offer targeted training, workshops, and continuous learning for practical Gen AI experience.
• Revamp data infrastructure: Build a robust infrastructure that integrates diverse data and supports secure, seamless human-AI collaboration.
• Form a cross-functional AI body: Establish a team of leaders, experts, and stakeholders to drive and oversee AI-human collaboration initiatives.

Striding into the future

GCCs are leading AI innovation, freeing teams from routine tasks and shifting focus to strategic growth. Instead of simply executing projects, they will craft brand-driven strategies and transform operations into profit centres. Human-AI collaboration will turn GCCs into powerful digital twins, acting as second headquarters for their organisations.