For organisations establishing a Global Capability Centre, AI is increasingly becoming part of the strategic conversation. But there is a difference between adding AI capabilities to a GCC and designing a GCC for an environment where AI is becoming part of how work gets done.
That distinction is becoming increasingly important.
Recent research from Zinnov highlights the need for organisations setting up GCCs to consider an AI-first future from the outset, with AI influencing workflows, talent strategy and operating models rather than being retrofitted later.
Start with the Business, Not the Technology
AI strategy should not begin with a list of tools, models or use cases. It should begin with the business.
Where can AI improve engineering productivity? Which processes could benefit from intelligent automation? Where can data and AI strengthen decision-making? Which products, platforms or customer experiences could be enhanced through AI?
The answers will differ from one organisation to another.
A GCC supporting product engineering may require a very different AI strategy from one focused on data, enterprise applications or business operations. The objective should not be to make every function AI-first, but to identify where AI can create meaningful business value and build the right capabilities around those areas.
Align AI With Business Priorities
AI also changes how GCC talent needs to be organised.
Strong engineering and domain expertise will remain essential, but the ability to work across disciplines will become increasingly important. AI engineers, data specialists, product teams, domain experts and technology leaders need greater interaction rather than operating as isolated functions.
Recent EY research on AI-first GCCs highlights this shift, pointing to new workforce models, evolving roles and the need to align talent, governance and operating structures with AI-led ways of working.
This means AI expertise needs to be connected to engineering, product, data and domain capabilities so that AI becomes part of how solutions are designed and delivered.
Design the Technology Foundation Accordingly
AI readiness also depends on the technology foundation underneath it.
Data architecture, integration, cloud infrastructure, security and governance become increasingly important as AI moves from experimentation into production.
For a new GCC, this presents an opportunity. Organisations can consider these requirements while designing the technology environment instead of having to retrofit them later.
Governance is equally important. Responsible AI, security, human oversight and lifecycle monitoring need to form part of the operating environment as AI adoption grows. EY's research identifies architecture, governance, workforce and operating-model design as interconnected elements of an AI-first GCC.
Build AI Capabilities That Can Move Into Production
AI readiness is not only about having the right strategy and infrastructure. Organisations also need the engineering depth to turn AI opportunities into solutions that can be deployed, integrated and scaled across the enterprise.
As an OpenAI Select Partner, SA Technologies combines access to OpenAI's AI capabilities with experience across AI consulting and engineering, data modernisation, product engineering and enterprise delivery.
For GCCs, this combination can support a practical path from experimentation to production, helping teams identify relevant use cases, build solutions around them and integrate AI into existing technology environments.
The objective is to create an environment where AI capabilities can evolve alongside the business, rather than remain isolated initiatives.
Design for the GCC's Next Phase
A GCC may begin with a focused technology charter and gradually expand into AI engineering, data, product development or other specialised areas.
The initial design should therefore leave room for that progression.
The specific AI tools and models used several years from now may be very different from those being adopted today. What is more durable is the foundation: adaptable talent, strong engineering, accessible data, effective governance and an operating model that allows people and AI systems to work together.
The recent GCC landscape reflects this broader shift, highlighting the growing strategic role of India-based GCCs and the increasing importance of AI capabilities within the ecosystem. The report also points to maturity becoming increasingly a design choice made early in the GCC journey.
Building for What Comes Next
For organisations evaluating a GCC today, AI should be considered across the design of the centre, from talent and technology to workflows, governance and engineering capabilities.
Building with this perspective creates a stronger foundation for the GCC to adapt as AI becomes increasingly embedded in enterprise technology and operations.