From cost centres to AI engines: How GCCs are driving enterprise reinvention

As GCCs move beyond their traditional role as cost and delivery hubs, they are increasingly becoming strategic engines for enterprise-wide transformation. The rise of AI and Agentic AI is accelerating this shift, pushing GCCs to rethink operating models, talent, data, governance and the very way work gets done. In this conversation, Sundeep Gandhi, Chief Commercial Officer – GCC, Accenture discusses what it takes to build an AI-native GCC, why agentic AI is becoming a priority, the challenges around talent and enterprise alignment, and how GCCs can translate AI investments into measurable business value.

GCCs have evolved from cost optimisation centers to strategic innovation hubs. As enterprises enter the AI era, how do you see the role of GCCs changing, and what defines an AI-native GCC?

GCCs are at an important inflection point. The conversation has moved beyond where work gets delivered to how these centers can help shape enterprise reinvention. Accenture’s GCC Pulse Survey shows that 29% of GCCs are already operating as catalysts for enterprise-wide transformation and innovation, while 75% have moved beyond AI experimentation to scaling AI and data initiatives across the business. They are also playing the role of talent catalysts by helping drive workforce transformation across the organisation.

An AI-native GCC is not defined by the adoption of AI tools alone. It is defined by the ability to embed AI into the operating model, decision-making, talent strategy and core business processes. These centers bring together technology, data, domain expertise and responsible governance to accelerate innovation, improve speed to market and become the reinvention engines for the enterprise.

Agentic AI is rapidly becoming a major area of enterprise investment. What makes agentic AI fundamentally different from earlier AI and GenAI initiatives, and what opportunities does it create for GCCs?

The next promise of AI is now shifting the focus from individual task-level productivity to the reinvention of work itself. Agentic AI is different because it can reason, plan, orchestrate actions and execute multi-step workflows across systems and functions, within clearly defined guardrails. Agentic AI necessitates a paradigm shift in mindset and a significant augmentation in organisation capability.

For GCCs, this creates a significant opportunity because they sit at the intersection of technology, operations and business functions. Our research shows that 70% of GCCs plan to prioritise agentic AI over the next one to two years, reflecting a clear shift from isolated use cases to enterprise-wide transformation.

The opportunity is not simply to deploy AI agents, but to redesign how work gets done. GCCs that combine AI capabilities with strong data foundations, responsible governance, business context and talent will be well positioned to help enterprises scale agentic AI safely and translate it into measurable business outcomes.

Many organisations are positioning their GCCs as the control towers for enterprise-wide AI execution. What capabilities, governance models, and leadership structures are essential for GCCs to successfully orchestrate AI at scale?

Scaling AI is becoming less of a technology challenge and more of an enterprise coordination challenge. Our survey found that 82% of leading GCCs align their strategy and KPIs with those of the parent organisation, and they are 3.8 times more likely to have cross-functional technology and domain teams collaborating to scale AI.

Three capabilities are essential. First, clear alignment between GCC and enterprise leadership on the business outcomes AI is expected to deliver. Second, cross-functional teams that bring together technology, data, domain and industry expertise. Third, governance frameworks that define accountability, responsible AI principles, security, compliance and risk oversight as AI scales across the enterprise.

Leadership is equally important. GCC leaders need to be part of enterprise decisions on technology, talent and transformation. The organisations moving fastest are those where GCCs help shape strategy and standards, not just execute programs.

From your experience working with global enterprises, what are the biggest challenges organisations face while transforming traditional GCCs into AI-native operating models, and how can these barriers be overcome?

Most organisations already have access to AI technologies but moving to an AI native operating model is a fundamental shift. The differentiator now is their ability to adapt people, processes, data foundations and operating models around them. According to our GCC Pulse Survey, 74% of GCCs identify talent gaps as their biggest barrier to innovation. Many organisations also continue to face challenges around enterprise alignment, legacy processes, data readiness and change management.

Becoming AI-native requires more than deploying AI tools. It requires reskilling talent, redesigning workflows, strengthening data and technology foundations, aligning more closely with enterprise priorities and building governance structures that support AI at scale. The leading GCCs recognise that AI transformation is operating model transformation. They embed AI into how decisions are made and how work gets done, rather than treating it as a standalone technology initiative.

Talent remains one of the biggest constraints in the AI journey. How should GCCs rethink hiring, reskilling, and workforce development to build sustainable AI capabilities, especially in areas such as data science, AI engineering, and AI governance?

In the AI era, talent advantage will be defined less by hiring alone and more by how quickly the workforce can learn and adapt and apply new skills to business priorities. While demand for skills in AI, data, cloud and cyber security continues to rise, our survey shows that 67% of GCCs identify skills availability and retention as a key challenge, and 74% cite talent gaps as a critical barrier to innovation.

Leading GCCs are taking a dual approach: acquiring critical skills where needed while continuously reskilling existing talent. They are also building multidisciplinary teams that combine AI engineering, data science, business process knowledge, industry expertise and responsible AI governance.

The next competitive advantage will not be access to talent alone. It will be the ability to build, adapt and deploy new skills at the pace of technological change, while creating clear career pathways that keep people engaged in high-value work.

As AI agents begin to automate increasingly complex business processes, how should enterprises balance innovation with responsible AI, security, regulatory compliance, and human oversight within their GCCs?

Trust is emerging as one of the biggest determinants of AI adoption at scale. AI, cybersecurity, regulatory compliance and human oversight need to be designed into AI programs from the outset. GCCs are well positioned to play this role because they bring together business, technology, data and operational expertise under one structure.

The organisations that will scale AI successfully are those that create clear accountability around decision-making, establish robust governance frameworks, and keep humans in the loop for judgment, oversight and exception handling. Responsible AI and innovation are not competing priorities. Trust is what enables AI to move from pilots to enterprise-scale value.

Looking ahead three to five years, what will distinguish the world’s most successful GCCs from the rest? What strategic priorities should technology and business leaders focus on today to build AI-first, globally competitive capability centers?

The next wave of leading GCC leaders will not be distinguished by their size or scale alone, but by the influence they have on enterprise value they create. The most successful GCCs will combine AI, data, talent and deep growth and reinvent business expertise to accelerate innovation, strengthen resilience and help shape new ways of working. For leaders, the priorities today are clear – build AI capabilities at scale, invest continuously in talent, strengthen responsible governance, align closely with enterprise strategy and create operating models that turn innovation into measurable business outcomes.