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AI-powered GCCs: A turning point that cannot be ignored 

For decades, Global Capability Centers (GCCs) have delivered consistent value to enterprises through a well-established model built on scale, talent access, and cost efficiency. 

That model has worked. It has scaled globally and has become deeply embedded in enterprise operating strategies. 

As Artificial Intelligence (AI) reshapes how work gets designed, executed, and measured, it is also challenging the very foundations on which GCC value has historically been built. What was once a model optimized for efficiency is now being tested against expectations of speed, intelligence, and business impact. 

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Cost efficiency and scaled talent, while still important, are no longer sufficient on its own. Enterprises are increasingly looking to GCCs to: 

  • Embed AI and automation into core operations  
  • Drive faster innovation and time-to-market  
  • Improve decision-making and risk management  
  • Deliver measurable business outcomes  

The gap between intent and impact 

While expectations are evolving rapidly, the pace of execution has been uneven. Across GCCs, there is visible momentum around AI. Many organizations have launched pilots, explored use cases, and invested in experimentation. 

Yet, the translation of this activity into enterprise-wide impact remains limited. Exhibit 1 outlines Everest Group’s AI infusion framework, which categorizes adoption into three scenarios – traditional, evolution, and reinvention. 

A significant proportion of GCCs continue to operate within traditional execution-led models, while only a small subset is demonstrating measurable outcomes from AI adoption at scale. In many cases, AI initiatives remain confined to isolated use cases rather than being integrated into core workflows. 

This gap between ambition and realization is not simply a matter of time. It reflects deeper structural challenges around operating models, data foundations, talent readiness, and governance. Without addressing these underlying elements, scaling AI impact remains difficult. 

This disconnect raises a more fundamental question: can the current GCC model evolve incrementally, or does it require a more structural shift? 

In many cases, AI is being introduced as an additional capability layered onto existing processes. 

However, its impact is more foundational. 

AI influences how workflows are designed, how decisions are made, and how performance is evaluated. As a result, incremental adoption tends to deliver localized improvements, but not systemic change. 

When AI is embedded more deeply into the operating model, the outcomes begin to look different: 

  • Productivity improvements that are structural rather than incremental  
  • Shorter innovation and development cycles  
  • Greater reliance on data-driven and predictive decision-making  
  • A shift in how work is distributed between humans and machines  

Implications beyond technology 

As this shift takes shape, its impact extends beyond technology into how GCCs are structured and evaluated. 

  • Workforce models are evolving.
    As AI takes on more routine and repeatable tasks, the nature of work within GCCs is shifting. Roles that were once heavily execution-driven are gradually moving toward higher-value activities such as judgment, orchestration, and decision support. 

This reflects a broader move toward human-AI collaboration, where AI augments execution and humans focus on context, oversight, and decision-making. Over time, this is expected to influence workforce composition, skill requirements, and career paths, with greater emphasis on AI literacy and domain expertise. 

  • Traditional performance metrics are being re-evaluated.
    Historically, GCC performance has been measured through indicators such as cost savings, utilization, and headcount growth. These metrics were well aligned with a delivery-centric, labor arbitrage model. 

However, as AI becomes more embedded, enterprises should look beyond these measures. There is increasing focus on outcomes such as speed of execution, productivity gains, quality improvements, and overall business impact. This shift in measurement is subtle but significant. It changes how success is defined and how GCCs position their value within the enterprise. 

Together, these changes signal a broader transition in how GCCs operate and how their contribution is evaluated. 

The growing importance of ecosystem partnerships 

As GCCs begin to address these structural shifts, another trend becomes increasingly important: the role of ecosystem partnerships. 

Building AI capabilities entirely in-house can be complex, time-intensive, and difficult to scale. The pace of innovation, combined with the need for specialized skills and robust technology foundations, is prompting many GCCs to look outward. 

Ecosystem partnerships are emerging as a key enabler in this context. External partners can help: 

  • Accelerate implementation timelines through pre-built solutions and accelerators  
  • Provide access to specialized AI and domain expertise  
  • Support capability building and workforce upskilling  
  • Strengthen governance, risk management, and compliance frameworks  

As a result, the role of GCCs is evolving from standalone delivery units to orchestrators of a broader ecosystem of providers, platforms, and specialists. 

This shift is not just about execution efficiency. It is about enabling GCCs to move faster, reduce risk, and translate AI initiatives into meaningful business outcomes. 

The questions GCC leaders are now facing 

Taken together, these shifts are prompting GCC leaders to rethink long-standing assumptions and navigate a new set of strategic questions: 

  • How can AI move beyond pilots into enterprise-scale impact?  
  • What changes are required in operating models to fully embed AI?  
  • How should talent strategies evolve in response to human-AI collaboration?  
  • What role should ecosystem partnerships play in accelerating this transition?  

While the direction of change is increasingly clear, the path forward is still being defined. Many organizations are at different stages of this journey, with varying levels of readiness across technology, talent, and governance. 

Understanding these shifts, and what it takes to respond effectively, will be critical for GCCs looking to remain aligned with enterprise priorities. 

To explore these themes in greater depth, read our full viewpoint on AI-powered GCCs: the New Value Engine for Enterprises. 

If you enjoyed this blog, check out, How global capability centers are driving transformation: The evolving landscape – Everest Group Research Portal, which delves deeper into another topic relating to GCCs. 

If you have any questions, please contact Ravneet Kaur ([email protected]) or Yashika Roy ([email protected]).