Introduction
Global capability centers have moved well beyond their original role. In many organizations, they take ownership of delivery, contribute to product development, and influence how work flows across teams.
The way these centers are designed has not kept pace with that shift. The model still draws from an earlier reality where work was segmented, moved across locations, and scaled through headcount. That approach continues to deliver results, though the nature of work has changed in ways it does not fully account for.
AI is one of the forces behind that change. Tasks are being broken down differently, decision-making is moving closer to execution, and cycles that once took weeks now move faster. In many cases, these changes are addressed after a GCC is already in place, which often leads to adjustments later rather than being built into how the center operates from the start.
There is also a growing tendency to view AI adoption and GCC strategy as separate choices. Some organizations experiment with automation first and delay decisions on distributed teams. In practice, both shape how work gets done. Treating them together usually leads to a more coherent operating model.
How AI Changes the GCC Operating Model
GCC operating models have already evolved over time. Many centers now operate with embedded ownership and are closely tied to business outcomes. AI changes how these models behave once work begins to move faster and dependencies shift.
From Pre-AI to AI-Enabled Models
Dimension 
Pre-AI Model
AI-Enabled Model
The effect becomes visible through the pace of work and the expectations placed on teams. As organizations move ahead with AI across functions, alignment between the GCC and the rest of the enterprise becomes more important. When that connection is weak, teams tend to work on parallel tracks and progress slows.
The GCC as an AI Incubation Environment
A GCC often sits close to where work is executed, which makes it a practical place to observe how new approaches hold up under real conditions.
Work rarely changes all at once. Teams try small adjustments, see how they affect delivery, and build from there. This kind of iteration becomes easier when both execution and feedback sit close together. The cycle from testing an idea to refining it tends to shorten in that environment.
Cost structure plays a supporting role. Running multiple iterations, stepping back when something does not hold, and trying again requires space to experiment. When those cycles are manageable, teams are more likely to push beyond pilot stages and understand how changes work at scale.
Over time, this creates a pattern where the GCC contributes to how approaches evolve before they are adopted more widely.
What AI Readiness Actually Requires
AI readiness shows up differently across functions, which is why it rarely works as a single transformation program. The shift becomes easier to understand when viewed through how teams operate day to day.
Engineering provides a useful reference point.
How AI Changes Engineering Team Design
Dimension
Before AI
With AI
The shift becomes clearer in how work is organized. As execution speeds up, more attention moves to planning and decision quality. Teams spend less time on repetitive work and more time coordinating how outcomes are shaped.
Other functions move through similar changes, though the details differ. Finance teams adjust reporting and compliance processes. Customer support teams change how interactions are handled. HR teams revisit hiring, training, and performance tracking. Each area moves at its own pace, which is why a single approach applied across all functions tends to miss important nuances.
From Local Experiments to Global Practice
When a team within a GCC develops a way of working that holds up over time, it often spreads. Other teams begin to adopt similar approaches, and the direction of learning becomes more distributed.
This changes how the GCC is viewed within the organization. Delivery remains important, though there is also an expectation that teams contribute to how work improves. Patterns that emerge in one area begin to influence how other teams operate.
Over time, this builds a different connection between the center and the rest of the enterprise. The GCC becomes part of how change is understood and applied, rather than a structure that follows decisions made elsewhere.
Five Questions Leadership Should Ask Before Building a GCC
- Is AI enablement built into the GCC design from the outset?
- Are teams expected to deliver outcomes alone, or also contribute to improvements in how work is carried out?
- Has AI readiness been evaluated separately for each function?
- Are early AI efforts being tested in environments that support iteration and learning?
- Do performance metrics reflect both efficiency and changes in how work evolves?
The Window for Structural Advantage
Organizations that consider AI readiness early tend to build operating models that adjust more easily as conditions change. Others often return to these decisions later, when changes take more effort to absorb.
As work continues to evolve, the ability to adjust becomes more relevant than the initial structure itself. In that context, the GCC plays a role in how organizations respond to shifting ways of working, rather than remaining fixed around earlier assumptions.
