It’s worth sitting with this number. Over 1,700 Global Capability Centers in India alone employ over 1.6 million experts, and that figure is continually rising. Eighty-three percent of GCCs are already scaling generative AI initiatives, according to the EY GCC Pulse Report 2025. Boardrooms are buzzing with activity. Approval of budgets is underway. The goals for headcount are increasing each quarter.

Silently, though, something is falling behind.

GCC leaders are facing the same unsettling reality across industries and regions: the more quickly a center expands, the more difficult it is to manage it effectively. At 1,000 persons, procedures that were effective at 250 begin to show flaws. Governance that seemed adequate in one place turns into a guessing game in five. Neither talent nor technology are to blame. The majority of firms are unaware of this operational maturity issue until the symptoms become difficult to ignore.

Growth Creates Complexity Faster Than Expected

Nobody sets out to build a complicated GCC. It happens gradually, and then all at once.

A new function gets added here, a geography comes online there, a transformation mandate lands on top of an already-stretched team. Each addition makes sense on its own. But collectively, they create layers of operational interdependency that the original setup was never designed to handle.

What used to be a quick conversation between two teams now needs a defined escalation path. What used to run on institutional knowledge now needs documentation, ownership, and a clear accountability chain. The visibility that leadership had when the center was smaller starts to blur. And by the time most organizations notice, the complexity is already embedded in how the center operates day to day.

The Hidden Operational Challenges Behind GCC Expansion

The difficult aspect of GCC complexity is that it seldom manifests itself in a significant way. It develops in the background, manifesting as little inefficiencies that no one has time to address, decisions that take longer than necessary, and quality gaps that are hard to identify.

Manual interventions accumulate. Errors tend to congregate at break points in workflows that span teams, time zones, and systems. Furthermore, governance, which ought to be the glue holding everything together, frequently ends up contributing to the issue. One after another, layers of oversight are added in a reactive manner until the framework intended to facilitate decision-making is really impeding it. The rest is caused by inconsistent process execution across locations, which results in quality variation that is costly to identify and even more costly to correct.

This is nothing out of the ordinary. This is just the reality that lies beneath the delivery metrics for the majority of GCCs that operate at scale.

Why Traditional Operating Models Struggle at Scale

Most GCCs were built with a particular scope in mind, a specific size, a defined set of functions, and a reasonable set of assumptions about how work would flow. The problem is that GCCs rarely stay that size.

When scope expands but operating models do not, the math stops working. Headcount grows. Complexity grows. But the workflows underneath stay the same, and suddenly there are twice as many people managing the same process that was already showing strain. That is not scale. That is cost without productivity.

Organizations often underestimate how much their operating model needs to evolve alongside their delivery ambitions. Process redesign gets treated as a secondary concern, something to come back to once growth stabilizes. But growth does not stabilize. And the operational debt that accumulates in the meantime quietly limits everything the GCC is trying to achieve.

The Talent and Capability Management Gap

Filling seats is not the hard part. Most GCCs figure that out. The harder problem is building a workforce that can actually lead what the center is being asked to do.

Demand for professionals who genuinely understand AI, automation, data science, and digital operations at a strategic level continues to outrun supply. Attrition in these roles is high, and when experienced people leave, they take context with them that rarely gets documented before they go. Leadership readiness is another gap that does not get enough attention. Delivery scope at many GCCs has expanded well beyond what the current leadership layer was originally developed to manage.

Upskilling, structured career pathways, and visible growth opportunities are not benefits. They are operational necessities for any GCC trying to hold onto the people it actually needs to function at this level.

AI and Automation: Solving Complexity at Scale

There is a version of AI adoption that genuinely transforms how a GCC operates. And there is a version that just adds a new layer of complexity on top of the existing one. The difference usually comes down to what was done before the technology was introduced.

Automating a fragmented or poorly documented process doesn’t solve the issue. It locks the problem in and speeds it up. It’s those GCCs that have first cleaned up their process base, put in place clear governance and then brought technology into an environment ready to absorb it that have experienced genuine, sustainable value from automation.

When done in that order, the results make sense: less manual intervention, improved exception management, real-time visibility of operational performance, and leadership that can make decisions based on data rather than gut feel. That’s when automation starts to solve complexity, rather than add to it.

Building the Next-Generation GCC

Size is not the differentiator anymore. Some of the most effective GCCs are not the largest ones. They are the ones where operational discipline has kept pace with ambition.

What separates them is not the technology they have deployed or the number of functions they cover. It is the quality of the foundation underneath. Governance that is designed to move fast, not just to provide oversight. Processes that are standardized at the core but flexible enough to work across different regulatory environments. Operational insights that are embedded into how the center is actually run, not produced as a quarterly report that leadership glances at and sets aside.

The shift from cost efficiency to enterprise value creation is real. But it does not happen just because a GCC adds new capabilities. It happens when the operating model is mature enough to support them.

Conclusion

Growth is not the risk. Growth without the operational maturity to sustain it is.

The GCCs that will genuinely matter at an enterprise level over the next decade are not going to be defined by how fast they scaled. They will be defined by how well they managed what scaling brought with it. That means stronger process foundations, governance that actually enables speed, and AI integration that is strategic rather than reactive.

Pierian Services partners with organizations to design and scale GCCs and Shared Services Centers that are built for exactly this level of complexity, from governance and process design to finance operations and digital enablement.

FAQs

1. Why do GCCs struggle to scale even when they have strong talent and technology in place?

Because talent and technology sit on top of processes and governance, and if those foundations are weak, scale makes things worse rather than better. Most GCC scaling problems are not resource problems. They are structural ones that growth simply makes more visible.

2. At what stage should a GCC start thinking about AI and automation integration?

After core processes are documented, standardized, and governed. Introducing automation into inconsistent or fragmented workflows accelerates the problems rather than solving them. Getting the process foundation right first is what makes the technology investment actually deliver.

3. How is a next-generation GCC different from the traditional shared services model?

Traditional shared services were built around cost reduction and transactional execution. A next-generation GCC is expected to drive enterprise value, and that requires operational discipline, leadership depth, and intelligent automation working together, not just a larger team doing more of the same work.