Articles

The AI-native GCC play: Focused and built to scale

- Shreya Kapoor

The promise or potential of AI continues to dominate conversations in Global Capability Centers (GCC). However, the next logical stage–converting that potential into action and sustainable impact–has proven to be elusive.

Research by Zinnov-ProHance shows that 92% of GCCs now run AI pilot programs or are growing their AI initiatives. Yet, value creation has been uneven, with more than 70% of organizations failing to establish methods for measuring ROI.

CIOs face a clear challenge: while the ambition for AI is high, most GCCs struggle to move beyond pilot projects because they cannot demonstrate tangible business value. The core issue is not technology implementation, but the absence of an operating model that embeds business alignment, data governance, talent development, and a culture rooted in outcomes rather than inputs.

The path forward is to evolve from a transactional cost center that implements AI for limited efficiency gains to an AI-native GCC, where the operating model and business mandate drive differentiation.

What AI-Native GCCs deliver

An AI-native GCC makes a big leap from streamlining processes with bolt-on AI tools or setting up AI testbeds for isolated experiments. AI-native GCCs take AI adoption to the next level by fundamentally re-architecting the core to embed intelligence, automation, and data-driven decision-making into every layer, including platforms, data, and operations. The result is a strategic capability hub that enables scalable, repeatable value creation.

Rather than optimizing a single product line or function with AI tools, an AI-driven GCC becomes a springboard for enterprise growth with scalable platforms, data products, and governance, supporting diverse business units and geographies. This multiplier effect - building capabilities once and deploying them enterprise-wide, helps eliminate redundant effort and accelerate digital transformation.

AI-driven GCCs deliver outsized value due to four distinct characteristics.

Pillar Strategic mandate for the CIO Measurable business outcome
Automated global governance GCCs operate in multiple jurisdictions with complex data flows and regulatory environments. The CIO should use this capability to integrate governance and policy into a single platform that automates compliance for data residency, security, and privacy in all markets. Compliance speed: Reduce the time and cost to enter new markets or launch products by ensuring they are compliant by design, not manually or retroactively.
Platform reuse and operational leverage Unlike siloed business units with redundant solution development, the GCC is chartered to build once and deploy it several times. CIOs can make sure that a single golden path is created as a high-quality internal platform that is robust, standardized, and enables self-service across all product teams. Time to market: Reduce the development cycle by allowing product teams to focus on business logic and customer experience, not rebuilding core infrastructure or tooling.
Data and ML intelligence as products The GCC is uniquely positioned to source, cleanse, and harmonize data across the enterprise. IT & business leaders need to position the GCC as the main source of high-quality reusable AI models and curated data sets, served as products to internal clients to build and maintain catalogs. Innovation speed: Enable business units to experiment and innovate faster by delivering high-quality intelligence instead of having them build their own data pipelines and models.
SRE-focused AI operations As the steward of shared global infrastructure, the GCC is solely accountable for service stability across the enterprise. The CIO should charter the GCC to use Site Reliability Engineering (SRE) principles, applying data and automation to govern service levels proactively, manage incidents across business units, and guarantee the reliability of shared digital services. Digital trust: Maintain customer confidence and brand reputation by ensuring the enterprise's digital presence is available, reliable, and performs well across all markets.

Why GCCs fail to unlock ROI

Studies on AI-native GCCs point toward three structural barriers that are preventing GCCs from realizing optimum business outcomes from their AI investments.

1. Fragmented data infrastructure

45% of GCC leaders point to the absence of a unified, secure data infrastructure as a primary barrier to scaling AI across the enterprise.

2. Lack of structured governance

Only 7% of GCCs have established a dedicated Center of Excellence (CoE) for risk and governance oversight, resulting in limited accountability and stalled adoption.

3. Limited visibility into AI adoption

63% of GCC leaders report minimal insight into how employees use AI, making it difficult to connect AI initiatives to productivity or business impact.

Without addressing these foundational gaps, AI initiatives risk remaining siloed pilots and preventing a GCC from transitioning from a cost center to a strategic value creator.

A three-stage strategic roadmap

For GCCs that are still anchored to a cost center mindset, the path to becoming AI-native will take a deliberate strategy that encompasses technology, process, and talent. Here is a blueprint that lays the path for transforming a GCC operating model into a driver of scalable, enterprise-wide value.

Stage 1: Build a unified foundation

The first priority is to reset the foundation. Scalable AI cannot be built on fragmented, inconsistent technology. This stage focuses on establishing a unified digital foundation across the GCC, setting the stage for accelerated outcomes. The Platform Core is critical in a GCC because it addresses redundant tooling across multiple business units and geographies.

A dedicated Platform Core team will build and manage shared tools, data infrastructure, and governance frameworks for all GCC teams. This team will define standard processes for software development and data science to drive consistency and efficiency.

The focus shifts from custom solutions for each business unit to a single, robust, reusable platform. Standardization then becomes the default mode. The Platform Core delivers templated workflows with embedded compliance and security, eliminating duplication and driving consistency across the enterprise.

Stage 2: Leverage the unified data platform

Next is the development of high-value AI and data products for the enterprise. This is driven by an Intelligence Core team made up of Machine Learning (ML) engineers and data scientists.

The Intelligence Core provides the base for building a catalog of reusable AI and data products, replacing duplication of efforts with standardized, scalable assets. Moreover, rather than developing separate models for each business unit, the team delivers ML algorithms that can be deployed across the GCC.

The CIO should work with the Intelligence Core team to identify and prioritize business problems that can be solved with a reusable AI model. The success of this strategy will not be based on how many models have been created. Instead, it will be based on how many of those models are utilized by different business units.

Stage 3: Establish an execution core

This stage is about proactively identifying opportunities to improve business processes, automating workflows, and creating new digital products for the enterprise.

The Execution Core will have a cross-functional team of business leaders, product managers, and IT engineers working with business leaders to develop solutions by leveraging the Platform Core and Intelligence Core.

The team will be responsible for measuring and reporting the impact of its work. The CIO must empower this team to form strategic partnerships with business leaders, with performance assessed by quantifiable business value such as reduced time-to-market and increased innovation. And fund them not as cost centers, but as capability investments.

The CIO’s mandate

As the Platform, Intelligence, and Execution Cores mature, the GCC will evolve into a data-driven innovation hub, serving as the central engine for enterprise-wide digital transformation. The Intelligence Core will proactively analyze global datasets to identify new business opportunities and market expansion paths.

Meanwhile, the Execution Core will move beyond embedded services to co-develop digital products and businesses with business units. This positions the GCC as a strategic partner, shaping the company’s future direction rather than simply delivering services.

The journey from a cost-focused GCC to an AI-native growth engine is a transformational one. It demands a fundamental shift in thinking about the GCC’s mission and purpose. A well-defined roadmap will be critical in helping CIOs achieve maximum potential and address the ROI paradox facing the market.

Request a consultation
About the author

Shreya Kapoor

Senior Content Strategist