This content was originally featured in a Forbes article in October 2025.
Introduction
The challenge facing private equity firms today isn’t whether to invest in AI, but how to implement it effectively at scale across diverse portfolio companies. Global talent hubs like India and Mexico are becoming focal points for AI-driven global capability centers (GCCs), where companies can access specialized talent and technology to scale AI initiatives efficiently.
How PE Portfolio Companies Can Scale AI While Protecting ROI
Private equity (PE) portfolio companies (PortCos) face a challenging balancing act. They’re under constant pressure to deliver growth and improve efficiencies, even as they navigate disruptive technologies and shifting market expectations.
Onshore innovation teams often struggle with access to talent at a reasonable cost for initiatives that require experimentation and velocity, especially within a more disciplined fiscal approach. Talent is limited, pilots are costly and demonstrating early ROI through productivity gains or cost savings is critical.
GCCs can serve as structured environments to pilot AI initiatives and identify scalable approaches beyond isolated experiments. By concentrating AI pilots in talent-rich hubs like Bengaluru or Guadalajara, companies can experiment more affordably, build repeatable success patterns and lay the groundwork for scalable adoption.
How Modern GCCs Reshape Enterprise Strategy
As AI adoption scales, some organizations are reimagining their GCCs as multidisciplinary platforms that go beyond support and operations, enabling experimentation, analytics and product innovation. Traditional GCCs delivered value by standardizing functions like finance, IT and customer support. Today’s GCCs integrate automation with AI capabilities, enabling companies to explore new approaches to efficiency, decision-making and product innovation.
The Three Drivers Of Modern GCC Impact
What makes AI-enabled GCCs a compelling model? It’s the combination of top-tier technical talent, embedded automation expertise and strong data engineering teams that lay the foundation for predictive, insight-driven execution.
- Top-Tier Technical Talent: AI-enabled GCCs tap into deep pools of engineers, data scientists and prompt specialists trained at leading institutions. These teams are fluent in the latest AI frameworks, comfortable working with production-grade systems and capable of rapidly translating business needs into technical execution.
- Embedded Automation Expertise: Rather than treating automation as an add-on, GCCs integrate it directly into core enterprise workflows. Teams are structured to optimize repetitive processes, embed intelligence into decision points and build scalable automation systems that evolve with the business.
- Strong Data Engineering Foundations: Effective AI starts with clean, well-structured data. GCCs bring specialized data engineering capabilities to consolidate fragmented sources, build reliable pipelines and ensure that AI models are powered by consistent inputs that drive real business insights.
How GCCs Deliver Measurable Business Outcomes
Some companies using AI-enabled GCCs report measurable improvements in operational efficiency and decision-making accuracy. The following anonymized examples offer a glimpse into what’s possible.
- Banking – KYC Compliance: The bank’s GCC team rolled out an AI-driven KYC refresh platform, which reduced turnaround time by approximately 60% and cut case-handling costs by 48%.
- Manufacturing – Cash-Conversion Cycle (CCC): A Mexico-based data-science pod rebuilt inventory analytics, cutting the CCC by 35 days (from 85 to 50) and releasing $750,000 in working capital.
- Financial Services – Credit Risk Accuracy: GenAI models added to a Bengaluru GCC’s risk engine improved credit-score prediction accuracy by 35%, reducing portfolio exposure.
These case studies tell a clear story: Start with a tight scope, iterate rapidly and measure real business gains in quarters, not years.
How GCCs Translate AI Ambition Into Enterprise Value
Scaling AI through GCCs is not just about cost and speed; it’s about controlled execution. Without the right governance model, even well-intentioned experiments can drift off-course. Top-performing GCCs implement joint governance, often pairing senior business leaders with GCC execution leads, to ensure each initiative supports clear, measurable business outcomes. This discipline helps AI initiatives stay grounded in value while moving at startup speed.
Looking Beyond The Near Term
As AI capabilities like synthetic data and agentic systems mature, they’ll require more than isolated proofs of concept. GCCs with the right setup can evolve from pilot grounds into integration hubs that operationalize these innovations across the enterprise.
Powering Up The GCC Engine
A century ago, factories that failed to electrify quickly were left behind, and many went out of business. AI-powered GCCs may offer a cost-effective approach to rethinking operational and analytical capabilities, similar to how past innovations transformed production processes.
For PE investors, the focus is shifting from deciding whether to use GCCs to determining how best to integrate them into portfolio companies’ AI strategies.
