Global Capability Centers Were Supposed to Be Strategic – AI Just Made the Gap More Expensive

The Global Capability Center (GCC) market is no longer a niche offshoring strategy; it’s a mainstream operating model, valued at over $600 billion USD in 2025 – and estimated to reach $900 billion USD by 2030, driven not by back-office support work but by AI programs, product development, and technology roadmaps.

In fact, last year marked the second consecutive year in which more than 300 new offshore and nearshore GCCs opened worldwide.

Yet most companies, as they build a GCC, still default to the same destination. India is the obvious choice: deep talent supply, government infrastructure, and a long track record. The country hosted over 1,700 GCCs as of FY 2024, contributing $64.6 billion in export revenue and employing roughly 1.9 million professionals.

T-Mobile’s recent move illustrates the pattern clearly: while cutting jobs in the U.S., the carrier opened a 250,000 square-foot GCC in the central Indian ‘City of Pearls’, Hyderabad – with plans to hire 1,000 people by 2027 covering software engineering, DevOps, data analytics and cybersecurity.

The Indian logic isn’t wrong. But the question is what companies consistently miss by treating it as the only answer.

The Problem With Building for Cost-First

The GCC model has been shifting faster than the default playbook reflects. Centers built primarily around efficiency metrics tend to struggle with the same setbacks: talent retention is harder, innovation is rarer, and the value delivered stays marginal.

The centers that actually work are thus built with a different mandate: teams with real accountability, operating close to the business, empowered to make calls rather than wait for direction.

That shift in purpose changes where you should build. A low-cost delivery arm operating at a distance can sit almost anywhere, but a team meant to function as a true extension of the core business – same cadence, context and actual real-time communication – needs something else. Time overlap, cultural proximity, low integration friction.

Without that, operational scalability stalls before it even gets going – because the coordination overhead compounds faster than the headcount does.

That’s where other regions like Latin America start to get harder to dismiss. But companies that recognize the opportunity hit the same structural wall at first.

“A traditional GCC can take several months, or even more than a year, to set up properly,” said Juan Felipe Velasco, managing director and co-founder at Latin America-based Workforce-as-a-Service (WaaS) provider Remoti.

“The WaaS model compresses that timeline because everything is already in one place. The infrastructure is built, the process is tested, and the company does not need to start from zero,” he added.

What Distributed Teams in Latin America Actually Bring

The biggest tech companies have already moved in: Google opened an AI research center in Brazil, partnering with the University of São Paulo; Microsoft committed US $1.1 billion to the region in 2020, building engineering hubs in Mexico; AWS partnered with the Colombian government on a program aimed at training over 500,000 tech professionals through 2028.

These aren’t exploratory bets – they reflect deliberate talent hub operations from giants that have already run the analysis.

Accenture puts 60% of the world’s highly-skilled workers in Asia Pacific, Africa, and Latin America – a share projected to reach 67% within the next four years. The talent distribution is already global, and the GCC map is just catching up.

“For many companies, India has been the default market for GCCs because of the scale, maturity, and long track record in technology delivery. But when companies only look at India, they often miss the broader strategic value that Latin America brings to the table,” Velasco stressed.

The value he describes isn’t mainly about cost, either. “LatAm offers a combination that is very hard to replicate: strong time zone alignment with the U.S., mid to senior technical talent, cultural proximity, bilingual communication, strong business context, and the ability to integrate quickly with U.S.-based teams.”

A team that surfaces blockers in real time, for example, operates differently than one that does it the next morning – and that difference compounds over months and years.

Retention and continuity tend to be stronger in the region, too – something that rarely shows up in GCC planning conversations until it becomes a problem.

The Real Bottleneck: Embedded Talent Infrastructure

Even companies sold on the LatAm case hit the same wall: building out workforce infrastructure the traditional way is slow.

“Companies need to solve entity setup, legal, payroll, compliance, recruiting, onboarding, equipment, benefits, tools, HR operations, retention and management layers before the team is fully productive,” Velasco explained.

In the current environment, that delay has a direct cost. Faster market entry isn’t just a nice-to-have when AI deployment timelines are compressing; it’s a competitive variable. A company that needs to stand up an AI engineering team abroad and waits a year to do it is playing a different game than one that can do so in weeks.

The WaaS model is built around a specific problem. Instead of constructing GCC infrastructure from scratch in a new market, its providers offer embedded talent infrastructure as a pre-built operating layer – talent, legal setup, compliance and payroll operations, equipment and HR already in place before the first hire signs.

Velasco described the difference in concrete milestones. “First qualified candidates in days, not months. First hires onboarded without opening a local entity. Payroll, contracts, benefits and compliance handled from day one. Equipment, tools and operational support included in the model. Ability to scale from one person to a full team without rebuilding the structure.”

He deemed it “speed with control”: the substance of a full GCC operation, without the yearlong build as a prerequisite for getting started.

AI Exposes the Infrastructure Problem

One question worth sitting with: if AI tools are automating significant portions of technical and operational work, do these centers still make sense at scale?

GCCs are increasingly running AI, data science, and automation at their core, according to Nova Bhojwani, SVP and global industry head at intelligent data provider Ness Digital Engineering. But that AI capability lives inside the workforce infrastructure – it doesn’t replace it.

“Imagine GCCs functioning as internal startups, pitching, prototyping, and commercializing ideas for their parent organizations […] Instead of being extensions of corporate headquarters, GCCs could become the nerve centers driving global strategy, product innovation, and decision-making,” Bhojwani wrote in a blog post.

Compliance and payroll operations still run. Legal requirements still apply across jurisdictions. Retention and continuity still compound or erode over time depending on how deliberately they’re managed. And distributed teams in LatAm or elsewhere don’t hold together on tooling alone.

“There is a common misconception around AI: that companies can replace the full operating model with tools alone. Mature companies understand that this is not enough. A GCC is an infrastructure model; AI can make teams more productive, but it does not replace the 360-degree infrastructure companies need to operate at scale,” said Velasco.

Therefore, the useful frame isn’t GCC versus AI; it’s AI-enabled distributed teams that still need real operational scalability underneath them. For firms building at speed in Latin America, WaaS is how that embedded talent infrastructure gets stood up without the traditional runway.

India will keep making sense for a lot of companies. But for those that need proximity, integration, and faster market entry alongside scale, the map has more on it than most GCC conversations acknowledge.