Amidst geopolitical tensions and global economic uncertainty, the Global Capability Center (GCC) model is proving its worth more than ever. The affirmation is so strong that global enterprises are actively setting up centers. Those who have set up shop are rapidly expanding their footprint.
India remains a top choice because it checks every box: stable policy, a mature ecosystem, ease of doing business, cost arbitrage, and a large pool of top-tier talent. The country has been attracting a wide spectrum of entrants, from traditional fintech and retail giants to digital natives like GitHub, Airbnb, Zoom, and Anthropic.
But all the hype apart, are GCCs really thriving in the AI era? Let’s dig a few inches deeper to uncover the reality.
What’s in this article:
- The strategic shifts and daily tug-of-war for GCC leaders
- How GCCs are ensuring talent stability
- Structural blockers stalling AI projects at the PoC stage
- How QBurst is closing the gap in the AI era
The Operational Reality
When an enterprise sets up a GCC, it is the responsibility of the center head to operationalize the headquarters’ strategic goals of transformation, innovation, value creation, revenue impact, sustainable growth, etc.
However, on a day-to-day basis, GCCs face a delicate tug of war:
- Strategic Vision vs. Tactical Wins: Aligning long-term transformation with the relentless demand for short-term wins.
- Capability Building: Developing the institutional muscle required for project delivery and excellence without disrupting day-to-day operations.
- Operational Efficiency: Creating room for experimentation while managing to keep “Business-as-Usual" lights on.
We are also seeing a shift in the power dynamics. Sponsorship for GCCs has moved from being purely CIO-led to a combined CFO and CIO mandate, and decision-making authority is increasingly moving to India.
Sponsorship for GCCs has moved from being purely CIO-led to a combined CFO and CIO mandate, and decision-making authority is increasingly moving to India.
Many GCCs are now managing the entire product lifecycle—from design and deployment to global support. The primary driver is no longer just cost arbitrage, but capability arbitrage.
The Talent Paradox
Despite the move toward ownership, GCCs face challenges with respect to talent acquisition and management.
The Indian tech talent pool, while vast, suffers from high parity and intense competition. GCCs often fight for the same top talent, resulting in higher hiring costs and a culture of high attrition. For a newly established GCC, even a few key exits can break strategic momentum and raise questions back at HQ regarding its stability.
While GCCs would love to bring every function in-house to prove their mettle to the HQ, in reality, this is a tough task. A mature GCC avoids this trap by embracing a partner ecosystem for two practical reasons:
- Agility: Specialized skills are often required cyclically; maintaining a massive permanent headcount for niche needs is inefficient.
- Maximizing RoI: A partner can absorb the low-complexity, high-volume tasks, freeing the GCC’s internal talent to focus on high-impact, ground-up innovation.
The AI Velocity Gap
The latest wave of AI innovation has significantly elevated what enterprises expect from their GCCs. It is no longer enough to achieve incremental productivity gains of 20%-30%. Enterprises now expect their GCCs to deliver more with the same resources or even less. This necessitates a revamp of roles, engineering, and communication between various systems. In short, AI is now the default factory setting for enterprises.
The pace of AI advancements now demands more mature and seasoned architects/Leads who can design and architect intelligent solutions and mentor teams in AI-first thinking. With this, the demand for entry-level roles is fading, leading to an inversion of the traditional talent pyramid.
While a few early adopters are taking it in their stride with AI-augmented engineering, the majority of GCCs are still testing AI as a “bolt-on” and are unable to move past the proof-of-concept stage to make it part of the foundational strategy.
The majority of GCCs are still testing AI as a “bolt-on” and are unable to move past the proof-of-concept stage to make it part of the foundational strategy.
This is because they keep hitting the same four blockers:
- Data Fragmentation: The outputs of AI models are only as good as the data they consume. Most enterprise data remains siloed and fragmented, making reliable outcomes nearly impossible.
- High Costs: Scaling API calls is expensive, given the high cost of securing GPU compute power, storage, and specialized talent needed to run these.
- Security Challenges: Companies are still worried about the IP/confidentiality and don’t have a definitive answer for the peripherals of these AI tools.
- Legacy Systems: High-performing AI tools are still unable to communicate with age-old mainframe-era applications, especially in the BFSI sector. (Anthropic argues that newer approaches will help close this gap, but we’ll have to wait and see.)
QBurst & GCCs: A Strategic Partnership
At QBurst, we view our relationship with GCCs as deeply "knitted" and as a natural extension of the GCC’s strategic core. We realized early on that simply throwing talent at a problem doesn’t work any longer, and enterprises need an AI-ready ecosystem that can sustain change. We see High AI-Q™ as a measure of structural maturity: how well a company’s data, architecture, and culture can actually sustain intelligent systems.
We see High AI-Q™ as a measure of structural maturity: how well a company’s data, architecture, and culture can actually sustain intelligent systems.
It starts with the vital work of data readiness. You can’t build intelligent enterprises on siloed data. We spend a lot of time helping enterprises transform their datasets into intelligent enterprise assets, asserting their readiness for AI consumption and integration.
The emergence of multi-task agents marks a fundamental departure from the era of rule-based workflows. We aren’t just building smarter chatbots; we’re designing complex, autonomous agentic systems that handle multi-step business processes that once needed human intervention. Our frameworks ensure that “Human in the lead” (not just "in the loop") while AI drives speed to ensure right outcomes and strategic alignment.
This shift in capability requires a shift in how we measure success. At QBurst, we have moved toward value-driven models, where delivery is focused on the business outcomes, not just the hours.
Ultimately, the future of the GCC isn't just about being a "center." It’s about being an integrated platform where the distinctions between headquarters and capability center disappear, leaving only a single, globally distributed, AI-native enterprise.
With the required agility, nimbleness, and the AI-proficiency, QBurst supports GCCs’ journey in transitioning their enterprises effectively and sustainably, while continuing to focus on outcomes that move the business forward.
