Insights
5 questions every executive team should ask before building a GCC
Bharath Krishnaswamy - UST, SVP
AI is redefining the GCC mandate. Before expanding headcount or delivery capacity, leaders must assess whether their GCC can accelerate AI adoption, strengthen ownership, reduce risk, and create measurable business value.
Bharath Krishnaswamy - UST, SVP
The role of Global Capability Centers is evolving. In the AI era, GCCs must move beyond cost efficiency and delivery scale to become strategic engines for AI adoption, product innovation, platform ownership, governance, and business value. Executive teams should reassess ownership, KPIs, operating models, and risk before investing further.
AI has moved the Global Capability Center decision into a new executive category. For years, enterprises used GCCs to expand access to talent, lower operating costs, and strengthen global delivery. That model created real value. The new mandate is broader: accelerate AI adoption, increase engineering velocity, own digital products, retain critical knowledge, reduce delivery risk, and build innovation capacity inside the business.
The urgency is measurable. Stanford HAI’s 2025 AI Index reported that organizational AI use rose from 55% in 2023 to 78% in 2024, while global private investment in generative AI reached $33.9 billion. This level of adoption changes the GCC strategy question. Executive teams now need to decide which capabilities deserve direct enterprise ownership and which can remain partner-led.
Industry signals point in the same direction. NASSCOM has described India's GCC ecosystem as being at a transformative juncture, with centers evolving into strategic hubs that influence global business dynamics. The implication for CIOs, CTOs, CDOs, and GCC leaders is clear: a GCC designed mainly for scale and efficiency will struggle to meet an enterprise mandate shaped by AI, product ownership, data, and speed.
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The old playbook is under pressure
Traditional GCC metrics still matter. Cost per FTE, SLA attainment, headcount growth, and utilization help leaders manage delivery control. They provide an incomplete view of whether the GCC is improving enterprise performance.
The executive questions have changed. Is AI moving from pilots into production? Are product releases reaching customers faster? Is institutional knowledge staying inside the enterprise? Are engineering teams creating reusable IP? Is vendor dependency falling in the areas that matter most?
This is where many organizations encounter execution risk. AI talent becomes fragmented. Governance is added after pilots spread. Business units duplicate AI investments. Delivery teams are measured on utilization while leadership expects product outcomes. Security and data controls vary across teams. These issues rarely appear in a traditional GCC scorecard, and they directly affect enterprise speed, resilience, and ROI.
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What an AI-native GCC looks like
An AI-native GCC is a strategic operating model where product teams, platform engineering, data, AI specialists, cybersecurity, and governance operate as integrated enterprise functions. It owns reusable platforms, engineering standards, AI enablement patterns, product knowledge, and decision rights.
The model also requires disciplined governance. NIST's AI Risk Management Framework gives organizations a voluntary, sector-agnostic structure for managing AI risks. ISO/IEC 42001:2023 provides requirements for establishing and continually improving an AI management system. For a modern GCC, these principles matter because AI adoption at scale depends on trust, accountability, monitoring, and repeatable controls.
The measurement system also changes. An AI-native GCC should be evaluated through metrics such as engineering cycle time, AI adoption rate, production AI use cases, internal IP created, automation realized, vendor dependency reduction, platform reuse, security posture, and business value delivered.
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Many enterprises have ambitions that belong in the AI-Native Era while their operating model, funding model, governance, and KPIs still reflect the Efficiency Era. That mismatch creates friction between board-level expectations and day-to-day delivery reality.
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Five questions every executive team should ask
Before approving the next GCC investment, leaders should answer five practical questions.
1. Which capabilities create competitive advantage when owned internally?
AI engineering, platform development, cybersecurity, enterprise data, product management, and domain-specific digital expertise often deserve a higher level of direct ownership.
2. Can the current model operationalize AI beyond pilots?
A successful AI strategy requires operating rhythms, governance, data access, engineering practices, and product ownership that allow teams to move from experimentation into production.
3. Where are we exposed to knowledge loss or vendor lock-in?
Every external dependency should be assessed by strategic importance, risk concentration, substitutability, and impact on institutional memory.
4. Which KPIs prove that the GCC is improving business outcomes?
Executives need a scorecard that connects the GCC to product velocity, AI adoption, customer impact, risk reduction, and measurable business value.
5. What can change in the next 90 days without disrupting current delivery?
The highest-value transformations usually begin with focused pilots: one product domain, one AI-enabled engineering workflow, one governance pattern, or one vendor dependency to reduce.
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The UST Nimbus perspective
At UST Nimbus, we believe GCC modernization should start with capability architecture before location, hiring, or tooling decisions. Enterprises need a clear view of the capabilities they should own, the operating model required to scale them, and the governance needed to manage AI responsibly.
A practical roadmap should protect today's delivery while building tomorrow's enterprise muscle. That means assessing maturity, identifying value pools, selecting quick-win pilots, redesigning KPIs, defining AI governance, and scaling proven patterns across teams. The objective is a lower-risk transition from global delivery capacity to an AI-ready enterprise operating model.
The next generation of GCCs will be evaluated by the capabilities they build: production AI adoption, faster product release cycles, stronger platform engineering, better knowledge retention, clearer governance, and measurable business impact.
The GCC decision has changed. The strategy needs to change with it.