HCM & HRIS

GCC leaders redefine accountability and AI strategies for measured business outcomes

October 3, 2026
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Singaravelu Ekambaram

Operationalising AI is a fundamentally different challenge from deploying it. It requires data foundations built for the enterprise’s specific domain and regulatory context />The mandate for global enterprise leaders is evolving rapidly. GCC heads are no longer judged primarily on operational continuity, attrition, or quality metrics. They are being asked harder, more strategic questions: how deeply is AI embedded in core workflows, what measurable business outcomes is the centre delivering, who owns the platform decisions, where should AI be hosted given jurisdictional risk and compute sovereignty, and who is accountable for the result?<br><br><!– PROMOSLOT_M –><div class=” article-detail-ad-slot=”” captionrendered=”1″ data-src=”https://etimg.etb2bimg.com/photo/134653730.cms” height=”442″ loading=”eager” src=”https://hr.economictimes.indiatimes.com/images/default.jpg” width=”590″></img></p> </div> <p>Addressing these questions requires more than strong delivery management alone. It calls for GCC leaders to bring a broader enterprise lens — connecting execution with business value, governance, talent strategy, and adoption so that AI can translate into measurable outcomes.</p> <p>The scale of what is at stake makes this urgent. India now hosts over 2,100 GCCs, with more than 506 Forbes Global 2000 companies represented. But growth in the number of centers is outpacing growth in the leadership model that governs them — and nowhere is that gap more consequential than in how GCCs are approaching AI.</p> <p><b>The real challenge is not adoption</b></p> <p>Most GCCs have moved past the question of whether to invest in AI. The EY India GCC Intelligence Report 2026 found that 58% are already investing in agentic AI, with a further 29% planning to do so within a year. The technology is accessible. The pilots are running. The harder question — the one that separates centers genuinely transforming from those generating activity — is whether the GCC can deliver outcomes from AI at scale.</p> <p>Operationalising AI is a fundamentally different challenge from deploying it. It requires data foundations built for the enterprise’s specific domain and regulatory context. It requires architecture decisions on AI hosting, compute sovereignty, and jurisdictional risk, made deliberately at the intersection of global mandates and local constraints, not resolved by default. It requires governance that evolves with adoption rather than being designed once and left static. And it requires an operating model built around end-to-end accountability — cross-functional, product-aligned teams that own outcomes, backed by new capabilities such as Agent Ops to govern agents in production and FinOps to prove their value, not siloed specialists who hand work across functions.</p> <p>The BFSI sector offers the clearest illustration. Accounting for approximately 40% of India’s GCC population, it is a sector defined by compliance complexity and transformation stakes that leave no room for AI deployments that work in pilots but fail at scale. In engagements like the one Cognizant runs for Citizens — where Citizens Pay operations run end-to-end out of Hyderabad — the accountability model is unambiguous: the GCC team owns the outcome, not just the delivery. That is what AI at scale actually requires.</p> <p><b>What the leadership shift looks like</b></p> <p>The leadership model that built these centers — skilled at managing relationships, translating headquarters directives into local execution — is not the model that will scale AI inside them. The shift is not primarily a skills question. It is an accountability question.</p> <p>GCC leaders getting this right are doing three things differently. They are articulating the centers’ AI contribution in business outcome terms, not operational metrics — making it legible to global leadership in commercial language. They are driving talent strategy as owners — investing in AI fluency, reshaping career architecture, extending workforce transitions to cover agents alongside people, and building the cross-functional ownership culture that AI delivery requires. And they are present in the rooms where enterprise strategy is shaped, not receiving it downstream.</p> <p>The structural constraint is real: many enterprises have not reconfigured the governance relationships that would give GCC leaders the authority their expanded mandates require. Resolving that is a headquarters challenge as much as a GCC one — and enterprises that do not address it will find their AI ambitions consistently outpacing their delivery capacity.</p> <p><b>The question that defines the next era</b></p> <p>The gap between what enterprises are asking their GCCs to deliver and the leadership model currently in place shows up most clearly in AI programs. The technology can be deployed. The architecture can be designed. What cannot be shortcut is the organizational capability to lead the transformation required to realise the value.</p> <p>The GCC leaders who will define the next era are not those who adopted AI earliest. They are those who built the accountability structures, the operating models, and the leadership depth to deliver from it — consistently, inside complex systems, at genuine scale.</p> <p><b><i>The author is <b>Singaravelu Ekambaram, SVP and Global Head of Delivery, Americas at Cognizant.</b></i></b></p> <p><b><i>Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.</i></b></p> <div> <div aria-label=

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