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TL;DR
Global Capability Centers (GCCs) are evolving from delivery centers into strategic enterprise hubs, but many continue to operate with governance models built for a cost-arbitrage era. The biggest risks today aren't talent or technology—they're governance gaps that slow decisions, dilute accountability and prevent AI, analytics and innovation from delivering business value.
This article explores six governance mistakes—across decision rights, cost, reporting, data quality, innovation, and technology—that quietly cost GCCs millions, and outlines what high-performing governance looks like in an AI-first enterprise.
For nearly two decades, Global Capability Centers (GCCs) were built around a simple business case: move work closer to talent, reduce costs, improve operational efficiency. That model created one of India's biggest business success stories.
India now hosts 2,117 GCCs across 3,728 units, employing 2.36 million professionals and generating USD 98.4 billion in annual revenue, with over 506 Forbes Global 2000 companies running operations from the country. But this scale no longer explains why some GCCs become enterprise growth engines while others plateau despite adding headcount and technology.
The operating model has changed.
Today's GCCs own AI programs, global products, planning, analytics, supply chain operations and enterprise platforms. They influence revenue, customer experience and strategic decision-making. In fact, nearly 90% of India's GCCs now operate as multi-functional centers, and ER&D-focused GCCs are growing 1.3 times faster than the overall GCC growth rate, a clear signal that higher-value, more complex work is shifting to India. Yet many continue to operate with governance models designed for shared-service organizations rather than enterprise capability hubs.
As Polestar Analytics has observed, the real shift is from measuring cost saved to measuring value created, a transition many GCCs have yet to operationalize.
The consequences rarely appear as headline failures. Instead, they emerge as slower decisions, duplicated technology investments, AI pilots that never scale, fragmented data ownership and investment approvals that move at the pace of governance rather than business demand. Industry estimates suggest poor data quality alone costs organizations an average of USD 12.9 million annually, and in CPG GCCs, where SKUs, trade promotions and distributor data span dozens of markets, the leakage is often significantly higher.
The question, therefore, is no longer whether governance matters. The question is whether governance is enabling enterprise value, or quietly limiting it.
In this article, we examine some governance gaps that continue to hold Global Capability Centers back — and why fixing them is becoming a boardroom priority, not just an operational one.
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The most expensive governance failures rarely come from poor execution. They come from unclear decision ownership. As GCCs scale, decision rights become fragmented across regional leaders, global functions and transformation offices. Governance forums continue to meet, but decisions move slowly because accountability is shared rather than assigned.
"GCCs probably need to focus on prioritising decisions rather than the pathways leading up to deliverables."
Governance should accelerate decisions, not become another layer between problems and resolution. Organizations need to evolve from being a doing organisation into a defining organisation.
Common mistakes
- Steering committees that review progress but lack decision authority.
- Dual reporting lines where regional and global leaders share accountability but neither owns the outcome.
- No defined escalation path when functions disagree on shared processes.
- HQ sponsors rotate, but governance charters and decision rights are never reset.
Business impact
Decision latency increases programme costs, duplicates effort and delays transformation. In CPG, delayed decisions on demand planning, pricing or inventory directly affect margin and working capital. A McKinsey analysis puts the cost of slow, ineffective decision-making at roughly 530,000 lost manager-days and $250M in wasted wages per year at a typical Fortune 500 firm, before opportunity cost. A separate 2026 review of decision-making research found only 39% of companies have a strong culture of data-driven decision-making, and poor operational decisions alone shave up to 3% off profits.
Many GCCs are positioned as strategic capability hubs, yet governed like traditional cost centres. The strategy talks about ownership, innovation and enterprise capability. The investment model still talks about FTE savings and rate cards.
"I'd like budgets to be based on value rather than cost."
He also reminded the panel of the AC Nielsen line he uses with his own CFO: "The price of light is cheaper than the cost of darkness." Until governance reflects that principle, capability ambitions remain constrained by legacy funding models.
Common mistakes
- Strategic investments evaluated primarily through a cost-arbitrage lens.
- FTE reduction treated as the primary measure of success.
- GCC leaders accountable for outcomes but unable to approve meaningful investments.
- Routine technology or vendor decisions requiring multiple HQ approvals.
Business impact
The organisation optimises for lower operating costs while underinvesting in productivity, resilience and long-term capability. The 2026 India GCC Landscape Report shows 92% of GCC leaders now say their centres deliver value beyond cost savings, and yet only 1 in 3 centres currently operate as a global profit centre rather than a cost centre. The Zinnov–NASSCOM GCC Landscape in India 2026 puts an even sharper number on it: only 5% of India's GCCs currently operate as full "Transformation Hubs" with AI-led operations and CXO mandates run from India. That gap between narrative and funding model is where strategic ambition quietly stalls.
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Most GCC dashboards are built to demonstrate performance rather than improve it. They report SLA compliance, utilisation and cost savings, but rarely show where decisions are slowing down, approvals are stuck or operational risks are accumulating.
"There's a whole bunch of reporting that happens that the organisation can live without... it's a rear-view look when the business needs knowledge on the go."
Common mistakes
- Dashboards designed for executive reporting instead of operational management.
- No visibility into decision velocity or approval bottlenecks.
- Output metrics replacing business outcome metrics.
- Status reporting substituting for proactive risk reporting.
As AI reshapes productivity, Anirban Nandi argued that organisations should move away from activity metrics towards outcome metrics, measuring "decision quality, decision speed, automation impact, business value." He was particularly sharp on one trap: "Tokens are always easy to count. It's just like the lines of code written, hours worked, tickets closed... but activity and impact are not the same thing."
Business impact
Leadership sees operational compliance while business decisions remain slow, risks surface late and governance becomes reactive. Gartner's May 2026 research found only 27% of executives believe they have a comprehensive strategy for measuring and enabling AI-era work, and just 20% believe their workforce is truly AI-ready, meaning most reporting today is measuring the wrong things at the wrong cadence. The Zinnov–NASSCOM 2026 view makes the same point for GCCs: what future-ready centres need is "continuous value telemetry," not month-end SLA decks.
Strong governance starts with trusted data and responsible AI. Explore the framework every modern GCC needs to scale AI with confidence.
Data quality usually fails at organisational boundaries rather than inside individual teams. The GCC owns analytics, business functions own the source data and IT owns the platforms. When data quality deteriorates, governance turns into a debate over ownership instead of accountability.
Shiraz Mishra, Business Head – International Markets, Polestar Analytics highlighted at the roundtable that organisations often focus on AI capabilities while overlooking "the data foundation challenge." Arvind reinforced the point by emphasising the importance of "clarity of assumptions" and "traceability of the data pathway" behind every business insight, a discipline life sciences learned three decades before the AI era.
Common mistakes
- Shared ownership with no single accountable data owner.
- Master and reference data governance introduced after processes have scaled.
- Delivery teams measured on quality while upstream business owners escape accountability.
- Different functions and markets maintaining different definitions of the same business metrics.
Business impact
Poor-quality data undermines forecasting, planning, reporting and AI adoption, forcing organisations to spend more time fixing data than creating value from it. Gartner still pegs the average annual cost of poor data quality at $12.9M per organisation, but the AI era has raised the stakes: enterprises spent $1.5 trillion on AI in 2025, and 73% of enterprise data leaders now rank data quality as the single biggest barrier to AI success, ahead of model accuracy, compute cost and talent. 60% of companies report little to no value from AI investments so far, and the root cause is the same one CPG has always struggled with: promotions that fail, inventory that over-forecasts and pricing decisions the business can no longer defend.
Most GCCs know how to build pilots. Few know how to govern scale. The technology works. The business case is validated. Then the momentum disappears because nobody owns production deployment, enterprise funding, adoption or global rollout.
Arvind stressed that experimentation should be aligned to strategic outcomes before organisations debate scale.
"AI doesn't create value when it's deployed. AI actually creates value when the behaviour changes."
Common mistakes
- Innovation positioned as a mandate without dedicated funding.
- No sandbox or structured experimentation framework.
- Successful pilots with no defined pathway to enterprise rollout.
- Innovation measured using BAU delivery metrics instead of adoption and value realised.
Business impact
The organisation pays for experimentation but never captures enterprise-scale value, leaving innovation trapped in proof-of-concept mode. Gartner's latest analysis shows the failure rate has actually got worse than the earlier 30% warning: at least 50% of GenAI projects were abandoned after proof of concept by end-2025, on grounds of poor data quality, weak risk controls, escalating cost or unclear business value. Meanwhile the 2026 India GCC report shows over 70% of Indian GCCs are now trying to move from AI pilots to enterprise-grade deployment this year, and 45% of India GCC leaders now sit in global C-suite decisions. The ones that scale are the ones that governed the transition on paper before they announced it in a town hall.
Technology fragmentation is often a governance failure, not a technology failure. When enterprise approval cycles cannot match business demand, teams build their own solutions. Shadow IT becomes the fastest path to delivery.
Common mistakes
- Local teams deploying automation or reporting tools because enterprise approvals take months.
- Multiple platforms performing the same capability with no rationalisation owner.
- HQ IT controlling licensing while GCC teams own deployment priorities.
- Technology decisions made independently across functions instead of through enterprise governance.
Business impact
Duplicated technology investments increase operating costs, complicate integration and introduce unnecessary security and compliance risks. Gartner projects that by 2027, 75% of employees will acquire, modify or create technology outside IT's visibility, up from 41% in 2022, and its May 2026 Global Labor Market Survey already finds 88% of employees with enterprise AI access are also using personal AI tools for work, the fastest-growing category of shadow tech and the hardest to see. Under India's DPDP Act 2023, every unsanctioned tool handling personal data is a direct regulatory exposure for the parent. A quarterly tools audit, a single sanctioned catalogue and a chargeback model tied to usage is the minimum bar; anything less transfers risk from the GCC to the group audit committee.
The governance models that consistently create enterprise value have one thing in common. They optimise for better decisions, not more oversight.
High-performing GCCs share six operating traits that map directly to the gaps above. They establish clear decision rights with named owners, not shared accountability. They align budgets to business value rather than FTE cost. They measure decision quality and velocity instead of activity and utilisation. They assign end-to-end ownership for data across business, GCC and IT. They create structured pathways for scaling innovation from pilot to production. And they govern technology as an enterprise capability rather than a collection of functional tools.
The signals of this shift are visible on the ground. Decision velocity shows up as a leadership KPI. Innovation portfolios are published every quarter with what graduated, what was killed, and why. Value telemetry replaces month-end SLA decks. Data ownership sits with a named business owner, not a delivery team.
As AI becomes embedded across planning, finance, supply chain and customer operations, governance must evolve beyond compliance. It must provide the operating discipline that enables organisations to move faster, innovate responsibly and make decisions with confidence.
Ultimately, governance should answer one question. Does it make the business easier to run? If the answer is no, the governance model needs to evolve.
Is your GCC still measured by cost — or by business outcomes? Learn what it takes to become an enterprise value multiplier.
As GCCs evolve into enterprise capability hubs, governance can no longer be treated as a control function. It has become a strategic capability that determines how quickly organisations make decisions, scale innovation and create business value.
At Polestar Analytics, we help Global Capability Centers build modern governance frameworks across data, AI, analytics and enterprise operations — enabling faster decisions, stronger accountability and measurable business outcomes. Because in the next generation of GCCs, competitive advantage won't come from doing more work — it will come from governing it better.
Traditional governance metrics such as SLA compliance, utilisation and cost savings provide limited insight into business impact. Leading GCCs increasingly measure governance through decision velocity, investment outcomes, innovation adoption, data trust and business value realised. If governance consistently accelerates decisions and enables enterprise outcomes, it is creating value — not just oversight.
As GCCs move beyond execution into AI-led decision support and enterprise transformation, governance must evolve from process control to decision enablement. Priorities include establishing clear decision rights, strengthening data ownership, aligning funding with business value, creating governance pathways for scaling successful AI pilots, and defining enterprise-wide accountability for AI and analytics initiatives.
The most effective GCCs standardise governance principles — not every decision. Global teams should define enterprise guardrails around risk, compliance and strategic priorities, while empowering GCC leaders to make operational and investment decisions within clearly defined boundaries. This balance improves agility without compromising enterprise control.
Governance often becomes a constraint long before business performance declines. Common indicators include slow decision cycles, recurring cross-functional ownership disputes, innovation pilots that fail to scale, inconsistent business metrics across markets, increasing reliance on shadow IT, and leadership meetings focused more on status updates than decision-making. These signals typically indicate that governance is slowing execution rather than enabling it.