How Can GCCs Become Strategic Hubs for Enterprise AI and Analytics?

Most large organizations already run serious AI and analytics work out of their Global Capability Centers (GCCs). Dashboards, forecasting models, data pipelines, and Gen AI pilots are often built there. Yet when a business executive wants to grasp why margins are diminishing in some geography or which brands to back next year, the GCC is usually asked for the data, not a PoV.

That gap gets harder to justify every year. Nasscom-Zinnov report outlines that, India hosts around 2,117 GCCs employing roughly 2.36 million professionals and generating about $98.4 billion in FY26 revenue. More than 1,200 of them have AI/ML capabilities, generative and agentic AI make up 52% of their AI use cases, and India holds nearly 28% of the global GCC AI talent pool.

The opportunity is to move beyond delivering analytics and start owning the decisions and capabilities those analytics support. GCCs that make this rudimentary shift requires five things: 

• Clear mandate
• Effective operating model
• Trusted data foundation
• Domain-led talent 
• Outcome-based measurement

And cost optimization becomes an advantage of this transformation, rather than its primary objective.

What is the maturity path from reporting factory to decision hub?

Most AI and analytics GCCs move through three stages. Each one is earned through a different kind of trust from HQ.

The progression is from execution trust to domain expertise and, ultimately, judgment. The move is happening faster than expected, complex problem-solving already makes up 38.1% of GCC work portfolios, and nearly 75% of India’s GCCs have the potential to become transformation hubs within five years.

But the common trap is staying at stage 1 with better tools. Replacing a BI platform or adding a GenAI assistant to a reporting factory does not make it a decision hub.

Which operating model works best for an AI and analytics GCC?

The model that works well is hub-and-spoke:

• A central data and AI CoE that owns the MLOps, platform, data governance, reusable standards and components.
• Embedded domain pods aligned with business functions such as RGM, supply chain, commercial and finance. Each pod has a product owner who works tightly with Head Quarters counterparts.
• Product-based funding, with budgets tied to AI and analytics products and their outcomes instead of headcount.
• Defined decision rights. Establish which suggestions the GCC can act on, which needs business approval and who remains responsible for the outcome.

This structure delivers the strategic benefits of GCCs at full strength: platform scale from the centre, business context from the pods. It also gives an enterprise GCC strategy a clean path to expand, one domain pod at a time.

Why should the GCC own the enterprise data foundation?

Most organizations run analytics with fragmented data. All departments keep their own data, metrices and dashboards, but the problem arises when the same question get different answers.

A GCC sits across all of those functions, which makes it the natural owner of the shared layer: common data models, a governed metrics layer, data products and the pipelines that feed every report and model. Once the supply chain head’s service-level question and the CFO’s margin question come from the same numbers, the GCC becomes the place the business goes for the truth.  

That’s where enterprise AI and analytics win or fail. According to Infosys AI-first GCC index 2026, GCCs are already spending around 15% of their operating budget on AI, and models built on untrusted data rarely provides real decisions. Here’s what they need to see:

• Prioritise decisions over deliverables. Every model, report, or analysis ties back to a decision someone will take.
• Trace every assumption. Keep the data lineage from source to insight visible, so suggestions can be defended.
• Test and control. Prove incremental impact with proper experiment design before talking about scale.

How do GCCs move AI and analytics from pilots into everyday decisions?

Value shows up when behaviour changes. A planner overrides the forecast less often. A brand manager shifts spend because of a model recommendation. That means redesigning workflows, incentives and decision rights around the insight, alongside building it.

Three practices help a GCC cross that line:

1. Bring recommendations, with options: Swap “here’s the dashboard we built” to “here’s the problem we encountered”, the options we measured, and what we suggest.
2. Retire low-value reporting: A lot of recurring reports are seen by limited set of people, and they look backward when they need business to answer. Diminishing them frees capacity for high value work and earns credibility with finance.
3. Own the outcome after launch: Stay accountable for impact and adoption, past the go-live date.

That’s how the GCC benefits for enterprises you can see on a P&L.

What talent does a strategic AI and analytics GCC need?

Tech know-how is just the entry point. The hardest profile is the connector, someone who connects data skills with real domain knowledge and drive adoption.

Let’s see it through CPG lens. For example – A RGM analyst pulls a shipment and scan data and refresh promo dashboards all days of the week. Now, in the present situation, the same analyst is expected to know why a promo depth decreases baseline volume or why a retailer cuts on a price-pack change or where a trade spends leaks between net and gross revenue. So, knowing all these things takes years of domain depth.

That’s why GCC talent and skill development requires deliberate investments that directly impacts GCC operational efficiency. Build in-house where these capabilities requires longevity; use partners for short-lived demand.

How should GCCs measure business value from AI and analytics?

Activity metrices like (tickets closed, token consumption, reports produced,  lines of code) talks less about impact. A team that works on 60 dashboards a quarter looks busy; if only 6 get opened, it’s busy on the wrong things.

A better scorecard for GCC business value tracks:

• Adoption: how many people use the dashboards, models and AI products
• Data trust: the share of key decisions made on governed, single-source metrics
• Model stability: whether forecasts and predictions hold up as conditions shift
• Governance health: escalations raised and how fast they close
• Operational impact: measured against a real baseline

Revenue is one of the alarming indicators. GCCs that look at these leading signals early earns the judgment trust stage 3 depends on, and that’s where GCC transformation and innovation turns into an operating model the enterprise runs on.

Conclusion

Therefore, becoming a strategic arm for enterprise AI & Analytics is not about expanding delivery capacity or developing more models. It needs GCCs that take responsibilities of business bottlenecks, decision accountabilities, and establish trusted AI and data abilities. 

We at Polestar Analytics assists GCCs power their abilities to scale their AI and analytics, data foundations, and convert insights into measurable business impacts. Get in touch with our GCC experts to know more.

FAQs on GCCs as AI and analytics hubs

1. How can a GCC convince global leadership to give it greater ownership of business decisions?

Demonstrate impactful business outcomes via top-priority use cases, showcasing how GCC-powered suggestions can deliver sustained value, improve decision quality and accelerate outcomes.

2. How should a GCC prioritise AI and analytics use cases across competing business functions?

Consider use cases by scalability, business impact, adoption potential and data readiness, prioritizing on value adding decisions with transparent ownership and measurable results.

3. What should a GCC agree with headquarters before taking accountability for AI-driven decisions?

Define business accountability, escalation protocols and human oversight, decision rights, and transparent ownership of suggestions, risks and results.