Beyond Cost Centres: The New Metrics Defining GCC Value
By Indrajit Mitra, AI & Data Science Head
For years, GCC performance was easy to explain.
More teams. More delivery. More cost efficiency. More work moving out of the enterprise core and into India.
That story is no longer enough.
In today's boardroom, the sharper question is different: did the GCC help the business move faster, decide better, reduce friction, or create measurable value?
That question is changing how GCCs are being evaluated. A cloud migration is no longer impressive only because workloads moved. An AI model is not valuable only because it reached production. An automation programme does not prove impact only because hours were saved. The real test is whether any of it changed the speed, quality, or economics of business decisions.
India's GCC sector has reached 2,117 centres, USD 98.4 billion in revenue, and 2.36 million professionals in FY2026. Leaders have described this as a "fundamental reset", with GCCs moving from scale to value, and taking ownership of global products, platforms, and business outcomes.
The headline number is impressive. But it is not the only thing that matters and hence the wrong thing to anchor on honestly.
The more important question is what the next USD 100 billion gets measured by. For many GCC leadership teams, that scorecard is still catching up.
Activity metrics built the industry. Value metrics will define what comes next.
Cloud and Modernisation: From Workloads to Wait Times
Most cloud reviews still open the same way. A migration percentage. A workload count. An infrastructure spend variance. All of these are useful. None of them fully answers the business question: is anything moving faster now?
The frame has to shift. Modernisation is no longer only a technology programme. It is a wait-time problem.
How long between a code commit and a customer touching it?
How long to spin up a new market, a new product line, or a new pricing experiment?
How long to recover when something breaks at three in the morning in another time zone?
These are business numbers. They also happen to be measurable.
The DORA software delivery performance metrics have become one of the clearest ways to connect engineering performance with business responsiveness: deployment frequency, lead time for changes, change failure rate, and failed deployment recovery time.
The honest test for a modernised GCC portfolio is not how many workloads sit on a hyperscaler. It is whether a product manager in the US, Europe, or APAC ships a feature this week instead of next quarter because the platform team in India removed a dependency.
That is what executives mean when they say agility. The cloud bill is a lagging indicator. The release calendar is the leading one.
Old metric: applications migrated
New metric: time-to-change
AI and Automation: From Accuracy to Adoption
This is where the conversation gets uncomfortable, and where most of the value is hiding.
The data is now too consistent to ignore. MIT's NANDA initiative found that just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. BCG has reported a similar pattern in banking, where fewer than 10% of banks have measurable GenAI use cases in operation.
The instinctive read on this data is that the models are not good enough.
That is not the real issue.
The harder problem is learning, integration, and workflow adoption inside the enterprise. Generic tools work well for individuals because they are flexible. They stall inside enterprises when they do not adapt to how decisions are actually made, governed, and acted on.
That is why many AI metrics reported by GCCs are beside the point.
Accuracy, precision, recall, and latency tell you whether the model works. They do not tell you whether anyone used the model's output to make a decision they would not otherwise have made.
A demand forecast that improves MAPE by four points is a technical improvement. A demand forecast that planners trust enough to change a purchase order is business value.
Those are different things. Only one shows up on the P&L.
The metrics that matter are downstream.
Adoption rate among intended users, not logins, but the share of relevant decisions where the AI output was actually consulted. Decision-override rate, which shows whether trust is forming or weakening. Cycle time from insight to action. Revenue or margin attributable to AI-influenced decisions, measured through proper experimental design rather than asserted in a deck.
For automation, the same rethink is needed.
Hours saved is an incomplete metric. In many cases, those hours were never fully converted into business value. The stronger metric is friction removed from a customer-facing, revenue-generating, or risk-sensitive workflow.
How many cases closed straight-through?
How many exceptions were reduced?
How many handoffs disappeared?
How much faster did the process become for the customer or the business user?
That is the right shape of metric.
Old metric: models deployed
New metric: decisions improved
The New GCC Scorecard
If cloud, modernisation, AI, and automation are placed on one page, the pattern becomes clear.
Capability | What we used to measure | What actually matters |
Cloud | Workloads migrated, infrastructure cost reduction | Time-to-change, recovery time, platform reuse |
Modernisation | Applications upgraded, technical debt retired | Deployment frequency, change failure rate, resilience |
AI | Models in production, accuracy gains | Adoption, decision-override rate, P&L attribution |
Automation | Hours saved, bots deployed | Friction removed, straight-through processing rate |
The common thread is simple.
Every right-hand-column metric points outward, toward the business, the customer, the cycle, or the decision. Every left-hand-column metric points inward, toward the function delivering the work.
That is the shift the best GCCs are making: from inward reporting to outward attribution.
This is harder than it sounds. Outward attribution needs three things many GCCs still lack.
First, a baseline for the business outcome before the intervention.
Second, an instrumented workflow that captures what changed after.
Third, a governance forum where the business sponsor co-owns the number instead of the GCC owning it alone.
The Zinnov-NASSCOM GCC Landscape in India 2026 report shows that only 5% of India's GCCs currently operate as Transformation Hubs, where AI-led operations and CXO mandates from India become the frontier. That means most GCCs still have headroom. Much of that headroom is not in capability. It is in measurement.
What future-ready GCCs need to build is something closer to continuous value telemetry.
Value telemetry means measuring whether technology interventions are changing business behaviour while the work is happening, not months later in a review deck.
The idea borrows from engineering vocabulary on purpose. The discipline is similar: instrument the system, watch the signal, alert when it drifts, and understand what caused the change.
The signals are different: adoption, trust, cycle time, productivity impact, customer-visible outcomes, margin movement. But the operating model is the same as any well-run engineering or SRE function.
You do not review value quarterly. You watch it. And when it moves, you know why.
The GCCs that get there first will have an advantage that compounds. The ones still running migration dashboards in 2027 will find their parent organisations asking harder questions about strategic value.
What Comes Next
The 2,117 GCCs in India today will not all make this transition at the same pace. Some will not need to. Captive shared services with a clear remit and stable cost equation remain a legitimate model.
But for GCCs whose parent organisations have invested in product ownership, R&D mandates, cloud platforms, AI-first operating models, and digital transformation charters, the measurement framework has to catch up with the work.
Right now, in many cases, it has not.
The shift from cost centre to value engine does not happen because a leadership team declares it in a town hall. It happens when the dashboard the GCC head presents to the global CEO has the same metrics the global CEO presents to the board.
When those two pages start to look like each other, the conversation about what India's GCCs contribute changes. It stops being only about delivery. It starts being about strategy.
The strongest GCCs will not be the ones with the largest dashboards. They will be the ones that prove, in business language, that a decision moved faster, a workflow carried less friction, a product reached the market sooner, or a customer outcome improved.
That is the next scorecard. And it is the one every GCC leader should be preparing for now.
For GCCs making this transition, the next step is clear: build the capability and build the measurement discipline around it. This is where the right partners become relevant, helping enterprises connect data, AI, cloud, and modernisation initiatives to outcomes the business can see, measure, and trust.
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