India’s global capability centers (GCCs) are moving away from the offshore delivery model that built them and toward ownership of product engineering, advanced analytics, cybersecurity, AI research and enterprise transformation. That shift shows up in India-specific reports from Zinnov and nasscom, the Government of India, PwC India and EY India. What those reports do not establish is measured business gain from AI. Adoption, leadership sentiment and capability growth are documented; the surveys cited here do not measure the financial or operational return on AI work inside these centers.
This article covers what is changing in mandate, leadership, operating model and talent, what each current figure actually measures, and how to keep AI activity separate from value. All figures refer to India unless stated otherwise.
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From cost unit to capability owner
The traditional GCC was a cost and service unit. It executed work that headquarters defined and was judged on service levels and labor-cost savings. PwC India describes the change as a move from offshore delivery toward product engineering, advanced analytics, cybersecurity, AI research and enterprise-wide transformation, based on a study of senior executives at these centers (PwC India, “Navigating the skills imperative for India’s GCCs in the AI era”). The Economic Survey 2024–25 adds that GCCs are progressing into high-end engineering roles such as product managers and architects (Government of India, Economic Survey 2024–25, Chapter 8).
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It helps to treat the change as a set of linked shifts rather than a single switch. The table below uses five editorial dimensions to compare the two models. It structures the comparison; it is not a validated maturity instrument. Where a center sits depends on its decision rights and how it measures value, not on its headcount or its title.
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| Dimension | Cost-led support unit | Enterprise capability center |
|---|---|---|
| Mandate | Transactional service delivery to a parent function | Ownership of product, engineering, AI research or enterprise transformation work |
| Decision rights | Execution under headquarters direction | Local ownership of architecture, product or business decisions |
| AI maturity | Experiments and isolated pilots | AI embedded in operating workflows; whether outcomes are measured is a separate question |
| Talent model | Hiring for narrow delivery roles | Interdisciplinary AI, data, domain, architecture and leadership capability |
| Value measure | Cost and service levels | Innovation, resilience, productivity and accountable business outcomes |
What the India numbers measure
The FY2026 landscape
Zinnov-nasscom’s FY2026 India GCC Landscape Report counts 2,117 GCCs, $98.4 billion in revenue and 2.36 million talent (Zinnov-nasscom India GCC Landscape Report 2026). These are totals for India, not global figures. The report frames the landscape around three structural shifts: an AI operating-model gap, a compressed maturity timeline and a migration of enterprise authority to India-based leaders. Those are the publisher’s characterizations. The measurements behind each one should be checked in the full report before they are quoted as findings.
The government series
A Press Information Bureau release dated 11 December 2025 gives a count of more than 1,700 GCCs and a revenue series of $40.4 billion in FY19 rising to $64.6 billion in FY24, growing at 9.8% a year (Press Information Bureau, Government of India, GCC release, 11 December 2025). The 9.8% rate is consistent with compounding across the five years from FY19 to FY24. The series ends at FY24, so it cannot show the most recent year.
Why the two counts cannot be compared directly
The two reports use different reference periods, and the sources do not establish that they count centers the same way. Read them side by side only with their dates and bases in view.
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|---|---|---|
| Number of GCCs | 2,117 | More than 1,700 (reference date not stated) |
| Revenue | $98.4 billion (FY2026 landscape) | $64.6 billion in FY24, up from $40.4 billion in FY19 |
| Talent | 2.36 million | Not stated |
| Growth rate | Not stated | 9.8% a year, FY19 to FY24 |
| Period covered | FY2026 | FY19 to FY24 revenue series |
Do not subtract one count from the other or present the gap as growth. Before comparing, check how each report defines a GCC, which entities it includes, and the date of its count.
Leadership and architecture: where authority is moving
The Economic Survey 2024–25 reports that 35% of transformation hubs have a strong presence of architects, citing nasscom’s Strategic Review 2024. That is a share of a specific group of centers, not evidence that architecture by itself produces better results. The survey’s term “transformation hubs” should not be assumed to match Zinnov-nasscom’s Transformation Hub stage without checking each report’s definition.
The same chapter cites a projection that GCC global leadership roles will rise from 6,500 to more than 30,000 by 2030. That is a forecast of demand, not a count of roles that exist or have been filled. Read it as a signal that leadership pipelines will need to widen.
Zinnov-nasscom’s point about enterprise authority migrating toward India-based leaders is the organizational side of the same shift. In practice it is a question of who signs off on architecture, product direction and business trade-offs, and whether the people doing that work sit inside the center.
The maturity ladder and its timeline
Zinnov-nasscom describes a four-stage framework for GCC maturity, in ascending order:
- Outpost
- Satellite
- Portfolio Hub
- Transformation Hub
The stage names give leadership teams a shared vocabulary, but the criteria that separate one stage from the next are not set out in this article. Use the stages only after reading the definitions in the full report, and record the evidence behind each placement. A center’s stage should follow what it owns and decides, not its title or payroll size.
The “compressed maturity timeline” points to a shorter path up that ladder than a conventional multi-year build-out would suggest. Treat it as the report’s characterization, and check its basis before using it as a planning assumption.
Talent: the constraint that sets the mandate
PwC India’s report is built on a survey of 200 senior GCC executives across eight industries. Its central argument is that sustaining the GCC opportunity depends on talent development keeping pace with AI-era demands. The report’s title, “Navigating the skills imperative for India’s GCCs in the AI era,” shows how the publisher frames the question. The findings reflect what those executives report; they are not a census of GCCs.
The December 2025 PIB release links workforce programs to skills including cybersecurity, cloud, analytics and AI. Read alongside the Economic Survey’s emphasis on architects and product managers, the pattern is a move from narrow delivery skills toward a broader mix of roles. The sources name the following capabilities:
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- AI and data: the AI skills the PIB release links to its workforce programs, and the AI-era demands PwC India says talent development must meet.
- Architecture: the Economic Survey reports strong architect presence in 35% of transformation hubs.
- Product management: product managers, named by the Economic Survey as a high-end engineering role GCCs are moving into.
- Cybersecurity and cloud: both appear in the PIB release’s list of program skills.
- Analytics: listed by PIB alongside AI, and central to the advanced analytics mandate PwC India describes.
- Domain knowledge and leadership: part of the talent model used in this article. The 30,000-role projection shows the scale of leadership demand but does not name specific competencies.
AI activity is not AI impact
EY’s 2025 GCC Pulse Survey frames AI as moving from experimentation toward enterprise scale. EY India’s article on the survey reports that 92% of surveyed leaders affirm GCCs contribute beyond cost arbitrage (EY India, based on the 2025 GCC Pulse Survey). The “AI-native” language is EY’s framing of the shift. The survey does not show that every center is AI-native, and the 92% figure records what leaders say, not an independently measured economic result.
Three claims are often blended together. Keep them apart:
- Adoption asks whether AI is in use: pilots, tools, deployed models or workflows. EY’s finding about the move from experimentation to scale sits here.
- Capability asks whether the center can build, run and govern AI systems: architecture, data foundations and skilled roles. The architect and product-manager figures sit here.
- Impact asks whether a measured outcome moved against a baseline. None of the India-level sources cited in this article establish that.
Before a claim of AI-driven value goes into a board paper or a budget request, put it through four questions:
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- What was the baseline, and when was it measured?
- Does the center control the metric, or is it a group-level number the center only contributes to?
- Has the effect been separated from other changes in the same period?
- Does the evidence come from operating data or from a survey of opinions?
Rewiring sequence for leadership teams
- Write the mandate as decisions. List the architecture, product and business decisions the center should own, and those that remain at headquarters. Assign a named owner to each.
- Place the center on the maturity ladder. Use Zinnov-nasscom’s four stages, state the criteria you applied, and attach evidence to each placement.
- Set outcome baselines before scaling AI work. Choose metrics the center controls, such as cycle time, defect escape rate, cost per transaction or release frequency, and fix the measurement method before the first pilot starts. These are examples chosen for illustration, not benchmarks from the sources.
- Build the talent plan from the roles the mandate needs. Plan for architecture and product roles, AI and data engineering, cybersecurity, cloud and leadership. Include development of existing staff alongside hiring.
- Report adoption, capability and impact on separate lines in governance reviews, applying the four questions above to any impact claim.
- Set a review interval and a stop rule. Decide in advance which result would change the mandate or end a program.
Failure modes to watch for
- Pilots counted as deployment. A center with several pilots has adopted AI in a narrow sense. It has not necessarily changed how work is run.
- Leadership sentiment reported as value. A high share of leaders who see a center’s contribution beyond cost is a positive signal, not a return figure.
- Hiring for delivery while the mandate expands. A center that asks for architects and product owners but recruits only for ticket-based delivery will stall at its current stage.
- National totals read as a local profile. An India-wide total says little about how any single center is configured.
Where the evidence stops
- Geography. Every figure here is India-specific. The sources do not provide comparable data for other GCC locations, so they do not support a global pattern or a country ranking, and not every center is following the path described above.
- Survey design. The PwC India study of 200 executives and the EY 2025 pulse survey each use their own samples and frameworks. Check sample design, respondent mix and question wording in the full reports before reusing a percentage.
- Projections and characterizations. The 30,000-plus leadership roles by 2030 figure is a projection. The maturity framework and its three structural shifts are the publisher’s characterization, not a validated measure of performance.
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