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Global capability centers (GCCs) are moving away from a model built mainly on offshore delivery and cost reduction. Their mandate is widening toward product engineering, advanced analytics, cybersecurity, AI model development and enterprise transformation, and AI is speeding that change. But the AI evidence available today measures adoption and leaders’ perceptions. It does not yet prove measured business impact, so a center should be judged on what it owns and can demonstrate, not on how many AI initiatives it runs. India has the richest published evidence on this shift, so this article uses it as the case study. All figures are India-specific.
What a GCC was built to do, and what is changing
A global capability center is an offshore unit that a multinational runs for its own operations. For many years the brief was largely transactional: deliver application support, testing, processing and standard development at lower cost, with direction and major decisions made at headquarters, and success measured in cost and service levels.
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That brief is widening. Centers are increasingly asked to own outcomes, not only tasks, and AI pushes this further because it makes higher-value work, such as designing AI-enabled workflows or running analytics on enterprise data, a natural extension of delivery. The sources describe an expanding role. They do not say delivery work disappears, and this article does not claim it does. Whether the same shift is under way in GCC hubs in other countries is not established by the evidence discussed here, so the analysis stays with India.
The India numbers, and why two counts disagree
Two recent government and industry reports give different totals for India’s GCC base. Zinnov and nasscom’s India GCC Landscape Report 2026 covers FY2026. A Press Information Bureau release from 11 December 2025 reports a center count for 2025 and a revenue series for FY19 to FY24. Both describe India’s landscape, not global totals.
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| Figure | Zinnov and nasscom, India GCC Landscape Report 2026 | Government of India, PIB release of 11 December 2025 |
|---|---|---|
| Period | FY2026 | Center count stated for 2025; revenue series covers FY19 to FY24 |
| GCC count | 2,117 | More than 1,700 |
| Revenue | $98.4 billion | $40.4 billion in FY19, rising to $64.6 billion in FY24 |
| Growth | Not stated on the landscape page | 9.8% annually, as the release reports |
| Workforce | 2.36 million talent | Not stated in the PIB release |
The reports cover different periods and appear to rely on different counting approaches, so the gap between “more than 1,700” and 2,117 is not a measure of growth over that span. Before citing the two together, check each report’s definition of a GCC and its cut-off date.
The mandate is moving up the value chain
PwC India’s report, Navigating the skills imperative for India’s GCCs in the AI era, describes a transition from offshore delivery toward product engineering, advanced analytics, cybersecurity, AI model development and enterprise-wide transformation. Zinnov-nasscom’s framing and India’s Economic Survey point the same way. The descriptions below are an interpretation of what each item means in practice; they are not survey findings.
- Product engineering: the center helps shape a product roadmap and owns technical delivery against it, instead of building to specifications written elsewhere.
- Advanced analytics: teams turn enterprise data into decisions the business acts on, with the center accountable for model quality and data pipelines.
- Cybersecurity: the center owns security engineering and controls, not only the operation of tools.
- AI model development: teams build, test and maintain models tied to named business use cases.
- Enterprise transformation: the center leads process and technology change that spans the parent company.
Leadership and architecture
India’s Economic Survey 2024–25, Chapter 8 (published 2025), says GCCs are progressing into high-end engineering roles such as product managers and architects. It reports that 35% of transformation hubs have a strong presence of architects, citing the nasscom Strategic Review 2024. The same chapter cites a projection that GCC global leadership roles will rise from 6,500 to more than 30,000 by 2030, again drawing on the nasscom Strategic Review 2024.
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Two qualifications apply. The 35% figure covers transformation hubs, not all centers. The 30,000 figure is a projection, not an observed outcome.
Decision rights and the maturity ladder
Zinnov-nasscom’s FY2026 landscape describes three structural shifts: an AI operating-model gap, a compressed maturity timeline, and a migration of enterprise authority to India-based leaders. It also sets out a four-stage maturity framework: Outpost, Satellite, Portfolio Hub and Transformation Hub. This article does not define those stages. Confirm the criteria in the full Zinnov-nasscom report before placing a specific center on the ladder, and treat the labels as shared vocabulary until then.
Decision rights are where the mandate change becomes concrete. A center that can sign off architecture, product or business decisions is in a different position from one that executes decisions made elsewhere.
A five-axis way to assess a center
These dimensions help structure a conversation about a center’s position. They are not a validated maturity instrument. Use them to locate a center on each axis, then check what it claims against what it can show.
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| Axis | Execution-led pattern | Ownership-led pattern | Evidence to ask for |
|---|---|---|---|
| Mandate | Transactional service delivery | Ownership of product, engineering, AI model development or enterprise transformation | Charter and budget lines that name the outcomes the center answers for |
| Decision rights | Execution under headquarters direction | Local ownership of architecture, product or business decisions | Named decision owners and a decision log |
| AI maturity | Experiments and isolated pilots | AI embedded in operating workflows | Which use cases run in production, and which have measured results |
| Talent model | Hiring for narrow delivery roles | Interdisciplinary AI, data, domain, architecture and leadership capability | Role mix, skills plan and internal career paths |
| Value measure | Cost and service levels | Innovation, resilience, productivity and accountable business outcomes | Outcome metrics with baselines and named owners |
AI activity: what the survey data shows and what it does not
EY India’s article, based on its 2025 GCC Pulse Survey, describes AI moving beyond experimentation toward enterprise scale in GCCs. The same survey reports that 92% of surveyed leaders affirm GCCs contribute beyond cost arbitrage. Read both as leaders’ reported views from one survey. The 92% is a perception result, not an independently measured economic outcome, and EY’s headline term “AI-native” describes a direction of travel rather than a measured state across centers.
Adoption, capability and impact are different claims
Three claims are often blurred in GCC communications:
- Adoption: AI tools or use cases are running in the center.
- Capability: the center has the people, data and architecture to build and operate AI.
- Impact: a measured change in productivity, revenue, quality or resilience, compared against a baseline.
A center can score well on the first two and still lack evidence for the third.
Checks before claiming AI value
- Record the baseline before a use case goes live, so there is something to compare against.
- Name the metric and the population it covers, such as a process, a team or a product line.
- Separate what leaders say from what operating data shows.
- Attribute a gain to AI only after ruling out parallel process or staffing changes.
Talent: a constraint and an enabler
PwC India’s report, based on a survey of 200 senior GCC executives across eight industries, argues that sustaining the GCC opportunity depends on talent development keeping pace with AI-era demands. It captures executives’ views on skills rather than measured workforce outcomes.
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Talent works in both directions
A shortage limits how far the mandate can move, and a deliberate development plan is how the center moves it. The talent question is therefore a strategic constraint on the shift and also the means of making it.
Where workforce programs fit
The Government of India’s December 2025 PIB release links workforce programs to skills including cybersecurity, cloud, analytics and AI. The same release carries the FY19 to FY24 revenue series discussed above. Linking training to skills does not by itself show that those programs changed a center’s mandate or productivity.
Quick Recap
A sequence for the transition
- Write the mandate as outcomes the center is accountable for, and name the business owner on the parent side for each one.
- Map decision rights for architecture, product and data, showing which decisions move to the center and which stay at headquarters.
- Build the talent plan around the roles the new mandate needs, including architects, product managers, and AI and data engineers, and decide for each role whether to build internally or hire.
- Fund scale-up of an AI use case only once it has an agreed outcome metric and an owner who reports on it.
- Review the operating model on a fixed cadence against outcome data, and close pilots that cannot show a measurable change.
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