Engagement model · GCC Transformation

GCC Transformation: From Legacy GCC to AI-Powered Scale

A four-stage journey that takes your GCC from legacy operations to modern platforms, AI adoption across every team, and growth in output using AI.

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Quick answerGCC transformation takes an existing Global Capability Center (GCC), also called a captive center, through four stages: from a legacy GCC, to modernization of platforms and delivery, to AI adoption across teams, to scaling output with AI. The result is a center that owns products and outcomes, and grows its output without matching growth in headcount.

We build and run GCCs in the Tier 1 hubs of Mumbai, Pune, Gurgaon, Noida, Hyderabad, Chennai, Bangalore, Ahmedabad and Kolkata, and in the Tier 2 city of Gandhinagar.

Diagnosis

Signs your GCC needs transformation

If three or more sound familiar, it is time to transform an underperforming GCC.

  • Headquarters sends tasks, not outcomes or products
  • Attrition is high and top talent leaves for product roles
  • Cost per head keeps rising without more value delivered
  • AI and automation are pilots, not part of daily delivery
  • Tooling and processes differ team by team
  • Leaders can't show business impact in board-level terms
  • All senior roles and decision makers sit at headquarters
  • Legacy platforms and technical debt slow every release

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The journey

Our 4-stage GCC transformation journey

Legacy GCC → Modernization → AI Adoption → Scaling with AI. Each stage builds on the last, and each delivers value on its own.

  1. Stage 1

    Legacy GCC

    Assess where you are today: operating model, technical debt, talent mix and cost.

  2. Stage 2

    Modernization

    Cloud, DevOps, modern data platforms, engineering practices and a redesigned operating model.

  3. Stage 3

    AI Adoption

    GenAI copilots, AI Pods, an AI-driven software factory, team upskilling and Responsible AI.

  4. Stage 4

    Scaling with AI

    More output without matching headcount growth, and new AI centers of excellence.

Stage 1 · Legacy GCC

Stage 1, Legacy GCC: assess where you are today

A legacy captive center was usually set up to take on well-defined work at lower cost, and many of today's GCCs started that way. Over the years they built up technical debt, inherited tools and a talent mix geared to execution rather than ownership.

We start with a diagnostic of the center as it is today: how decisions are made, where technical debt slows delivery, which skills are missing and what each team really costs. The output is a baseline and a sequenced roadmap for the stages that follow.

Stage 2 · Modernization

Stage 2, Modernization: modern platforms and ways of working

Legacy GCC modernization clears the path for AI. Without modern platforms and practices, AI stays stuck in pilots.

Cloud

Migrate and modernize legacy applications onto well-governed cloud platforms.

Cloud practice

DevOps

CI/CD, infrastructure as code and release automation for faster, safer delivery.

DevOps practice

Modern data platforms

Data foundations that make analytics and AI possible.

Data & AI practice

Engineering practices

Product-aligned teams, code quality, test automation and observability.

Operating model redesign

A redesigned GCC operating model: clear ownership, decision rights and leadership roles inside the center.

FinOps

Cost visibility and accountability as workloads move to the cloud.

FinOps
Stage 3 · AI Adoption

Stage 3, AI Adoption: AI in everyday delivery

AI adoption in your GCC works when it is part of how every team delivers, not a side project.

GenAI copilots

Assistants for engineers, analysts and operations teams, with guardrails.

AI Pods

Cross-functional teams that take AI use cases from idea to production on a fixed cadence.

AI Services

AI-driven Software Factory

AI-assisted coding, testing and documentation across the engineering lifecycle.

Team upskilling

AI literacy for everyone, and deeper GenAI tracks for builders.

Learning & Development

Responsible AI

Governance, evaluation and human oversight for every AI use case.

Enterprise AI

Use-case portfolio

AI use cases prioritized by value, moved from pilots into production.

Stage 4 · Scaling with AI

Stage 4, Scaling with AI: more output, not more headcount

Scaling your GCC with AI means growing output faster than headcount, once AI is part of daily delivery.

Non-linear productivity

Teams deliver more with AI-assisted engineering, testing and operations.

New AI centers of excellence

AI, data and platform CoEs that serve the whole enterprise.

GCC Scale Up

Reusable platforms

Shared AI and automation platforms that every new team builds on.

GCC 1.0 vs GCC 2.0

From GCC 1.0 to GCC 2.0

GCC 1.0 compared with GCC 2.0
GCC 1.0 (legacy GCC)GCC 2.0 (AI-powered GCC)
MandateExecute tasks for HQOwn products, platforms and outcomes
Value measureCost savings per headBusiness impact and speed to market
TalentDelivery-focused rolesProduct, engineering and AI leaders
TechnologyInherited tools, manual workModern platforms, AI and automation
Decision rightsAt headquartersShared, with leadership in the center
GrowthOutput grows with headcountOutput grows faster than headcount, using AI
Outcomes

Outcomes to expect at each stage

Stage 1

A clear baseline

An honest view of cost, debt and skills, and a roadmap leadership can fund.

Stage 2

Faster, more reliable delivery

Shorter cycle times, fewer incidents and lower cost to serve.

Stage 3

AI in production

AI use cases that reach users, and teams that use AI every day.

Stage 4

Scale without matching headcount

More output and new capabilities from the team you have.

Client proof

GCC transformation in practice

Healthcare & Life SciencesGCC TransformationCaptive center

Modernizing regulated cloud and data work for a healthcare and life sciences leader

Regulated cloud, data and automation work delivered from India for a global healthcare and life sciences enterprise.

GCCs for Healthcare & Life Sciences

Challenge

Move capability-center work up the value chain into cloud, data and automation while staying inside GxP and privacy controls.

Approach

Relevance Lab redesigned delivery around platforms and automation, and brought cloud and data engineering skills into the existing operating model.

Outcome

A capability center trusted with higher-value engineering work, with compliance built into delivery rather than checked afterwards.

See all GCC case studies

Locations

Where we build GCCs: Tier 1 and Tier 2 cities across India

We set up and run GCCs in nine Tier 1 hubs and the fast-growing Tier 2 city of Gandhinagar, so you can balance talent depth, cost and business continuity.

  • Tier 1MumbaiIndia's financial capital, with depth in banking and financial services talent.
  • Tier 1PuneDeep engineering, product and automotive technology talent.
  • Tier 1GurgaonDelhi NCR's corporate hub, with deep technology and business services talent.
  • Tier 1NoidaDelhi NCR's large IT and technology services cluster, at competitive cost.
  • Tier 1HyderabadOne of India's fastest-growing GCC hubs, with strong engineering and data talent.
  • Tier 1ChennaiStrong in SaaS, financial services technology and engineering.
  • Tier 1BangaloreIndia's largest technology talent pool and its most established GCC hub.
  • Tier 1AhmedabadGujarat's commercial hub, with a growing technology talent base at competitive cost.
  • Tier 1KolkataEastern India's largest talent market, with a large graduate pool at lower cost.
  • Tier 2GandhinagarHome to GIFT City, India's international financial services center.
FAQ

Frequently asked questions

Straight answers about GCC Transformation. Can't see your question?

Ask a GCC expert

GCC transformation takes an existing Global Capability Center through four stages: legacy GCC, modernization, AI adoption and scaling with AI. It moves the center from executing tasks to owning products, platforms and outcomes for the parent company.

Our journey has four stages: assess the legacy GCC; modernize platforms, delivery and the operating model; adopt AI across teams with copilots, AI Pods and upskilling; then scale output using AI without matching growth in headcount.

A legacy GCC, often an older captive center, was set up mainly to execute well-defined work at lower cost. It typically carries technical debt, inherited tools and a talent mix geared to execution, which makes AI adoption hard.

GCC 2.0 describes an AI-powered capability center that works as an innovation hub. It owns products and outcomes, leads on AI and digital work, and has leadership and decision rights in the center itself rather than only at headquarters.

The Stage 1 assessment typically takes four to six weeks. Modernization and AI adoption then run in waves over the following months, with measurable outcomes reported to leadership every quarter.

Not necessarily. Transformation works for fully owned centers and for centers run with a partner. Some clients pair it with a Managed GCC for functions they don't want to build in-house.

Content last reviewed: October 2026

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