The Rapid Adoption of Agentic AI and Generative AI in GCCs:
- srkgamechangers
- Jan 7
- 4 min read
What Is Really Changing on the Ground
By Ramma Shiv Kumar | Global Capability Centres Expert
Over the last year, conversations around Artificial Intelligence in Global Capability Centres (GCCs) have quietly—but decisively—shifted.
This is no longer about pilots, proofs of concept, or curiosity-led experimentation. What I am seeing across GCC ecosystems today is deliberate adoption of Agentic AI and Generative AI as part of the GCC operating model itself—particularly in mid-size and neo-size GCCs that are designed for speed, ownership, and enterprise impact.
AI is no longer a technology initiative.It is becoming an organisational design decision.
From Automation to Agency
For years, automation helped GCCs improve efficiency by standardising processes and reducing manual effort. Agentic AI represents a fundamentally different shift.
Agentic systems are designed to:
Understand context
Make recommendations
Take autonomous actions within defined guardrails
Learn continuously from outcomes
When combined with Generative AI, these systems move beyond task execution into reasoning, synthesis, and decision support. The nature of work inside GCCs changes—from “doing” to thinking, deciding, and influencing.
Why Mid-Size and Neo-Size GCCs Are Leading This Shift
Interestingly, the most advanced AI adoption is not always happening in the largest GCCs.
Many mid-size and neo-size GCCs (roughly 300–2,000 employees) are moving faster because they have:
Clear outcome ownership, rather than purely transactional mandates
Lean governance structures, enabling faster experimentation
Closer proximity between talent and decision-makers, reducing friction
These conditions allow intelligence to be embedded directly into workflows instead of being layered on as an afterthought.
What Adoption Looks Like in Practice (Across the Ecosystem)
Across the GCC ecosystem, I see consistent patterns emerging—particularly among centres that have been set up with product, platform, or capability ownership.
For instance, Atlassian’s India GCC has publicly spoken about embedding Generative AI into developer experience and product engineering workflows, enabling teams to accelerate design, testing, and collaboration rather than treating AI as a standalone tool.
Similarly, Intuit’s India GCC is widely recognised as a product and platform hub, where AI-driven insights support decision-making across customer experience, risk, and financial platforms. The emphasis here is not just on efficiency, but on augmenting judgment and ownership at the GCC level.
In the retail and consumer ecosystem, Target’s India GCC has demonstrated how AI-first operating models can support analytics, digital engineering, and decision intelligence—allowing GCC teams to influence upstream decisions rather than operate purely downstream.
These examples are illustrative of broader ecosystem patterns rather than isolated success stories. What stands out is not the size of these centres, but the clarity of mandate and the intentional design of intelligence-led operating models.
What This Means for Leaders and Talent
As Agentic AI becomes embedded, GCC leadership models must evolve.
Leaders are now required to:
Design human–AI collaboration models
Redefine roles around judgment, ethics, and decision ownership
Invest in AI literacy beyond technology teams
The most valuable professionals in this environment are not those who execute tasks faster, but those who can:
Ask better questions
Interpret AI outputs critically
Combine domain knowledge with contextual reasoning
This is where GCCs can build sustainable, differentiated capability.
Governance Cannot Be an Afterthought
Rapid AI adoption without governance creates invisible risk.
The GCCs that are scaling Agentic AI responsibly are doing three things early:
Defining clear decision boundaries for AI agents
Embedding ethical and compliance checks directly into workflows
Assigning accountability for AI-driven outcomes
Agentic AI does not reduce responsibility.It raises the bar for clarity, ownership, and leadership discipline.
The Bigger Shift Underway
The adoption of Agentic AI and Generative AI is accelerating a broader transition across GCCs:
From:
Execution centres
To:
Intelligent capability hubs
Mid-size and neo-size GCCs that get this right will not need to justify their relevance. They will become integral to enterprise strategy, product direction, and decision-making.
The real question for leaders is no longer:
“Should we adopt AI in our GCC?”
It is:
“How do we redesign our GCC so intelligence becomes native to how it operates?”
Those who answer this well will define the next chapter of the GCC story. I observed during my conversation with mid size GCC heads as well.
A Final Reflection for GCC Leaders
If you lead or influence a GCC today, it may be worth pausing to reflect:
Where can Agentic AI create true ownership—not just efficiency?
Which decisions should remain human, and which should be AI-augmented?
What operating model changes are we delaying because they feel uncomfortable?
In the coming years, competitive advantage will belong not to the GCCs that adopt AI fastest—but to those that design for intelligence with intent and responsibility.
Looking ahead to 2026 and beyond, I see GCCs hitting USD 110 billion by 2030, with AI at the core. Pune will continue shining as a hub, with enablers like policy support and innovation teams driving growth. (more insights in next paper). As Founder of SRKGameChangers, I'm excited to partner with more organizations to build AI-native GCCs.
