Overview
The Intelligence Shift Is Already Here
In our first editorial interview, we examined what it takes to build and lead a high-performing Global Capability Center. We covered talent strategy, operating models, governance, and the journey from cost center to strategic hub.
This second interview goes deeper into the defining challenge of the current moment: the transformation of GCCs into AI-native organizations.
AI is not an add-on for today’s GCCs. Across sectors—from BFSI and technology to manufacturing and healthcare—GCC leaders are confronting a fundamental question that goes beyond productivity tools and copilot deployments.
“What does it mean to lead a GCC when the work itself is being reimagined by intelligence?”
This interview surfaces aggregated insights from GCC heads, CDOs, and transformation leaders across India, Eastern Europe, and Southeast Asia to answer that question with candor and clarity.
| 78% | 3× | $2.4T |
| Of GCCs report active AI pilots in production as of 2026 | Increase in AI-related GCC headcount demand over 24 months | Projected value unlocked by AI-enabled GCCs by 2030 |
What this Interview Covers
Six Dimensions of the AI-First GCC
01 - AI Adoption
From Pilot to Production
Where do most GCCs actually stand on the AI maturity curve today? There’s a lot of noise about transformation—but what’s really happening on the ground?
Honestly, there’s a significant gap between the narrative and the reality. Most GCCs fall into what we’d call the structured experimentation phase—they have pilots running, they’re measuring outcomes, but they haven’t yet operationalized AI across their delivery model. Only a small cohort—roughly 15 to 20 percent of larger GCCs—have moved into genuine production deployment where AI is reshaping how work gets done at scale.
The pilots-to-production gap is real, and it’s being driven by three friction points: data readiness, change management, and governance. Most enterprises underestimated all three. You can spin up a copilot in a sprint. Building the infrastructure to run AI reliably, at scale, with accountability? That takes 18 to 24 months of sustained effort.
What use cases are generating real value—not just good demo material, but measurable business outcomes?
The highest-impact use cases cluster in three areas. First, intelligent process acceleration—where AI is embedded into existing workflows like finance reconciliation, compliance monitoring, or QA automation. These are not glamorous but they deliver 40 to 60 percent cycle time reduction quickly.
Second, knowledge augmentation for knowledge workers—engineering, legal, research, and product teams using AI to compress analysis time, generate first drafts, synthesize research, and navigate complex documentation.
Third, and this is where the more advanced GCCs are playing, AI-native product development—where the GCC is not just using AI internally but building AI-powered features and platforms for the enterprise’s end customers. That’s where you see the strategic repositioning happen.
02 - Workforce Transformation
Reskill, Rehire, or Both?
The talent conversation in GCCs has shifted dramatically. How are leaders thinking about what the AI-era workforce actually looks like?
The honest answer is that most GCC leaders are grappling with this in real time. The traditional model was to hire at scale for relatively standardized skill sets—software engineers, analysts, BPO operators—and manage them through structured processes. That model is under pressure.
What’s emerging is a tiered workforce architecture. At the top, you have AI architects, prompt engineers, and data scientists who design and govern intelligent systems. In the middle, a large cohort of AI-augmented professionals—people whose jobs have fundamentally changed because AI handles the lower-complexity portions. At the base, roles that have been automated or significantly reduced in headcount.
The reskilling investment required is substantial. Leading GCCs are running 200- to 300-hour AI literacy programs, not 10-hour certifications. They’re creating internal AI academies. And critically, they’re building career pathways that don’t dead-end—because talent will leave if they don’t see where their AI journey takes them professionally.
There’s anxiety in many GCC communities about job displacement. How should leaders be addressing this without it becoming a cultural liability?
Transparency is non-negotiable. The leaders who handle this well are the ones who communicate early, honestly, and with specificity about what is changing and what isn’t. The worst thing you can do is create an information vacuum where rumor fills the space.
But there’s also a reframe that’s important. Most of the net job loss being attributed to AI in GCCs is, on closer examination, a compression of the volume of low-complexity tasks—not an elimination of the professionals doing them. A finance analyst who previously spent 60 percent of their time on data extraction now spends that time on synthesis and recommendations. That’s a better job.
The cultural liability comes when leadership isn’t honest about the transition costs—that reskilling is hard, that some roles will shrink, that new growth will look different. Sugarcoating this destroys trust faster than the change itself.
03 - Agentic Operating Models
The Next Architecture
The industry is starting to talk seriously about agentic AI—systems that can plan, execute, and iterate with minimal human intervention. What does this mean for GCC operating models?
Agentic AI is the most structurally significant shift GCCs are facing over the next three to five years. Current AI deployments are largely assistive—they help a human do something faster. Agentic systems are autonomous actors within workflows: they can receive objectives, break them into sub-tasks, use tools and data, and complete end-to-end processes with human checkpoints rather than human execution.
Today’s GCC is structured around teams of humans executing defined processes. The agentic GCC will be structured around portfolios of intelligent workflows—where humans design, govern, and improve agent pipelines rather than executing within them.
Some GCCs are already prototyping this in DevOps, finance operations, and IT support. The early evidence is striking: a 12-person team managing agent pipelines that handle the equivalent workload of 60 to 80 FTEs. That’s not a productivity improvement—it’s a structural redesign.
How do you actually manage a workforce that is partially human and partially agentic? What does governance look like?
This is where most organizations don’t have clean answers yet—and we should be honest about that. The governance frameworks for agentic systems are nascent. What leading GCCs are doing is borrowing from software engineering practices: version control for agent configurations, rigorous testing environments, staged rollouts, and well-defined escalation triggers.
The key governance principle is bounded autonomy—defining precisely what decisions an agent can make independently, what requires a human approval gate, and what must be escalated.
The other critical element is audit and explainability. Enterprise stakeholders—compliance, legal, finance leadership—will not accept outcomes they cannot trace. Building interpretability into agentic systems from the ground up, not as an afterthought, is a non-negotiable for GCCs operating in regulated industries.
04 - GCC LEADERSHIP
Leading in the AI Era
What does effective GCC leadership look like in this environment? What’s changed compared to the profile of a successful GCC head five years ago?
Five years ago, the effective GCC leader was primarily a delivery executive—someone who could manage scale, optimize operations, navigate complex stakeholder environments, and build talent pipelines. That skillset remains necessary but is no longer sufficient.
Today’s GCC leaders are being asked to be technology architects of their organization’s future. They need enough technical fluency to evaluate AI investments critically—not to write code, but to ask hard questions about build-versus-buy, data strategy, and integration risk.
What’s also changed is the relationship with the global parent. The most effective GCC leaders are no longer order-takers executing on a mandate delivered from headquarters. They are genuine co-architects of enterprise strategy—bringing India’s or Eastern Europe’s talent density and digital capabilities into conversations about where the enterprise should go next.
That shift in relationship with headquarters is significant. How do the best GCC leaders actually earn and maintain that co-creator status?
It’s earned through what we call proactive strategic contribution—bringing ideas, capabilities, and initiatives to the table that headquarters didn’t ask for and couldn’t have anticipated. The leaders who do this well are deeply curious about what the parent company is trying to achieve, not just what they’ve been asked to deliver.
Practically, this means building a strong feedback loop between what you see on the ground—talent trends, technology developments, competitive signals—and the strategic dialogue at the enterprise level.
The GCC leaders who get stuck in execution mode typically have one of two problems: they’ve been hired into a governance structure that doesn’t give them the airtime, or they haven’t yet built the internal trust to be taken seriously as a strategic voice. Both are solvable—but solving them requires intentionality.
05 — Responsible Ai
Risk, Trust & Governance
AI risk and ethics are often treated as compliance checkbox items in organizations. How should GCC leaders be thinking about responsible AI more seriously?
The compliance framing is genuinely dangerous, because it encourages a minimum-viable posture rather than a proactive one. The organizations that treat responsible AI as a competitive differentiator—not a regulatory constraint—are the ones building durable advantages.
The most important question is: who owns AI risk in your GCC? In most organizations, the answer is unclear—it falls somewhere between legal, IT, and the business units, with no one having explicit accountability.
What leading GCCs are building is a dedicated AI governance function—not a massive bureaucracy, but a small, empowered team with clear authority over AI deployment standards, model evaluation criteria, bias monitoring, and incident response.
Data security and IP protection become more complex when AI is in the mix—especially for GCCs handling sensitive enterprise data. How are leaders navigating this?
This is one of the most practically challenging areas, and the right answer varies significantly by industry. In financial services, healthcare, and defense-adjacent sectors, the data governance requirements around AI are extremely tight.
The architecture choices matter enormously. Where does inference happen—in a public cloud, on a private instance, or on-premise? How is training data segmented? What controls prevent sensitive data from being ingested into shared model environments? These are not theoretical questions.
The good news is that the enterprise AI vendor landscape has matured significantly. Private deployment options, data residency controls, and enterprise-grade security features are now standard. The challenge is implementation discipline—making sure that the controls you’ve specified are actually enforced at every layer of the stack.
06 — The Future Gcc
What 2030 Looks Like
If we look out five years, what does the leading GCC look like? And equally—what does a GCC that failed to adapt look like?
The leading GCC of 2030 is almost unrecognizable compared to the GCC of 2020. It’s smaller in terms of headcount-per-unit-of-output—but far more impactful. It operates with a hybrid workforce of humans and intelligent systems. It functions as a center of AI excellence, not just for its own operations but as a capability it exports to the rest of the enterprise.
It has moved from delivering well-defined work to originating new value—driving product innovation, entering new markets, creating proprietary data assets and AI models that the parent company treats as strategic IP.
The GCC that failed to adapt? It’s running higher cost-per-outcome ratios than AI-native competitors. It’s fighting talent attrition because people want to work on intelligent problems, not legacy processes. Its leadership has been unable to evolve the relationship with headquarters from a delivery model to a strategic model.
What’s your single most important piece of advice for a GCC leader sitting with this interview right now, trying to figure out where to focus?
Start with a brutally honest AI maturity assessment—not the self-reported kind, but a structured evaluation of your data infrastructure, your talent’s actual AI capability, and the gap between your AI narrative and your AI reality.
Most GCC leaders know their center is behind where it needs to be. The ones who act on that with urgency and specificity—who identify the three to five AI initiatives that will produce compounding returns and resource them properly—are the ones who will be in the leading cohort in 2028.
And don’t let perfect be the enemy of progress. The goal is to move into learning fast enough that your organization is compounding AI capability at pace with the technology’s own evolution. Every month you wait is a month of learning you don’t get back.
Who Should Read This
Built for Decision-Makers in the GCC Ecosystem
| GCC Leadership | Enterprise Executives | Strategy & Consulting | Ecosystem Partners |
| GCC Heads & Center Directors Chief Digital & Technology Officers AI & Transformation Leaders | CXOs evaluating AI GCC strategy COOs and CFOs assessing model evolution Boards overseeing digital transformation | GCC advisory and setup firms Operating model architects Digital transformation consultants | AI technology vendors & platforms Talent & workforce solutions providers Investors in GCC-adjacent sectors |
