By Manish Goyal, Managing Director and Head – GCC, Alvarez & Marsal
For decades, the Global Capability Center (GCC) story in India has been one of scale, with more seats, more processes, more functions moved offshore. Over time, GCCs expanded far beyond their roots as cost-and-capacity engines, becoming centers for analytics, technology, research, and increasingly complex problem-solving. Today, that evolution is intersecting with another powerful force: AI’s ability to transform and accelerate work.
It’s worth being honest about how disorienting this moment feels from inside the industry. Over the years, the consulting playbook has remained remarkably consistent, with seasoned experts applying frameworks, institutional knowledge, and analytical rigor to solve complex business problems. AI is now beginning to challenge that model, fueling both concern about the industry’s traditional economics and excitement about what AI-enabled delivery can achieve.
Early predictions that AI would simply “eliminate consultants” have given way to a more grounded view. Increasingly, the consensus is that AI will hollow out the research, synthesis, and deck-production work that historically justified large teams, while sharply increasing the value of professionals who can exercise judgment, build trusted relationships, and deliver measurable results.
That shift raises an uncomfortable but necessary question for every firm with a large India-based delivery footprint: if AI can now do much of what GCCs were built to do, what happens to the GCC itself?
The answer, I’d argue, is the opposite of what the anxious version of this story predicts. GCCs are not the casualty of the AI transition. They are best positioned to lead it. The next chapter isn’t about building bigger centers. It’s about building smarter ones, at exactly the moment AI is rewriting the economics of every knowledge-intensive industry, consulting included.
Why GCCs Are the Natural Home for AI Transformation
Start with what a GCC actually is today. Over the past decade, India’s consulting GCCs have moved well past their original cost-and-capacity mandate. They’ve built deep analytics and technology capability, taken ownership of proprietary assets and benchmarking IP, and increasingly co-own client deliverables rather than simply executing tasks designed elsewhere.
That combination—technology depth, domain knowledge, delivery discipline at scale, and global operating experience—is precisely what’s needed to industrialize AI inside a professional services firm. Piloting a use case is easy; almost anyone can spin up a proof of concept. Turning a hundred scattered pilots into secure, reliable, reusable capability that a global business can depend on is hard. That is a GCC’s core competence, applied to a new kind of asset.
This is why the relationship between AI and the GCC is best understood as symbiotic rather than adversarial. AI doesn’t just make GCCs work faster; it gives them a path to move up the value chain, build proprietary tools, and take on genuinely strategic mandates. In turn, GCCs give the wider firm a safe, technically sophisticated place to experiment, industrialize, and scale AI without disrupting live client work. The GCC makes the firm AI-fluent; AI makes the GCC strategic. Neither happens as effectively alone.
What This Requires Firms To Change
Realizing that potential isn’t automatic, nor is it just a matter of hiring a few machine learning engineers into an existing team. It requires rethinking how GCCs are structured and measured.
The old GCC operating model was built on three layers—analysts doing the work, managers reviewing it, and leaders building the organization. The AI-native version looks different: a strong middle layer of subject matter experts spending most of their time on judgment and client problem-solving rather than managing juniors; a layer of automated, AI-driven workflows with humans firmly in, on, or around the loop; and an enterprise platform layer that provides governance, security, and reusable tools underneath it all.
Success metrics need to change alongside the structure. Headcount, utilization, and cost-per-FTE were reasonable proxies for value in a labor-arbitrage model. They mean far less in an intelligence-arbitrage model, where the better measures are time-to-market, reusable IP created, adoption of AI products across the global business, and the revenue those products generate.
Just as important, AI transformation cannot be something the GCC pursues in isolation, disconnected from the businesses it serves. It needs genuine sponsorship from global business leaders and shared governance—an AI operating committee spanning business units and the GCC—so that innovation stays tethered to client priorities rather than becoming a well-funded science project.
The GCCs That Will Pull Ahead
The GCCs that lead in the AI era won’t necessarily be the largest. They’ll be the ones that function as AI laboratories for rapid experimentation, AI factories that industrialize what works, domain intelligence hubs that fuse technical AI skill with deep sector expertise, and talent engines that continuously reskill their people rather than only recruiting new ones.
Done well, this doesn’t just make the GCC more efficient but changes its purpose. The GCC stops being a place that executes work designed elsewhere and becomes a place where the global firm’s next operating model gets invented first, then exported everywhere else.
The question every consulting leader with a GCC should be asking isn’t “How will AI affect our GCC?”
It’s, “How fast can our GCC help the rest of the firm catch up to the AI era?” The firms that get this right will discover their GCC was never just a delivery engine. It was always the test bed for what comes next.