The Evolution of Global Capability Centers in the AI Era: From Cost Centers to Strategic Innovation Hubs

The Future of Global Capability Centers in the AI Era

By Mr. Alouk Kumar In today's fast-changing business environment, Global Capability Centers (GCCs) have evolved from cost-cutting back offices to strategic innovation centers. With artificial intelligence transforming industries globally, these centers are at a pivotal point of inflection -- on the verge of driving the change or becoming obsolete. From Cost Centers to Strategic Innovation Hubs The former emphasis on labor arbitrage was the exclusive way offshore operations operated in the past. Those days are long gone. Modern GCCs now function as advanced operations that provide specialized expertise across all functions, including engineering, analytics, and research and development. Various large multinational organizations utilize their GCCs to achieve both support-driven operations and enterprise-wide transformation initiatives. This evolution wasn't accidental. Intentional capital investment in talent development combined with technological integration and cultural fusion with parent organizations produced this evolution. Research has yielded significant benefits because GCCs consistently produce innovations that guide their main organizations into the future. AI's Transformative Impact on GCCs GCCs encounter both unprecedented possibilities and existential challenges because of the AI revolution. Three main technologies, such as machine learning combined with natural language processing and robotic process automation, enable substantial operational efficiency improvements. Traditional GCC roles are endangered through machine automation systems. Smart GCC leaders now increase their investments in AI implementation. Management in GCC countries accepts artificial intelligence as a strategic instrument that enhances workforce capacities instead of considering it as a threatening invention. Technology centers established by GCCs in major Asian innovation areas maintain AI Centers of Excellence for specialists to integrate human mentoring with artificial intelligence systems. The Shifting Talent Landscape The skills that made GCCs successful in the past won't guarantee their future. Today's GCC talent requirements extend far beyond technical competence to include: AI literacy and implementation expertise: Understanding AI fundamentals and how to apply them to business challenges is now essential for GCC professionals across all levels. Experience with data governance and ethics: As AI systems make increasingly consequential decisions, GCC teams must understand how to implement ethical frameworks and governance structures. Strong business acumen and domain knowledge: Technical skills alone aren't enough; understanding the business context is crucial for developing truly valuable AI solutions. Change management capabilities: The introduction of AI systems requires careful change management to ensure adoption and minimize disruption. Cross-cultural collaboration skills: As global operations become more integrated, the ability to work effectively across cultural boundaries becomes increasingly valuable. This shift has sparked fierce competition for AI-savvy talent in GCC hotspots across Asia, Eastern Europe, and Latin America. Forward-thinking centers are responding by partnering with universities, creating AI training academies, and implementing continuous learning programs. Beyond Labor Arbitrage: The New Value Proposition As routine processes become increasingly automated, the GCC value proposition is fundamentally changing. The centers that thrive will be those that can: Orchestrate seamless human-machine collaboration: Creating systems where humans and AI complement each other's strengths leads to outcomes neither could achieve alone. Generate actionable insights from vast data repositories: Converting raw data into strategic business insights creates tremendous value for parent organizations. Accelerate innovation through cross-functional expertise: GCCs with diverse skill sets can rapidly prototype and test new ideas before global implementation. Scale AI solutions across global operations: Successful pilots must be effectively scaled to create enterprise-wide impact, a process GCCs are uniquely positioned to facilitate. Serve as digital transformation laboratories: GCCs can function as safe spaces to experiment with transformative technologies before wider deployment. Many financial institutions have already reimagined their GCCs as "innovation centers" where teams prototype cutting-edge AI applications before global rollout, achieving significant improvements in processing times while maintaining high accuracy. The Road Ahead: Challenges and Opportunities Despite promising developments, significant challenges remain. Data privacy regulations, cybersecurity concerns, and ethical considerations around AI deployment create complex operating environments. Meanwhile, geopolitical tensions and supply chain vulnerabilities have prompted many organizations to reconsider their global footprints. For GCC leaders, navigating these challenges requires strategic foresight and operational agility. The most successful will develop hybrid models combining centralized expertise with distributed innovation networks. They'll also invest heavily in responsible AI frameworks that balance innovation with ethical considerations. Conclusion The future of Global Capability Centers in the AI era won't be determined by technology alone. It will depend on leadership vision, organizational culture, and the ability to seamlessly integrate human and artificial intelligence. As we move deeper into this transformative period, one thing is certain: GCCs that embrace AI as a strategic imperative rather than a tactical tool will not only survive but thrive -- evolving from valued business partners into indispensable innovation engines driving their organizations forward. (The author is Mr. Alouk Kumar, Founder and CEO of Inductus, and the views expressed in this article are his own)

Building the AI Future Workforce in GCCs.

Building Future-Ready GCCs with AI: Empowering Enterprise IT, Not Just Engineering Cool Tech By Shishir Choudhary We're living in the age of AI euphoria. Today's boardroom conversation seems to revolve around "AI-first" strategies, with ambitious visions of end-to-end automation, predictive everything, and zero-touch operations. AI is being hailed as the magic wand that will transform enterprise performance overnight. But beneath the buzzwords and press releases lies a harsh reality: most enterprise AI initiatives stall, struggle to scale, or quietly fade into irrelevance. Why? Because while everyone is chasing AI as a silver bullet, very few are laying the groundwork needed to make it real. This article is a grounded exploration of what it takes to prepare enterprises and their Global Capability Centers (GCCs) for this AI-powered future, not by chasing hype but by solving the real, often unglamorous, business problems that matter. Enterprise IT today is at a crossroads. It is no longer just about "keeping the lights on." It's about orchestrating transformation, supporting business growth, and enabling innovation -- all while managing cost, complexity, and risk. In this context, Global Capability Centers (GCCs) have emerged as crucial allies -- not as flashy innovation hubs chasing the next shiny tech, but as strategic enablers of enterprise value. Redefining Enterprise IT: The Nervous System of the Business Enterprise IT is not about building breakthrough apps or chasing unicorn ideas. It's the backbone that connects supply chains, customers, products, warehouses, and decisions. It spans ERP, SCM, CRM, HRMS, and everything else. It runs on complex architectures, supports thousands of users, and evolves at the pace of business -- and, unfortunately, at the speed of the slowest legacy system. When it comes to GCCs enabling Enterprise IT, the game is about: Solving high-impact business problems using available tech. Scaling what works, not just prototyping what's cool. Building deep domain knowledge to bring business and IT closer. What's Holding Tech Initiatives Back? Despite significant investments, many technology initiatives stall or fail to scale. Why? Lack of Business Alignment Initiatives are often tech-led rather than business-backed. Without a clear ROI or stakeholder pull, they fade out post-PoC. Organizational Silos IT, data, business, and operations often work in isolation, leading to disjointed execution. Change Management Gaps Even the best technology hits a wall without adoption and behavioral change. Technical Debt Legacy systems and fragmented data slow everything down -- from integration to insights. Missing Talent You need tech and domain fluency. Too many teams have one without the other. Why Is AI Even Harder to Scale in Enterprise IT? Now add AI to the mix -- and things get tougher. Data is dirty and scattered. AI thrives on clean, contextual data. Most enterprises have neither. Business processes are nuanced. AI models need deep process understanding -- not generic training sets. Systems don't talk to each other. AI needs to be integrated into ERP, CRM, WMS, and FSM, but most enterprise landscapes aren't AI-ready. What Can GCCs Do Differently? Instead of chasing tech fads, future-ready GCCs must focus on solving real business problems by smartly applying mature technologies. 1. Control the Data, Control the Innovation Data is the fuel. Without it, even the best AI model is a glorified guesser. Build a strong Data CoE: Own MDM, analytics platforms, and quality frameworks. Set enterprise-wide data governance standards. Focus on data readiness before pushing for AI. Think: Data Products, not Dashboards. 2. Solve Business Problems, Not Just Build Tech GCCs must know the business inside out. AI without domain context is just noise. Run business immersion programs for tech teams. Co-create with business -- not just deliver tickets. Redefine KPIs: Measure business outcomes, not project velocity. If you know the business, you'll know what to automate, optimize, or reimagine. 3. Build for Scale, Not Just Showcases AI won't deliver real value if only used in small experiments. To make it work across the business, you need the basics in place: Set up systems that can talk to each other easily. Put processes in place to manage and improve AI over time. Design solutions that fit into everyday business, not just stand-alone as one-off pilots. 4. Innovate Frugally, Scale Boldly You don't need billion-dollar labs. You need business-led innovation. Set up AI Sandboxes or Innovation Pods. Prioritize 2-3 high-impact use cases -- e.g., service parts forecasting, warehouse optimization. Partner with startups and product vendors where needed. Use frugal engineering to fail fast, learn fast, and scale what works. 5. Invest in and Leverage Your Ecosystem You don't have to build everything yourself -- the right partners can accelerate your journey. Tap into startups and universities for fresh ideas, rapid experimentation, and emerging talent. Work with IT service partners who understand your business and can help you scale proven solutions quickly. Co-create with ecosystem players to share risk, speed up innovation, and stay ahead without reinventing the wheel. The smartest GCCs know when to build, when to buy, and when to partner. (The author is Shishir Choudhary, Business Head GCC India, YASH Technologies, and the views expressed in this article are his own)

AI is 'Eating' Global Capability Centers, And Here's Why You WANT to Be on the Menu

The biggest mistake? Assuming GCCs will look the same in five years. They won't. Enterprises that find themselves stuck with obsolete, low-value operations will watch wistfully from the side of the road as competitors accelerate with AI-powered execution. This is the moment for GCC leaders and decision makers to step up. AI -- including myriad specific "agents" that supplement humans doing specific tasks and workflows -- isn't here to replace GCCs -- it's here to supercharge them. But success requires a new model, new metrics, and new mindsets. Here are nine key characteristics of the emerging future-state GCC. 1. Keep Humans in the Loop of Every Critical Workflow. AI is powerful, particularly as agents get increasingly capable, but human expertise remains the foundation. AI doesn't make judgment calls, challenge assumptions, ask new questions, or drive creative problem-solving -- people do. The best GCCs will have humans orchestrating AI-powered workflows, ensuring compliance, and maintaining strategic oversight. An AI-powered GCC can provide real-time insights that enable more accurate, timely, and informed HUMAN decisions. 2. Embed Foundational AI + Last-Mile AI. The best GCCs will leverage foundational AI platforms (think: ChatGPT, Claude, Gemini, Mistral, Llama, etc.) for broad automation, and have customized Last-Mile agentic AI solutions to fine-tune workflows for specific business needs and technology environments (like Ascendion's own AVA+). 3. Commercials Include AI-Powered Arbitrage: Working from lower cost locations -- from across the globe or across your state for wage arbitrage -- will still be an important value lever, but AI arbitrage is the new differentiator. The ability to optimize cost, speed, quality, and transparency through AI-driven efficiencies will define the next wave of GCC success. 4. Set Up An Agent Foundry For AI At Scale. GCCs need an Agent Foundry -- a team dedicated to building, monitoring, and managing the swarm of AI agents that optimize IT, business processes, and customer interactions. Every company evolves -- people, tech stacks, processes -- are never static. Agents need to evolve with demand, so every GCC will a "foundry" so that agentic workflows evolve along with the company. 5. Ensure Co-Innovation: Human Collaboration Remains Key. AI doesn't innovate. People using AI innovate. The best GCCs will be co-innovation hubs -- places where enterprise leaders, AI engineers, and business strategists collaborate to drive impact. AI-powered tools remove friction from efficient cross functional collaboration by breaking down geographical barriers, enabling teams across different regions to communicate and collaborate along intelligent workflows, improving productivity and velocity. 6. Demand New Commercial Models: Process & Output-Based. The days of traditional T&M pricing are numbered. Leading GCCs will shift toward AI-Arbitrage commercial models -- billing based on process efficiencies and business outcomes, not headcount. 7. Don't Go It Alone: Success Takes An Ecosystem. Next-gen GCCs will integrate deeply with AI ecosystem partners -- AWS, Microsoft, ServiceNow, Salesforce, other business platforms, and service partners -- to extend AI-powered capabilities. 8. Hubs & Nodes: The New Operating Model. Next-gen GCCs will deliver cost control and scalable execution by combining hubs of remote or near-shore teams -- including remote workers -- with smaller but centralized client-accessible nodes for strategic collaboration and co-innovation. 9. Manage Risk & Compliance: The AI Trust Factor. AI-driven workflows can bring real and perceived risks related to security, compliance, and ethics. The next-gen GCC must lead in AI governance for responsible, transparent AI so threats are detected, data remains safe, compliance is more streamlined, and errors reduced. Strong governance -- by humans -- is non-negotiable to secure trust, ensure regulatory compliance, and protect organizational reputation.