Every executive conversation about agentic AI eventually reaches the same crossroads: What should we agentify? Who should manage it? Where do we actually implement it? How do we oversee it? Where do we find the talent to build, operate, and continuously improve it? Most enterprises look for answers within their technology department or by creating a new AI team. The answer is sitting in your GBS.
Global Business Services was created to do something most enterprise functions were never meant to do: coordinate capabilities across geographies, functions, and delivery models at scale. That coordinating skill is exactly what agentic AI requires. The organizations succeeding with agents today are not using fragmented IT systems or small-scale innovation labs. Instead, they are deploying through their GBS organizations. The results are incomparable. Siemens is a prime example of this.
GBS as the Orchestration Layer for Agentic AI
Agentic AI is not simply a technology rollout but a shift in operating models. Agents make decisions, initiate actions, handle exceptions, and escalate issues without waiting for human instructions at every step. To operate reliably on an enterprise level, they need three things that most organizations cannot provide from a single function or location: process standardization across the entire enterprise, a deep and diverse pool of talent, and a governance infrastructure capable of managing autonomous actions without losing accountability.
GBS has spent years building exactly that. The GBS organization that has evolved beyond cost arbitrage has already done the groundwork: harmonizing processes across business units, developing analytics and AI talent routes within its GCCs, establishing governance cadences that span multiple regions, and gaining the institutional knowledge to run complex, multi-function operations continuously. That same foundation is what agentic AI needs to move from proof of concept to enterprise-wide impact.
The GCC is where the work runs. GBS is where the strategy, governance, and orchestration live. That distinction matters enormously when you are deploying agents across finance, HR, procurement, and customer operations simultaneously.
The Data Is Already Telling Us This
SSON’s 2026 State of the Industry report is clear on this point. Nearly 40% of SSO and GBS leaders are that generative and agentic AI projects sit within their own function. They are not waiting for IT to give them a mandate. Instead, they are developing, managing, and expanding these projects themselves. More than half are creating dedicated AI and agentic leadership roles within their GBS structures. The convergence of talent attraction, process maturity, and transformation goals is happening simultaneously.
Everest Group’s analysis of more than 100 GCC organizations reinforces the trend. GBS organizations that have shifted their delivery model from just transactional service providers to true transformers are generating 1.6 times the value of those focusing solely on cost savings. The integrator stage, which emphasizes innovation, customer experience, and enterprise transformation, delivers twice the value. Agentic AI serves as the accelerator that helps the organization achieve desired outcomes faster, but only if GBS is positioned to adopt, govern, and operationalize it at scale.
What This Means for GBS Leaders and CXOs
If you lead GBS or oversee the transformation mandate for your enterprise, the question is no longer whether agentic AI will reshape how work is done. It will. The real question is whether your GBS organization is set up to be the launchpad or if it will be left managing the exception queue after the agents have already taken over.
Three factors determine readiness. Process standardization is the foundation. Agents cannot rely on tribal knowledge or inconsistent documentation of workflows. If large volumes of work across your GBS still depend on undocumented or fragmented processes, that must be your first priority. Next is an AI-native talent strategy. The skills needed for agentic AI are completely different from those for transactional delivery.
Prompt engineering, designing agent workflows, outcome-based quality assurance, and autonomous governance are separate skills. Your talent pipeline must adapt accordingly. Governance architecture is the final piece. Agentic AI without accountability frameworks creates risk, not efficiency. GBS must own the governance structure, clearly defining where agents operate independently, where human judgment is essential, and how exceptions are managed, escalated, and learned from.
The Window Is Narrowing
New GBS organizations are being designed with agentic AI as a core principle, not a future addition. Established GBS functions that delay this shift will find themselves overtaken by leaner, more intentionally built competitors who have fewer legacy constraints and much clearer design.
Organizations that move decisively today will develop the process depth, talent maturity, and governance strength needed to scale agentic AI more quickly and reliably than those treating it as a future initiative. Agentic AI rewards preparedness. Your GBS is your preparedness.
