The immense potential of artificial intelligence that can autonomously execute complex, multi-step tasks has captured the imagination of business leaders, yet its practical application often remains just over the horizon. While the concept of agentic AI promises a future of unparalleled efficiency, many organizations find themselves struggling to translate this vision into tangible enterprise value. The key to unlocking this potential lies not in aggressive, speculative adoption, but in a deliberate, methodical strategy. For Global Business Services (GBS) units, which stand at the crossroads of an organization’s most critical functions, this structured approach provides a clear path to transforming operations and solidifying their role as strategic enterprise partners.
The Dawn of Agentic AI Separating Hype from Enterprise Reality
Agentic AI represents a significant leap beyond conventional automation, introducing systems capable of reasoning, planning, and executing goal-oriented actions across various software environments. This is not merely about creating content, as with generative AI, or forecasting trends, as with predictive AI. Instead, agentic AI is defined by its ability to act, orchestrating workflows and making decisions to achieve a specified objective. Its promise lies in creating a more dynamic, responsive, and autonomous operational backbone for the enterprise.
However, a tangible gap exists between this compelling vision and the current state of deployment. The initial enthusiasm for agentic systems has been tempered by the complex foundational work required to scale them effectively. Much like the experience with generative AI, where a 2025 survey revealed that a majority of GBS organizations had yet to complete a single project, the adoption of agentic AI remains in its early stages. This reality check underscores the critical need for a structured framework, one that moves beyond hype and focuses on building the necessary process and data maturity to harness its power successfully. For GBS leaders, the journey begins with acknowledging that true transformation requires preparation, not just experimentation.
The Strategic Imperative Why GBS Is the Ideal Proving Ground for Agentic AI
The true power of agentic AI is unlocked at the software orchestration layer, where it moves beyond automating isolated tasks to intelligently managing end-to-end processes. This capability to connect disparate systems, interpret context, and take independent action is precisely what makes it a game-changer for GBS. The benefits extend far beyond incremental efficiency gains, offering the potential to drastically improve service levels, reduce operational risk, and provide deeper business insights. By automating complex decision-making workflows, GBS can accelerate its evolution from a cost-focused back-office function into a high-value strategic partner to the enterprise.
Global Business Services and Global Capability Centers (GCCs) are uniquely positioned to serve as the launchpad for these agentic ecosystems. By design, GBS sits at the intersection of core enterprise functions, with deep visibility into the processes and data flows across finance, HR, procurement, and IT. This central vantage point provides the ideal environment to pilot, govern, and scale AI agents that can operate across departmental silos. GBS’s existing expertise in process optimization and service delivery makes it the natural leader to guide the organization into a future where interconnected AI agents work alongside human teams to drive enterprise-wide value.
A Methodical Blueprint Five Steps to Deploy Agentic AI in GBS
A successful agentic AI program is not the result of a single, brilliant pilot but the outcome of a deliberate, phased approach. For GBS leaders, moving from an initial concept to a scaled, enterprise-wide deployment requires a clear blueprint. The following five-step framework provides actionable guidance, ensuring that each stage builds upon the last to create a robust foundation for adoption. This methodical journey is designed to manage complexity, mitigate risk, and maximize the strategic impact of agentic AI.
Building the Foundation Mastering Your Operational Landscape
The first and most critical step is to develop a comprehensive understanding of the existing operational environment. Before any AI can be effectively deployed, an organization must meticulously document and analyze its current business processes, workflows, and data ecosystems. This involves mapping how data moves from end to end, identifying key systems of record and APIs, and assessing the quality and structure of available information. A clear picture of data pipelines, governance protocols, and security measures is essential to prepare for seamless AI integration and ensure agents can make informed, reliable decisions.
Case in Point A Global Logistics Firm’s Foundational Analysis
A leading global shipping and logistics firm exemplified this foundational approach when preparing for its automation journey. The company operated seven GBS centers supporting over 80 highly complex and manually intensive processes with significant regional variations. To pave the way for transformation, it first undertook an exhaustive mapping of these workflows. This deep analysis allowed the firm to identify the highest-impact automation opportunities while also pinpointing potential risks related to process inconsistencies and data fragmentation, creating a clear blueprint for its AI strategy.
From Problem to Pilot Defining Scope and a Strategic Operating Model
With a clear understanding of the operational landscape, the next step is to identify a high-impact business problem and frame it as a discrete, manageable pilot. This use case should have well-defined objectives, clear metrics for success, and a scope that is ambitious yet achievable. Selecting the right problem—one that addresses significant costs, service-level challenges, or compliance risks—ensures that the pilot demonstrates tangible business value and builds momentum for broader adoption.
Simultaneously, it is crucial to establish a suitable operating model to govern the initiative and ensure it can be extended beyond the initial pilot. Whether through a centralized Center of Excellence (COE), a democratized citizen-led approach, or a strategic partnership model, structural clarity is vital. This governing framework provides the oversight needed to manage environmental complexity, mitigate risks, and create a standardized pathway for scaling successful agentic AI solutions across the enterprise.
Real-World Application A Multinational Bank’s Scaled Discovery
The experience of a large multinational bank in Australia highlights the power of a well-defined operating model. After initial success automating several non-core processes, the bank established an Automation COE to tackle its most complex workflows. Using a unifying software platform, the COE successfully managed over 100 discovery projects in less than 14 months. This structured approach not only validated numerous use cases but also created a repeatable, scalable pathway for deploying automation across the organization’s core functions.
Achieving Enterprise Impact Scaling from a Successful Pilot
A successful pilot is not the end goal but a stepping stone toward enterprise-wide transformation. The strategy for scaling involves leveraging the insights, technologies, and governance frameworks established during the pilot phase to address larger, more complex business challenges. This means expanding from a single use case to transforming entire functions, such as procurement or financial advisory. Building on initial wins, GBS can demonstrate the exponential value of agentic AI and secure the executive buy-in needed for broader, more ambitious initiatives.
The objective of scaling is to embed agentic capabilities deep within the organization’s operational fabric. In a procurement process, for example, an AI agent can move beyond simple data extraction to evaluate vendor risk, cross-reference compliance standards, verify budgets, and even initiate negotiations, all while maintaining a perfect audit trail. This level of sophisticated orchestration delivers an impact that is an order of magnitude greater than standalone automation, driving significant gains in efficiency, compliance, and strategic decision-making.
At Scale The Logistics Firm’s AI-First Transformation
The global shipping provider’s methodical journey culminated in a full-scale, AI-first transformation. The foundational work of mapping processes and data allowed it to build a robust technology stack capable of handling complex documents, applying rule-based reasoning for country-specific exceptions, and orchestrating work across global teams. This foundation supported the rollout of 16 interconnected initiatives, leading to exponential growth in automation and delivering significant, measurable efficiency gains across its GBS operations.
The Future Vision From Standalone Automation to Agentic Ecosystems
The journey outlined here demonstrated that the ultimate role of agentic AI was not to replace human judgment but to extend and augment it, enabling teams to make faster, more consistent, and better-informed decisions at scale. The recommended blueprint emphasized a critical shift in mindset for GBS leaders: moving away from viewing automation as a series of isolated tasks and toward building an interconnected ecosystem of AI agents. In this model, agents share insights, learn from one another, and coordinate their actions to optimize outcomes for the entire enterprise.
This strategic approach positioned GBS and GCCs as the primary beneficiaries and natural leaders of the enterprise’s transition into the agentic era. Their central position at the nexus of processes and data across finance, HR, and IT provided the ideal launchpad for these ecosystems. By adopting a methodical, five-step deployment framework, GBS leaders were able to leapfrog incremental improvements and operate at a level of end-to-end process orchestration, solidifying their transformation into indispensable strategic partners.
