Presented by EdgeVerve
Before addressing Global Business Services (GBS), let’s take a step back. Could agentic AI, the type of AI capable of taking goal-driven action, transform not just GBS but any type of enterprise? And has it done so yet?
Like many new technologies, in this case the rhetoric has outstripped deployment. While 2025 was “supposed to be the year of agentic AI,” according to VentureBeat Contributing Editor Taryn Plumb, it didn’t turn out that way. Relying on input from Google Cloud and integrated development environment (IDE) company Replit, Plumb explained in a December 2025 VentureBeat post that what’s missing are the fundamentals needed to scale.
Given the experience with Large Language Model (LLM)-based generative (Gen)AI, this result is not surprising. In a survey conducted at the February 2025 Shared Services and Outsourcing Network (SSON) Summit, 65% of GBS organizations responded that they had yet to complete a GenAI project. One can safely say that the recent adoption of agentic AI for enterprises, including GBS, is still in its early stages.
The role of agentic AI in global business services
Nevertheless, there are good reasons to focus on the tremendous potential of agentic AI and its application in the GBS field.
Moving away from the hype, agent AI unlocks capabilities in the orchestration layer of software workflows that were not practical before. It does this through a number of techniques, including (but not requiring) LLM. Although enterprises may lack some of the fundamentals needed to truly deploy agentic AI at scale, those prerequisites are not out of reach.
As far as GBS and Global Competence Centers (GCCs) are concerned, they are already undergoing a transformation from back-office extensions to increasingly strategic enterprise partners. Agentic AI is a natural fit because its standard use cases include IT operations or customer-service agents, with functionality already within the existing GBS and GCC wheelhouse.
So yes, agentic AI could potentially transform the GBS sector. Industry leaders can move toward large-scale deployment by taking a systematic approach.
Five Steps to Deploying Agentic AI in GBS
Agentic AI isn’t the only game in town. As mentioned, there is GenAI, which is primarily used for content creation. But broadening the scope, we can also point to predictive AI and document AI, which are used for forecasting and data extraction respectively. (Neither requires an LLM.) Exposure to AI that already exists bodes well for the future of agentic AI.
First, these flavors of AI are mutually supporting, stacked (rather than siloed) in modern systems. Agent AI, in particular, is positioned to attract others. Second, after GenAI passes the hype cycle, industry leaders may be willing to take a more measured – and productive – approach to agentic AI.
Instead of rushing to become a pilot, it would be wise to prepare carefully for the industry (Steps 1-3). When paired with the right testing project (Step 4), these actions can pave the way for scaled-up deployment of agentic AI (Step 5):
Know your processes. Business tasks can be complex. Consider a top global shipping and logistics firm whose thousands of full-time employees across seven GBS centers supported more than 80 processes with highly complex, manually intensive workflows with wide regional variations. Understanding existing processes and workflows first allows such an organization to be able to rethink or rework them.
Know your data. Closely related are the data on which the workflow depends. How does this data flow from one end to the other? What do pipelines look like? Where are the major APIs? Is the data structured or unstructured? What resources, including data platform (system of record) and vector database (context engine), both are needed for AI agents to make good decisions? What type of data governance and security is prevalent? How might they change in an agentic AI landscape?
identify the problem. In the case of the shipping firm mentioned above, the complexity and variation of workflows, as well as their manual intensity, exposed it to significant costs, defaults in service level agreements (SLAs), poor customer experience, and increased compliance and legal risks. Once named, a problem logically becomes a possible use case with different objectives.
Pilot an Operating Model. Options include consolidating efforts in Centers of Excellence (CoE), democratizing development through a citizen-led approach, and partnering through the Build-Operate-Transform-Transfer (BOTT) model. Without structural clarity, it is difficult for even promising AI pilots to move beyond their initial domain. The model should also reflect reality. The potential for involving multiple, parallel agents in the pursuit of coordinated goals, agent AI is still constrained by environment, complexity, risk, and governance.
scale up. Successful pilots take their own next steps. Take the fragmented experience of a large multinational bank in Australia. After automating many non-core processes through Automation CoE, the bank realized it needed to analyze and improve its most complex workflows. It selected a state-of-the-art software platform that enabled it to complete over 100 exploration projects in less than 14 months. Thus pilots can grow, becoming enterprise-wide initiatives.
What agentic AI looks like at enterprise scale
Only scale can produce real impact. The shipping provider, with its seven GBS centers, ended up with technology capable of building data pipelines, digitizing complex documents, applying rule-based logic to country-specific exceptions, and organizing work in teams. That foundation led to AI-first transformation, rapid increases in automation, and significant efficiency gains across approximately 16 initiatives.
By exposing capabilities at the orchestration layer – enabling contextual perception, cross-domain collaboration, and autonomous action aligned with governance – agentic AI can turbo-charge the operations of both AI and humans.
Consider a purchasing process. While Document AI can extract data from purchase orders, skipping some manual checks, an AI agent can also evaluate vendor risk, cross-reference compliance standards, verify budget availability and initiate negotiations while keeping audit logs for regulatory reporting. In a financial advisory scenario, while predictive AI could analyze trends, an AI agent could take further action, assisting professionals in particular business units on targeted strategic investments.
Note that the agent is not replacing human judgment, but rather expanding it, ensuring that decisions are made faster, more consistently, and at scale.
From Standalone Automation to Agentic Ecosystem in GBS
GBS is uniquely positioned to lead the enterprise into the agentic AI era. By design, GBS sits at the intersection of processes and data across multiple business units. Finance, human resources, supply chain and IT all flow through the shared services model. This central vantage point makes GBS an ideal launchpad for building an agentic AI ecosystem.
An ecosystem differs from standalone automation. Agents do not work alone. Rather, they operate as part of an interconnected system. They share insights, learn from each other and coordinate to optimize results at the enterprise level. Deployed within a GBS or GCC, agent AI can accelerate their ongoing transformation, enabling them to make the leap into incremental automation and work at the level of end-to-end process orchestration.
N. Shashidhar is SVP and global head of product management at EdgeVerve.
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