Presented by EdgeVerve
Before we talk about Global Business Services (GBS), let’s take a step back. Can agentic AI, the type of AI that can take targeted action, transform not just GBS, but any form of enterprise? And has that already happened?
As with many new technologies, the rhetoric in this case has outpaced the stakes. Although 2025 was “supposed to be the year of agentic AI,” that turned out not to be the case, according to VentureBeat editor Taryn Plumb. Drawing on input from Google Cloud and integrated development environment (IDE) company Replit, Plumb reported in a December 2025 VentureBeat post that what was missing were the foundations needed to scale.
Given the experience with generative AI based on the Large Language Model (LLM), this outcome is not surprising. In a survey conducted in February 2025 Shared Services & Outsourcing Network (SSON) Summit65% of GBS organizations responded that they had yet to complete a GenAI project. It’s safe to say that adoption of the more recently arrived AI is still in its early stages for companies, including GBS.
The Role of Agentic AI in Global Business Services
Nevertheless, there are good reasons to focus on the enormous potential of agentic AI and its application to the ABS sector.
Stripped of all hype, Agentic AI unlocks possibilities in the orchestration layer of software workflows that were previously impractical. It does this through a range of techniques, including (but not requiring) LLMs. While enterprises are indeed missing some of the fundamentals needed to deploy AI at scale, these prerequisites are not out of reach.
As for GBS and Global Capability Centers (GCCs), they have already undergone a makeover, from back-office extensions to increasingly strategic business partners. Agentic AI is a natural fit because one of the standard use cases involves IT operations or customer service agents, functionality that is already within the existing GBS and GCC wheelhouse.
So yes, agentic AI could potentially transform the GBS sector. Industry leaders can best take a step further toward scaled implementation by taking a methodical approach.
Five steps for deploying agentic AI in GBS
Agentic AI isn’t the only game in town. As noted, there is GenAI, which is mainly used for content creation. But if we broaden the scope, we can also point to predictive AI and document AI, which are used for forecasting and data extraction respectively. (Neither of which requires LLMs.) Exposure to pre-existing AI bodes well for the future of agentic AI.
First, these forms of AI are mutually supportive and are stacked (rather than in silos) in modern systems. Agentic AI in particular is positioned to leverage the others. Second, after experiencing the GenAI hype cycle, industry leaders may be inclined to take a more measured – and productive – approach to agentic AI.
Rather than rushing into a pilot, the sector would do well to prepare carefully (steps 1-3). Combined 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 management can be complicated. Take a leading international freight forwarding and logistics company, whose thousands of full-time employees in the seven GBS centers supported more than 80 processes with highly complex, manually intensive workflows with large regional differences. Only by first understanding existing processes and workflows does an organization like this stand a chance of rethinking or reworking them.
Know your data. Closely related is the data on which workflows depend. How does this data flow from start to finish? What do the pipes look like? Where are the main APIs? Is the data structured or unstructured? Do the sources include data platforms (recording systems) and vector databases (context engines), both of which AI agents need to make good decisions? What type of data management and security prevails? How might these change in an agentic AI scenario?
Identify the problem. In the case of the shipping company mentioned above, the complexity and variability of the workflows, as well as their manual intensity, exposed the company to significant costs, expiration of Service Level Agreements (SLAs), poor customer experience and increased compliance and legal risks. Once named, a problem logically becomes a potential use case with separate objectives.
Test an operating model. Options include consolidating efforts into a Center of Excellence (COE), democratizing development through citizen-led approaches, and collaborating through Build-Operate-Transform-Transform-Transfer (BOTT) models. Without structural clarity, even promising AI pilots are difficult to expand beyond their original domain. The model must also reflect reality. It is likely that multiple, parallel agents are involved in the pursuit of coordinated goals, and Agentic AI is still limited by environment, complexity, risk, and governance.
Scale up. Successful pilots lead to their own next steps. Consider the fragmented experience of a large multinational bank in Australia. After automating several non-core processes through Automation COE, the bank realized it needed to analyze and improve its most complex workflows. It selected an over-the-top software platform that allowed it to complete more than 100 discovery projects in less than 14 months. Pilots can therefore grow into company-wide initiatives.
What agent AI looks like at enterprise scale
Only scale can deliver real impact. The shipping company, with its seven GBS centers, ultimately had technology that could build data pipelines, digitize complex documents, apply rules-based reasoning to country-specific exceptions, and orchestrate work across teams. That foundation led to an AI-first transformation of approximately 16 initiatives, exponential growth in automation, and significant efficiency gains.
By unleashing capabilities at the orchestration layer – enabling contextual perception, cross-domain collaboration, and autonomous action aligned with governance – AI can supercharge operations, both AI and human.
Think of a purchasing process. While document AI can extract data from purchase orders, eliminating certain manual checks, an AI agent can also evaluate supplier risk, compare compliance standards, verify budget availability, and even initiate negotiations, while maintaining audit logs for regulatory reporting. In a financial advisory scenario, while predictive AI can analyze trends, an AI agent can take further action and assist professionals in certain business units with targeted strategic investments.
Keep in mind that the agent does not replace human judgment, but augments it, making decisions faster, more consistently, and at scale.
From standalone automation to agentic ecosystems in GBS
GBS is uniquely positioned to lead the enterprise into the age of agentic AI. By design, GBS sits at the intersection of processes and data from multiple business units. Finance, HR, supply chain and IT all flow from the shared services model. This central vantage point makes GBS an ideal launching pad for creating agentic AI ecosystems.
An ecosystem differs from standalone automation. Agents do not perform tasks in isolation. Instead, they work 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, Agentic AI can accelerate their ongoing transformation, allowing them to take automation one step further and operate at the level of end-to-end process orchestration.
N. Shashidar is SVP & Global Head, Product Management at EdgeVerve.
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