Presented by EdgeVerve
Before addressing Global Business Services (GBS), let’s take a step back. Can agentic AI, the type of AI able to take goal-driven action, transform not just GBS but any kind of enterprise? And has it done so yet?
As with many new technologies, rhetoric has outpaced deployment in this case. While 2025 was “supposed to be the year of agentic AI,” it didn’t turn out that way, according to VentureBeat Contributing Editor Taryn Plumb. Leaning on input from Google Cloud and integrated development environment (IDE) company Replit, Plumb reported in a December 2025 VentureBeat post that what has been missing are the fundamentals required to scale.
Given the experience of Large Language Model (LLM)-based generative (gen)AI, this outcome is not surprising. In a survey conducted at the February 2025 Shared Services & Outsourcing Network (SSON) summit, 65% of GBS organizations responded that they had yet to complete a GenAI project. One can safely say that the adoption of the more recently arrived agentic AI is still in its very nascent stages for enterprises, including GBS.
The role of agentic AI in Global Business Services
There are good reasons, nonetheless, to focus on the tremendous potential of agentic AI and its application to the GBS sector.
Stripped of hype, Agentic AI unlocks capabilities in the orchestration layer of software workflows that weren’t practical before. It does so through a range of techniques, including (but not requiring) LLMs. While enterprises may indeed be missing certain fundamentals needed to deploy agentic AI at scale, those prerequisites are not out of reach.
As for GBS and Global Capability Centers (GCCs), they have already been undergoing a makeover, from back-office extensions into increasingly strategic enterprise partners. Agentic AI is a natural fit because one of its standard use cases involves IT operations or customer-service agents, functionality already within the existing GBS and GCC wheelhouse.
So yes, agentic AI could potentially transform the GBS sector. Industry leaders can best move toward scaled deployment by taking a methodical approach.
Five steps for deploying agentic AI in GBS
Agentic AI is not the only game in town. As noted, there’s GenAI, used primarily for content creation. But broadening the scope, we can also point to predictive AI and document AI, used respectively for forecasting and data extraction. (Neither requires LLMs.) Exposure to preexisting AI bodes well for the future of agentic AI.
First, these flavors of AI are mutually supportive, stacked (rather than siloed) in modern systems. Agentic AI, in particular, is positioned to draw upon the others. Second, having lived through the hype cycle of GenAI, industry leaders may be inclined to take a more measured – and productive – approach to agentic AI.
Rather than rushing into a pilot, the industry would do well to prep carefully (steps 1-3). When combined with the right test project (step 4), these actions can pave the way for a scaled-up deployment of agentic AI (step 5):
Know thy processes. Business operations can be complicated. Consider a top global shipping and logistics firm, whose thousands of full-time employees at its seven GBS centers supported more than 80 processes involving highly complex, manually intensive workflows with wide regional variations. Only by first understanding existing processes and workflows does an organization like this stand a chance of being able to rethink or rework them.
Know thy data. Closely related are the data that workflows depend upon. How do these data flow from end to end? What do the pipelines look like? Where are the key APIs? Are the data structured or unstructured? Do the resources include data platforms (systems of record) and vector databases (context engines), both of which AI agents need to make good decisions? What kind of data governance and security prevail? How might those change in an agentic AI scenario?
Identify the problem. In the case of the shipping firm mentioned above, the complexity and variation of the workflows, as well as their manual intensity, exposed it to significant costs, lapses in service level agreements (SLAs), poor customer experience and heightened compliance and legal risks. Once named, a problem logically becomes a potential use case with discrete objectives.
Pilot an operating model. Options include consolidating efforts in a Center of Excellence (COE), democratizing development through citizen-led approaches, and partnering through Build-Operate-Transform-Transform-Transfer (BOTT) models, among others. Without structural clarity, even promising AI pilots are difficult to extend beyond their initial domain. The model should also reflect reality. Likely involving multiple, parallel agents in pursuit of coordinated goals, Agentic AI is still constrained by environment, complexity, risks and governance.
Scale up. Successful pilots lead to their own next steps. Take 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 enabled it to complete more than 100 discovery projects in under 14 months. Pilots thus may grow, becoming enterprise-wide initiatives.
What agentic AI looks like at enterprise scale
Only scale can yield 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 reasoning across country-specific exceptions and orchestrating work across teams. That foundation led to an AI-first transformation of about 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 – agentic AI can turbo-charge operations, both AI and human.
Consider a procurement process. While document AI can extract data from purchase orders, obviating certain manual checks, an AI agent could also evaluate vendor risk, cross-reference compliance standards, verify budget availability and even initiate negotiation while keeping audit logs for regulatory reporting. In a financial advisory scenario, while predictive AI can analyze trends, an AI agent could take further action, assisting professionals in particular business units on targeted strategic investments.
Note that the agent isn’t replacing human judgment, but extending it, ensuring decisions are made faster, more consistently and on a scale.
From standalone automation to agentic ecosystems 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, HR, supply chain and IT all flow through the shared services model. This central vantage point makes GBS an ideal launchpad for creating agentic AI ecosystems.
An ecosystem differs from standalone automation. Agents don’t perform tasks in isolation. Rather, they work as part of an interconnected system. They share insights, learn from one another and coordinate to optimize outcomes at the enterprise level. Deployed within a GBS or GCC, Agentic AI can accelerate their ongoing transformation, enabling them to leapfrog incremental automation 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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