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Before discussing Global Business Services (GBS), let’s take a step back. Will agent AI, the type of AI capable of goal-driven action, revolutionize not just GBS but any type of business? And has it been done?
As with many new technologies, rhetoric trumps deployment in this case. While 2025 is “supposedly the year of the agent AI,” it hasn’t been, 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 is missing are the fundamentals needed to measure.
Given the experience with Large Language Model (LLM)-based generative (gen) AI, this result is not surprising. In a survey conducted in February 2025 Shared Services & Outsourcing Network (SSON) summit65% of GBS organizations responded that they have not yet completed a GenAI project. It is safe to say that the adoption of the latest AI agent is still in its nascent stages for businesses, including GBS.
The role of agent AI in Global Business Services
There are good reasons, however, to focus on the great potential of agent AI and its application in the GBS sector.
Taken off the hype, Agentic AI opens up capabilities at the orchestration layer of software workflows that weren’t practical before. This is done through a variety of techniques, including (but not necessarily) LLMs. Although businesses may be missing some of the fundamentals needed to deploy agent AI at scale, those requirements are not insurmountable.
As for GBS and Global Capability Centers (GCCs), they have undergone a transformation, from back-office extensions to more strategic business partners. Agentic AI is a natural fit because one of its standard use cases involves IT operations or customer service agents, the functionality is within the existing GBS and GCC wheelhouse.
So yes, agent AI can change the GBS sector. Industry leaders can best move toward increased deployment by taking a strategic approach.
Five steps for deploying agent AI in GBS
Agentic AI isn’t the only game in town. As noted, there is GenAI, which is used primarily for content creation. But expanding the scope, we can also point to predictive AI and document AI, which are used respectively for prediction and data extraction. (It doesn’t even require LLMs.) Exposure to early AI bodes well for the future of agentic AI.
First, these flavors of AI support each other, layered (rather than siloed) on modern systems. Agentic AI, in particular, is set up to take over others. Second, having lived through the GenAI hype cycle, industry leaders may be inclined to take a more scalable – and productive – approach to agent AI.
Rather than rushing into a pilot, the industry would do well to prepare well (steps 1-3). When combined with the right test project (step 4), these actions can pave the way for an enhanced AI agent deployment (step 5):
Know your processes. Business operations can be complex. Consider a leading global shipping and logistics company, whose thousands of full-time employees in seven GBS centers support more than 80 processes involving highly complex, manually intensive workflows with significant regional variations. It is only by first understanding the existing processes and workflows that an organization like this has the chance to rethink or change them.
Know your data. Closely related is the data that workflows rely on. How is this data from end to end? What do the pipelines look like? Where are the key APIs? Is the data structured or unstructured? Do the resources include data platforms (systems of record) and vector databases (context machines), which both AI agents need to make good decisions? What kind of data management and security prevails? How does an AI agent scenario change?
Identify the problem. In the case of the shipping company mentioned above, the complexity and diversity of the workflows, as well as their manual intensity, exposed it to significant costs, failure of service level agreements (SLA), poor customer experience and increased compliance and legal risks. Once named, a problem logically becomes a potential use case with discrete goals.
Pilot an operating model. Options include consolidating efforts in a Center of Excellence (COE), democratizing development through citizen-led approaches, and collaboration through Build-Operate-Transform-Transform-Transfer (BOTT) models, among others. Without structural clarity, even promising AI pilots will find it difficult to pursue beyond their initial domain. The model must also reflect reality. Presumably involving multiple, coherent agents in pursuit of coordinated goals, Agentic AI is still constrained by environment, complexity, risks and governance.
exalted. Successful pilots lead their own next steps. Get the fractional experience of a large multinational bank in Australia. After automating many non-core processes through Automation COE, the bank realized that it was necessary to analyze and improve the most complex workflows. It chose an over-the-top software platform that enabled it to complete more than 100 discovery projects in less than 14 months. Pilots can thus grow, becoming enterprise-wide initiatives.
What agent AI looks like at enterprise scale
Only scale can have a 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 throughout the country with specific exceptions and orchestrating the work of teams. That foundation led to an AI-first transformation of 16 initiatives, exponential growth in automation and significant efficiency gains.
By unleashing the capabilities of the orchestration layer – enabling context perception, cross-domain collaboration, and autonomous action aligned with management – agent AI can turbo-charge operations, both AI and human.
Consider the buying process. While document AI can extract data from purchase orders, avoiding some manual checks, an AI agent can also evaluate vendor risk, cross-reference compliance standards, verify budget availability and even initiate negotiations while keeping audit logs for regulatory reporting. In a financial advisory scenario, while predictive AI can analyze trends, an AI agent can take additional action, helping professionals in particular business units with targeted strategic investments.
Note that the agent does not replace human judgment, but extends it, ensuring that decisions are made faster, more consistently and to a certain extent.
From standalone automation to GBS ecosystem agents
GBS is uniquely positioned to lead the business into the agent 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 into the shared service model. This central vantage point makes GBS an ideal launchpad for creating an agentic AI ecosystem.
An ecosystem is different from standalone automation. Agents do not perform tasks alone. Instead, they work as part of an interconnected system. They share insights, learn from each other and coordinate to optimize results at the business level. Deployed within GBS or GCC, Agentic AI can accelerate their continuous innovation, enabling them to leapfrog incremental automation and operational level end-to-end process orchestration.
N. Shashidar is SVP & Global Head, Product Management at EdgeVerve.
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