Introduced by EdgeVerve
Earlier than addressing International Enterprise Providers (GBS), let’s take a step again. Can agentic AI, the sort of AI in a position to take goal-driven motion, remodel not simply GBS however any type of enterprise? And has it finished so but?
As with many new applied sciences, rhetoric has outpaced deployment on this case. Whereas 2025 was “supposed to be the 12 months of agentic AI,” it didn’t prove that method, in accordance to VentureBeat Contributing Editor Taryn Plumb. Leaning on enter from Google Cloud and built-in growth setting (IDE) firm Replit, Plumb reported in a December 2025 VentureBeat post that what has been lacking are the fundamentals required to scale.
Given the expertise of Massive Language Mannequin (LLM)-based generative (gen)AI, this consequence is not shocking. In a survey carried out at the February 2025 Shared Services & Outsourcing Network (SSON) summit, 65% of GBS organizations responded that that they had but to full a GenAI challenge. One can safely say that the adoption of the extra just lately arrived agentic AI is nonetheless in its very nascent levels for enterprises, together with GBS.
The position of agentic AI in International Enterprise Providers
There are good causes, nonetheless, to focus on the great potential of agentic AI and its software to the GBS sector.
Stripped of hype, Agentic AI unlocks capabilities in the orchestration layer of software program workflows that weren’t sensible before. It does so by means of a variety of methods, together with (however not requiring) LLMs. Whereas enterprises could certainly be lacking sure fundamentals wanted to deploy agentic AI at scale, these conditions are not out of attain.
As for GBS and International Functionality Facilities (GCCs), they’ve already been present process a makeover, from back-office extensions into more and more strategic enterprise companions. Agentic AI is a pure match as a result of one in all its normal use circumstances entails IT operations or customer-service brokers, performance already inside the present GBS and GCC wheelhouse.
So sure, agentic AI might probably remodel the GBS sector. Business leaders can finest transfer towards scaled deployment by taking a methodical strategy.
5 steps for deploying agentic AI in GBS
Agentic AI is not the solely recreation on the town. As famous, there’s GenAI, used primarily for content material creation. However broadening the scope, we will additionally level to predictive AI and doc AI, used respectively for forecasting and information extraction. (Neither requires LLMs.) Publicity to preexisting AI bodes nicely for the way forward for agentic AI.
First, these flavors of AI are mutually supportive, stacked (fairly than siloed) in fashionable techniques. Agentic AI, specifically, is positioned to draw upon the others. Second, having lived by means of the hype cycle of GenAI, trade leaders could also be inclined to take a extra measured – and productive – strategy to agentic AI.
Reasonably than dashing right into a pilot, the trade would do nicely to prep rigorously (steps 1-3). When mixed with the proper take a look at challenge (step 4), these actions can pave the method for a scaled-up deployment of agentic AI (step 5):
Know thy processes. Enterprise operations might be sophisticated. Contemplate a prime world transport and logistics agency, whose hundreds of full-time workers at its seven GBS facilities supported greater than 80 processes involving extremely complicated, manually intensive workflows with huge regional variations. Solely by first understanding present processes and workflows does a company like this stand an opportunity of having the ability to rethink or rework them.
Know thy information. Carefully associated are the information that workflows rely upon. How do these information movement from finish to finish? What do the pipelines appear like? The place are the key APIs? Are the information structured or unstructured? Do the sources embrace information platforms (techniques of report) and vector databases (context engines), each of which AI brokers want to make good selections? What sort of information governance and safety prevail? How may these change in an agentic AI situation?
Establish the downside. In the case of the transport agency talked about above, the complexity and variation of the workflows, in addition to their guide depth, uncovered it to vital prices, lapses in service stage agreements (SLAs), poor buyer expertise and heightened compliance and authorized dangers. As soon as named, an issue logically turns into a possible use case with discrete aims.
Pilot an working mannequin. Choices embrace consolidating efforts in a Heart of Excellence (COE), democratizing growth by means of citizen-led approaches, and partnering by means of Construct-Function-Rework-Rework-Switch (BOTT) fashions, amongst others. With out structural readability, even promising AI pilots are tough to prolong past their preliminary area. The mannequin also needs to mirror actuality. Possible involving a number of, parallel brokers in pursuit of coordinated targets, Agentic AI is nonetheless constrained by setting, complexity, dangers and governance.
Scale up. Profitable pilots lead to their very own subsequent steps. Take the fragmented expertise of a giant multinational financial institution in Australia. After automating a number of non-core processes by means of Automation COE, the financial institution realized it wanted to analyze and enhance its most complicated workflows. It chosen an over-the-top software program platform that enabled it to full greater than 100 discovery initiatives in beneath 14 months. Pilots thus could develop, turning into enterprise-wide initiatives.
What agentic AI seems like at enterprise scale
Solely scale can yield actual influence. The transport supplier, with its seven GBS facilities, ended up with expertise able to constructing information pipelines, digitizing complicated paperwork, making use of rule-based reasoning throughout country-specific exceptions and orchestrating work throughout groups. That basis led to an AI-first transformation of about 16 initiatives, exponential progress in automation and vital effectivity beneficial properties.
By unleashing capabilities at the orchestration layer – enabling contextual notion, cross-domain collaboration, and autonomous motion aligned with governance – agentic AI can turbo-charge operations, each AI and human.
Contemplate a procurement course of. Whereas doc AI can extract information from buy orders, obviating sure guide checks, an AI agent might additionally consider vendor threat, cross-reference compliance requirements, verify finances availability and even provoke negotiation whereas holding audit logs for regulatory reporting. In a monetary advisory situation, whereas predictive AI can analyze developments, an AI agent might take additional motion, aiding professionals specifically enterprise items on focused strategic investments.
Notice that the agent isn’t changing human judgment, however extending it, guaranteeing selections are made quicker, extra constantly and on a scale.
From standalone automation to agentic ecosystems in GBS
GBS is uniquely positioned to lead the enterprise into the agentic AI period. By design, GBS sits at the intersection of processes and information throughout a number of enterprise items. Finance, HR, provide chain and IT all movement by means of the shared companies mannequin. This central vantage level makes GBS an excellent launchpad for creating agentic AI ecosystems.
An ecosystem differs from standalone automation. Brokers don’t carry out duties in isolation. Reasonably, they work as a part of an interconnected system. They share insights, be taught from each other and coordinate to optimize outcomes at the enterprise stage. Deployed inside a GBS or GCC, Agentic AI can speed up their ongoing transformation, enabling them to leapfrog incremental automation and function at the stage of end-to-end course of orchestration.
N. Shashidar is SVP & International Head, Product Administration at EdgeVerve.
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