A world of new possibilities for shared service teams
Shared services teams have moved past their early mission to drive operational efficiency by centralizing and standardizing tasks. Many have already embraced a Global Business Services (GBS) model, breaking down departmental silos, looking beyond incremental efficiency gains, and unlocking end-to-end process improvement.
Now, agentic AI has arrived to spur shared services teams into a potentially game-changing transformation. They’re gaining fresh power to coordinate, execute, and improve work across Procurement, Finance, HR, IT, and other functions – and even to rethink how work works.
In the Middle East, the opportunity for GBS leaders is especially clear. Delivering digital transformation, at scale, is often high on the corporate agenda, with many companies asking their shared services functions to support faster growth, better public and employee services, and more resilient operations.
Increasingly, that coordinating role extends to owning the AI capability itself – the platform and licensing that operating companies and functions draw on. This is especially true for the region's many diversified, multi-operating-company conglomerates where GBS is often the only function positioned to own an AI platform.
This unlocks a new role for GBS leaders, as prominent drivers of strategic transformation scaled across their wider organizations and operational structures. Instead of being measured on their ability to deliver cost savings and process efficiencies, they'll increasingly be recognized for creating additional value, and delivering an extraordinarily wide range of business outcomes.
How AI agents are transforming shared services
Automation has been in shared services’ DNA for years, but AI automation enabled by agents is an altogether different proposition.
Traditional automation applies predefined rules. An RPA bot might download a supplier invoice, extract the key data, match it to the company's open POs, and approve payment if the details fall within set tolerances.
Agentic AI, meanwhile, can plan multi-step interactions to meet set goals. To continue the example, imagine an AI agent taking up an invoice that the RPA bot can’t approve. It can investigate the issue (consulting supplier comms and historical purchasing patterns), contact the supplier, and either provide a recommended next step, or execute the step itself.
In this way, AI agents’ ability to interpret goals, reason across information, collaborate with humans and other agents, marshal RPA and generative AI tools, and take action when required is a game changer for Accounts Payable/Receivable – and for every other shared service.
And where automations like RPA freed human colleagues from some of the burden of repetitive, manual, or low value work, agentic AI kicks these benefits into an altogether higher gear. It gives people back time and headspace for the work only humans can do: judgment calls, creative leaps, or high-stakes negotiations. But the real gain isn’t just measured in hours reclaimed. It's a quiet renegotiation of labor between human and machine expertise, agents handling execution, people setting direction, each sharpening the other in the process.
What changes when agents become part of the workforce?
In short, everything. When you understand agentic AI’s transformative potential, it’s unsurprising that 85% of Finance and Shared Services leaders say they’re aiming to become “agentic enterprises” within the next two to three years. Opting otherwise is the equivalent of choosing to sit in the slow lane and get overtaken by competitors.
The transformational impact of AI agents will be especially visible in high-scale, high-volume service environments – such as finance and fintech operations in the UAE, and energy and industrial shared services in Qatar.
But if GBS and shared services leaders in the Middle East are to successfully implement and scale agentic AI, they first need to evaluate its impact, and evolve their operating models accordingly. That involves asking a few key questions:
- What work should agents do? There are some tasks that are better left to traditional, rules-based automation. Defining which processes will benefit from agentic AI, and which are most ready for it, is vital.
- Where and how will humans be involved? When a decision is low risk, an AI agent can often be trusted to make the correct call. But when a decision has much more riding on it, GBS leaders will need to limit agent autonomy, and create human-in-the-loop escalation models.
- How will we measure performance? As GBS leaders use agentic AI to unlock widespread innovation, they’ll need to establish robust mechanisms for evaluating both the performance of AI agents and their impact on business outcomes.
- How will we handle exceptions? Sometimes AI agents won’t be able to find the necessary data to confidently reach a decision, or a technical glitch will prevent them from accessing your CRM and ERP. Planning for exceptions minimizes risk and safeguards ROI.
- Who owns the platform? Increasingly, the answer for multi-entity organizations is GBS: centralizing the platform, licensing, and underlying capability at the group level, and letting operating companies and business units consume it operationally, rather than requiring every part of the business to build, license, and maintain its own agentic AI stack.
- Who owns agent governance? Companies are moving to appoint a cross-disciplinary AI governance board or council, bringing together representatives from IT, Security, Legal, Compliance, Risk, Data Governance, Enterprise Architecture and business teams. There’s a lot to steer – from which use cases go into production, to where AI agents should have limited autonomy.
From process risk to intelligent orchestration
For businesses to maximize agentic AI’s benefits, deployment needs to be part of a wider transformation strategy. Crucially, agentic AI needs the right operational foundation to succeed.
No shared services organization starts out with AI-ready business processes. Most have processes that rely on fragmented systems. Processes that contain manual workarounds and – despite efforts to standardize – regional variations. Or processes that, when the buck has to stop somewhere, are found to lack a definite owner.
Introduce AI agents into such an environment, and it’s all too easy to:
- Optimize one task, but create new problems downstream
- Give AI agents incomplete or outdated business rules
- Fail to account for exceptions or process variants
- Struggle to demonstrate compliance and AI ROI
Solving these issues puts shared services teams firmly on the path to embodying their new role, as a driver of business value and strategic transformation.
Five key steps to scaling AI agents successfully
There are five key steps every GBS leader should take before scaling AI agents. Completing these preparations quickly is particularly important in the Middle East, where shared services organizations aren’t only being asked, “Where can AI agents reduce manual work?”, but “How can we scale intelligent operations responsibly across complex, high-growth environments?”
Let’s dig into that checklist.
- Rethink process visibility
74% of finance and shared services leaders say fragmented systems stop them having a real-time view of their processes. That’s one problem that needs to be tackled right off the bat.
Successful agentic AI implementation and scaling starts with an accurate, real-time view of business operations. It’s what lets GBS leaders effectively evaluate and prioritize agentic AI use cases. And it’s one of the keys to empowering AI agents to make contextually-aware decisions, and deliver genuine value.
- Rethink standardization
GBS leaders are hardwired to think about workflow standardization. But with agentic AI they need to start thinking in terms of consistent capabilities – for example, making sure every department is able to expose the data agents need to operate across them. It’s a change that goes hand-in-hand with the evolving role of GBS teams, as they move beyond driving consistent processes, to driving better business outcomes.
- Rethink controls
Overseeing the governance of agentic AI for shared services requires a similar mental shift. Effective controls don’t squander AI agents’ powers of reasoning by mandating a particular action. Instead, they set operational limits – from the level of autonomy an AI agent can exert, to the sources of information it can access. Agent decisions and actions must be traced and audited, while AI security standards and regulatory policies must be developed and applied across the business.
- Rethink talent
It’s a mistake to view AI agents as a means of reducing headcount. Instead, GBS leaders must consider how agents and humans will work side by side, and take the time to define each and every hand-off. It’s only through effectively orchestrating AI and people, across systems, that GBS teams will deliver business-level impact.
Where AI agents do take over everyday tasks, they’ll free up individuals with a deep understanding of the business to take on higher-value roles, from process owners and AI supervisors, to exception managers and governance leads.
- Rethink value
With agentic AI, the breadth of the value GBS leaders can drive expands. Classic metrics like percentage of transactions automated or hours saved should be joined by metrics that speak to business-level outcomes – from accelerated revenue, to improvements in customer service and customer experience.
How to create the right foundation for agentic shared services
There’s a reason “rethink process visibility” is the first step on our checklist. From giving AI agents the context they need to succeed, to establishing appropriate controls for agentic automation, to measuring and maximizing value – it all begins with that clear view of how your business actually runs.
Those bolded words are important. Frontier AI models are trained on the public internet so they’re generalists, loaded with how a business runs in theory. They’re not specialists, lacking the context and understanding of how any given business runs in practice.
And that’s something a lot of businesses still don’t know themselves – a business process always looks much neater on paper than it does in reality. This gap often becomes visible for the first time right after a major ERP migration – the process is finally standardized on paper, the system is live, and leaders discover how much still happens outside it, in workarounds nobody signed off on.
To understand how their business actually runs, GBS leaders must create a real-time, dynamic digital twin of their business operations – drawing on system data as well as how the work actually gets done at the desktop level, not just how it's recorded. This helps them identify the best use cases for AI agents, so value is realized rapidly, and momentum can grow. It will also allow them to:
- Monitor agent actions. The actions of AI agents are naturally captured in the digital twin letting GBS leaders monitor and audit their performance.
- Connect agentic automation to measurable outcomes, including upticks in a host of metrics, from productivity and cash flow, to cycle time and service quality – effectively demonstrating AI ROI.
- Understand process variants and exceptions. If they’re a problem, GBS leaders can fix the issue before introducing AI. If they’re natural and unavoidable, well, that’s all important context for AI agents. On the subject of which…
This is where the ownership question matters most. The strongest architecture we see, especially among regional conglomerates, has the platform, the licensing, and the capability sitting at the GBS level – a single foundation, built and governed once. That's what turns agentic AI from a series of one-off, business-unit projects into a scalable, group-wide capability.
While creating a digital twin of business operations is a huge leap forward for many GBS organizations, it’s not enough to successfully implement and scale agentic AI. GBS leaders need a platform that, as well as generating this digital twin, empowers them to:
- Feed AI agents with operational context. Equipping them to reason, decide, and act based on the business’s operational reality.
- Let operating companies build and implement agents on their own terms – using the agent building platforms that make most sense for the business – while drawing on the shared context, controls, and licensing GBS provides centrally.
- Orchestrate AI agents at scale alongside humans and traditional automation, across systems, departments, and workflows, and across every operating company using the platform.
Choosing the right platform for analyzing, designing, and operating AI-driven services – owned by GBS, and inherited by every operating company – will help GBS leaders in the Middle East and to close the gap between ambitious transformation strategies and operational execution.
The future of shared services belongs to the process-ready
It’ll be the GBS leaders who think fast, and act first, that benefit most from the rise of agentic AI. The Middle East leaders who understand both the challenge and the opportunity, and start reimagining their operating model, today – preparing their processes, data, controls, and teams for the agentic future. For many, especially those leading GBS across multiple operating companies, that reimagined operating model starts with a single decision: own the capability once, centrally, and let the rest of the business consume it.
By expediting this crucial groundwork, shared services leaders will put themselves on the fast track to not only demonstrable ROI, but an influential new role. Soon, they’ll be head of the function that – above all others – drives and delivers timely transformation and next-level business outcomes.