When conflict disrupted operations across the region earlier this year, the organisations that adapted fastest tended to share one trait. They had already automated a meaningful share of their work. The pattern held across the banks, airlines, government entities and logistics companies we work with.
Airlines had to rebook and reroute passengers at short notice. Contact centres absorbed a surge in demand without adding headcount. Where processes were already automated and coordinated, teams could take on more of that load, and several were already layering AI onto workflows that were running reliably. Organisations starting from scratch had much less to work with.
AI is key to enabling enterprises to scale their operations fast to build resilience and business continuity. It can help enterprises respond faster, but its value depends on how effectively it is leveraged to perform to its maximum potential, returning the most valuable ROI. For GCC organisations pursuing ambitious AI agendas, business orchestration - the layer that coordinates AI agents, robots, people and systems across long-running, end-to-end business processes- is what helps convert AI potential to measurable business results.
The work around the model
Conversations about agentic AI with business leaders usually start with the model, including which one to choose and how to scale it. Yet choosing the model is often the more straightforward part. The harder work lies in connecting AI to core systems, coordinating workflows that move between people and applications, and keeping control of what happens at each stage.
A 2026 UiPath survey of 590 C-suite and IT leaders at large enterprises across the US, Europe and Asia found that only 31% consider AI fully embedded in their business, while 11% remain at the pilot stage. The most commonly cited obstacles were data quality (38%), integrating agents with existing workflows and systems (37%), and governance and compliance (33%).
Consider a bank that runs a successful AI pilot in its finance team. If that pilot cannot connect to existing approval chains, legacy platforms and other departments, scaling it across the business becomes considerably harder. Multiply that across departments and an organisation can accumulate promising experiments without seeing a corresponding improvement in how it operates.
AI can produce answers, but turning them into business outcomes requires agents, software robots, APIs and people to work through processes from start to finish, with governance built in. Business orchestration provides that connecting layer, allowing enterprises to coordinate work across different technologies, maintain visibility and manage workflows when conditions change or something goes wrong. Only 29% of organisations in the same survey have fully embedded orchestration. Among them, 89% reported that their agentic AI projects met or exceeded their return-on-investment expectations. For GCC enterprises, many of which operate a mix of legacy platforms and newer technologies, the priority should be to connect and coordinate these environments before adding more intelligence to them.
Every agent needs a job description, a manager and an exit plan
Orchestration becomes even more important as organisations move from deploying a handful of AI agents to potentially managing hundreds or thousands across different business functions.
Agentic AI is advancing faster than many organisations can keep up with, while expectations of full autonomy are running ahead of what enterprises can safely deploy. Governance is struggling to keep pace, and what may seem manageable with a handful of agents can become a serious operational risk when hundreds are operating across the business.
Enterprises need to manage agents with the same discipline they apply to their workforce. Employees are onboarded, given credentials, supervised and monitored, and their access is revoked when they leave. Agents require similar controls, particularly because they cannot exercise human judgement or take responsibility for the consequences of their actions.
Every agent should have a clearly defined job, with specific tasks, permissions and boundaries around what it can do. It also needs a designated manager who understands where it operates, monitors its activities and determines when human intervention is required. Equally important is an exit plan, ensuring agents can be retired and their access revoked when processes change, their performance falls short or they are no longer needed.
These controls cannot operate effectively in isolation. Enterprises need a central orchestration layer that shows which agents are operating, what systems they can access, how their work connects to other processes and when human oversight is required.
In banking, for example, know-your-customer checks, compliance reviews and trade finance combine high volumes and established rules with frequent exceptions that require judgement. An agent can collect documents, flag discrepancies and prepare a recommendation, while decisions with material consequences remain subject to appropriate human authority and oversight. Orchestration coordinates these activities and maintains an audit trail of every step, whether completed by a person or an agent. For regulated organisations, these processes must also meet applicable national data residency and sovereignty requirements from the outset.
Start with the outcome, then orchestrate the work
Finance, procurement and HR offer a practical starting point, because many organisations already automate parts of them. A company with an established financial reconciliation process, for instance, has a foundation on which to introduce agentic capabilities. The value will come from connecting those capabilities to the systems, approvals and people around the process.
For business leaders, the starting point should always be the outcome rather than the technology. This means identifying a small number of high-value processes, understanding how they operate and addressing existing inefficiencies before introducing AI. Getting the data foundations right, building governance into the process and establishing orchestration across the workflow should follow. Buying technology first and searching for a use case later risks creating a fragmented collection of platforms without delivering meaningful business value.
The UAE and Saudi Arabia share a strong, government-led commitment to AI, although they are approaching adoption from different starting points. The UAE started investing in automation earlier, and many organisations there are now scaling what they have built. e&, which has worked with UiPath since 2018, now plans to introduce AI-led capabilities such as process mining and document processing across its internal operations. Saudi Arabia is building new capabilities quickly and at scale, guided by Vision 2030, data sovereignty requirements and investment in local talent. Much of today’s agentic AI activity there is concentrated in government, utilities and aviation, though the private sector has strong foundations of its own, with some of UiPath’s largest automation deployments in the Middle East at Saudi financial institutions. Agentic adoption in that sector is expected to accelerate. Whatever their starting point, enterprises in both markets will need the same foundations to turn national ambition into results across the business.
The organisations that were better placed to handle this year’s disruptions had invested in automation foundations long before they were tested. As enterprises across the Gulf move towards increasingly autonomous operations, orchestration will turn those investments into coordinated, resilient business operations.
Join me at UiPath FUSION Dubai on 2 November at the Museum of the Future, where we’ll explore how enterprises can scale agentic AI while building the resilience and governance needed for the future.
Karl Crowther is Vice President - Middle East Africa, India and Southeast Asia at UiPath
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Meet UiPath at UiPath FUSION Dubai on November 2nd, 2026
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