The conversation in boardrooms across the UAE and Saudi Arabia has begun to shift from developing more AI capabilities to ensuring that they work efficiently across the entire organisation. Typically, every novel technology follows the same chronology. First comes the capability phase, followed by questions about how that capability should be managed. Quite inevitably, AI too has arrived at this inflection point. As organisations move from experimentation and AI pilots to enterprise-wide production, the focus is shifting to how those capabilities are managed, governed and scaled. The need for oversight and accountability is becoming increasingly critical.
The UAE and Saudi Arabia are rapidly establishing themselves as global leaders in enterprise AI adoption, with organisations increasingly moving AI initiatives from pilot projects into production. In the public sector, the UAE has announced plans to transform 50% of government sectors, services and operations through agentic AI within the next two years. As AI becomes embedded in day-to-day operations, however, the conversation is beginning to shift from deploying AI to managing it effectively at scale.
The Orchestration Gap in Scaling Hundreds of AI Agents
While deploying a single AI agent may be relatively straightforward, coordinating hundreds of them across systems, business functions, employees and other AI agents across an organisation is significantly more complex. Without the right orchestration, much of this coordination remains manual, making it difficult to manage AI at scale. Without visibility, organisations lack a complete view of what AI agents are doing, what decisions have been made, and the current status of a process. When AI agents operate across multiple departments, accountability becomes difficult to establish without a unified control plane. As a result, governance policies may be applied inconsistently across the organisation, increasing the risk of duplicated effort, conflicting decisions and fragmented oversight. Often, such issues do not surface until weeks later, making them significantly more costly and time-consuming to identify and resolve.
For example, ADNOC has publicly announced the deployment of more than 115 AI agents across functions including finance, HR, procurement and audit. As organisations scale AI deployments of this magnitude, maintaining a central control plane becomes increasingly important to ensure visibility, governance and traceability across every AI-driven process. This can be especially challenging when something goes wrong, requiring organisations to understand what happened, why a decision was made and where a process may have failed — and this is less a technology challenge than a management one. Addressing it requires effective orchestration, rather than simply deploying more AI agents.
Orchestration as a competitive layer
According to Deloitte Middle East’s report, more than 80% of companies in the Middle East are feeling the pressure to increasingly adopt AI agents into their workflows. Furthermore, organisations across the region are rapidly moving beyond AI experimentation and into enterprise-scale deployment. This raises the stakes significantly, signalling the urgency of incorporating orchestration as a foundational capability within the organisational framework.
Amid the growing agent sprawl, workflow productivity is no longer the only metric at stake. Orchestration provides the connective layer that coordinates AI agents, business systems and human decision-making into a single governed workflow. Organisations that implement a robust orchestration layer from day one are better positioned to scale AI securely, consistently and with greater accountability. Over time, this becomes a competitive advantage, enabling organisations to move faster while maintaining governance and control.
Governance from Day One
AI agents without governance are floating liabilities within a company’s digital ecosystem. As CFOs and other investors demand greater visibility into the business value and risks associated with AI investments, businesses cannot afford to treat governance as an afterthought. It must be embedded into AI deployments from day one, enabling organisations to understand not only what AI agents are doing, but also why decisions were made and who remains accountable for the outcomes. An ungoverned AI agent operating within an organisation’s framework without clear policies, audit trails or oversight can quickly become an operational and business liability. When decisions are questioned, organisations must be able to explain what happened, why it happened and how the outcome was reached.
Without that level of visibility and accountability, AI introduces not only operational risk, but also regulatory, financial and reputational consequences.
Governance as a Business Imperative
KPMG’s UAE Tech Report 2026 shows how fast this is moving locally: 97% of UAE organisations report embedding AI agents into their workflows, products and services — the highest rate of any region in KPMG’s global survey. Yet the same report finds AI transparency is UAE organisations’ top future AI risk concern, cited more than anywhere else in the world. This comes on the heels of a joint report published by six international cybersecurity watchdogs who warn that without proper governance, AI agents functioning on production infrastructure can cause risks such as cascade failures, privilege escalation, and irreversible action execution.
The challenge for organisations today is to scale enterprise AI without compromising governance and accountability. When governance is treated as an afterthought, organisations often find themselves retrofitting controls onto systems that were never designed to support them. The result is reduced traceability, weaker auditability and greater operational risk—particularly when organisations need to explain how an AI-driven decision was reached or who remains accountable for the outcome. In this regard, the UAE and Saudi Arabia are already poised to pursue regulations that clearly outline governance, transparency, and accountability across AI deployments.
The cloud era was not made remarkable by those who constructed the fastest servers. Rather, organisations that built it around policies supporting reliability, observability, and governance emerged as its biggest beneficiaries. In a similar vein, the UAE and Saudi Arabia are gearing up not just as global pioneers of AI adoption, but also as its leaders as enterprise AI enters its next phase—one defined by governance, accountability and trust.
This opinion piece is authored by Karl Crowther, Vice President, Middle East Africa, India and South East Asia, UiPath.
Source: Tahawul Tech