The Middle East’s artificial intelligence (AI) market is growing exponentially. According to a Q4 2025 report, by late 2024, some 60% of Middle Eastern enterprises were in the throes of rapid adoption, with 90% of GCC business leaders reporting that their organizations were using generative AI.

by Saif Mashat, Vice President, MEA ServiceNow

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These figures exceed global averages. Investment in AI is becoming bolder across the Middle East. In May 2025, Saudi Arabia’s Public Investment Fund (PIF) partnered with NVIDIA, AMD, AWS, and others and invested US$15 billion to launch HUMAIN, an AI one-stop-shop that includes data centers, other AI infrastructure, and multimodal Arabic LLMs. And Egypt is laser-focused on raising AI’s economic contribution to more than 7.7% by 2030.

We are staring at a bright horizon where generative and agentic AI are poised to accelerate growth by fine-tuning the trajectories of businesses from Cairo to Muscat. Every aspect of how we work – search, gain insights, make decisions – will be subject to overhaul. We are overwhelmed, which probably explains why the average score in ServiceNow’s Enterprise AI Maturity Index dropped nine points in 2025.

So, we need a plan; and to plan, we must squint into that bright future and try to discern what lies ahead. Here are seven ways we can expect the Middle East work environment to change by 2030.

1. Reimagined workflows

Depending on when the organization started digitization, its business logic may have gone from analog to online and then been optimized by various tools. But outside AI-native startups, businesses’ workflows are ill-suited to the AI era. Workflows automated by AI are not necessarily optimized by AI. They must be reimagined, not least by breaking down operational and data silos. Organizations must not only imagine new ways to work with AI; they should reinvent how work flows throughout the business.

2. AI orchestration

We won’t just work with AI. We will be managers for AI as if they were colleagues. Agentic workforce management with purpose-built agents that work to KPIs – outcomes rather than just outputs – will become standard. Human managers will oversee governance, supported by rich dashboards that show confidence and impact. Goals, constraints, and success metrics will be determined by human agents while AI agents get it done. Each run will teach a different lesson. Teams will learn and improve.

3. End-to-end autonomy

Once agents become sufficiently attuned to business goals and can remain compliant while coordinating operations across apps, data, and partners, the business will have created an ecosystem of almost full autonomy. Agents will plan, execute, and even improvise without the need for human intervention. The only exceptions will be high-risk or low-confidence scenarios, where humans orchestrate the system for improvements in speed and accuracy. Autonomous workflows will be transparent, policy-adherent, auditable, and measured in value per run, allowing a sustainable learning-improvement cycle to take root.

4. Dynamic roles

Organizational charts will evolve to become work charts. Static hierarchies will be swept aside by new dynamic teams composed of the right humans and the right agents as determined by live skills graphs and agent registries. Based on required outcomes, pop-up teams will spring into action to grasp arising opportunities. Based on business rules and shared dashboards, humans will step in for judgment and

guidance, and everyone (human and AI) will build a performance record that will guide future pop-up teams.

5. Simulations as standard

Leaders and teams will run simulations inside high-fidelity digital-twin setups. Customers, products, and operations can be judged against thousands of what-if runs so the organization optimizes its operations for cost, risk, sustainability, and human experience before going live. Further, every real-world outcome will be digested by the twin to improve future decisions.

6. New productivity metrics

Value will replace volume as the formal unit of measurement for productivity. This could be in direct terms such as value per run, or more indirect measurements like lower exceptions counts, faster cycle times, first-pass yields, or human-to-agent leverage. Agentic workforce management measures will include clear views of where business intelligence brings value and where it does not. Teams along the seniority chain will be rewarded for outperformance, incentivizing compounded improvement across thousands of autonomous runs.

7. Always-on innovation

By the time the agentic workforce is embedded, the enterprise’s people will have had the chance to pursue the creative instincts that set them apart from AI. Their AI agents can help them pilot these ideas safely through autonomous ideation pipelines that accelerate the notion-to-ship time-lag. Underpinned by continuous upskilling, the business will then evolve at the speed of its imagination, bringing personalized learning paths to its people, and improving the match of skills to real-time opportunity.

Destination: Horizon

Bright skylines can confuse and pull us off course. But you will notice that the changes highlighted here are not a catalog of how AI will supplant humans; they are more a future diary of how we allow the best parts of being human to emerge and add business value. AI agents will muscle through the mundane and we will put our empathy and imagination to their best possible use – solving our most pressing challenges.