Building resilient Middle East supply chains with AI

Another day, another disruption

Interrupted global supply chains are now the norm, with 85% of Supply Chain leaders feeling the need to respond to these disruptions more quickly. And nowhere is this more evident than in the Middle East.

Sitting at the center of complex international supply networks, the region hosts many of the world's most important trade routes. As a major energy producer and exporter, with an unpredictable climate, ongoing geopolitical uncertainty, a rapidly growing population, and a high dependency on imported goods, the Middle East is disproportionately exposed to any disruption.

Artificial intelligence (AI) provides the answer to building more resilient, adaptive supply chains that can withstand disruption. And the deployment of AI technology across the region is supported by a multitude of transformation agendas. From the Qatar National Vision, Saudi Vision, and Abu Dhabi Economic Vision, to Dubai Vision 2030, and Oman’s Vision, the Middle East is preparing to become a hub for future-ready, AI-powered supply chains.

But despite this strong support, less than a third (31%) of GCC (Gulf Cooperation Council) businesses have so far scaled AI deployment beyond the pilot stage. Why? Because AI is missing the operational context it needs to reason correctly, decide sensibly, and act reliably to boost supply chain resilience.

Why AI needs the right context to build supply chain resilience

Around half of Supply Chain leaders already use AI for a variety of purposes, our 2026 research reveals.

• Automated inventory management (55%)
• Demand forecasting (51%)
• Supplier relationship management (48%)
• Transport network and route planning (45%)
• Tariff management (44%)

But to fully leverage AI in supply chain resilience, it must understand how that supply chain – and the business as a whole – actually runs. And right now it has too many operational blind spots.

Out-of-the-box AI tools designed for supply chain optimization may be incredibly powerful, but they lack the company-specific business rules, enriched data, and end-to-end context required for accurate decision-making. The data platforms that feed them may have robust data storage and analytics, but they lack context of their supply chains uniquely operate.

This is challenging to deliver because it demands process reality — current state, memory of how it got there — combined with the business knowledge that turns that state into meaning: the operating model, the semantics, the patterns, the rules that define every unique supply chain operation. Without that foundation, the intelligence that sits on top is out of reach: why outcomes occur, what happens next, what to do about it.

As a result:

• Each new AI use case has to rebuild its own view of the business, so the same integration and modeling work is repeated every time.
• Without a unified view and the right context, AI often generates hallucinations and suboptimal outcomes that ignore critical dependencies.
• Supply Chain leaders are unable to validate decisions or trust AI with critical workflows.

What AI needs to know to make trusted decisions

The operational context Enterprise AI needs to make supply chains more resilient and adaptive covers a huge range of categories. It will need information about supplier performance and customer needs as well as demand patterns and market trends. It may need to know about financial targets, manufacturing capabilities, product types, inventory levels, and regulatory requirements. On top of this it will need to understand business priorities and real-time operational signals such as weather forecasts and traffic conditions.

Let’s explore some examples of sector-specific context AI would need to increase supply chain resilience for industries in the Middle East:

Energy supply chains

As a major producer of oil and natural gas, the Middle East is seeing continuous disruptions to energy infrastructure and exports. As Corey Alemand, Celonis’ Industry Lead for Energy explains, “Geopolitical shifts and economic nationalism are fracturing supply chains, while the dual mandate to fund low-carbon initiatives and maintain legacy assets creates a permanent squeeze on working capital.”

To optimize energy provision, any AI solution will need:

• Information about assets such as power plants, substations, pipelines, transformers, and storage facilities. This may include production and distribution capacities, and equipment age and history.
• Data on grid operations, including transmission capacity, maintenance schedules, planned outages, and possible congestion.
• The status of fuel supplies including supplier details and inventory availability.
• Trends in energy demand, as well as how these are impacted by real-time fluctuations.
• Core priorities for the business, such as maintaining grid reliability, maximizing renewable energy utilization, or protecting critical infrastructure.

• Read more about how the energy industry is industrializing Enterprise AI.

Retail supply chains

For retailers, supply chain disruption from broader factors like geopolitical uncertainty and climate events are being exacerbated by changing consumer preferences and growing demand for seamless, omnichannel retail experiences. To build resilient supply chains for retail, AI must be able to optimize the movement of goods from their supply network to customers while balancing priorities like costs and customer satisfaction.

To achieve retail supply chain resilience AI will need:

• Customer behavioral data including loyalty and changing preferences.
• Product information such as SKU hierarchy, lifecycle, margins, and shelf life.
• Warehouse and distribution center information, including capacities, replenishment schedules, cross-docking and returns capabilities, and safety stock levels.
• Store insights including performance, localized promotions, regional demand, storage capacity, and click-and-collect arrangements.
• Logistics optimization data, including shipping costs, carrier performance, optimal delivery routes, and shipment tracking options.

• Discover more about how retailers and CPG brands are transforming their Middle East supply chains.

Food supply chains

The Middle East is heavily dependent on imports, with the GCC countries importing 85% of their food. Although these countries are currently food-secure, supply chain disruption can easily lead to spoilage and waste, lower availability of fresh produce, reduced variety, and higher costs which are passed on to wholesalers, retailers, and ultimately consumers.

To adapt effectively to disruptions in the food supply chain, AI needs to understand things like:

• How seasonality impacts both supply and demand.
• The storage requirements, packaging needs, and shelf-life of individual foodstuffs.
• Which substitutes will see a demand peak when another foodstuff is scarce.
• Which alternative suppliers are most reliable in times of shortage as well as their minimum order requirements and lead times.
• The quality standards and procurement policies the business must adhere to.
• Factory and warehouse capacity to cope with peaks and troughs in demand.

• See how PepsiCo is speeding up inventory movement, reducing shipping costs, and optimizing working capital – all while accounting for regulations and potential shipping embargos.

From supply chain visibility to adaptive orchestration

So how can businesses in the Middle East make the most of AI, and the operational context that feeds it, to build more resilient, adaptive supply chains? This is a five-step progression:

Step one: Visibility

Gaining end-to-end visibility to understand what has happened historically throughout the supply chain, and what is happening today is already a major step forward for many Middle East businesses. But it’s no longer enough just to identify where the issues are and observe the downstream impact.

Step two: Diagnosis

Once a business has visibility of its supply chain and can determine where bottlenecks and delays are occurring, it can go one step further and understand why they are happening, identifying the root causes of issues so they can be addressed.

Step three: Foresight

In addition to understanding what is happening right now, businesses can use AI to understand what is likely to happen next. This insight allows them to take preventative action to keep supply chains flowing before potential disruptions impact their performance.

Step four: Recommendation

Using information about what has happened, what is happening, and what is likely to happen next, AI can recommend the best action to take to achieve supply chain goals. It can either trigger AI agents or other forms of automation to take those actions, or can pass recommendations to a human for approval.

Step five: Orchestration

The final step is getting AI solutions and agents to work together, and alongside existing systems and human teams. Supply Chain leaders need the tools to orchestrate complex workflows so handovers happen seamlessly and new bottlenecks aren’t created.

Celonis provides the foundation for AI-enabled supply chain resilience

The Celonis platform empowers businesses to progress through these five stages. Each of the five steps depends on the same thing: AI that understands how the business actually runs. The Celonis platform supplies that understanding, and the means to act on it.

At the foundation is the Celonis Context Model — the CCM — which translates the reality of a business into a language AI can reason over, across three layers:

1. Process data. The CCM sources live process data from wherever it sits — ERP, CRM, lakehouse, custom and legacy systems — and creates a view of the operation: not tables of records, but orders, shipments, suppliers, and invoices with the relationships and event history that connect them. That is what makes the current state genuinely end-to-end, and what preserves the memory of how each entity reached the state it is in.
2. Business knowledge. On top of that, the CCM holds what the data cannot tell you on its own: the operating model of how work is organised and should flow, the semantics that determine what counts as a late delivery or a blocked invoice, the operational patterns that describe how the network normally performs, and the business intent and governance that set which actions are permitted and which need a human. This is what turns an observed delay into a diagnosed root cause.

1. Intelligence. With both in place, the CCM discovers how processes actually run and why outcomes occur, predicts what is likely to happen next from the memory of how comparable cases unfolded, simulates the effect of a proposed action before it is taken, and recommends the next best step.

Insight is not the end of the job. A recommendation that nobody acts on changes A recommendation nobody acts on changes nothing, so Celonis also provides the tools to build AI-native applications on top of the context model. Because they all draw on the same operational context, work can be orchestrated across people, systems, suppliers, and agents — sanctioned by the business, executed by agents or passed to a human, and traceable end to end.

By combining hindsight, insight, and foresight, the CCM gives both supply chain teams and AI agents the operational clarity they need to reason correctly, decide sensibly, and act reliably. It’s the indispensable layer for deploying and scaling efficient AI supply chain leaders can trust.

With the CCM, Supply Chain leaders can:

• Create end-to-end visibility across fragmented systems.
• Identify bottlenecks, variants, and hidden inefficiencies.
• Understand how disruption affects adjacent processes.
• Prioritize AI use cases based on business value.
• Feed AI with process context and business knowledge.
• Orchestrate actions across people, systems, suppliers, and agents.
• Continuously measure outcomes and improve resilience over time.

Take action on adaptive, AI-powered supply chains

Supply chain disruption isn’t going away, with 89% of respondents to the World Economic Forum’s Global Risks Report anticipating an unsettled, turbulent, or even stormy outlook over the next 10 years.

Increasing resilience and adaptability through AI is the answer, but resilient supply chains do not come from AI alone. They come from AI that understands how the supply chain actually runs and can be trusted to improve it. The Celonis Context Model gives AI the intelligence it needs to see disruptions earlier, understand downstream impact, predict what will happen next, recommend the next best action, and orchestrate teams, agents, and systems to optimize supply chain performance.

Discover more about how Celonis supports supply chain transformation, or speak to one of our experts today.