Infrastructure investments in Saudi Arabia are entering a new phase, measured not only by what has been built in airports, ports, logistics corridors, energy systems, and digital infrastructure, but by the ability of these assets to work as one integrated system. The future value will not come from increasing capacity alone, but from improving the decisions that determine how aircraft, ships, cargo, energy, and data move in real time. In this phase, artificial intelligence becomes part of the operational question itself: how can large assets be turned into more efficient, reliable, and disruption-resilient operations?

In a special interview with Asharq Al-Awsat, Bilal Abu Ghazaleh, founder and CEO of 1001, a sovereign AI development startup that raised $30 million in a Series A funding round, believes that the difference between building assets and operating them efficiently is the difference between capability and performance. He says, 'Building an airport, port, or corridor gives you capacity, but it doesn't automatically give you the best use of it,' adding that 'a larger asset does not manage itself efficiently; it creates a larger number of decisions that must be made correctly.'

Bilal Abu Ghazaleh, founder and CEO of 1001 (Company)

Intelligence from Design

This idea appears fundamental in reading the next phase of Saudi transformation. Every new asset does not only add space or operational capacity, but also adds a new network of relationships and mutual dependencies. A new terminal at an airport, a new berth at a port, or a new logistics corridor does not operate in isolation from the rest of the system. A delay of one aircraft or one ship can change the gate or berth plan, then impact truck traffic, customs, warehouses, delivery schedules, and human resources.

Abu Ghazaleh explains that adding a terminal, berth, or corridor also means 'adding thousands of new links between things that affect each other.' At the speed and scale at which Saudi Arabia is moving, no human team, no matter how experienced, can keep all these relationships in mind and make the best decision every time in real time, he says.

But this difficulty carries an opportunity. Countries with old infrastructure often have to introduce AI later on top of systems that have accumulated over decades, whereas Saudi Arabia, in new projects like King Salman International Airport or new ports and railways, can embed the operational intelligence layer from the design stage, not after years of operation. Abu Ghazaleh considers that 'most countries in the world are stuck trying to install AI on top of old systems,' while the Kingdom can design intelligence within the asset from the start.

Solving Complex Problems

In airports and ports, the most complex problems are not always a lack of capacity, but a coordination failure. Therefore, it is not enough to build more facilities, hire more workers, or add a new conventional program. When a ship is delayed at a port the size of Jeddah Islamic Port, for example, or a disruption occurs at a major airport, a chain of sequential decisions begins: which berth to use? How to reschedule cranes? What happens to trucks and trains? How to reorganize the yard, warehouses, and resources?

Abu Ghazaleh states, 'The hardest problems in airports and ports are not capacity problems, but coordination problems,' stressing that they cannot be solved by pouring more concrete or increasing the number of workers. Adding people may increase the coordination burden and does not necessarily provide a unified view of all the overlapping variables.

Traditional software does not fully bridge the gap either, because the core problem lies in data fragmentation across different systems: one for transport, another for warehouses, a third for resource planning, a fourth for customs or maintenance. Each system performs a specific function within its scope, but it does not see the entire process. Thus, the challenge becomes building a living model of the process that unifies data, relationships, and rules, and makes decision-making based on a single view of the system.

Artificial intelligence can improve decisions for rescheduling berths, cranes, and trucks when a ship or aircraft is delayed (Shutterstock)

The Role of Operational AI

Abu Ghazaleh emphasizes that the starting point is not the same in every sector; the greatest benefit of AI may be in using capacity for an airline, managing disruptions at a port, improving cargo flows in a logistics group, or reducing energy consumption in another asset. Therefore, he says, 'We don't start with guesses,' but by understanding the process from within.

1001's methodology, according to Abu Ghazaleh, involves placing engineers inside client teams to understand how the organization actually works, not as it appears in diagrams or presentations. These engineers map out workflows, data, and the highest-value problems, then determine with the operations teams the first use case that can deliver a clear impact. Afterwards, a 'living model of the process' is built, akin to a working digital map showing assets, processes, rules, and relationships between them, updated in real time.

The key here is that value does not come from just one use case. Once this foundation is built, subsequent use cases become faster. Abu Ghazaleh notes that the first use case often takes the longest, but the second and third benefit from the same model, until what used to take 16 weeks can be executed in about 4 weeks. He adds that the return can be substantial, with a single use case potentially generating over $100 million in value in the first year.

When a Ship is Delayed

To explain the difference between automation and intelligence, Abu Ghazaleh uses the example of a ship arriving several hours late. This event does not just change one schedule; it breaks the original plan. The berth assigned to it may become needed for another ship, the cranes and crews waiting for it become idle, the containers it carries are linked to trucks, trains, and delivery appointments that are no longer suitable, while the yard has been arranged according to the old arrival schedule.

Automation can handle some routine procedures, such as sending an alert, updating a schedule, or reallocating a slot according to fixed rules. But when reality deviates from the plan, executing a specific rule is not enough. What is needed is to rethink the entire process and determine the best recovery plan across thousands of variables in minutes.

Abu Ghazaleh states that this is the kind of decision that AI can improve, because it 'sees the entire process at once' and can replan quickly: which berth should the delayed ship take? How to rearrange cranes and the yard? How to reschedule trucks and trains together according to actual constraints? However, this does not eliminate the human role; the plan is presented to the operator with its rationale, and the final decision remains under their control.

Using AI in critical infrastructure requires human oversight, explainability, auditability, and recording of every decision (Shutterstock)

Intelligence is Part of the Process