AI helps Saudi real estate shift from delays to data-driven delivery

ALKHOBAR: Saudi Arabia’s real estate sector is entering a new phase of artificial intelligence adoption as large-scale developments move beyond digital experimentation and into day-to-day operations on construction sites.

Across the Kingdom’s built environment, developers and contractors are increasingly using AI to improve planning, optimize construction schedules and mitigate delays on complex projects.

The transition comes as Saudi Arabia accelerates the delivery of giga-projects and smart cities under Vision 2030, placing unprecedented pressure on developers, consultants and contractors to execute projects at a scale rarely seen in global real estate markets.

For Matthew Marson, managing director at JLL EMEA, the debate is no longer about whether AI has a role in real estate, but whether organizations can deploy it strategically enough to generate measurable outcomes.

JLL is currently supporting the construction of a new giga-project city in Saudi Arabia, according to responses shared with Arab News. The development is currently in its infrastructure and early vertical construction stages.

“We’re currently supporting the construction of a new giga-project city in Saudi Arabia. The project is in its infrastructure and early vertical construction phase, and managing the schedule is a task of immense logistical intricacy,” Marson said.

Large construction projects are particularly vulnerable to delays caused by supply chain disruptions, contractor dependencies and evolving site conditions. Studies have consistently shown that major infrastructure developments frequently experience cost overruns and schedule slippages, making accurate planning increasingly critical for both developers and governments.

In Saudi Arabia, where projects are advancing at exceptional speed and scale, even limited disruptions can have significant implications for budgets and delivery timelines.

“On a project of this scale, even a minor delay in one area can create significant ripple effects, impacting timelines and budgets across the entire development,” Marson said.

To manage those risks, he said its team is using ALICE Technologies, an AI-powered construction planning platform backed by JLL Spark, the company’s venture capital arm.

The platform runs large-scale simulations to test multiple construction scenarios and identify more efficient delivery strategies.

“To manage this complexity, our team is leveraging AI to optimize the construction lifecycle. Using a platform called ALICE Technologies (an investment from our venture capital fund, JLL Spark) the tool runs millions of simulations to proactively identify the most efficient and resilient delivery plan,” Marson said.

According to ALICE Technologies, the platform can reduce construction timelines by up to 17 percent and labor costs by as much as 14 percent.

JLL added that comparable large-scale programs using the platform have recovered between 30 and 60 days of schedule time that would otherwise have been lost to delays.

The value of the technology became particularly clear when a key supplier of specialized components reported a multi-week shipping delay, threatening to create a bottleneck during a critical construction phase.

Under traditional project management processes, such a disruption could trigger weeks of manual rescheduling as teams coordinate subcontractors, revise dependencies and redistribute resources across the development.

Instead, project managers entered the updated timeline constraints directly into the AI platform.

“Within hours, the system processed the downstream effects of the delay and generated a revised, optimized plan. This is work that would traditionally take weeks of manual effort,” Marson said.

The system then resequenced tasks that could be completed earlier, adjusted resource allocations and updated schedules across the supply chain network.

“This allowed the project to absorb the disruption with minimal impact, turning a significant logistical challenge into a manageable, data-driven schedule adjustment,” Marson said.

The example reflects a broader shift in how real estate companies are approaching AI adoption. Rather than treating AI as a standalone innovation initiative, developers and consultants are increasingly embedding it into core planning and operational decision-making.

JLL’s research estimates that 70 percent of commercial real estate activities could be supported by AI by 2030. At the same time, the company notes that while 82 percent of corporate leaders view AI strategy as critical, only 24 percent have implemented AI across their organizations.

For Saudi Arabia, that gap represents both a challenge and an opportunity. The Kingdom continues to invest heavily in digital infrastructure, smart cities and large-scale urban development under Vision 2030, whose framework places economic diversification, private-sector expansion and quality-of-life improvements at the center of national transformation efforts.

Yet in real estate, successful AI adoption depends on more than access to advanced technology. Industry experts say it also requires reliable data, strong governance, organizational readiness and leadership capable of integrating AI into core business decisions.

Without those foundations, AI risks remaining a vendor-driven experiment rather than evolving into a practical tool for improving project delivery.

For now, the clearest gains appear in areas where complexity is highest and outcomes can be directly measured, including construction scheduling, resource planning, predictive maintenance, asset performance and portfolio analysis.

In large-scale developments, the benefits are particularly pronounced because delays and inefficiencies can rapidly multiply across contractors, materials and project timelines.

AI is unlikely to replace human judgment in real estate development. But it can provide project teams with faster visibility into risk and a wider range of responses when conditions shift unexpectedly.

For Saudi Arabia’s rapidly evolving built environment, that may prove to be AI’s most immediate and practical value.

The next phase of adoption is unlikely to be measured by how many companies experiment with AI tools, but by how effectively they use them to keep projects on track.