Artificial intelligence (AI) is increasingly being integrated into supply chain operations across Saudi Arabia, transitioning from experimental uses to practical, operational applications, according to experts in the field. Industry leaders highlight the growing role of AI in enhancing forecasting, planning, and resilience within the Kingdom’s retail and fast-moving consumer goods (FMCG) sectors.

Yahyah Pandor, vice president and general manager for the Middle East, North Africa, and Turkey (MENAT) region at Blue Yonder, describes this shift as structural. He explains that the rising complexity driven by omnichannel demand in Saudi Arabia requires a layered AI approach rather than relying on a single model.

Pandor outlines the current most effective AI framework as one that combines machine-learning forecasting, demand sensing, lead-time and variability prediction, and AI-driven decision layers embedded within planning workflows. This approach allows companies to integrate external factors such as weather conditions, promotional activities, competitor pricing, and social trends into their forecasting processes, contributing to improved inventory management, replenishment, and customer service.

In line with Saudi Arabia’s Vision 2030, which emphasizes economic diversification and localization, AI is also influencing supply chain design through supplier selection and network optimization. Pandor notes that supplier evaluation now extends beyond cost considerations, incorporating criteria such as local content contribution, resilience, lead-time reliability, and the ability to transfer capabilities. AI-powered digital twins, particularly in logistics and infrastructure sectors, are gaining traction to support these efforts.

Despite these advancements, challenges remain, particularly in integrating AI with existing legacy enterprise resource planning (ERP) systems. Pandor identifies system architecture rather than AI model development as the primary obstacle. He observes that the market is still evolving from guided autonomy to more advanced, self-healing supply chain planning tools, with near-term benefits primarily emerging in exception management and demand sensing rather than fully replacing human planners.

Operational readiness is cited as the key constraint in broader AI adoption. The so-called MLOps gap—referring to the gap between machine learning model development and operational deployment—poses significant hurdles. However, once operationalized effectively, AI significantly enhances scenario planning and disruption response capabilities. Pandor emphasizes that automation must be explainable, bounded, and closely integrated with execution to bolster supply chain resilience.

Platforms developed by companies like Blue Yonder reportedly process over 25 billion AI predictions daily, underlining the scale of AI’s influence. Overall, AI is becoming central to Saudi Arabia’s enterprise supply chain operations, supporting the Kingdom’s ongoing efforts to modernize and localize its economic infrastructure.