This study examines the impact of artificial intelligence (AI)-based forecasting on forecast accuracy and operational efficiency within Al Kazemi Group, a diversified trading and logistics organization operating in the Middle East. The research aims to evaluate whether AI-driven predictive models outperform traditional forecasting methods in improving decision-making, inventory management, and overall organizational performance. An action research approach was adopted, involving a three-month implementation of an AI forecasting tool across key departments, including Operations, Supply Chain, Finance, and Management.

Data were collected through semi-structured interviews with selected stakeholders and analyzed using thematic analysis, supported by internal performance records. The findings reveal that AI-based forecasting significantly reduces forecast errors, enhances real-time decision-making, and improves inventory optimization by lowering safety stock requirements. Additionally, automation of data processing increased operational speed and reduced manual workload.

However, the study also identifies socio-technical challenges, particularly employee concerns regarding job security and limited understanding of AI systems. These findings highlight that while AI offers substantial benefits in accuracy and efficiency, successful adoption depends on effective change management and workforce training. The study concludes that AI-driven forecasting can provide a sustainable competitive advantage when combined with a human-centered implementation strategy.