AI-Driven Demand Planning: A Systematic Review of Adoption, Barriers and Strategic Implications

Agri-food organisations face a deepening governance challenge: managing demand un-certainty, supply chain volatility, and food waste under tight operational margins and in-creasing sustainability pressures. While artificial intelligence (AI) offers transformative potential for logistics and operations management, the organisational dimensions of its adoption, including strategic alignment, human capital development, and change management, remain insufficiently synthesised in the literature. This study investigates AI-driven demand planning as a management and organisational innovation, presenting a systematic review of 37 peer-reviewed studies (2015–2025) following the PRISMA protocol. Thematic synthesis across four analytical pillars, such as forecasting model applications, inventory and waste management practices, strategic impacts and resilience, and methodological overviews, reveals that advanced AI tools can reduce the mean absolute percentage error (MAPE) by 20–40% over traditional statistical methods in empirical case studies, with direct consequences for logistics performance, food waste reduction, and inventory governance. Critically, the review identifies persistent organisational barriers, particularly for SMEs: data governance deficiencies, high costs of technology adoption, workforce skill gaps, and the need for structured change management to institutionalise AI-based planning systems. The findings demonstrate that AI integration in agri-food supply chains constitutes a fundamental organisational transformation, requiring aligned strategies in innovation management, human resource development, supply chain governance, and sustainable business development. This review contributes to the administrative and management sciences by providing a structured, evidence-based framework for managers, policymakers, and practitioners navigating the organisational transition towards AI-enabled agri-food operations.