Artificial Intelligence and the Future of the Global Wood Industry: A Sustainable and Smart Manufacturing Perspective

The global wood industry is undergoing a profound transformation driven by rapid advances in Artificial Intelligence (AI), digital technologies, and sustainable manufacturing practices. Traditional wood production systems are increasingly transitioning toward intelligent manufacturing environments that integrate machine learning, computer vision, robotics, the Internet of Things (IoT), and big data analytics. These technologies improve operational efficiency, reduce production costs, enhance product quality, optimize raw material utilization, and support environmentally sustainable practices. This review paper examines the current and emerging applications of Artificial Intelligence across the global wood industry, including forest resource management, timber harvesting, wood processing, quality inspection, predictive maintenance, inventory management, and digital supply chain optimization. This is a conceptual review article: it synthesizes and triangulates existing literature to build a theoretical framework, and does not present new primary or statistically validated data. In addition to the technological and environmental dimensions, this revised edition incorporates a psychological and human-factors perspective, examining how workforce trust, cognitive load, perceived job security, and organizational readiness shape the real-world adoption of AI in wood-processing environments. Based on an extensive review of recent scientific literature and current industry statistics, this paper proposes a conceptual framework named the Artificial Intelligence for Sustainable Wood Industry (AI-SWI) Framework, which illustrates the integration of AI technologies with smart forestry, intelligent manufacturing, digital supply chains, human-centered management, and sustainable industrial development. The findings indicate that AI has become a strategic driver of competitiveness within the global wood industry, with documented benefits including 20-50% reductions in unplanned equipment downtime, defect-detection accuracies exceeding 79% mAP in recent deep-learning models, and wildfire-detection accuracies above 98% in AI-IoT forestry monitoring systems. Nevertheless, several challenges — including high implementation costs, cybersecurity concerns, insufficient digital infrastructure, workforce skill gaps, psychological resistance to automation, and data management issues — continue to hinder widespread adoption. Finally, the paper outlines future research directions and provides practical recommendations for policymakers, industrial managers, and researchers seeking to accelerate the digital transformation of the wood sector. Keywords: Artificial Intelligence; Wood Industry; Smart Manufacturing; Industry 5.0; Sustainability; Internet of Things; Machine Learning; Digital Supply Chain; Circular Economy; Human Factors and Psychology of Automation.