Enhancing Team Efficiency and Quality Management through AI-Driven 3D Game Development

This study utilizes?a multi-level efficiency analysis to examine the impact of artificial intelligence (AI) on team efficiency and approach to content quality management during 3D game creation. While AI is being increasingly integrated?into game development, its concrete advantages across different phases of production and types of content are still unclear. The study examines ten projects in 3D content production narrative from commercial, education, and entertainment sectors and investigates key production phases, namely, pre-production, asset?creation, production, and post-production. Results show that post-production provides the?best and highest efficiency (0.913) sustained over a longer period, with central production processes showing the lowest efficiency gains (0.042). AI demonstrates its true strength?with technical, rule-based work (rendering, data processing, etc) rather than in hyper-creative processes, and the largest efficiency gains are to be found in industry projects. The research stresses the necessity of the strategic integration of AI and organizational preparedness beyond just access to?tools. Interestingly, productivity is at its highest when AI tools account for 30–70% of working hours,?revealing superior outcomes from a well-balanced collaboration between AI and local staff. These findings support a cautious trajectory of AI integration that begins with the automation of repetitive, machine-processable tasks to facilitate the efficiency of a team-generation process outputting high-quality content. This study provides significant?theoretical and practical implications to maximize the utilization of AI in managing the short-term game industry workflow and to explore in a grounded way whether and how AI implementation could better enhance productivity and quality management, offering empirical findings for such future AI use.