The Impact of AI on Game Development: A Scoping Review of Transformations, Career Implications, and Emerging Challenges

Artificial intelligence (AI) is increasingly transforming the game industry by reshaping development processes, production workflows, and career structures. In this study, we present a scoping literature review examining the impact of AI on game development and employment within the industry, with particular attention to the perspectives of displacement, task transformation, and productivity effects. Drawing on academic research and industry reports published between 2020 and 2026, while retaining earlier foundational literature where relevant, this study analyzes how AI technologies such as generative AI, machine learning, procedural content generation, and large language models are integrated across major stages of game production, including game art, production management, programming, and live operations. Our findings indicate that AI can support efficiency, accelerate development cycles, and enable new forms of personalization and creative experimentation. At the same time, AI adoption may alter workforce dynamics by increasing the automation potential of routine and entry-level tasks, especially in areas such as asset creation and quality assurance. Rather than fully replacing workers, however, AI more commonly reconfigures job roles and increases demand for hybrid skill sets that combine technical knowledge, creativity, and AI literacy. Our study also identifies emerging opportunities in AI-assisted design, data analytics, and machine learning integration. Despite these benefits, AI adoption introduces important challenges related to employment polarization, intellectual property, workforce adaptation, ethical governance, and market concentration. We conclude that AI is fundamentally reshaping the game industry and highlight the need for balanced strategies that support innovation while preserving creative diversity, sustainable career pathways, and equitable access to technological opportunities. Overall, the strongest evidence concerns AI capabilities, workflow changes, and task-level transformation, whereas direct evidence of actual employment outcomes and long-term occupational change remains limited and uneven.