The integration of Generative Artificial Intelligence (GAI) into next-generation networks and communication systems heralds a transformative shift in the digital landscape. As GAI applications like advanced natural language processing, real-time video generation, and complex data synthesis has become increasingly prevalent, ensuring robust Quality of Service (QoS). This paper outlines the critical aspects of QoS for GAI, exploring the unique challenges and potential solutions within the context of next-generation networks, including 5G, 6G and beyond. Major QoS parameters such as latency, bandwidth, reliability, and scalability are examined in relation to GAI's demanding requirements. The basic need for low latency in real-time applications, high bandwidth for data-intensive processes and robust reliability to ensure uninterrupted service are emphasized. The scalability of network resources to accommodate fluctuating demands is also considered essential for maintaining QoS in dynamic GAI environments. Furthermore, the paper discusses the implications of integrating QoS mechanisms with existing and emerging standards and protocols. The role of Machine Learning (ML) and Deep Learning (DL) in predictive analytics and adaptive QoS strategies is explored, showcasing how these technologies can preemptively address network issues before their impact on service quality. This paper underscores the importance of continued research and development in this field to achieve seamless, efficient, and high-quality AI services in future communication networks.