## 1 Introduction  
The evolution of communication systems is primarily influenced by service requirements. As shown in Figure 1, the International Telecommunication Union Radiocommunication Sector (ITU-R) report identifies the integration of communication and artificial intelligence (AI) as one of the new use cases for sixth-generation (6G) mobile networks [1, 2]. Future 6G mobile networks will pursue higher data speeds at a gigabit-per-second magnitude [3]. To support the vision of 6G networks, AI will become a key enabler for various 6G applications and scenarios. As intelligent applications evolve, next-generation networks are anticipated to thoroughly integrate AI, information technology (IT), and communication technology (CT), which are referred to as AICT in this paper. This transition is converting the communication system from traditional data pipelines into intelligent service platforms [4, 5]. In the era of fifth-generation mobile networks (5G), the integration of IT and CT has already been explored through concepts such as cloud-network convergence and service-based architectures [6, 7]. Building upon this foundation, AICT is introducing AI technologies to further enhance the intelligence of communication systems. AICT introduces two key concepts: AI for Network (AI4 Net) and Network for AI (Net4 AI). AI4 Net highlights the use of AI technologies to optimize network performance,which has been extensively studied in both academic and industrial contexts. The 3 GPP Radio Access Network (RAN) and Service & System Aspect (SA) working groups (WGs) have investigated AI use cases, attributes, and lifecycle management for the RAN, the core network, and network management systems [8; 9; 10; 11; 12]. Diverse AI-based approaches have been proposed to enhance use cases such as mobility management, load balancing, energy saving, beam management, and interference management [13; 14; 15; 16; 17]. For instance, in [13], AI automates and refines cell reselection, cell handover, and base station mobility configuration parameters for mobility management, which can reduce network measurement overheads and improve user service experience. In [17], a model-free distributed self-learning interference suppression (SLIM) scheme for autonomous networks is proposed without information exchange between base stations to reduce manual intervention and minimize the impact of inter-cell interference. However, current discussions on AI4 Net lack validation within existing networks. The additional overhead introduced by AI and its actual impact on network performance still require further testing and evaluation. In contrast, Net4 AI focuses on improving network capabilities to support diverse AI applications. In this paradigm, the network not only is the pipeline for traditional data transmission, but also provides the computing resources, data, and models necessary for AI processing. By transforming communication networks into intelligent service platforms, the communication service providers can develop AI-driven applications on top of this infrastructure. While the AI4 Net model merely applies AI technologies to optimize network performance, Net4 AI can also be exposed as a service to third parties, providing models, computing power, or data analytics to support applications such as autonomous driving, AI-driven industrial Internet of Things (IoT), and smart cities [18; 19]. This approach enables telecom operators to expand their revenue models beyond traditional connectivity services. In the 5G and 5G-advanced stage, AI and communication systems function more like two independent entities, with AI integrated as an add-on feature to the existing communication networks. Under this approach, AI algorithms must be separately designed  
Figure 1: Typical 6G use cases defined by the ITU-R.  
and deployed for each application scenario, potentially leading to redundant network functions and inefficient resource utilization.