The telecommunications landscape is grappling with an unprecedented surge in complexity, where managing network performance, energy consumption, and operational costs has become a monumental task for human engineers alone. As the industry charts a course toward a more intelligent and self-sufficient future, Samsung is pioneering a pragmatic, step-by-step strategy to transform the ambitious concept of fully autonomous networks into an operational reality. Rather than pursuing a futuristic overhaul, the company is focusing on deploying artificial intelligence to solve today’s most pressing challenges, thereby building operator trust through measurable gains. This methodical approach is anchored in software-centric architectures, unified data strategies, and a clear-eyed focus on practical, high-impact AI use cases that deliver immediate value, paving the way for the gradual evolution from human-assisted automation to genuine network autonomy. This journey prioritizes tangible results over theoretical promises, ensuring that each advancement is a stable foundation for the next.
From Theory to Tangible Gains
Samsung’s strategic application of AI begins by targeting solvable problems with clear economic incentives, a prime example being energy efficiency within the Radio Access Network (RAN). Power optimization presents an ideal entry point for AI because of its direct impact on operating expenses and the availability of abundant, well-structured network data for machine learning models to analyze. The company successfully demonstrated that its AI-driven optimization could dramatically outperform existing manual or static power-saving methods. In a collaboration with a major operator, engineers validated that Samsung’s AI could achieve maximum power savings of 35%. This was accomplished by having the AI analyze real-time and historical traffic patterns for individual cells, allowing it to dynamically and intelligently determine when specific network components could be powered down. This approach enables far more aggressive and granular power-saving strategies tailored to local usage conditions, all without degrading network performance or the user experience, proving that AI can deliver substantial operational savings today.
Beyond pure cost reduction, Samsung is applying AI to enhance network performance in areas traditionally reliant on the specialized and time-consuming efforts of human experts. One of the most complex challenges in mobile networks is RAN optimization, which involves constantly fine-tuning parameters to balance capacity and coverage. In one notable success, an AI system was tasked with optimizing antenna parameters across a live network. By continuously analyzing performance data and making micro-adjustments, the system achieved a significant 9% increase in network throughput without any corresponding loss of connectivity or coverage footprint. These targeted wins are strategically crucial for the broader adoption of AI in telecommunications. They serve as powerful proof points that build operator confidence, demonstrating that AI systems can not only handle routine tasks but also augment and improve upon the work of seasoned engineers. This creates essential momentum, encouraging operators to embrace automation for increasingly complex and mission-critical functions.
Building the Foundation for Autonomy
The ability to scale AI from isolated pilot projects to network-wide deployment rests on two foundational pillars, the first of which is a comprehensive and unified data strategy. Historically, network data has been fragmented and locked within proprietary silos corresponding to the RAN, the core network, and various management domains. This separation prevents a holistic view of network operations and limits the potential of AI. Samsung’s approach directly confronts this issue by systematically dismantling these data silos. The objective is to aggregate and normalize information from across the entire network architecture into a common data layer. By creating this single source of truth, AI models can be trained on richer, more contextualized datasets. This enables the system to identify complex interdependencies between different network segments and make more intelligent, coordinated decisions, moving beyond localized optimizations to true end-to-end network management and laying the essential groundwork for advanced autonomous capabilities.
The second critical pillar supporting the journey to autonomy is “softwarization,” a fundamental architectural shift that involves decoupling network software from the specialized hardware on which it has traditionally run. This move toward a more software-centric model creates an unprecedented level of flexibility and agility, allowing network functions to be treated as dynamic workloads. Operators gain the ability to intelligently place these workloads wherever they can achieve the best performance and efficiency, whether on-premises for security, in a regional data center for centralized control, or at the network edge to support low-latency applications. This architectural freedom is essential for an autonomous network, which must be able to reconfigure itself in real-time to respond to changing demands and network conditions. By embracing softwarization, Samsung is helping build the fluid and adaptable infrastructure necessary for AI to effectively manage and orchestrate network resources without the constraints imposed by rigid, hardware-defined systems.
The Path to Trusted Self-Governance
Samsung’s long-term vision culminates in a carefully managed progression from partial automation to full-fledged network autonomy, a journey supported by its CognitiV Network Operations Suite. This evolution is designed as a phased process, intentionally moving from a state of “assisted automation,” where AI acts as a sophisticated advisor by offering recommendations to human operators, toward the ultimate goal of “trusted autonomy,” where the system is empowered to take direct, independent action on the network. This gradual transition is critical for an industry where reliability and stability are paramount. By allowing operators to validate the AI’s decisions and observe its positive impact at each stage, Samsung methodically builds the confidence required to cede greater control. This shift will ultimately reduce operational complexity, lower long-term operating costs, and, most importantly, reallocate invaluable human expertise away from routine maintenance and toward more strategic, high-value tasks such as service innovation and future network planning.
The industry’s deliberate journey toward network autonomy, guided by pragmatic and value-driven AI implementations, is ultimately establishing a new operational paradigm. The initial focus on tangible use cases like energy savings and RAN optimization proved to be a pivotal strategy, as it provided undeniable evidence of AI’s practical benefits and fostered essential operator trust. The parallel development of foundational pillars—unifying disparate data sources and embracing the flexibility of softwarization—created the robust and agile infrastructure necessary for these intelligent systems to thrive. This methodical progression, moving from AI-driven recommendations to direct, autonomous actions, transformed how networks are managed. It demonstrated that the path to a self-governing telecommunications future is not a single leap but a series of well-executed steps that enhance efficiency and performance at every stage.
