The telecommunications industry stands at a pivotal juncture, grappling with escalating network complexity and the relentless demand for higher performance, which has made the vision of a fully autonomous network not just an ambition but a necessity. Samsung is charting a deliberate and pragmatic course toward this future, turning theoretical concepts into operational reality by focusing on a strategy rooted in software-centric architectures, unified data frameworks, and a portfolio of high-impact AI use cases. This approach is not a sudden leap into full autonomy but rather an incremental journey designed to build operator trust through measurable, real-world gains. By prioritizing solvable problems with clear economic benefits today, the company is systematically laying the foundational stones for the trusted, self-governing networks of tomorrow, aiming to redefine operational efficiency and reallocate human expertise toward innovation rather than routine maintenance. This evolution is critical for navigating the next wave of digital transformation.
Practical Applications and Tangible Wins
A cornerstone of Samsung’s strategy involves targeting high-value, data-rich problems where AI can deliver immediate and quantifiable improvements, with energy efficiency in the Radio Access Network (RAN) serving as a prime example. Power optimization presents an ideal starting point due to its direct impact on operational expenditures and the availability of abundant, structured data from network components. The company successfully demonstrated that its AI-driven energy-saving solutions could substantially outperform conventional, static methods. In a collaboration with a major operator, engineers validated that the AI model achieved a maximum power consumption saving of 35%. This was accomplished by having the AI analyze the unique traffic patterns of individual cells and dynamically determine the optimal moments to power down network components. This proves that AI enables more aggressive and granular power-saving policies that are precisely tailored to local usage conditions, all without compromising the quality of service for end-users, thereby building a strong business case for further AI integration.
Beyond the clear-cut benefits of energy savings, Samsung is strategically applying AI to address more intricate operational challenges that have traditionally depended on the deep, specialized knowledge of human engineers, such as RAN optimization. One notable success story involved leveraging AI to intelligently balance the perennial trade-off between network capacity and coverage. By optimizing crucial antenna parameters in real time, the AI system achieved a 9% increase in average user throughput without any degradation in connectivity or coverage area. These targeted victories are instrumental in the broader journey toward autonomy. They serve as powerful proof points that build essential confidence among network operators, demonstrating that AI systems can not only replicate but often enhance the outcomes of complex, manual optimization tasks. This growing trust creates the necessary momentum for operators to embrace more extensive automation, viewing AI not as a replacement for human expertise but as a powerful tool to augment it and unlock new levels of performance.
Foundational Pillars for Scaling AI
Two foundational pillars are essential for elevating AI from isolated pilot projects to a network-wide operational standard: a unified data strategy and a commitment to a software-centric architecture. Samsung’s approach methodically dismantles the traditional data silos that have long existed between the RAN, the core network, and various management domains. The primary objective is to aggregate and harmonize this disparate data into a common, accessible layer. This unification allows AI models to gain a holistic view of the entire network, enabling more sophisticated, cross-domain analysis and decision-making. The second pillar is “softwarization,” which involves decoupling network software functions from the underlying proprietary hardware. This fundamental shift introduces a new dimension of architectural flexibility, empowering operators to dynamically place network workloads and functions where they can achieve the best possible performance, efficiency, and resilience, whether in a central data center, at the network edge, or in a public cloud environment.
This foundational work supports Samsung’s vision for a gradual but steady progression from basic automation to complete operational autonomy, a journey orchestrated by its CognitiV Network Operations Suite. The ultimate goal is to evolve AI systems from their current role as advisory tools that offer recommendations to a future state where they can take direct, trusted, and independent action on the network. This critical transition from “assisted automation” to “trusted autonomy” is where the most profound long-term value of AI in telecommunications will be realized. By enabling networks to self-diagnose, self-heal, and self-optimize, this evolution will dramatically reduce operational complexity and lower operating costs. More importantly, it will free human network engineers from the burden of repetitive, manual tasks, allowing them to redirect their expertise toward more strategic, high-value initiatives such as service innovation, network planning, and enhancing the customer experience.
From Incremental Gains to an Autonomous Future
The strategic deployment of AI within telecommunications networks marked a significant shift from theoretical exploration to practical implementation. By focusing on tangible outcomes in areas like energy savings and RAN optimization, the groundwork was laid for broader acceptance. These early successes were not merely technical achievements; they were crucial in building the trust required for operators to cede greater control to automated systems. The establishment of unified data layers and flexible, software-defined architectures proved to be the essential enablers, creating an environment where AI could learn and operate effectively across previously fragmented network domains. This deliberate, step-by-step methodology ensured that each phase of automation was validated by measurable performance gains, paving a clear and viable path toward a future where networks can manage their own complexity. The journey transformed the concept of network autonomy from a distant aspiration into an achievable operational paradigm.
