Self-Organizing Network Artificial Intelligence (AI) Global Market Report 2025

The self-organizing network artificial intelligence (AI) market size has grown rapidly in recent years. It will grow from $5.19 billion in 2024 to $6.18 billion in 2025 at a compound annual growth rate (CAGR) of 19.2%. The growth during the historic period can be attributed to the increasing implementation of machine learning and AI algorithms in telecom networks, rising investment in intelligent network management systems, growing need for self-healing and self-optimization features, and a stronger focus on predictive maintenance and proactive fault detection.

The self-organizing network artificial intelligence (AI) market size is expected to see rapid growth in the next few years. It will grow to $12.32 billion in 2029 at a compound annual growth rate (CAGR) of 18.8%. The growth during the forecast period can be attributed to rising demand for automation and operational efficiency, increased 5G deployments and network densification, growth in data traffic and subscriber numbers, and expanded adoption of AI and machine learning algorithms. Key trends in the forecast period include advancements in AI-driven network automation, enhanced integration of machine learning for real-time optimization, innovation in self-healing and self-configuring networks, and integration of generative AI for network analytics.

The rise in adoption of 5G networks is expected to drive the growth of the self-organizing network artificial intelligence (AI) market. 5G networks are the latest generation of mobile connectivity, delivering high-speed data, ultra-low latency, and increased capacity to support advanced technologies such as immersive gaming and the Internet of Things. The growing adoption of 5G networks is driven by the increasing demand for high-speed, low-latency connectivity to support real-time digital applications. 5G networks enhance self-organizing network AI by providing high-speed, low-latency connectivity, enabling real-time data processing and decision-making. They improve network efficiency by supporting AI-driven automation, optimization, and predictive maintenance, enhancing overall service quality and user experience. For example, in December 2023, according to the Office of Communications (Ofcom), a UK-based regulator and competition authority, the level of outdoor 5G coverage provided by at least one mobile network operator rose to 85-93% in 2023, up from 67-78% in 2022. Therefore, the rise in adoption of 5G networks is driving the growth of the self-organizing network AI market.

Key companies in the self-organizing network AI market are focusing on developing advanced solutions, such as AI-driven radio resource management, to optimize network performance, improve user experience, and reduce operational costs. AI-driven radio resource management (RRM) refers to the use of artificial intelligence and machine learning algorithms to dynamically allocate, optimize, and manage radio spectrum and network resources in a telecom network. For instance, in March 2025, Huawei Technologies Co. Ltd., a China-based technology company, launched the AI Core Network at the Mobile World Congress (MWC), marking a significant milestone in intelligent telecommunications. This network architecture integrates artificial intelligence into the core of telecom systems, enabling seamless connectivity and collaboration among AI agents. It is designed to deliver intelligent automation, real-time decision-making, AI-driven radio resource management, and optimized network performance across diverse service scenarios, supporting the development of next-generation smart networks.

In July 2023, Amdocs Limited, a US-based software and services company, acquired TEOCO Corporation for an undisclosed amount. Through this acquisition, Amdocs Limited aims to strengthen its network optimization and analytics capabilities by integrating TEOCO Corporation’s advanced service assurance, analytics, and network planning solutions to enhance end-to-end network intelligence and operational efficiency. TEOCO Corporation is a US-based telecommunications software company providing self-organizing network AI solutions.

Major players in the self-organizing network artificial intelligence (ai) market are Huawei Investment & Holding Co. Ltd., Cisco Systems Inc., Qualcomm Incorporated, Telefonaktiebolaget LM Ericsson, NEC Corporation, Nokia Oyj, ZTE Corporation, Juniper Networks Inc., Amdocs Limited, Ciena Corporation, Mavenir plc, Viavi Solutions Inc., RadiSys Corporation, Comarch SA, Parallel Wireless Inc., Rohde & Schwarz India Private Limited., Airspan Networks (India) Private Limited, Infovista SAS, P.I. Works Inc., Samsung Electronics Co. Ltd., Innovile, and AirHop Communications Inc.

Europe was the largest region in the self-organizing network artificial intelligence (AI) market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in self-organizing network artificial intelligence (AI) report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the self-organizing network artificial intelligence (AI) market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the information technology sector, particularly in hardware manufacturing, data infrastructure, and software deployment. Higher duties on imported semiconductors, circuit boards, and networking equipment have raised production and operational costs for tech firms, cloud service providers, and data centers. Companies relying on globally sourced components for laptops, servers, and consumer electronics are facing longer lead times and increased pricing pressures. In parallel, tariffs on specialized software tools and retaliatory measures from key international markets have disrupted global IT supply chains and reduced overseas demand for U.S.-developed technologies. To navigate these challenges, the sector is accelerating investments in domestic chip fabrication, diversifying supplier bases, and adopting AI-driven automation to enhance operational resilience and cost efficiency.

The self-organizing network artificial intelligence (AI) market research report is one of a series of new reports that provides self-organizing network artificial intelligence (AI) market statistics, including self-organizing network artificial intelligence (AI) industry global market size, regional shares, competitors with a mobility data-as-a-service market share, detailed self-organizing network artificial intelligence (AI) market segments, market trends and opportunities, and any further data you may need to thrive in the self-organizing network artificial intelligence (AI) industry. This self-organizing network artificial intelligence (AI) market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Self-organizing network artificial intelligence (AI) refers to the use of AI and machine learning algorithms to enable telecom networks to automatically configure, optimize, and heal themselves without human intervention. Its primary purpose is to improve network efficiency, enhance user experience, reduce operational costs, and accelerate fault detection and resolution by predicting and responding to network issues in real time.

The key components of self-organizing network artificial intelligence (AI) are software, hardware, and services. Self-organizing network AI software is an automated system that uses AI and machine learning to dynamically optimize, configure, and manage telecom networks, enhancing performance, efficiency, and reliability with minimal human intervention. The various network types include 2G or 3G, 4G or LTE, 5G, and others, and it is deployed through on-premises and cloud modes. It finds applications in network optimization, network configuration management, network healing, network planning and design, and other areas, serving end users such as telecom operators, enterprises, managed service providers, and others.

The self-organizing network artificial intelligence (AI) market consists of revenues earned by entities by providing services such as multi-technology integration, high-traffic optimization, custom module development and integration, outsourced services, and performance optimization services. The market value includes the value of related goods sold by the service provider or included within the service offering. The self-organizing network artificial intelligence (AI) market also includes sales of network optimization software, automated fault management tools, performance monitoring solutions, radio access network (RAN) analytics platforms, and AI-driven network planning and orchestration products. Values in this market are ‘factory gate’ values; that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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