DELRAY BEACH, Fla., Sept. 16, 2026 /PRNewswire/ -- According to MarketsandMarkets, the AI in Network Market is projected to grow from USD 8.38 billion in 2026 and to reach USD 21.52 billion by 2032, at a CAGR of 17.0% during 2026-2032.
Browse 300 market data Tables and 55 Figures spread through 350 Pages and in-depth TOC on "AI in Network Market - Global Forecast to 2032"
AI in Network Market Size & Forecast:
- Market Size Available for Years: 2020-2032
- 2025 Market Size: USD 7.06 billion
- 2026 Market Size: USD 8.38 billion
- 2032 Projected Market Size: USD 21.52 billion
- CAGR (2026-2032): 17.0%
AI in Network Market Trends & Insights:
- The AI in Network Market is growing rapidly, driven by the increasing complexity of hybrid and multicloud networks, rising AI workloads, and demand for real-time network visibility and automation.
- By offering, the services segment is expected to register the highest CAGR of 20.4% during the forecast period.
- By end user, the enterprises segment is projected to grow at the fastest rate from 2026 to 2032.
- By region, Asia Pacific is expected to be the fastest-growing region in the global AI in Network Market.
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Growing network complexity, rising data volumes, and increasing demand for automation and real-time visibility are accelerating AI adoption in network solutions. Enterprises and service providers are increasingly investing in AI-powered network management, predictive analytics, intelligent monitoring, and automation to improve network performance, reduce downtime, and optimize resource utilization. The expansion of cloud, 5G, and edge computing further increases the need for intelligent network infrastructure that can analyze large volumes of data and enable faster decision-making.
The growing deployment of AI-enabled networking technologies enables continuous analysis of network traffic, performance, anomalies, and infrastructure health. These solutions support predictive maintenance, automated fault detection, traffic optimization, capacity planning, and faster incident resolution. AI-driven decision-making helps reduce operational costs and downtime while improving network security and user experience. As organizations prioritize network automation, resilience, and intelligent infrastructure management, AI adoption in networks is expected to expand across developed and emerging economies.
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By solution, the AI-driven network management segment is expected to hold the largest market share during the forecast period.
The AI-driven network management segment is expected to account for the largest share of the global AI in Network Market, driven by growing network complexity, rising data volumes, and the need for automated, real-time network operations. Enterprises and communication service providers are increasingly adopting AI-driven solutions for network monitoring, configuration, optimization, fault detection, and performance management. The growing adoption of 5G, edge computing, cloud, hybrid-cloud, IoT, and software-defined networking is further driving demand for intelligent network management. These solutions leverage machine learning, predictive analytics, generative AI, and automation to detect anomalies, predict failures, optimize resources, and accelerate incident resolution, helping organizations reduce operational costs, minimize downtime, and improve network performance. Integrating AI with network observability, digital twins, intent-based networking, and closed-loop automation is further enabling self-optimizing networks. As digital transformation accelerates, AI-driven network management is becoming critical for scalable, resilient, and autonomous network operations.
By service, the managed services segment is expected to witness a higher growth rate during the forecast period.
The managed services segment is expected to grow rapidly as enterprises increasingly outsource AI-driven network monitoring, optimization, and maintenance to improve performance and reduce operational complexity. The growing complexity of hybrid, multi-cloud, and 5G networks, along with rising network traffic, is driving demand for real-time monitoring, predictive maintenance, automated troubleshooting, and performance optimization.
Managed services also support AI adoption by providing AI-powered analytics and automation without significant in-house investments. The increasing deployment of AIOps, edge computing, software-defined networking, and cloud-based network environments is enabling proactive fault detection, enhanced security, and improved network resilience, further driving segment growth.
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Asia Pacific is expected to register the highest growth rate during the forecast period.
Asia Pacific is projected to register the highest growth rate in the AI in Network Market during the forecast period, driven by rapid 5G deployment, increasing network complexity, rising investments in cloud and edge infrastructure, and strong adoption of AI-driven network optimization across countries such as China, Japan, South Korea, India, and Singapore. Telecom operators and enterprises are increasingly deploying AI for network optimization, predictive maintenance, traffic management, network security, and autonomous operations to improve network performance, reduce operational costs, and enhance user experience. The region's transition toward AI-native and 6G networks is further accelerating market growth, with operators focusing on software-defined architectures, distributed AI, and AI-enabled RAN capabilities. A notable recent development is the March 2026 collaboration between NTT DOCOMO and SK Telecom, which produced a joint white paper outlining the technical requirements and enabling technologies for evolving vRAN into AI-RAN, reinforcing Asia Pacific's leadership in AI-native network development.
Top Companies in AI in Network Market:
The Top Companies in AI in Network Market are Cisco (US), HPE (US), Nokia (Finland), Ericsson (Sweden), Huawei (China), Arista Networks (US), NVIDIA (US), Extreme Networks (US), Broadcom (US), IBM (US), Fujitsu (Japan), Dell Technologies (US), Ciena (US), NEC (Japan), Forward Networks (US), BlueCat Networks (Canada), A10 Networks (US), ScienceLogic (US), Versa Networks (US), Dynatrace (US), Infoblox (US), EfficientIP (France), Riverbed (US), NETSCOUT (US), SolarWinds (US), NetBrain (US), ManageEngine (US), VIAVI Solutions (US), Selector (US), Aviz Networks (US), Nile (US), NetBox Labs (US), Celona (US), Shabodi (Canada), and A5G Networks (US).
These players have adopted various growth strategies, such as partnerships, agreements, collaborations, new product launches, enhancements, and acquisitions, to expand their footprint in the AI in Network Market.
AI in Network Market - Investment & funding +Merger & Acquisition
Investment Funding Context
The AI in Network Market is witnessing increasing investment from telecom operators, network equipment providers, hyperscalers, and technology companies as AI-native infrastructure becomes a strategic priority for 5G-Advanced and 6G networks. Investment is increasingly directed toward AI-RAN, GPU-accelerated network infrastructure, edge AI, intelligent network automation, and programmable RAN platforms. For instance, NVIDIA announced a USD 1 billion investment in Nokia to accelerate AI-RAN innovation and the transition toward AI-native 6G networks, while SoftBank is developing its AITRAS platform with NVIDIA to combine AI computing and RAN infrastructure. In parallel, national initiatives such as South Korea's government-backed Hyper-AI Network project are supporting the development and commercialization of AI-RAN technologies, indicating that public and private funding is increasingly converging around AI-native network infrastructure.
Revenue Shift Context
The market is shifting from conventional connectivity and rule-based network management to AI-enabled, autonomous, and programmable networks that can dynamically optimize performance, energy consumption, and customer experience. AI is increasingly embedded directly into the RAN and network operations layer, creating opportunities for operators to improve network efficiency while developing new services around AI workloads, edge intelligence, network slicing, and differentiated connectivity. Ericsson's AI in RAN software, for example, is designed to enable real-time optimization of radio networks, while commercial trials with T-Mobile have shown up to 15% higher downlink throughput and nearly 10% higher spectral efficiency. At the same time, network monetization is emerging as a key revenue priority, with operators increasingly exploring premium, low-latency, secure, and AI-optimized connectivity services rather than relying solely on traditional bandwidth-based models.
Mergers & Acquisitions
Mergers & acquisitions in the AI in Network Market are increasingly focused on combining networking infrastructure, optical connectivity, network observability, and AI-driven operations. Recent transactions have strengthened vendor capabilities in AI-native networking, high-speed optical connectivity, and network intelligence, driven by demand for AI workloads, data-center connectivity, and autonomous network operations.
AI IN NETWORK MARKET: MERGERS & ACQUISITIONS, JANUARY 2025-JUNE 2026
Month & Year | Deal Type | Company 1 | Company 2 | Description |
July 2025 | Acquisition | HPE (US) | Juniper | HPE completed its acquisition of Juniper |
February 2025 | Acquisition | Nokia (Finland) | Infinera | Nokia completed its billion-dollar acquisition of |
Company Revenue Share Details
The AI in Network Market remains highly competitive and fragmented, with no single vendor controlling the overall market because AI networking spans switching, interconnects, optical infrastructure, network management, and observability. NVIDIA has strengthened its position through its AI networking portfolio, including Ethernet, InfiniBand, and high-speed interconnect technologies, while Cisco continues to expand its AI networking capabilities across data-center networking, security, and observability. Arista Networks has strengthened its position through high-performance Ethernet switching and AI-focused data-center networking solutions. HPE has further expanded its networking presence by acquiring Juniper Networks, adding AI-native networking, routing, switching, and network management capabilities. Other major participants, including Broadcom, Nokia, and Huawei, contribute through Ethernet switching, networking silicon, optical infrastructure, and telecom networks. Overall, competition is increasingly shaped by demand for AI-optimized Ethernet, high-speed interconnects, intelligent network operations, and autonomous networking, while the absence of standardized standalone AI-networking revenue reporting makes precise company revenue-share comparisons difficult.
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