AI in Networks Market by Offering (Router & Switches, AI Networking Platform, Management Software, Software Defined Networking), Function (Optimization, Cybersecurity, Predictive Maintenance), Technology (Gen AI, ML, NLP) - Global Forecast to 2029
AI in Networks Market Size Share Industry Growth and Trends
[235 Pages Report] The global AI in Networks market is expected to be valued at USD 10.9 billion in 2024 and is projected to reach USD 46.8 billion by 2029 and grow at a CAGR of 33.8% from 2024 to 2029. The AI in networks market is experiencing high growth driven by increasing adoption of 5G technology, edge computing, IoT and connected devices, and expansion of smart cities. Increasing deployment of 5G networks has led to the vast amount of network data, generated by high bandwidth application such as video streaming and online gaming, driving network operators to integrate AI driven solutions to manage network data and allocate resources to reduce network congestion. Network operators are also integrating AI driven solutions to automate network operations and predictive maintenance, to reduce human dependency and errors, leading to efficient network management. Additionally, as the demand for cloud services is on the rise, there is demand for AI driven network solutions in data centers to optimize network operations. Data center providers are investing heavily towards AI networking solutions to automate and manage network operations, to monitor performance and reduce latency.
AI Impact on the Networks Market
Integrating AI algorithms such as machine learning, Gen AI, and deep learning in networks is becoming increasingly evident as networks become more complex. With the increasing deployment of 5G networks and IoT devices, the demand for advanced networking solutions to manage and automate network operations has grown significantly. AI algorithms help network operators in automation and optimization, network security, predictive maintenance, troubleshooting, performance monitoring, and operational cost reduction.
For instance, machine learning algorithms help identify patterns of cyber threats by analyzing a vast amount of network traffic data to mitigate threats in real-time. These AI-driven security solutions are used for network security, fraud detection, and prevention in areas like billing and subscriber management, helping telecom service providers (TSPs) reduce losses and improve their financial performance.
Attractive opportunities in the AI in networks market
AI in Networks Market Forecast to 2029
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AI in Networks Market Trends & Dynamics
Driver: Rising adoption of 5G technology
The rise in internet and mobile penetration around the globe is driving the demand for 5G technology for high bandwidth applications such as streaming and online gaming services to rise significantly. Network operators invest heavily in developing AI-driven solutions to manage and optimize network traffic. AI in networks allows operators to efficiently perform network management tasks such as traffic routing, resource allocation, and network security. As the 5G technology advances, the demand for cybersecurity solutions will also rise, driving the AI in networks market.
Restraint: Data privacy and security concerns in AI in networks
Integration of artificial intelligence technology in the networking leads to various risks affiliated with collecting, storing, and transmitting network traffic data. AI driven network collect users and network operations data information, creating a high risk environment of privacy breaches, due to the rising cyberthreats. These cyberattacks may lead to unauthorized access to network and user data, disrupting network operations. Additionally, data generated by connected and Iot devices such as smartphones, smart home systems, surveillance system is collected by network, leads to concerns regarding unauthorized surveillance and cyberattacks.
Opportunity: Increasing prevalence of smart city initiatives
Rapid urbanization has led to the exapsnion of smart cities globally. Countries around the world are investing heavily towards smart infrastructure by integrating advanced technologies such as artificial intelligence (AI). For instance, smart city ecosystem consist of various sensors and connected and IoT devices, and to ensure efficient transmission and processing of data generated by these sensor and devices. AI driven network solutions play a vital role in collecting and processing of data, identifying anomalies and equipment failure based on present and historical data, helping network operator to schedule maintenance in advance and reduce downtime.
Challenge: Rapid change in the technology landscape
As the technology landscape evolves rapidly, AI presents a major challenge in the network market. As new technologies appear and current technology evolves, companies in the ecosystem must continuously invest in the research and development of changing market demand and advancements. Additionally, intense competition in the market and pressure to offer innovative solutions further restrict companies from maintaining market leadership. Companies' negligence in identifying the technological shift can result in a decline in market share and revenue.
AI in Networks Market Ecosystem
The AI in networks market is dominated by established and financially sound manufacturers with extensive experience in the industry. These companies have diversified product portfolios, cutting-edge technologies, and strong global sales and marketing networks. Leading players in the market include NVIDIA Corporation, Cisco Systems, Inc. (US), Telefonaktiebolaget LM Ericsson (Sweden), Hewlett Packard Enterprise Development LP (US), and Arista Networks, Inc. (US).
Based on the offering, the software in the AI in networks market holds the highest market share during the forecast period.
Software offering is expected to hold the highest share in the AI in networks market during the forecast period. Software solutions in the AI in networks market are highly customized, catering to different requirements of organizations. This includes tailored features, functionalities, and enhanced user interface. Additionally, there is a rise in the demand for cloud-based AI solutions enabling network operators to perform network management tasks from virtually anywhere. This shift towards cloud-based software solutions, reducing physical infrastructure requirements, drives the growth of software offerings.
Based on the technology, AI in the network market for machine learning holds the highest market share during the forecast period.
Machine learning technology in AI in networks market is expected to hold the highest market share during the forecast period. This growth is attributed to its ability in identifying network performance issues and anomalies. Machine learning algorithms help network operators in system monitoring, and identify network anomalies during predictive maintenance, leading to reduced network congestion and efficient resource allocation for high bandwidth networks. The ability to reallocate resources based on network congestion also helps network operators in reducing energy consumption.
Based on the end-use industry, AI in networks for telecom service providers holds the highest market share during the forecast period.
The AI in Networks market for telecom service providers (TSPs) is projected to hold the highest market share during the forecast period. This growth is attributed to the increasing integration of advanced technologies such as AI/ML by network operator to automate and optimize network operations. Telecom operators are integrating AI powered virtual network assistants which helps in quickly identifying network performance issues and suggest actions for network improvement. These virtual assistants reduce the operationsal costs by reducing the human intervention for the network maintenance.
AI in networks market in North America will hold the highest market share during the forecast period.
The AI in networks market for North Ameirca is expected to hold the highest market share during the forecast period. This growth is attributed to the presence of leading AI and network technology companies in the region. These companies are investing heavily towards the advancement of technologies such as AI, 5G, edge computing, due to the high internet penetration rate in the region. The demand for high bandwidth network application such as video streaming and online gaming also on the rise, driving the investments and innovations towards AI driven solutions in network management.
AI in Networks Market by Region
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Top Companies AI in Networks Market - Key Market Players
The AI in networks companies is dominated by players such as
- NVIDIA Corporation (US),
- Cisco Systems, Inc. (US),
- Telefonaktiebolaget LM Ericsson (Sweden),
- Hewlett Packard Enterprise Development LP (US),
- Arista Networks, Inc. (US) and others.
Scope of the AI in Networks Market Report
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Report Metric |
Details |
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Estimated Market Size
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USD 10.9 billion in 2024 |
| Projected Market Size | USD 46.8 billion by 2029 |
| Market Growth Rate | grow at a CAGR of 33.8% |
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Market size available for years |
2020-2029 |
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Base year considered |
2023 |
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Forecast period |
2024-2029 |
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Forecast units |
Value (USD Million) |
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Segments Covered |
By Offering, By Deployment mode, By Technology, By Network Function, and By End-Use Industry |
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Geographies covered |
North America, Europe, Asia Pacific, and RoW |
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Companies covered |
The major market players include NVIDIA Corporation, Cisco Systems, Inc. (US), Telefonaktiebolaget LM Ericsson (Sweden), Hewlett Packard Enterprise Development LP (US), Arista Networks, Inc. (US), Juniper Networks, Inc. (US), Ciena Corporation (US), Extreme Networks (US), Fujitsu (Japan), Huawei Technologies Co., Ltd. (China), Nokia (Finland). (Total 25 players are profiled) |
AI in Networks Market Highlights
The study categorizes the AI in networks market based on the following segments:
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Segment |
Subsegment |
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By Offering |
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By Technology |
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By Deployment Mode |
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By Network Function |
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By End-Use Industry |
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By Region |
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Recent Developments
- In June 2024, Arista Networks announced the Arista Etherlink AI platforms, designed to deliver optimal network performance for the most demanding AI workloads, including training and inferencing.
- In April 2024, Cisco launched Catalyst 9300 Series Switches. These are Cisco’s lead stackable access platforms for the next-generation enterprise and have been purpose-built to address emerging trends in Security, IoT, Mobility, and Cloud.
- In March 2024, NVIDIA announced a new wave of networking switches, the X800 series, designed for massive-scale AI. NVIDIA X800 switches are end-to-end networking platforms that enable trillion-parameter-scale generative AI essential for new AI infrastructures.
- In January 2024, Juniper Networks announced the industry's first AI-Native Networking Platform, purpose-built to leverage AI to assure the best end-to-end operator and end-user experiences. Juniper’s AI-Native Networking Platform unifies all campus, branch, and data center networking solutions with a common AI engine and Marvis Virtual Network Assistant (VNA).
- In January 2024, Extreme Networks, Inc. announced 4000 Series cloud-managed switches. The new 4000 Series includes the 4120 and 4220 families and extends Extreme’s innovative Universal Switching portfolio. By leveraging ExtremeCloud solutions, the 4000 Series dramatically reduces the time it takes to deploy and manage new switches.
- In June 2023, Cisco launched the Networking Cloud Platform to simplify the management of networking gear through a single, common interface.
- In May 2023, Juniper Networks announced the latest innovation to its award-winning AI-driven enterprise portfolio, the Juniper Mist Access Assurance service. This new service leverages Mist AI and a modern microservices cloud to provide a full suite of network access control (NAC) and policy management functions via the same flexible and simple framework already included in Juniper’s wired access, wireless access, indoor location, SD-WAN, and secure client-to-cloud portfolio.
Frequently Asked Questions (FAQs):
What are the major driving factors and opportunities for the AI in networks market?
Some of the major driving factors for the growth of this market include the Rising adoption of 5G technology, Increased demand for network efficiency, Proliferation of IoT devices, and Increase in data traffic. Moreover, the Rising demand for enhanced analytics, the Increasing prevalence of smart city initiatives, and the rising demand for network automation are critical opportunities for the AI in networks market.
Which region is expected to hold the highest market share?
North America is projected to capture the highest market size in AI networks due to the presence of leading technology companies, advanced technological infrastructure, and significant investments in research and development. Additionally, the early adoption of emerging technologies like AI, Gen AI, and Machine Learning contributes to the robust growth of the AI in networks market in North America, making its position as a dominant player in the global market landscape.
Who are the leading players in the global AI in networks market?
Companies such as NVIDIA Corporation, Cisco Systems, Inc. (US), Telefonaktiebolaget LM Ericsson (Sweden), Hewlett Packard Enterprise Development LP (US), and Arista Networks, Inc. (US) are the leading players in the market.
What are some of the technological advancements in the market?
Network automation and optimization are undergoing a technological revolution due to the increasing adoption of advanced technologies in networks involving AI. The integration of cutting-edge analytics and machine learning algorithms, which provides real-time network insights is on the rise. Companies are increasingly investing in these technologies to automate network management tasks and securing the networks from cyberattacks, reducing the human dependency.
What are some of the macroeconomic facors impacting the AI in networks market?
Macroeconomic factors such as interest rates, inflation, GDP growth, unemployment, and debt will significantly impact the AI in networks market . Government initiatives, enterprise investments, borrowing costs, research and development highly depends on these factors. High inflation leads to increase in interest rates, restricting businesses to minimize spending on AI technology research and development. Reduce in AI investments may lead to delay in the development of AI driven solution affecting the AI in networks market.
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The study involved four major activities in estimating the current size of the AI in networks market. Exhaustive secondary research collected information on the market, peer, and parent markets. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. Both top-down and bottom-up approaches were employed to estimate the complete market size. After that, market breakdown and data triangulation were used to estimate the market size of segments and subsegments.
Secondary Research
Secondary sources for this research study included corporate filings (such as annual reports, investor presentations, and financial statements), trade, business, professional associations, white papers, certified publications, articles by recognized authors, directories, and databases. The secondary data was collected and analyzed to determine the overall market size, further validated through primary research.
List of major secondary sources
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SOURCE |
Web Link |
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Federal Communications Commission (FCC) |
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National Institute of Standards and Technology (NIST) |
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Ministry of Electronics and Information Technology (MeitY) |
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Ministry of Industry and Information Technology (MIIT) |
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Ministry of Internal Affairs and Communications (MIC) |
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The AI Association |
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National Security Commission on Artificial Intelligence - NSCAI |
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
Both top-down and bottom-up approaches were used to estimate and validate the AI in networks market size and its various dependent submarkets. The key players in the market were identified through secondary research, and their market share in the respective regions was determined through primary and secondary research. This entire procedure involved the study of annual and financial reports of top players and extensive interviews with industry leaders such as chief executive officers (CEOs), vice presidents (VPs), directors, and marketing executives. All percentage shares and breakdowns were determined using secondary sources and verified through primary sources. All the possible parameters that affect the markets covered in this research study were accounted for, viewed in extensive detail, verified through primary research, and analyzed to obtain the final quantitative and qualitative data. This data was consolidated and supplemented with detailed inputs and analysis from MarketsandMarkets and presented in this report.
Bottom-Up Approach
The bottom-up approach was used to determine the overall size of the AI in networks market from the revenues of the key players and their shares in the market. The overall market size was calculated based on the revenues of the key players identified in the market.
- Identifying various end-use industries using or expected to implement AI in networks
- Analyzing each end-use sector, along with the significant related companies and AI in networks providers
- Estimating the AI in networks market for end-use industries
- Understanding the demand generated by companies operating across different end-use industries
- Tracking the ongoing and upcoming implementation of projects based on AI in networks technology by end-use industries and forecasting the market based on these developments and other critical parameters
- Carrying out multiple discussions with key opinion leaders to understand the type of AI in networks products designed and developed vertically, helping analyze the breakdown of the scope of work carried out by each significant company in the AI in networks market
- Arriving at the market estimates by analyzing AI in networks companies as per their countries and subsequently combining this information to arrive at the market estimates by region
- Verifying and cross-checking the forecasts at every level through discussions with the key opinion leaders, including CXOs, directors, and operations managers, and finally with domain experts at MarketsandMarkets
- Studying various paid and unpaid sources of information, such as annual reports, press releases, white papers, and databases
Top-Down Approach
In the top-down approach, the overall market size has been used to estimate the size of the individual markets (mentioned in the market segmentation) through percentage splits from secondary and primary research.
The most appropriate immediate parent market size has been used to implement the top-down approach to calculate the market size of specific segments. The top-down approach was implemented for the data extracted from the secondary research to validate the market size obtained.
Each company's market share was estimated to verify the revenue shares used earlier in the top-down approach. This study determined and confirmed the overall parent market size and individual market sizes by using the data triangulation method and validating data through primaries. The data triangulation method is explained in the next section.
- Focusing on top-line investments and expenditures being made in the ecosystems of various end-user industries
- Building and developing the information related to the market revenue generated by key AI in network manufacturers
- Conducting multiple on-field discussions with the key opinion leaders involved in the development of AI in network products in various end-use industries
- Estimating geographic splits using secondary sources based on multiple factors, such as the number of players in a specific country and region, the offering of AI in networks, and the level of solutions offered in end-use industries
Data Triangulation
After arriving at the overall market size from the above estimation process, the market has been split into several segments and subsegments. The data triangulation procedure has been employed wherever applicable to complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments. The data has been triangulated by studying various factors and trends from both the demand and supply sides. Additionally, the market size has been validated using top-down and bottom-up approaches.
Market Definition
AI in Networks integrates artificial intelligence technologies within network infrastructure to enhance efficiency, security, and overall performance. By leveraging machine learning, deep learning, and advanced analytics, AI-driven solutions can dynamically manage network traffic, detect and mitigate anomalies, enhance cybersecurity measures, and optimize resource allocation. These capabilities enable real-time responses to network demands, proactive maintenance, and robust protection against cyber threats. AI in Networks is crucial for supporting the increasing complexity and scale of modern network environments, driven by the proliferation of connected devices and the growing demand for high-speed, reliable internet services across various industries.
Key Stakeholders
- Telecommunications Companies
- Network Equipment Manufacturers
- Software Providers
- Cloud Service Providers
- Enterprises and Businesses
- Internet Service Providers (ISPs)
- Data Centers
- Cybersecurity Firms
- Regulatory Bodies and Government Agencies
- Research Institutions and Universities
- Investors and Venture Capitalists
- End-Use Industries
Report Objectives
- To define, describe, and forecast the AI in Networks market by offering, deployment mode, technology, network function, end-use industry, use-case, and region
- To forecast the size of the market segments for four major regions—North America, Europe, Asia Pacific, and the Rest of the World (RoW)
- To provide detailed information regarding the major factors influencing the growth of the market (drivers, restraints, opportunities, and challenges)
- To strategically analyze micro markets concerning individual growth trends, prospects, and contributions to the total market
- To provide a detailed overview of the AI in Networks market’s value chain, the ecosystem, technology trends, use cases, regulatory environment, and Porter’s five forces analysis.
- To strategically profile the key players and comprehensively analyze their market shares and core competencies
- To analyze the opportunities in the market for stakeholders and describe the competitive landscape of the market
- To analyze competitive developments, such as collaborations, partnerships, product developments, and research & development (R&D), in the market
Available Customizations
With the given market data, MarketsandMarkets offers customizations according to the specific requirements of companies. The following customization options are available for the report:
- Detailed analysis and profiling of additional market players (up to 5)
- Additional country-level analysis of the AI in networks market
Product Analysis
- Product matrix, which provides a detailed comparison of the product portfolio of each company in the AI in networks market.
Growth opportunities and latent adjacency in AI in Networks Market