AI in Networks Market Size Share & Trends
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
OVERVIEW
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
The AI in networks market is projected to reach USD 46.87 billion by 2029 from USD 10.93 billion in 2024, 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 networ operators to integrate AI driven solutions to manage network data and allocate resources to reduce network congestion.
KEY TAKEAWAYS
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BY REGIONBy region, Asia Pacific is expected to grow at the fastest CAGR of 37.1% during the forecast period.
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BY OFFERINGBy offering, the software segment is expected to hold the highest market share of 38.9% in 2029.
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BY TECHNOLOGYBy technology, Generative AI is expected to grow at the fastest CAGR during the forecast period.
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BY END USERBy end user, the telecom service providers segment is expected to register the highest share of 47.7% in 2025.
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COMPETITIVE LANDSCAPE - KEY PLAYERSNVIDIA Corporation, Cisco Systems, Inc., Telefonaktiebolaget LM Ericsson, and Hewlett Packard Enterprise Development LP, were identified as some of the star players in the AI in networks market, given their strong market share and product footprint.
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COMPETITIVE LANDSCAPE - STARTUPS/SMESDriveNets and Ordr, among others, have distinguished themselves among SMEs by securing strong footholds in specialized niche areas, underscoring their potential as emerging market leaders.
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.
TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS
The AI in networks market is witnessing several transformative trends and disruptions impacting customer businesses; for instance, the shift toward autonomous network management, where AI-driven automation is streamlining operations and improving efficiency. This trend is driven by the increasing complexity of networks, the demand for real-time decision-making capabilities, and the need to optimize resource utilization.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
MARKET DYNAMICS
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Rising adoption of 5G technology
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Increased demand for network efficiency
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High implementation costs
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Data privacy and security concerns
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Rising demand for enhanced analytics
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Increasing prevalence of smart city initiatives
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Rapid changes in technology landscape
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Compatibility and interoperability issues
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
Driver: Rising adoption of 5G technology
The rising adoption of 5G technology is a significant driver for the AI in networks market due to the complexities and performance requirements associated with 5G networks. Unlike previous generations, 5G offers ultra-low latency, high-speed data transfer, and the ability to connect a massive number of devices simultaneously. These capabilities demand sophisticated network management and optimization techniques, which AI is uniquely equipped to provide.
Restraint: High implementation costs
High implementation costs represent a significant restraint for AI in the network market due to several reasons. For instance, the integration of AI technologies into existing network infrastructure requires substantial upfront investments. This includes not only the cost of acquiring AI software and hardware but also the expenses associated with redesigning network architectures, training staff, and potentially hiring specialized AI professionals.
Opportunity: Rising demand for enhanced analytics
The rising demand for enhanced analytics presents a significant opportunity for the AI in networks market by revolutionizing how network data is leveraged for strategic insights and operational improvements. AI-driven analytics empower network operators to extract deeper, more meaningful insights from vast amounts of data generated by network activities, user interactions, and device behaviors.
Challenge: Rapid changes in technology landscape
Rapid changes in the technology landscape pose a significant challenge for the AI in networks market due to several intertwined factors. Firstly, technological advancements occur at a breakneck pace, introducing new hardware, software, and methodologies that AI systems must adapt to and integrate with. For instance, the evolution from 4G to 5G networks requires AI algorithms to swiftly adjust to new data transmission speeds, network architectures, and latency requirements. This necessitates constant updates and upgrades to AI models and infrastructure, which can strain resources and disrupt operational continuity.
AI IN NETWORKS MARKET SIZE SHARE & TRENDS: COMMERCIAL USE CASES ACROSS INDUSTRIES
| COMPANY | USE CASE DESCRIPTION | BENEFITS |
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Vodafone integrated Nokia’s cutting-edge AI platform into its network infrastructure. This platform is designed to analyze vast amounts of network data in real time and automate various network management tasks. | With optimized resource allocation and real-time performance monitoring, Vodafone was able to maintain high-quality network services, even under varying demand conditions. The improvements in network reliability and service quality translated to higher customer satisfaction. |
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IBM provided its Watson AI platform, a powerful suite of AI tools and services, to assist AT&T in optimizing its network operations. Using AI algorithms, Watson analyzed vast amounts of data generated by AT&T’s network devices and systems. | By predicting and preventing potential failures, AT&T experienced fewer network outages and service interruptions. The predictive maintenance capabilities allowed AT&T to address issues before they escalated into significant problems, enhancing overall network stability. |
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Huawei provided its comprehensive AI-driven platform to Deutsche Telekom, focusing on intelligent operation & maintenance (O&M) solutions. The AI platform automated the configuration and management of network devices, reducing manual intervention and the likelihood of human error. | The automation of routine maintenance tasks and the reduction in manual labor led to significant cost savings. Predictive maintenance also minimized the need for emergency repairs, further lowering expenses. The AI-driven dynamic allocation of network resources ensured optimal utilization of available bandwidth and infrastructure. |
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Ericsson’s Network Intelligence platform utilizes machine learning algorithms to analyze vast amounts of network data in real time. This enables the identification of patterns, trends, and anomalies that might not be visible through traditional monitoring methods. | The AI-driven optimization led to more efficient use of network resources, enhancing overall network performance. Enhanced routing and load balancing resulted in faster data transmission and reduced bottlenecks. With accurate traffic forecasting, Telefónica could dynamically adjust network parameters to handle peak loads more effectively. |
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MARKET ECOSYSTEM
The AI in networks market ecosystem spans the entire value chain, from semiconductor and material suppliers enabling high-performance hardware to AI network providers delivering intelligent networking platforms and solutions. System integrators and telecom operators play a critical role in deploying and optimizing these AI-driven networks, while hyperscalers, cloud providers, and enterprises act as key end users driving adoption across data centers and telecom networks.
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET SEGMENTS
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
Wireless Gigabit Market, By Offering
AI-Networking Platform segment is expected to dominate the AI in networks market during the forecast period. The integration of AI in networking platforms is driving the AI in networks market by transforming how networks are managed and optimized. AI networking platforms reduce the need for manual intervention, automate complex processes, and provide actionable insights that improve decision-making. This results in cost savings, enhanced network efficiency, and greater agility in responding to changing network demands.
Wireless Gigabit Market, By Technology
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.
Wireless Gigabit Market, By End User
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.
REGION
Asia Pacific is expected to be the fastest-growing region across the AI in networks market during the forecast period
The AI in networks market in the Asia Pacific region is experiencing significant growth, driven by rapid digital transformation, increased adoption of smart technologies, and the expansion of 5G infrastructure. Countries like China, Japan, South Korea, and India are at the forefront of integrating AI into network management to enhance operational efficiency, reduce costs, and improve user experiences.
AI IN NETWORKS MARKET SIZE SHARE & TRENDS: COMPANY EVALUATION MATRIX
In the AI in networks market matrix, NVIDIA Corporation (Star) and Juniper Networks, Inc. (Emerging Leader) lead with a strong market presence and a wide product portfolio, driving large-scale adoption across various end users, including telecom service providers and data centers.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
KEY MARKET PLAYERS
MARKET SCOPE
| REPORT METRIC | DETAILS |
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| Market Size in 2024 (Value) | USD 10.93 Billion |
| Market Forecast in 2029 (Value) | USD 46.87 Billion |
| Growth Rate | CAGR of 33.8% from 2024–2029 |
| Years Considered | 2020–2029 |
| Base Year | 2023 |
| Forecast Period | 2024–2029 |
| Units Considered | Value (USD Billion) and Volume (Million Units) |
| Report Coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
| Segments Covered |
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| Regional Scope | North America, Europe, Asia Pacific, RoW |
WHAT IS IN IT FOR YOU: AI IN NETWORKS MARKET SIZE SHARE & TRENDS REPORT CONTENT GUIDE
DELIVERED CUSTOMIZATIONS
We have successfully delivered the following deep-dive customizations:
| CLIENT REQUEST | CUSTOMIZATION DELIVERED | VALUE ADDS |
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| Telecom Equipment Manufacturer (Global) |
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| Network Software & AI Platform Provider |
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| Hyperscaler / Cloud Service Provider |
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| System Integrator / Managed Service Provider |
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RECENT DEVELOPMENTS
- 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.
- 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.
- 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.
- 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).
Table of Contents
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Methodology
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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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 and Top-Down 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
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.
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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