세계의 AI 기반 통신망 시장 예측(-2032년) - 제공 내용별, 전개 방식별, 기술별, 용도별, 최종 사용자별, 지역별 분석

According to Stratistics MRC, the Global AI-Powered Telecom Networks Market is accounted for $1.12 billion in 2025 and is expected to reach $8.15 billion by 2032 growing at a CAGR of 32.8% during the forecast period. Telecom systems enhanced by artificial intelligence transform conventional network management into smarter, automated, and adaptive environments. Using machine learning algorithms and real-time analytics, operators can foresee performance bottlenecks, identify irregularities, optimize bandwidth allocation, and minimize outages. AI improves customer satisfaction through faster response, intelligent troubleshooting, and tailored service options. As 5G, IoT devices, and rising data traffic strain traditional systems, automation ensures consistent speed, lower latency, and strong security. These intelligent networks reduce operational expenses, boost energy efficiency, and enable advanced capabilities such as network slicing and autonomous service monitoring.

According to NVIDIA's 2024 State of AI in Telecommunications Report, based on a global survey of over 400 telecom professionals 95% of telecom companies are either using or planning to use AI in their operations.

Market Dynamics:

Driver:

Rising data traffic & 5G expansion

Increasing internet usage and 5G rollout are key reasons behind the growth of AI-driven telecom networks. As smartphones, cloud applications, and IoT devices generate heavy data loads, conventional network management becomes inefficient. AI tools automatically manage bandwidth, forecast congestion, and optimize network paths to maintain low latency and steady performance. 5G-supported innovations like industry automation, connected mobility, and smart infrastructure need highly responsive and intelligent networks. By using AI, telecom operators minimize disruptions, ensure seamless performance, and improve customer service. With data consumption expanding every year, AI-based automation is becoming critical to handle traffic efficiently and support next-generation digital services.

Restraint:

High implementation & integration costs

Setting up AI-enabled telecom networks involves heavy financial commitments for software licenses, intelligent hardware, cloud servers, and expert personnel. Older network systems must be modified or replaced, which raises the cost further. Smaller telecom companies find it difficult to invest in large-scale AI rollouts due to budget limitations. Employees also need training to operate automation tools and analytics platforms, adding additional expenses. Migrating to AI-driven environments requires advanced IT infrastructure, data security systems, and continuous system maintenance. These high upfront and operational costs discourage many operators from adopting AI solutions quickly, particularly in developing markets, slowing overall industry growth.

Opportunity:

Rising demand for autonomous network operations

Telecom companies are moving toward smart networks that manage themselves with minimal human intervention. AI enables automatic troubleshooting, bandwidth adjustment, predictive maintenance, and real-time system monitoring. This lowers operational risks and speeds up problem resolution. Autonomous networks also improve security, reduce outages, and deliver consistent service quality. As digital traffic grows, telecom firms look for solutions that reduce manual effort and control expenses. Cloud-based platforms, virtualized cores, and edge infrastructure strengthen the need for intelligent automation. Because of these advantages, adoption of AI-driven autonomous network operations presents a major market opportunity for technology developers and telecom service providers worldwide.

Threat:

Vendor dependency and proprietary technologies

Many AI telecom platforms rely on exclusive tools, patented software, and custom-built hardware. Operators may become locked into one vendor's ecosystem for upgrades, support, and security patches. This reduces flexibility in choosing technology partners and increases long-term costs. Migrating to new vendors becomes complicated because of data compatibility and integration problems. Proprietary systems also create interoperability gaps when networks combine solutions from multiple providers. If a vendor changes policies, raises prices, or ends product support, telecom operators face service risks and financial pressure. Therefore, heavy reliance on a limited number of technology suppliers represents a serious threat to market stability.

Covid-19 Impact:

COVID-19 created strong momentum for AI-enabled telecom networks as digital dependence expanded worldwide. Remote working, video conferencing, telemedicine, and streaming services generated heavy network loads, requiring smarter automation. AI supported operators by optimizing traffic flow, predicting faults, and improving quality of service during peak demand. With restrictions on field workforce, remote diagnostics and intelligent monitoring became essential to run critical infrastructure. The pandemic emphasized the need for autonomous and resilient network systems capable of scaling without manual intervention. Although some projects were postponed due to economic slowdown, the overall outcome was positive, driving long-term investments in AI-based telecom innovation.

The cloud-based segment is expected to be the largest during the forecast period

The cloud-based segment is expected to account for the largest market share during the forecast period because it offers high scalability, flexible integration, and reduced infrastructure burden for operators. Cloud systems allow telecom companies to launch AI features quickly, automate network functions, and analyze live traffic without extensive physical installations. They enable centralized monitoring, remote troubleshooting, and seamless expansion of capacity as demand rises. Cloud environments also support virtualized network functions, edge connectivity, and continuous software updates, improving service efficiency. With increasing reliance on 5G, IoT, and digital services, cloud-driven AI platforms provide cost savings, faster innovation, and stronger performance, making them the most widely adopted deployment approach in the telecom industry.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate because it enables networks to learn from data and make intelligent decisions without manual input. Telecom companies rely on machine learning tools to predict congestion, detect faults, and optimize performance in real time. With rising data volumes from IoT devices, 5G services, and digital applications, machine learning provides accurate insights for traffic management, cyber security, and service customization. It enhances automation across network layers, reducing operational complexity and improving reliability. Its versatility and ability to handle large, dynamic datasets drive strong adoption, making machine learning the segment with the highest growth rate.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to its mature infrastructure, rapid 5G deployment, and early integration of AI technologies by telecom firms. The region's well-established research ecosystem, significant investment in next-gen networks, and favorable policies encourage adoption of AI for operations, service personalization, and network resilience. Leading operators there implement automation, big-data analytics, and machine-learning across their network stacks ahead of global peers. As data volumes escalate and network complexity grows, North America's advanced capabilities and readiness give it a substantial advantage in leveraging AI-based telecom solutions and driving the largest regional market share.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by expanding 5G deployments and massive mobile user populations. Nations such as China, India, Japan, and South Korea are rapidly embedding AI into telecom operations for automation, intelligent traffic handling, and smart customer support. Regional telecom providers are upgrading networks with cloud platforms, analytics, and AI-based orchestration to manage soaring data demand and IoT connectivity. Government initiatives promoting digital transformation, affordable services, and advanced infrastructure further boost progress. With rising competition and heavy investment in next-generation networks, APAC is positioned to achieve the highest growth rate in this sector.

Key players in the market

Some of the key players in AI-Powered Telecom Networks Market include Vodafone, Bharti Airtel, Reliance Jio, Huawei Technologies, IBM, Microsoft, Intel, Cisco Systems, Google Cloud, Nokia, NVIDIA, Ericsson, Juniper Networks, Sand Technologies and XenonStack.

Key Developments:

In November 2025, Microsoft Corp. has signed an approximately $9.7 billion deal to purchase AI cloud capacity from IREN Ltd., becoming the Australian company's largest customer. The five-year agreement will provide Microsoft access to Nvidia Corp. accelerator systems in Texas built using the GB300 architecture for AI workloads and include a 20% prepayment.

In March 2025, Huawei has announced the signing of a cooperation agreement with Telecom Egypt - WE. The agreement aims to equip Telecom Egypt's network with advanced technological solutions in preparation for the launch of 5G services in Egypt, ensuring high-quality broadband for users.

In March 2025, Bharti Airtel said it has signed an agreement with Elon Musk's SpaceX to bring high-speed satellite internet service Starlink to India. In an exchange filing on the BSE, Bharti Airtel said Starlink would sell its services in India and explore opportunities to collaborate with Airtel's existing telecom infrastructure.

Offerings Covered:

• Solution
• Services

Deployment Modes Covered:

• Cloud-Based
• On-Premises

Technologies Covered:

• Machine Learning
• Natural Language Processing (NLP)
• Deep Learning
• Big Data Analytics

Applications Covered:

• Autonomous Network Optimization
• Predictive Fault Detection & Maintenance
• AI-Powered Customer Experience Platforms
• Telecom Fraud Detection Systems
• Virtual Agent & Chatbot Interfaces
• AI-Driven IT Operations (AIOps)
• Intelligent CRM & Campaign Automation
• AI-Augmented RAN Performance Analytics

End Users Covered:

• Telecom Operators
• Enterprises

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global AI-Powered Telecom Networks Market, By Offering

• 5.1 Introduction
• 5.2 Solution
• 5.3 Services
  • 5.3.1 Managed Services
  • 5.3.2 Professional Services

6 Global AI-Powered Telecom Networks Market, By Deployment Mode

• 6.1 Introduction
• 6.2 Cloud-Based
• 6.3 On-Premises

7 Global AI-Powered Telecom Networks Market, By Technology

• 7.1 Introduction
• 7.2 Machine Learning
• 7.3 Natural Language Processing (NLP)
• 7.4 Deep Learning
• 7.5 Big Data Analytics

8 Global AI-Powered Telecom Networks Market, By Application

• 8.1 Introduction
• 8.2 Autonomous Network Optimization
• 8.3 Predictive Fault Detection & Maintenance
• 8.4 AI-Powered Customer Experience Platforms
• 8.5 Telecom Fraud Detection Systems
• 8.6 Virtual Agent & Chatbot Interfaces
• 8.7 AI-Driven IT Operations (AIOps)
• 8.8 Intelligent CRM & Campaign Automation
• 8.9 AI-Augmented RAN Performance Analytics

9 Global AI-Powered Telecom Networks Market, By End User

• 9.1 Introduction
• 9.2 Telecom Operators
• 9.3 Enterprises

10 Global AI-Powered Telecom Networks Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Vodafone
• 12.2 Bharti Airtel
• 12.3 Reliance Jio
• 12.4 Huawei Technologies
• 12.5 IBM
• 12.6 Microsoft
• 12.7 Intel
• 12.8 Cisco Systems
• 12.9 Google Cloud
• 12.10 Nokia
• 12.11 NVIDIA
• 12.12 Ericsson
• 12.13 Juniper Networks
• 12.14 Sand Technologies
• 12.15 XenonStack