Global AI in Telecommunication Market 2025-2035

Description

List of Tables

AI in Telecommunication Market Size, Share & Trends Analysis Report by Component (Solution, and Service) by Application (Network Security, Network Optimization, Customer Analytics, Virtual Assistance, Self-Diagnostics, and Others) by Vertical (Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), and Others), Forecast Period (2026-2035)

Industry Overview

AI in the telecommunication market was valued at $4.6 billion in 2025 and is projected to reach $28.9 billion by 2035, growing at a CAGR of 20.2% during the forecast period (2026-2035). The increasing number of connected devices, IoT, and streaming services generates significant data volumes, necessitating real-time AI analysis, prediction, and optimization for market growth. The State of AI in Telecommunications: 2024 Trends report by NVIDIA Corp.

AI Adoption in Telecom

>90% of telecom operators are using AI in some form (pilot or full-scale).
56% of telecom leaders see AI as critical to business success (up from 42% in 2022).
53% believe AI provides a competitive advantage, a 14-point increase from last year.

Generative AI Momentum

43% of telcos are actively investing in generative AI.
Key applications include customer support automation, content generation, and predictive network optimization.
AI adoption is moving from experimental pilots to strategic, revenue-driving initiatives.

Top AI Use Cases

Customer Experience Enhancement: Personalized services, faster support, chatbots, and AI-driven insights.
Operational Efficiency: AI-driven network monitoring, predictive maintenance, and anomaly detection.
Revenue & Cost Impact: AI is improving both revenue generation and cost savings across operations.
Network Automation: Optimizing traffic management and reducing downtime with AI.

Market Dynamics

AI-powered network planning and optimization

The market growth is being fueled by improved service quality and reduced operational costs achieved through AI-powered network planning and optimization. According to research by the IBM Institute for Business Value, supported by GSMA Intelligence, Telecommunications and communications services providers who utilize cloud computing and AI in their networks enjoy benefits like better performance, scalability, cost savings, and new monetization potential, as per a study by the IBM Institute for Business Value in association with GSMA Intelligence. The global telco executive survey results

79% Make network performance the second priority after modernization.
62% of technology innovators apply legacy AI to network planning, twice the 40% baseline.
54% apply generative AI in network planning, reflecting cloud readiness's flexibility.
55% suffered a network security incident within the past year, yet only 42% rate it as a top challenge.

Growing Data Traffic and 5G Rollout

The sudden surge in mobile data consumption and the growth of global 5G networks are pushing telecom operators to embrace AI for real-time traffic inspection and dynamic resource allocation. For instance, in March 2025, Jio Platforms, AMD, Cisco, and Nokia joined forces to develop an Open Telecom AI Platform, with a mission to transform telecom operations by strengthening network security, improving efficiency, and lowering the total cost of ownership. This cutting-edge technology will employ different AI methods. The Telecom AI Platform, a multi-domain intelligence platform, will bring AI and automation to every level of network operation, for efficiency, security, and revenue opportunities for service providers. Employing agentic AI, LLMs, SLMs, and non-GenAI machine learning methods, it will automate and improve network management and operations, utilizing open APIs.

Market Segmentation

Based on the component, the market is segmented into solution and service.
Based on the application, the market is segmented into network security, network optimization, customer analytics, virtual assistance, self-diagnostics, and others (quality of service (QOS) enhancement, and network planning and optimization)
Based on the vertical, the market is segmented into machine learning (ML), natural language processing (NLP), deep learning (DL), and others (generative AI / large language models (LLMS), reinforcement learning (RL))

Machine Learning (ML): A Key Segment in Market Growth

Machine learning is a powerful tool that helps identify patterns, predict outcomes, and automate decisions based on data. It is used in healthcare, finance, retail, transport, and telecommunication to provide personalized recommendations, predictive analytics, and network optimization. Machine learning is critical to supporting telecom networks by forecasting failures, dynamically managing bandwidth, and self-tuning parameters. It also helps in predictive maintenance, traffic forecasting, security, and customer satisfaction through personalized services and fraud detection automation.

Regional Outlook

The global AI in telecommunication market is further divided by region, including North America (the US and Canada), Europe (the UK, Germany, France, Italy, Spain, Russia, and the Rest of Europe), Asia-Pacific (India, China, Japan, South Korea, Australia and New Zealand, ASEAN Countries, and the Rest of Asia-Pacific), and the Rest of the World (the Middle East & Africa, and Latin America).

Improved Customer Experience In North America

Telecom businesses are leveraging AI to enhance customer experience, automate support services, and enhance satisfaction, thus fueling market expansion. For instance, in January 2025, SK Telecom intended to launch a beta version of its AI-based personal agent, "Aster," for North American consumers. Aster is an 'agentic AI' intended to know the intentions of the users, providing customized planning, execution, reminders, and advice, and combines conversational search with Perplexity. To create an AI system for telecommunication applications, optimized through extensive testing and reinforcement learning.

Asia-Pacific Dominates Market Position

Asia-Pacific has a large share, as the growth in the market is spurred mainly by the potential of AI to improve network performance, reduce downtime, and improve service quality is fueling market expansion by maximizing network performance. For instance, in June 2025, India staged the third meeting of the International Telecommunication Union's Focus Group on Artificial Intelligence Native for Telecommunication Networks (FG-AINN) in New Delhi. The conference sought to promote AI-Native telecom networks, with the spotlight on efforts by India, such as 'Bharat Gen' and AI-enabled network automation initiatives. The focus group also underscored the significance of ethical, inclusive, and safe AI deployment, digital sovereignty, and regulatory framework evolution. The FG-AINN is revolutionizing telecom networks through AI integration, seeking to create self-optimizing, robust networks. The team is investigating areas such as intelligent public services, smart transport, and disaster-conscious communication infrastructure.

Market Players Outlook

The major companies operating in the global AI in telecommunication market include Google Cloud, IBM Corp., Intel Corp., Microsoft, NVIDIA Corp., among others. Market players are leveraging partnerships, collaborations, mergers, and acquisition strategies for business expansion and innovative product development to maintain their market positioning.

Recent Developments

In March 2025, NVIDIA and leaders in the telecom sector are working together to create AI-native wireless networks for 6G. These networks will incorporate AI to interconnect trillions of devices and offer better services, spectral efficiency, and new revenues for telecom companies. The collaboration plans to interconnect billions of devices seamlessly.
In March 2025, EY introduced EY.ai Telecom Agents, a collection of AI agents aimed at telecommunications companies. The solution runs across finance, network, customer service, and content life cycle management. It leverages the end-to-end NVIDIA AI platform, such as NVIDIA NIM microservices, NeMo Retriever, NeMo Guardrails, and NVIDIA Blueprints. The solution features a Contract Intelligence agent to boost vendor contract analysis, processing of performance data, scenario planning, customizable reporting metrics, and real-time vendor insights.
In June 2025, Tata Consultancy Services (TCS) collaborated with NVIDIA to create AI-native agents for the telecom sector. The partnership will enhance telecom operations and network management using AI-accelerated solutions, purpose-built AI models, and digital twins. TCS employs NVIDIA accelerated computing, such as NVIDIA DGX Cloud, NVIDIA AI Enterprise, NVIDIA NIM microservices, NVIDIA NeMo, and NVIDIA Blueprints, to optimize telecom operations and make them smarter and more effective. Use cases involve TCS Autonomous Network Anomaly Management, TCS Billing and Revenue Assurance Service, and TCS Smart Assist Framework for Service Assurance.
In September 2024, Dell Technologies collaborated with NVIDIA to drive AI deployments in telecom service providers. Dell AI for Telecom solution, a part of the Dell AI Factory, is designed to simplify and fast-track AI deployments. The program, which fuses Dell's AI capabilities, infrastructure, and services with software and silicon from the AI ecosystem, is designed to optimize network performance, improve customer experience, and deliver enterprise value. Also, to create and test telecom AI solutions for CSPs. Such solutions are the Amdocs amAIz platform, Iternal, Kinetica SQL-GPT, Synthefy, PowerEdge XR8000 server, and GPU-as-a-Service offerings. The solutions were designed to improve customer service, automate call center functionality, perform network debugging, create digital twins, enable AI deployments at the edge, and assist CSPs in designing and deploying GPU-as-a-Service solutions.

The Report Covers:

Market value data analysis of 2025 and forecast to 2035.
Annualized market revenues ($ million) for each market segment.
Country-wise analysis of major geographical regions.
Key companies operating in the global AI in telecommunication market. Based on the availability of data, information related to new products and relevant news is also available in the report.
Analysis of business strategies by identifying the key market segments positioned for strong growth in the future.
Analysis of market-entry and market expansion strategies.
Competitive strategies by identifying 'who-stands-where' in the market.

Product Code: OMR2028383

1. Report Summary

Current Industry Analysis and Growth Potential Outlook
Global AI in Telecommunication Market Sales Analysis - Component | Application | Vertical ($ Million)
AI in Telecommunication Market Sales Performance of Top Countries
1.1. Research Methodology
Primary Research Approach
Secondary Research Approach
1.2. Market Snapshot

2. Market Overview and Insights

2.1. Scope of the Study
2.2. Analyst Insight & Current Market Trends
- 2.2.1. Key AI in Telecommunication Market Trends
- 2.2.2. Market Recommendations

3. Market Determinants

3.1. Market Drivers
- 3.1.1. Drivers For Global AI in Telecommunication Market: Impact Analysis
3.2. Market Pain Points and Challenges
- 3.2.1. Restraints For Global AI in Telecommunication Market: Impact Analysis
3.3. Market Opportunities
- 3.3.1. Opportunities For Global AI in Telecommunication Market: Impact Analysis

4. Competitive Landscape

4.1. Competitive Dashboard - AI in Telecommunication Market Revenue and Share by Manufacturers
AI in Telecommunication Solution Comparison Analysis
Top Market Player Ranking Matrix
4.2. Key Company Analysis
- 4.2.1. Google Cloud
  - 4.2.1.1. Overview
  - 4.2.1.2. Product Portfolio
  - 4.2.1.3. Financial Analysis
  - 4.2.1.4. SWOT Analysis
  - 4.2.1.5. Business Strategy
- 4.2.2. IBM Corp.
  - 4.2.2.1. Overview
  - 4.2.2.2. Product Portfolio
  - 4.2.2.3. Financial Analysis
  - 4.2.2.4. SWOT Analysis
  - 4.2.2.5. Business Strategy
- 4.2.3. Intel Corp.
  - 4.2.3.1. Overview
  - 4.2.3.2. Product Portfolio
  - 4.2.3.3. Financial Analysis
  - 4.2.3.4. SWOT Analysis
  - 4.2.3.5. Business Strategy
- 4.2.4. Microsoft
  - 4.2.4.1. Overview
  - 4.2.4.2. Product Portfolio
  - 4.2.4.3. Financial Analysis
  - 4.2.4.4. SWOT Analysis
  - 4.2.4.5. Business Strategy
- 4.2.5. NVIDIA Corp.
  - 4.2.5.1. Overview
  - 4.2.5.2. Product Portfolio
  - 4.2.5.3. Financial Analysis
  - 4.2.5.4. SWOT Analysis
  - 4.2.5.5. Business Strategy
4.3. Top Winning Strategies by Market Players
- 4.3.1. Merger and Acquisition
- 4.3.2. Product Launch
- 4.3.3. Partnership And Collaboration

5. Global AI in Telecommunication Market Sales Analysis By Component ($ Million)

5.1. Solution
5.2. Service

6. Global AI in Telecommunication Market Sales Analysis By Application ($ Million)

6.1. Network Security
6.2. Network Optimization
6.3. Customer Analytics
6.4. Virtual Assistance
6.5. Self-Diagnostics
6.6. Others

7. Global AI in Telecommunication Market Sales Analysis By Vertical ($ Million)

7.1. Machine Learning (ML)
7.2. Natural Language Processing (NLP)
7.3. Deep Learning (DL)
7.4. Others

8. Regional Analysis

8.1. North American AI in Telecommunication Market Sales Analysis - Component | Application | Vertical ($ Million)
Macroeconomic Factors for North America
- 8.1.1. United States
- 8.1.2. Canada
8.2. European AI in Telecommunication Market Sales Analysis - Component | Application | Vertical ($ Million)
Macroeconomic Factors for Europe
- 8.2.1. UK
- 8.2.2. Germany
- 8.2.3. Italy
- 8.2.4. Spain
- 8.2.5. France
- 8.2.6. Russia
- 8.2.7. Rest of Europe
8.3. Asia-Pacific AI in Telecommunication Market Sales Analysis - Component | Application | Vertical ($ Million)
Macroeconomic Factors for Asia-Pacific
- 8.3.1. China
- 8.3.2. Japan
- 8.3.3. South Korea
- 8.3.4. India
- 8.3.5. Australia & New Zealand
- 8.3.6. ASEAN Countries (Thailand, Indonesia, Vietnam, Singapore, And Other)
- 8.3.7. Rest of Asia-Pacific
8.4. Rest of the World AI in Telecommunication Market Sales Analysis - Component | Application | Vertical ($ Million)
Macroeconomic Factors for the Rest of the World
- 8.4.1. Latin America
- 8.4.2. Middle East and Africa

9. Company Profiles

9.1. Amazon Web Services, Inc.
- 9.1.1. Quick Facts
- 9.1.2. Company Overview
- 9.1.3. Product Portfolio
- 9.1.4. Business Strategies
9.2. Capgemini Services SAS
- 9.2.1. Quick Facts
- 9.2.2. Company Overview
- 9.2.3. Product Portfolio
- 9.2.4. Business Strategies
9.3. Ericsson
- 9.3.1. Quick Facts
- 9.3.2. Company Overview
- 9.3.3. Product Portfolio
- 9.3.4. Business Strategies
9.4. Google Cloud
- 9.4.1. Quick Facts
- 9.4.2. Company Overview
- 9.4.3. Product Portfolio
- 9.4.4. Business Strategies
9.5. Huawei Technologies Co., Ltd.
- 9.5.1. Quick Facts
- 9.5.2. Company Overview
- 9.5.3. Product Portfolio
- 9.5.4. Business Strategies
9.6. IBM Corp.
- 9.6.1. Quick Facts
- 9.6.2. Company Overview
- 9.6.3. Product Portfolio
- 9.6.4. Business Strategies
9.7. Intel Corp.
- 9.7.1. Quick Facts
- 9.7.2. Company Overview
- 9.7.3. Product Portfolio
- 9.7.4. Business Strategies
9.8. Intellias Global Ltd.
- 9.8.1. Quick Facts
- 9.8.2. Company Overview
- 9.8.3. Product Portfolio
- 9.8.4. Business Strategies
9.9. Microsoft
- 9.9.1. Quick Facts
- 9.9.2. Company Overview
- 9.9.3. Product Portfolio
- 9.9.4. Business Strategies
9.10. Nexocode
- 9.10.1. Quick Facts
- 9.10.2. Company Overview
- 9.10.3. Product Portfolio
- 9.10.4. Business Strategies
9.11. Nokia Corp.
- 9.11.1. Quick Facts
- 9.11.2. Company Overview
- 9.11.3. Product Portfolio
- 9.11.4. Business Strategies
9.12. Nuance Communications, Inc.
- 9.12.1. Quick Facts
- 9.12.2. Company Overview
- 9.12.3. Product Portfolio
- 9.12.4. Business Strategies
9.13. NVIDIA Corp.
- 9.13.1. Quick Facts
- 9.13.2. Company Overview
- 9.13.3. Product Portfolio
- 9.13.4. Business Strategies
9.14. Q3 technologies, Inc.
- 9.14.1. Quick Facts
- 9.14.2. Company Overview
- 9.14.3. Product Portfolio
- 9.14.4. Business Strategies
9.15. Salesforce, Inc.
- 9.15.1. Quick Facts
- 9.15.2. Company Overview
- 9.15.3. Product Portfolio
- 9.15.4. Business Strategies
9.16. ServiceNow, Inc.
- 9.16.1. Quick Facts
- 9.16.2. Company Overview
- 9.16.3. Product Portfolio
- 9.16.4. Business Strategies
9.17. SUBEX
- 9.17.1. Quick Facts
- 9.17.2. Company Overview
- 9.17.3. Product Portfolio
- 9.17.4. Business Strategies
9.18. Super Micro Computer, Inc.
- 9.18.1. Quick Facts
- 9.18.2. Company Overview
- 9.18.3. Product Portfolio
- 9.18.4. Business Strategies
9.19. Tata Consultancy Services (TCS)
- 9.19.1. Quick Facts
- 9.19.2. Company Overview
- 9.19.3. Product Portfolio
- 9.19.4. Business Strategies
9.20. Wipro Ltd.
- 9.20.1. Quick Facts
- 9.20.2. Company Overview
- 9.20.3. Product Portfolio
- 9.20.4. Business Strategies