通信におけるAI市場- 世界の産業規模、動向、機会、予測、競合別、技術別、用途別、展開タイプ別、地域別、競争別、2019-2029年

通信におけるAIの世界市場規模は2023年に16億7,000万米ドルで、予測期間を通じてCAGR 15.22%で2029年には39億4,000万米ドルに達すると予測されています。

通信におけるAI市場とは、通信分野への人工知能技術の統合を指します。この市場には、通信サービスのパフォーマンス、効率、能力を向上させるためにAIが活用されるさまざまなアプリケーションが含まれます。通信におけるAIの主な応用分野には、ネットワークの最適化、予測保守、顧客サービスの自動化、不正行為の検出、パーソナライズされたマーケティングなどがあります。

AIを活用したソリューションにより、通信会社は膨大な量のデータをリアルタイムで分析し、よりスマートな意思決定とサービス提供の改善につなげることができます。例えば、AIはネットワーク・トラフィックを動的に管理し、混雑を防いで最適なパフォーマンスを確保するのに役立ちます。AIアルゴリズムによる予知保全は、潜在的な機器の故障を予見し、積極的な修理を促し、ダウンタイムと運用コストを削減します。さらに、AIチャットボットとバーチャルアシスタントは、問い合わせに即座に正確な回答を提供することで、顧客サービスを向上させる。

高速インターネットへの需要の高まり、コネクテッドデバイスの普及、5G技術の登場は、通信におけるAI市場の重要な促進要因です。複雑なプロセスを自動化し、実用的な洞察を提供することで、AIは業務効率を高めるだけでなく、通信業界におけるイノベーションの新たな機会を創出します。

主な市場促進要因

コネクテッドデバイスとIoTの普及

顧客体験の向上に対する需要

主な市場課題

データのプライバシーとセキュリティに関する懸念

統合と相互運用性の問題

主な市場動向

AIを活用したネットワーク最適化の採用拡大

AIを活用したカスタマーサービス・ソリューションの成長

目次

第1章 概要

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 顧客の声

第5章 世界の通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別（ソリューションとサービス）
  • 技術別（機械学習とディープラーニング、自然言語処理）
  • 用途別（顧客分析、ネットワークセキュリティ、自己診断、ネットワーク最適化、仮想アシスタンスなど）
  • 展開タイプ別（クラウドとオンプレミス）
  • 地域別
  • 企業別（2023）
• 市場マップ

第6章 北米の通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別
  • 技術別
  • 用途別
  • 展開タイプ別
  • 国別
• 北米：国別分析
  • 米国
  • カナダ
  • メキシコ

第7章 欧州の通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別
  • 技術別
  • 用途別
  • 展開タイプ別
  • 国別
• 欧州：国別分析
  • ドイツ
  • 英国
  • イタリア
  • フランス
  • スペイン

第8章 アジア太平洋地域の通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別
  • 技術別
  • 用途別
  • 展開タイプ別
  • 国別
• アジア太平洋地域：国別分析
  • 中国
  • インド
  • 日本
  • 韓国
  • オーストラリア

第9章 南米の通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別
  • 技術別
  • 用途別
  • 展開タイプ別
  • 国別
• 南米：国別分析
  • ブラジル
  • アルゼンチン
  • コロンビア

第10章 中東・アフリカの通信におけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別
  • 技術別
  • 用途別
  • 展開タイプ別
  • 国別
• 中東・アフリカ：国別分析
  • 南アフリカ
  • サウジアラビア
  • アラブ首長国連邦
  • クウェート
  • トルコ

第11章 市場力学

• 促進要因
• 課題

第12章 市場動向と発展

第13章 企業プロファイル

• IBM Corporation
• Microsoft Corporation
• Cisco Systems, Inc.
• Intel Corporation
• AT&T Inc.
• Nuance Communications, Inc.
• Evolv Technologies Holdings, Inc.
• Infosys Limited
• Salesforce, Inc.
• NVIDIA Corporation

第14章 戦略的提言

第15章 調査会社について・免責事項

Global AI in Telecommunication Market was valued at USD 1.67 billion in 2023 and is expected to reach USD 3.94 billion in 2029 with a CAGR of 15.22% through the forecast period.

The AI in Telecommunication market refers to the integration of artificial intelligence technologies into the telecommunications sector. This market encompasses a range of applications where AI is leveraged to enhance the performance, efficiency, and capabilities of telecommunication services. Key areas of AI application in telecommunications include network optimization, predictive maintenance, customer service automation, fraud detection, and personalized marketing.

AI-driven solutions enable telecom companies to analyze vast amounts of data in real-time, leading to smarter decision-making and improved service delivery. For instance, AI can help in dynamically managing network traffic to prevent congestion and ensure optimal performance. Predictive maintenance powered by AI algorithms can foresee potential equipment failures and prompt proactive repairs, reducing downtime and operational costs. Additionally, AI chatbots and virtual assistants improve customer service by providing instant, accurate responses to inquiries.

The growing demand for high-speed internet, the proliferation of connected devices, and the advent of 5G technology are significant drivers of the AI in Telecommunication market. By automating complex processes and providing actionable insights, AI not only enhances operational efficiency but also creates new opportunities for innovation within the telecommunications industry.

Key Market Drivers

Proliferation of Connected Devices and IoT

The proliferation of connected devices and the Internet of Things (IoT) is a significant driver of the AI in Telecommunication market. As the number of connected devices continues to grow exponentially, telecom networks face increasing pressure to handle the vast amounts of data generated. IoT devices, including smart home appliances, wearables, and industrial sensors, create a complex and dynamic ecosystem that requires advanced management and optimization.

AI plays a crucial role in managing the massive influx of data from these devices. Machine learning algorithms can analyze data in real-time to identify patterns, predict trends, and optimize network performance. For instance, AI can help in managing network traffic by dynamically allocating resources to ensure smooth and uninterrupted connectivity. This is particularly important in scenarios where multiple devices are competing for bandwidth.

AI enhances the ability of telecommunication companies to provide personalized services. By analyzing data from connected devices, AI can offer insights into user behavior and preferences, allowing for tailored service offerings and improved customer experiences. For example, AI-driven analytics can help telecom providers understand how customers use their devices and services, enabling them to develop targeted marketing campaigns and personalized recommendations.

To enhancing network performance and customer experience, AI also contributes to the security of IoT ecosystems. With the growing number of connected devices, the risk of cyber threats increases. AI-powered security solutions can detect and mitigate potential threats in real-time, ensuring the integrity and safety of the network and its connected devices. Machine learning algorithms can identify unusual patterns of behavior that may indicate a security breach and respond proactively to prevent attacks.

The proliferation of connected devices and IoT presents both challenges and opportunities for the telecommunications industry. AI technologies offer the tools needed to manage the complexity, optimize performance, and enhance security in this interconnected world. As the IoT market continues to expand, the integration of AI in telecommunications will be essential for sustaining growth and meeting the evolving demands of consumers and businesses alike.

Demand for Enhanced Customer Experience

The demand for enhanced customer experience is a powerful driver of the AI in Telecommunication market. In an increasingly competitive landscape, telecom companies are striving to differentiate themselves by providing superior customer service and personalized experiences. AI technologies offer innovative solutions to meet these expectations and improve customer satisfaction.

One of the primary ways AI enhances customer experience is through the automation of customer service. AI-powered chatbots and virtual assistants are capable of handling a wide range of customer inquiries, providing instant and accurate responses. These AI-driven solutions can manage routine tasks such as billing inquiries, service troubleshooting, and account management, freeing up human agents to focus on more complex issues. This leads to faster resolution times and higher customer satisfaction.

AI enables telecom companies to offer personalized experiences by analyzing customer data and behavior. Machine learning algorithms can process vast amounts of data to identify individual preferences and patterns. This allows telecom providers to tailor their services and marketing efforts to meet the unique needs of each customer. For instance, AI can recommend data plans based on a user's consumption patterns or suggest new services that align with their interests.

Predictive analytics is another area where AI significantly enhances customer experience. By leveraging historical data and machine learning models, AI can anticipate customer needs and proactively address potential issues. For example, predictive maintenance can identify network problems before they affect customers, enabling telecom companies to take preemptive action and minimize service disruptions. This proactive approach helps in maintaining high levels of customer satisfaction and loyalty.

AI also plays a crucial role in customer feedback analysis. Natural language processing (NLP) algorithms can analyze customer feedback from various sources, such as social media, surveys, and call center transcripts, to gain insights into customer sentiments and preferences. This valuable information helps telecom companies understand their customers better and make informed decisions to improve their services and products.

AI-driven solutions can enhance the efficiency of marketing campaigns by targeting the right audience with personalized messages. By analyzing customer data, AI can identify the most effective channels and timing for marketing efforts, increasing the likelihood of conversion and customer engagement.

Key Market Challenges

Data Privacy and Security Concerns

Data privacy and security concerns are significant challenges facing the global AI in Telecommunication market. As AI technologies become increasingly integrated into telecommunication systems, vast amounts of data are generated, collected, and analyzed. This data often includes sensitive information about users, such as their personal details, communication patterns, and usage behaviors. Ensuring the privacy and security of this data is paramount, and several challenges arise in this context.

One of the primary concerns is the potential for data breaches. With the increasing sophistication of cyber-attacks, telecom networks are at constant risk of being targeted by malicious actors. AI systems, while enhancing network capabilities, also introduce new vulnerabilities. Hackers may exploit AI algorithms to gain unauthorized access to sensitive data or disrupt network operations. For instance, adversarial attacks can manipulate AI models by feeding them misleading data, causing them to make incorrect decisions. This can have serious implications, from compromising user data to causing network outages.

The centralization of data in AI-driven systems can create attractive targets for cybercriminals. Telecom operators must implement robust security measures to protect data at all stages, including data in transit, at rest, and during processing. Encryption, secure access controls, and regular security audits are essential practices. However, the rapid evolution of AI and cyber threats requires continuous updates and advancements in security protocols, posing a significant challenge for telecom companies.

Another critical aspect is compliance with data protection regulations. Different regions have varying laws and regulations regarding data privacy, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. Telecom operators must ensure that their AI systems comply with these regulations, which often involve stringent requirements for data handling, storage, and user consent. Failure to comply can result in severe penalties and damage to the company's reputation.

The ethical use of AI in telecommunications is a growing concern. There are debates around the transparency and accountability of AI algorithms, particularly in how they collect and process user data. Users are increasingly aware of their privacy rights and demand greater transparency from telecom operators regarding data usage. Telecom companies must address these concerns by adopting ethical AI practices, which include ensuring that AI models are explainable, fair, and do not perpetuate biases.

Data privacy issues also extend to the sharing of data with third-party vendors and partners. Telecom operators often collaborate with external entities for various services, such as cloud storage, data analytics, and AI development. Ensuring that these partners adhere to the same stringent data privacy and security standards is crucial. This involves establishing clear data sharing agreements, conducting regular audits, and implementing robust data protection measures.

Integration and Interoperability Issues

Integration and interoperability issues are major challenges in the global AI in Telecommunication market. As AI technologies are introduced into existing telecommunication infrastructures, ensuring seamless integration and interoperability with current systems, protocols, and technologies becomes a complex task. These challenges can hinder the effective deployment and utilization of AI solutions, impacting the overall performance and efficiency of telecommunication networks.

One of the primary challenges is the complexity of legacy systems. Many telecom operators have long-standing infrastructures composed of various hardware and software components, often from multiple vendors. Integrating AI solutions into these heterogeneous environments requires significant effort to ensure compatibility and seamless operation. Legacy systems may lack the necessary interfaces or processing capabilities to support advanced AI algorithms, necessitating extensive upgrades or replacements. This process can be time-consuming, costly, and disruptive to ongoing operations.

The rapid evolution of AI technologies adds to the integration challenge. AI models and algorithms are continually advancing, leading to frequent updates and new versions. Telecom operators must ensure that their systems can accommodate these changes without causing disruptions. This requires a flexible and scalable architecture that can adapt to evolving AI technologies while maintaining compatibility with existing components. Achieving this level of adaptability is a significant technical challenge.

Interoperability issues also arise from the diverse range of AI applications and platforms used in telecommunications. Different AI solutions may utilize various data formats, communication protocols, and interfaces, making it difficult to achieve seamless interoperability. For instance, AI-driven network optimization tools, predictive maintenance systems, and customer service chatbots may all operate independently, leading to data silos and inefficiencies. Ensuring that these disparate systems can communicate and share data effectively is crucial for realizing the full potential of AI in telecommunications.

Standardization is a key factor in addressing integration and interoperability challenges. The lack of standardized protocols and interfaces for AI applications in telecommunications can lead to fragmentation and compatibility issues. Industry-wide standards and frameworks can facilitate smoother integration and interoperability by providing common guidelines and specifications. However, achieving consensus on standards involves collaboration among various stakeholders, including telecom operators, technology vendors, regulatory bodies, and industry organizations. This process can be slow and complex, delaying the adoption of standardized solutions.

Integrating AI solutions into telecommunication networks requires specialized skills and expertise. Telecom operators must invest in training their workforce and developing the necessary technical capabilities to manage and support AI deployments. This includes understanding AI algorithms, data management practices, and integration techniques. The shortage of skilled professionals in AI and telecommunications further exacerbates this challenge, making it difficult for operators to effectively implement and maintain AI solutions.

Another critical aspect is the need for robust testing and validation processes. AI solutions must be thoroughly tested to ensure they operate correctly within the existing telecommunication infrastructure. This involves verifying that AI models deliver accurate and reliable results, do not introduce new vulnerabilities, and comply with regulatory requirements. Developing comprehensive testing and validation frameworks is essential to mitigate risks and ensure the successful integration of AI technologies.

Key Market Trends

Increased Adoption of AI-Powered Network Optimization

One of the prominent trends in the global AI in Telecommunication market is the increased adoption of AI-powered network optimization. As telecommunication networks become more complex with the advent of technologies like 5G and the Internet of Things (IoT), the need for efficient and intelligent network management is more critical than ever. AI-powered network optimization solutions are being increasingly adopted to enhance network performance, reduce operational costs, and provide superior service quality.

AI algorithms can analyze vast amounts of network data in real-time, identifying patterns and trends that may not be apparent through traditional methods. This capability allows telecom operators to optimize network parameters dynamically, ensuring optimal performance even under varying traffic conditions. For instance, AI can help in load balancing by distributing network traffic efficiently across different network paths, preventing congestion and ensuring smooth connectivity.

Predictive analytics is another key aspect of AI-powered network optimization. By analyzing historical data and identifying patterns, AI can predict potential network issues before they occur. This proactive approach allows telecom operators to take preventive measures, minimizing downtime and maintaining high service levels. For example, AI can predict when certain network components are likely to fail and prompt maintenance before any disruption occurs.

AI-driven network optimization enhances the deployment and management of 5G networks. 5G technology introduces new challenges with its higher frequency bands, requiring more sophisticated management of network resources. AI can optimize the placement of small cells and antennas, ensuring optimal coverage and capacity. Additionally, AI can help in managing the network slicing feature of 5G, where multiple virtual networks are created on a single physical infrastructure, each tailored to specific requirements and use cases.

Energy efficiency is another area where AI-powered network optimization is making significant strides. Telecommunication networks are substantial consumers of energy, and optimizing energy usage is crucial for both cost savings and environmental sustainability. AI can analyze energy consumption patterns and identify opportunities for energy savings. For instance, AI can manage the power consumption of base stations, dynamically adjusting their operation based on traffic demand, leading to significant energy savings without compromising service quality.

AI-powered network optimization contributes to enhanced customer experience. By ensuring optimal network performance and minimizing downtime, AI helps telecom operators provide a seamless and reliable service to their customers. Additionally, AI can personalize network services based on user behavior and preferences, further enhancing customer satisfaction. For instance, AI can prioritize network resources for high-priority applications like video streaming or online gaming, ensuring a superior user experience.

The adoption of AI-powered network optimization also drives innovation in the telecom industry. Telecom operators are increasingly partnering with AI technology providers to develop advanced optimization solutions. These collaborations are leading to the development of cutting-edge technologies and solutions that push the boundaries of network performance and management.

Growth in AI-Driven Customer Service Solutions

The growth in AI-driven customer service solutions is a significant trend in the global AI in Telecommunication market. As telecom operators strive to enhance customer satisfaction and reduce operational costs, AI technologies are being increasingly adopted to transform customer service operations. AI-driven solutions, such as chatbots, virtual assistants, and predictive analytics, are revolutionizing the way telecom companies interact with their customers, providing faster, more efficient, and personalized service.

AI-powered chatbots and virtual assistants are at the forefront of this trend. These AI-driven tools are capable of handling a wide range of customer inquiries, from billing questions and service troubleshooting to account management and technical support. By leveraging natural language processing (NLP) and machine learning, chatbots can understand and respond to customer queries in real-time, providing accurate and relevant information. This not only reduces the workload on human agents but also ensures that customers receive immediate assistance, leading to higher satisfaction levels.

AI-driven customer service solutions enable 24/7 availability, allowing telecom operators to provide support round-the-clock. This is particularly beneficial in today's digital age, where customers expect instant responses regardless of the time of day. AI-powered virtual assistants can handle inquiries and resolve issues at any time, ensuring that customers are not left waiting for support. This continuous availability enhances the overall customer experience and builds loyalty.

Personalization is another critical aspect of AI-driven customer service solutions. By analyzing customer data and behavior, AI can provide tailored recommendations and solutions. For instance, AI can suggest the most suitable data plans based on a user's usage patterns or recommend new services that align with their interests. Personalized interactions not only enhance customer satisfaction but also increase the likelihood of upselling and cross-selling opportunities, driving revenue growth for telecom operators.

Predictive analytics is also playing a crucial role in transforming customer service. By analyzing historical data and identifying patterns, AI can predict potential issues and proactively address them before they impact the customer. For example, AI can predict when a customer is likely to experience a service disruption and take preventive measures to avoid it. This proactive approach reduces the number of customer complaints and enhances the overall service quality.

AI-driven customer service solutions also improve the efficiency of human agents. AI can assist agents by providing them with relevant information and insights during customer interactions. For instance, AI can analyze a customer's history and provide agents with suggested responses or solutions, enabling faster and more accurate resolution of issues. This not only improves the efficiency of customer service operations but also enhances the quality of interactions, leading to higher customer satisfaction.

The integration of AI in customer service is driving innovation in the telecom industry. Telecom operators are increasingly investing in AI research and development to create advanced customer service solutions. Collaborations with AI technology providers and startups are leading to the development of innovative tools and applications that push the boundaries of customer service excellence.

Segmental Insights

Component Insights

The solutions segment held the largest Market share in 2023. AI solutions in telecommunication often involve complex software applications, algorithms, and platforms that require significant upfront investment in research, development, and deployment. These solutions are designed to address specific challenges such as network congestion management, predictive maintenance, fraud detection, and customer service automation. They integrate seamlessly with existing telecom infrastructure, leveraging AI capabilities to improve efficiency and performance across various operational domains.

AI solutions offer tangible benefits that directly contribute to the bottom line of telecom operators. For instance, AI-driven network optimization solutions help reduce operational costs by dynamically managing network resources and minimizing downtime. Predictive analytics solutions enable proactive maintenance, which not only improves network reliability but also enhances customer satisfaction by preventing service disruptions. These value propositions create a strong business case for telecom operators to invest in AI solutions as they seek to gain a competitive edge in the market.

The increasing adoption of 5G technology, Internet of Things (IoT) devices, and digital transformation initiatives within the telecommunications industry drives the demand for AI solutions. Telecom operators are under pressure to deliver high-speed connectivity, low-latency services, and personalized customer experiences. AI solutions enable operators to meet these demands efficiently and effectively, positioning them as innovators in the market.

AI solutions typically generate higher revenue streams compared to AI services due to licensing or subscription models. Telecom operators pay for the usage of AI software, platforms, and tools, which contribute significantly to market revenues. Additionally, AI solutions can be scalable, allowing operators to expand their deployments as their operational needs grow, further increasing revenue potential over time.

Leading AI solution providers in telecommunication possess deep industry expertise and often collaborate with telecom operators to co-develop tailored solutions. These partnerships facilitate the customization and integration of AI technologies into specific telecom environments, ensuring alignment with operational objectives and regulatory requirements.

Regional Insights

North America region held the largest market share in 2023. North America, particularly the United States, is home to some of the world's largest and most innovative telecom companies. These companies have been early adopters of AI technologies, leveraging them to enhance network management, customer service, and operational efficiency. The region's robust telecommunications infrastructure provides a fertile ground for deploying advanced AI solutions, including those designed for 5G networks and IoT applications. Companies in North America often have substantial R&D budgets and a strong inclination towards investing in cutting-edge technologies, further propelling the adoption of AI in telecommunications.

The technological ecosystem in North America fosters a culture of innovation and entrepreneurship, with numerous AI startups and technology firms focused on developing AI solutions specifically tailored for the telecom industry. These startups benefit from access to venture capital funding, research universities, and a skilled workforce, enabling them to pioneer new AI applications and disrupt traditional telecom practices. The competitive landscape drives continuous innovation and the rapid evolution of AI technologies in telecommunication, further consolidating North America's leadership position.

North America's regulatory environment and policies are generally conducive to the adoption and deployment of AI technologies. Regulatory frameworks in the region often prioritize innovation and competition while ensuring consumer protection and data privacy. Clear regulatory guidelines provide telecom operators and AI solution providers with the confidence to invest in and scale AI deployments without significant regulatory barriers.

The market demand for high-speed connectivity and advanced telecom services in North America drives the adoption of AI to meet these evolving consumer expectations. AI-powered solutions enable telecom operators to offer personalized services, improve network reliability, and optimize resource allocation, thereby enhancing customer satisfaction and loyalty.

Strategic partnerships between telecom operators, technology providers, and research institutions in North America accelerate the development and commercialization of AI in telecommunication. These collaborations facilitate knowledge exchange, technology transfer, and the development of integrated AI solutions that address specific market needs and challenges.

Key Market Players

IBM Corporation

Microsoft Corporation

Cisco Systems, Inc.

Intel Corporation

AT&T Inc.

Nuance Communications, Inc.

Evolv Technologies Holdings, Inc.

Infosys Limited

Salesforce, Inc.

NVIDIA Corporation

Report Scope:

In this report, the Global AI in Telecommunication Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

AI in Telecommunication Market, By Component:

Solutions Services

AI in Telecommunication Market, By Technology:

Machine Learning & Deep Learning Natural Language Processing

AI in Telecommunication Market, By Application:

Customer Analytics Network Security Self-Diagnostics Network Optimization Virtual Assistance Others

AI in Telecommunication Market, By Deployment Type:

Cloud On-Premises

AI in Telecommunication Market, By Region:

North America
• United States
• Canada
• Mexico
Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
Asia-Pacific
• China
• India
• Japan
• Australia
• South Korea
South America
• Brazil
• Argentina
• Colombia
Middle East & Africa
• South Africa
• Saudi Arabia
• UAE
• Kuwait
• Turkey

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI in Telecommunication Market.

Available Customizations:

Global AI in Telecommunication Market report with the given Market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

Detailed analysis and profiling of additional Market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
• 1.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Formulation of the Scope
• 2.4. Assumptions and Limitations
• 2.5. Sources of Research
  • 2.5.1. Secondary Research
  • 2.5.2. Primary Research
• 2.6. Approach for the Market Study
  • 2.6.1. The Bottom-Up Approach
  • 2.6.2. The Top-Down Approach
• 2.7. Methodology Followed for Calculation of Market Size & Market Shares
• 2.8. Forecasting Methodology
  • 2.8.1. Data Triangulation & Validation

3. Executive Summary

4. Voice of Customer

5. Global AI in Telecommunication Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Solutions and Services)
  • 5.2.2. By Technology (Machine Learning & Deep Learning and Natural Language Processing)
  • 5.2.3. By Application (Customer Analytics, Network Security, Self-Diagnostics, Network Optimization, Virtual Assistance and Others)
  • 5.2.4. By Deployment Type (Cloud and On-Premises)
  • 5.2.5. By Region
  • 5.2.6. By Company (2023)
• 5.3. Market Map

6. North America AI in Telecommunication Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Technology
  • 6.2.3. By Application
  • 6.2.4. By Deployment Type
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States AI in Telecommunication Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Technology
      • 6.3.1.2.3. By Application
      • 6.3.1.2.4. By Deployment Type
  • 6.3.2. Canada AI in Telecommunication Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Technology
      • 6.3.2.2.3. By Application
      • 6.3.2.2.4. By Deployment Type
  • 6.3.3. Mexico AI in Telecommunication Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Technology
      • 6.3.3.2.3. By Application
      • 6.3.3.2.4. By Deployment Type

7. Europe AI in Telecommunication Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Technology
  • 7.2.3. By Application
  • 7.2.4. By Deployment Type
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany AI in Telecommunication Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Technology
      • 7.3.1.2.3. By Application
      • 7.3.1.2.4. By Deployment Type
  • 7.3.2. United Kingdom AI in Telecommunication Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Technology
      • 7.3.2.2.3. By Application
      • 7.3.2.2.4. By Deployment Type
  • 7.3.3. Italy AI in Telecommunication Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Technology
      • 7.3.3.2.3. By Application
      • 7.3.3.2.4. By Deployment Type
  • 7.3.4. France AI in Telecommunication Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Technology
      • 7.3.4.2.3. By Application
      • 7.3.4.2.4. By Deployment Type
  • 7.3.5. Spain AI in Telecommunication Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Technology
      • 7.3.5.2.3. By Application
      • 7.3.5.2.4. By Deployment Type

8. Asia-Pacific AI in Telecommunication Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Technology
  • 8.2.3. By Application
  • 8.2.4. By Deployment Type
  • 8.2.5. By Country
• 8.3. Asia-Pacific: Country Analysis
  • 8.3.1. China AI in Telecommunication Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Technology
      • 8.3.1.2.3. By Application
      • 8.3.1.2.4. By Deployment Type
  • 8.3.2. India AI in Telecommunication Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Technology
      • 8.3.2.2.3. By Application
      • 8.3.2.2.4. By Deployment Type
  • 8.3.3. Japan AI in Telecommunication Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Technology
      • 8.3.3.2.3. By Application
      • 8.3.3.2.4. By Deployment Type
  • 8.3.4. South Korea AI in Telecommunication Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Technology
      • 8.3.4.2.3. By Application
      • 8.3.4.2.4. By Deployment Type
  • 8.3.5. Australia AI in Telecommunication Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Technology
      • 8.3.5.2.3. By Application
      • 8.3.5.2.4. By Deployment Type

9. South America AI in Telecommunication Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Technology
  • 9.2.3. By Application
  • 9.2.4. By Deployment Type
  • 9.2.5. By Country
• 9.3. South America: Country Analysis
  • 9.3.1. Brazil AI in Telecommunication Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Technology
      • 9.3.1.2.3. By Application
      • 9.3.1.2.4. By Deployment Type
  • 9.3.2. Argentina AI in Telecommunication Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Technology
      • 9.3.2.2.3. By Application
      • 9.3.2.2.4. By Deployment Type
  • 9.3.3. Colombia AI in Telecommunication Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Technology
      • 9.3.3.2.3. By Application
      • 9.3.3.2.4. By Deployment Type

10. Middle East and Africa AI in Telecommunication Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Technology
  • 10.2.3. By Application
  • 10.2.4. By Deployment Type
  • 10.2.5. By Country
• 10.3. Middle East and Africa: Country Analysis
  • 10.3.1. South Africa AI in Telecommunication Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Technology
      • 10.3.1.2.3. By Application
      • 10.3.1.2.4. By Deployment Type
  • 10.3.2. Saudi Arabia AI in Telecommunication Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Technology
      • 10.3.2.2.3. By Application
      • 10.3.2.2.4. By Deployment Type
  • 10.3.3. UAE AI in Telecommunication Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Technology
      • 10.3.3.2.3. By Application
      • 10.3.3.2.4. By Deployment Type
  • 10.3.4. Kuwait AI in Telecommunication Market Outlook
    • 10.3.4.1. Market Size & Forecast
      • 10.3.4.1.1. By Value
    • 10.3.4.2. Market Share & Forecast
      • 10.3.4.2.1. By Component
      • 10.3.4.2.2. By Technology
      • 10.3.4.2.3. By Application
      • 10.3.4.2.4. By Deployment Type
  • 10.3.5. Turkey AI in Telecommunication Market Outlook
    • 10.3.5.1. Market Size & Forecast
      • 10.3.5.1.1. By Value
    • 10.3.5.2. Market Share & Forecast
      • 10.3.5.2.1. By Component
      • 10.3.5.2.2. By Technology
      • 10.3.5.2.3. By Application
      • 10.3.5.2.4. By Deployment Type

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

13. Company Profiles

• 13.1. IBM Corporation
  • 13.1.1. Business Overview
  • 13.1.2. Key Revenue and Financials
  • 13.1.3. Recent Developments
  • 13.1.4. Key Personnel/Key Contact Person
  • 13.1.5. Key Product/Services Offered
• 13.2. Microsoft Corporation
  • 13.2.1. Business Overview
  • 13.2.2. Key Revenue and Financials
  • 13.2.3. Recent Developments
  • 13.2.4. Key Personnel/Key Contact Person
  • 13.2.5. Key Product/Services Offered
• 13.3. Cisco Systems, Inc.
  • 13.3.1. Business Overview
  • 13.3.2. Key Revenue and Financials
  • 13.3.3. Recent Developments
  • 13.3.4. Key Personnel/Key Contact Person
  • 13.3.5. Key Product/Services Offered
• 13.4. Intel Corporation
  • 13.4.1. Business Overview
  • 13.4.2. Key Revenue and Financials
  • 13.4.3. Recent Developments
  • 13.4.4. Key Personnel/Key Contact Person
  • 13.4.5. Key Product/Services Offered
• 13.5. AT&T Inc.
  • 13.5.1. Business Overview
  • 13.5.2. Key Revenue and Financials
  • 13.5.3. Recent Developments
  • 13.5.4. Key Personnel/Key Contact Person
  • 13.5.5. Key Product/Services Offered
• 13.6. Nuance Communications, Inc.
  • 13.6.1. Business Overview
  • 13.6.2. Key Revenue and Financials
  • 13.6.3. Recent Developments
  • 13.6.4. Key Personnel/Key Contact Person
  • 13.6.5. Key Product/Services Offered
• 13.7. Evolv Technologies Holdings, Inc.
  • 13.7.1. Business Overview
  • 13.7.2. Key Revenue and Financials
  • 13.7.3. Recent Developments
  • 13.7.4. Key Personnel/Key Contact Person
  • 13.7.5. Key Product/Services Offered
• 13.8. Infosys Limited
  • 13.8.1. Business Overview
  • 13.8.2. Key Revenue and Financials
  • 13.8.3. Recent Developments
  • 13.8.4. Key Personnel/Key Contact Person
  • 13.8.5. Key Product/Services Offered
• 13.9. Salesforce, Inc.
  • 13.9.1. Business Overview
  • 13.9.2. Key Revenue and Financials
  • 13.9.3. Recent Developments
  • 13.9.4. Key Personnel/Key Contact Person
  • 13.9.5. Key Product/Services Offered
• 13.10. NVIDIA Corporation
  • 13.10.1. Business Overview
  • 13.10.2. Key Revenue and Financials
  • 13.10.3. Recent Developments
  • 13.10.4. Key Personnel/Key Contact Person
  • 13.10.5. Key Product/Services Offered

14. Strategic Recommendations

15. About Us & Disclaimer