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市场调查报告书
商品编码
1889283

电信人工智慧和分析市场预测至2032年：按组件、公司规模、营运商类型、部署类型、应用和地区分類的全球分析

Telecom AI and Analytics Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Enterprise Size, Operator Type, Deployment, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 预测，全球电信人工智慧和分析市场规模预计将在 2025 年达到 112.8 亿美元，到 2032 年达到 311.1 亿美元，预测期内复合年增长率 (CAGR) 为 15.6%。电信人工智慧和分析正在透过帮助服务供应商提升网路效能、提高客户满意度和实现复杂操作的自动化，从而改变整个产业。借助人工智慧模型、预测分析和大数据分析，电信业者能够及早发现问题、最大限度地减少停机时间并保持更高的网路稳定性。这些技术提高了营运效率、降低了营运成本，并增强了客户支援、收费和网路优化等领域的决策能力。随着 5G、物联网和云端生态系驱动的资料呈指数级增长，先进的分析技术使电信公司能够加强安全性、发现模式并改善策略规划。这种发展趋势使服务提供者能够更快地进行创新，并提供更聪明、更可靠的服务。

根据GSMA的数据，电信业在采用生成式人工智慧方面领先其他产业，70%的电信业者已全面或部分部署了人工智慧技术。此外，89%的电信业者计划在下一财年投资生成式人工智慧，这一比例在所有产业中最高，与保险业并列。

网路自动化和营运效率的需求日益增长

电信业者越来越依赖自动化来控制营运成本并维持可靠的效能，而人工智慧和分析技术已成为不可或缺的工具。人工智慧可以自动管理资源、优化频宽分配并实现节能的网路营运。预测分析透过及早发现技术问题来减少计划外维护。自动化工作流程可以加快配置、部署和问题解决速度，从而减少对人工流程的依赖。随着虚拟化系统的扩展、物联网的普及以及边缘应用的兴起，自动化对于维持服务稳定性至关重要。提高营运效率、提升生产力以及应对日益复杂的网路需求，正推动电信业者快速采用人工智慧和分析技术。

高昂的实施成本和整合挑战

电信业采用人工智慧和分析技术面临高昂的实施成本和复杂的系统整合需求，这极大地限制了其发展。部署先进的人工智慧工具需要大量的资金投入，尤其是在替换过时的基础设施和处理大型资料集方面。许多中小电信业者缺乏足够的预算或专业知识来有效地完成这种转型。将人工智慧整合到现有网路中常常面临许多挑战，包括相容性问题、资料结构分散化以及对专业技术人员的需求。此外，持续的培训、云端运算和模型维护成本也加重了营运商的负担。这些财务和营运方面的限制阻碍了人工智慧技术的广泛应用，也使得电信业者无法充分利用人工智慧驱动的分析能力。

对个人化客户体验解决方案的需求日益增长

用户对客製化数位体验的期望日益增长，为电信业的AI和分析技术创造了巨大的机会。 AI能够分析使用者使用模式、使用者旅程和即时行为，帮助营运商设计个人化服务套餐并提供相关提案。分析工具还有助于改善客户流失预测模型、识别盈利客户群并开发更有效的客户参与方式。智慧聊天机器人和自动化支援系统可以提高服务品质并缩短回应时间。随着用户对无缝和个人化互动的需求不断增长，电信业者可以利用AI洞察来凸显其价值主张并建立持久的客户忠诚度。这种对个人化日益重视正在加速AI驱动的客户分析和体验管理平台的普及应用。

科技快速变革和激烈的竞争压力

人工智慧、分析技术和电信基础设施创新领域的快速发展带来了巨大的竞争压力，威胁着市场稳定。持续的升级、技术变革以及新兴解决方案供应商的崛起，使得营运商难以保持技术领先。小规模的电信业者面临的压力最大，因为它们往往缺乏频繁现代化所需的财力和技术资源。快速变化增加了现有人工智慧投资过时的风险，从而降低了整体投资回报率。这种动态环境迫使电信公司不断重新评估其策略，造成了营运上的不确定性。对快速创新的需求最终会使长期规划变得复杂，并减缓人工智慧解决方案的稳定普及。

新冠疫情的感染疾病：

感染疾病透过数位化和变革网路运营，对电信人工智慧和分析市场产生了重大影响。随着远端连线需求的激增，营运商广泛使用基于人工智慧的工具来监控网路、管理流量高峰并维持服务品质。分析在需求预测、频宽优化和支援大规模数位化使用方面变得至关重要。疫情也促使电信业者加快对自动化、云端平台和虚拟化系统的投资，以提高网路韧性。儘管如此，经济的不确定性和供应链中断仍然延缓了一些人工智慧倡议。整体而言，疫情的影响喜忧参半，既推动了快速创新，也减缓了某些技术的应用。

预计在预测期内，软体领域将占据最大的市场份额。

在预测期内，软体领域预计将占据最大的市场份额，这主要得益于市场对灵活智慧平台日益增长的需求，这些平台融合了即时分析、机器学习和预测功能。电信业者更倾向于采用软体主导的解决方案，以增强网路营运、提升客户参与并实现关键业务流程的自动化。这些人工智慧平台易于部署在云端和边缘环境中，能够满足现代电信基础设施不断变化的需求。随着营运商对旧有系统进行现代化改造并推动数位转型，人工智慧软体对于产生洞察和优化工作流程至关重要。因此，软体领域在该市场中占据最强劲的地位。

预计在预测期内，云端基础市场将实现最高的复合年增长率。

由于其无与伦比的敏捷性、扩充性和经济高效的部署，预计在预测期内，云端基础市场将实现最高的成长率。电信营运商正在采用云端环境，使其能够处理大量资料流、执行即时分析并利用人工智慧功能，而无需依赖复杂的本地硬体。云端解决方案支援轻鬆升级、与 5G 和边缘运算的无缝整合以及高级分析的快速部署。随着数位转型的推进，电信营运商正在转向云端原生人工智慧工具，以提升网路效能、增强客户参与并优化营运。这种全行业向云端智慧的转变已使云端基础市场稳居成长最快的类别之列。

占比最大的地区：

预计在整个预测期内，北美将占据最大的市场份额，这主要得益于其先进的基础设施、激烈的市场竞争以及电信营运商对人工智慧的早期应用。该地区的电信公司正在大力投资分析和机器学习，以提高5G效率、实现网路任务自动化并提升客户服务品质。强大的云端运算和边缘运算平台为这项转型提供了支持，而有利的监管环境则促进了创新。此外，顶尖科技公司的存在和强大的研发能力也使得预测模型的大规模部署成为可能。因此，北美将继续成为推动电信人工智慧和分析技术发展的最重要地区。

复合年增长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率，这主要得益于数位基础设施的扩张、智慧型手机和宽频使用量的成长，以及中国和印度等主要市场5G的快速部署。该地区的电信营运商正致力于利用人工智慧驱动的分析来管理不断增长的流量负载、提供客製化服务并提高网路效率。此外，政府的支持性政策、城市数位化计划和物联网的普及也推动了相关投资。在对数据密集型应用的需求不断增长的推动下，亚太地区正崛起为人工智慧主导电信转型中最具活力和成长最快的地区。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 公司概况
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户要求，对主要国家进行市场估算与预测，复合年增长率（註：可行性需确认）
  • 竞争基准化分析
    • 根据主要企业的产品系列、地理覆盖范围和策略联盟基准化分析

目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球电信人工智慧和分析市场（按组件划分）

  • 软体
  • 服务

第六章：全球电信人工智慧与分析市场（依公司规模划分）

  • 中小企业
  • 大公司

7. 全球电信人工智慧和分析市场（按运营商类型划分）

  • 仅提供行动服务的营运商
  • 固网营运商
  • 收敛算子

第八章：全球电信人工智慧和分析市场（按部署类型划分）

  • 云端基础的
  • 本地部署

9. 全球电信人工智慧和分析市场（按应用划分）

  • 客户分析
  • 网路流量优化
  • 故障诊断和预测性维护
  • 诈欺侦测与安全
  • 虚拟助理和聊天机器人

第十章：全球电信人工智慧和分析市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • IBM Corporation
  • Microsoft Corporation
  • Intel Corporation
  • AT&T;
  • Cisco Systems
  • Nuance Communications
  • Salesforce
  • Nvidia
  • Amazon Web Services（AWS）
  • Nokia
  • Huawei Technologies Co. Ltd
  • Amdocs Inc.
  • Vodafone Ltd.
  • SK Telecom
  • American Tower Corporation
Product Code: SMRC32743

According to Stratistics MRC, the Global Telecom AI and Analytics Market is accounted for $11.28 billion in 2025 and is expected to reach $31.11 billion by 2032 growing at a CAGR of 15.6% during the forecast period. Telecom AI and analytics are reshaping the industry by helping service providers boost network performance, elevate customer satisfaction, and automate complex operations. Using AI models, predictive insights, and large-scale data analysis, telecom operators can identify issues early, minimize downtime, and maintain stronger network stability. These technologies improve operational efficiency, cut operational expenses, and enhance decision-making across areas such as customer support, billing, and network optimization. With rapid data growth from 5G, IoT, and cloud ecosystems, advanced analytics enable telecom firms to bolster security, uncover patterns, and refine strategic planning. This evolution empowers providers to innovate quickly and deliver smarter, more reliable services.

According to GSMA data, telecoms are ahead of most industries in generative AI adoption, with 70% of telcos having fully or partially implemented AI technologies. Furthermore, 89% of telecom operators expect to invest in generative AI in the next financial year, the joint highest alongside insurance.

Market Dynamics:

Driver:

Increasing need for network automation and operational efficiency

Telecom operators are increasingly turning to automation to control operational expenses and maintain reliable performance, making AI and analytics essential tools. AI automates resource management, improves bandwidth distribution, and supports energy-efficient network operations. Predictive analytics help reduce unexpected maintenance by detecting technical issues early. Automated workflows accelerate provisioning, configuration, and issue resolution, lowering reliance on manual processes. With expanding virtualized systems, IoT deployments, and edge-based applications, automation is critical for maintaining service stability. The push to streamline operations, enhance productivity, and manage growing network complexity is driving telecom providers to adopt AI and analytics at a rapid pace.

Restraint:

High implementation costs and integration challenges

The adoption of AI and analytics in telecom is heavily restricted by high deployment expenses and complex system integration needs. Advanced AI tools demand major financial investment, especially when replacing old infrastructure and processing massive data sets. Many smaller telecom firms lack the budget and expertise to manage these transitions efficiently. Integrating AI with existing networks often leads to compatibility hurdles, fragmented data structures, and the requirement for skilled professionals. Ongoing costs tied to training, cloud computing, and model maintenance further add to the burden. These financial and operational constraints delay broader adoption, preventing telecom operators from maximizing AI-enabled analytical capabilities.

Opportunity:

Rising demand for personalized customer experience solutions

Increasing expectations for customized digital experiences are generating significant opportunities for AI and analytics in the telecom industry. With the ability to study usage patterns, customer journeys, and real-time behavior, AI helps operators design personalized service bundles and deliver relevant recommendations. Analytics tools also strengthen churn prediction models, highlight profitable customer groups, and guide smarter engagement approaches. Intelligent chatbots and automated support systems enhance service quality and reduce response times. As users seek seamless, tailored interactions, telecom providers can leverage AI insights to differentiate their offerings and build lasting loyalty. This growing focus on personalization accelerates adoption of AI-driven customer analytics and experience management platforms.

Threat:

Rapid technological changes and high competitive pressure

The fast pace of innovation in AI, analytics, and telecom infrastructure creates significant competitive pressure that threatens market stability. Constant upgrades, shifting technologies, and emerging solution providers make it challenging for operators to remain up to date. Smaller telecom firms face the greatest burden, as they often lack the financial and technical capacity for frequent modernization. Rapid changes also heighten the risk of existing AI investments becoming obsolete, lowering overall return on investment. This dynamic environment forces telecom companies to continually reassess strategies, causing operational uncertainty. The need to innovate quickly ultimately complicates long-term planning and slows the steady adoption of AI solutions.

Covid-19 Impact:

Covid-19 had a major influence on the telecom AI and analytics market by accelerating digital adoption and transforming network operations. As remote connectivity needs surged, operators relied heavily on AI-based tools to monitor networks, manage traffic spikes, and ensure uninterrupted service quality. Analytics became crucial for forecasting demand, optimizing bandwidth, and supporting high-volume digital usage. The crisis also encouraged telecom providers to invest more in automation, cloud platforms, and virtualized systems to improve resilience. Despite this growth, economic instability and disrupted supply chains delayed certain AI initiatives. Overall, the pandemic created mixed effects, driving rapid innovation while also slowing some technological deployments.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, supported by growing demand for flexible, intelligent platforms that combine real-time analytics, machine learning, and predictive capabilities. Telecom companies favor software-driven solutions because they enhance network operations, improve customer engagement, and automate key tasks. These AI platforms are easily deployed in cloud and edge environments, catering to the evolving needs of modern telecom infrastructures. As operators modernize legacy systems and scale their digital transformation, AI software becomes essential for insight generation and optimizing workflows. Consequently, the software segment holds the strongest position in this market.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate because it offers unmatched agility, scalability, and cost-efficient deployment. Telecom companies are adopting cloud environments to process massive data streams, perform real-time analytics, and utilize AI capabilities without relying on complex on-premise hardware. Cloud solutions support effortless upgrades, smooth integration with 5G and edge computing, and quick implementation of advanced analytics. With digital transformation rising, operators depend on cloud-native AI tools to boost network performance, enhance customer engagement, and optimize operations. This industry-wide shift toward cloud intelligence firmly establishes the cloud-based segment as the highest-growth rate category.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, bolstered by cutting-edge infrastructure, fierce competition, and early uptake of AI by operators. Telecom firms in this region are investing heavily in analytics and machine learning to improve 5G efficiency, automate network tasks, and boost customer service quality. Robust cloud and edge computing platforms support this shift, and favorable regulations encourage innovation. Additionally, the presence of top-tier technology companies and strong research capabilities enables large-scale implementation of predictive models. Consequently, North America remains the most influential region in pushing forward the growth of telecom AI analytics.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to its expanding digital infrastructure, rising smart phone and broadband usage, and fast-paced 5G deployment in major markets such as China and India. Telecom companies in this region are turning to AI-powered analytics to manage increasing traffic loads, provide tailored services, and improve network efficiency. In addition, supportive government policies, urban digitization projects, and IoT adoption are driving investments. With rising demand for data-heavy applications, Asia Pacific emerges as the most vibrant and rapidly expanding region for AI-driven telecom transformation.

Key players in the market

Some of the key players in Telecom AI and Analytics Market include IBM Corporation , Microsoft Corporation, Intel Corporation, AT&T, Cisco Systems, Nuance Communications, Salesforce, Nvidia, Amazon Web Services (AWS), Nokia, Huawei Technologies Co. Ltd, Amdocs Inc., Vodafone Ltd., SK Telecom and American Tower Corporation.

Key Developments:

In November 2025, IBM and Atruvia AG have sealed a long-term collaboration that paves the way for sustainable and state-of-the-art IT platforms for the banking of tomorrow. Atruvia will use IBM z17, which was announced earlier this year, as a cornerstone supports its mission critical operations including the core banking system.

In November 2025, Nokia and Latvijas Mobilais Telefons (LMT) announced a strategic agreement to integrate Nokia's cutting-edge 5G radio technology with LMT's proven defense solutions. This collaboration will result in a high-capacity, secure, and resilient tactical communications system specifically designed for dedicated use cases in the region.

In October 2025, Cisco is launching a new routing system built for the intense traffic of artificial-intelligence workloads between data centers. Routing systems use AI algorithms to direct and manage the flow of tasks, information, or requests in various systems and applications. Cisco 8223 is optimized to efficiently and securely connect data centers and power the next generation of AI workloads.

Components Covered:

  • Software
  • Services

Enterprise Sizes Covered:

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

Operator Types Covered:

  • Mobile-Only Operators
  • Fixed-Line-Only Operators
  • Converged Operators

Deployments Covered:

  • Cloud-Based
  • On-Premise

Applications Covered:

  • Customer Analytics
  • Network Traffic Optimization
  • Fault Diagnostics & Predictive Maintenance
  • Fraud Detection & Security
  • Virtual Assistance & Chatbots

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 Application Analysis
  • 3.7 Emerging Markets
  • 3.8 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 Telecom AI and Analytics Market, By Component

  • 5.1 Introduction
  • 5.2 Software
  • 5.3 Services

6 Global Telecom AI and Analytics Market, By Enterprise Size

  • 6.1 Introduction
  • 6.2 Small & Medium Enterprises (SMEs)
  • 6.3 Large Enterprises

7 Global Telecom AI and Analytics Market, By Operator Type

  • 7.1 Introduction
  • 7.2 Mobile-Only Operators
  • 7.3 Fixed-Line-Only Operators
  • 7.4 Converged Operators

8 Global Telecom AI and Analytics Market, By Deployment

  • 8.1 Introduction
  • 8.2 Cloud-Based
  • 8.3 On-Premise

9 Global Telecom AI and Analytics Market, By Application

  • 9.1 Introduction
  • 9.2 Customer Analytics
  • 9.3 Network Traffic Optimization
  • 9.4 Fault Diagnostics & Predictive Maintenance
  • 9.5 Fraud Detection & Security
  • 9.6 Virtual Assistance & Chatbots

10 Global Telecom AI and Analytics 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 IBM Corporation
  • 12.2 Microsoft Corporation
  • 12.3 Intel Corporation
  • 12.4 AT&T
  • 12.5 Cisco Systems
  • 12.6 Nuance Communications
  • 12.7 Salesforce
  • 12.8 Nvidia
  • 12.9 Amazon Web Services (AWS)
  • 12.10 Nokia
  • 12.11 Huawei Technologies Co. Ltd
  • 12.12 Amdocs Inc.
  • 12.13 Vodafone Ltd.
  • 12.14 SK Telecom
  • 12.15 American Tower Corporation

List of Tables

  • Table 1 Global Telecom AI and Analytics Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Telecom AI and Analytics Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Telecom AI and Analytics Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Telecom AI and Analytics Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global Telecom AI and Analytics Market Outlook, By Enterprise Size (2024-2032) ($MN)
  • Table 6 Global Telecom AI and Analytics Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 7 Global Telecom AI and Analytics Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 8 Global Telecom AI and Analytics Market Outlook, By Operator Type (2024-2032) ($MN)
  • Table 9 Global Telecom AI and Analytics Market Outlook, By Mobile-Only Operators (2024-2032) ($MN)
  • Table 10 Global Telecom AI and Analytics Market Outlook, By Fixed-Line-Only Operators (2024-2032) ($MN)
  • Table 11 Global Telecom AI and Analytics Market Outlook, By Converged Operators (2024-2032) ($MN)
  • Table 12 Global Telecom AI and Analytics Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 13 Global Telecom AI and Analytics Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 14 Global Telecom AI and Analytics Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 15 Global Telecom AI and Analytics Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global Telecom AI and Analytics Market Outlook, By Customer Analytics (2024-2032) ($MN)
  • Table 17 Global Telecom AI and Analytics Market Outlook, By Network Traffic Optimization (2024-2032) ($MN)
  • Table 18 Global Telecom AI and Analytics Market Outlook, By Fault Diagnostics & Predictive Maintenance (2024-2032) ($MN)
  • Table 19 Global Telecom AI and Analytics Market Outlook, By Fraud Detection & Security (2024-2032) ($MN)
  • Table 20 Global Telecom AI and Analytics Market Outlook, By Virtual Assistance & Chatbots (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.