查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2144327

2034年網路營運人工智慧市場預測：按解決方案類型、部署模式、技術、應用、最終用戶和地區分類的全球分析

AI in Network Operations Market Forecasts to 2034 - Global Analysis By Solution Type, Deployment, Technology, Application, End User, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球網路營運人工智慧市場規模將達到 193 億美元，並在預測期內以 20.6% 的複合年成長率成長，到 2034 年將達到 862 億美元。

在網路維運領域，人工智慧（AI）指的是應用人工智慧和機器學習技術來自動化、最佳化和管理網路基礎設施及服務。該市場涵蓋多種解決方案，例如網路監控與可觀測性、網路自動化與編配、AIOps平台、網路效能管理、網路安全與威脅偵測、故障偵測與根本原因分析以及容量規劃與最佳化。這些解決方案可部署在雲端、本地端以及混合式環境。

網路日益複雜，營運自動化的需求日益成長。

受雲端運算、虛擬化、5G 和物聯網等技術的推動，現代網路日益複雜，這是推動人工智慧在網路維運領域應用的主要因素。傳統的網路管理方式已無法因應現代網路規模和動態變化。人工智慧能夠自動執行監控、異常檢測、故障排除和效能最佳化等任務。企業正在採用 AIOps 來降低營運成本、提升服務質量，並讓員工騰出精力專注於策略性措施。隨著網路複雜性的不斷成長，對人工智慧驅動的網路運維解決方案的需求也在不斷擴大，從而支撐著市場的持續成長。

數據品質挑戰和整合複雜性

數據品質和整合複雜性方面的挑戰是限制市場發展的主要因素。人工智慧模型需要大量高品質數據才能學習並做出準確預測。網路資料通常分散在多個工具和系統中，造成整合難題。資料格式不一致和資料不完整都會影響模型效能。將人工智慧解決方案與現有網路管理系統整合需要付出相當大的努力。這些數據和整合方面的挑戰可能會減緩人工智慧在網路營運中的應用，尤其是在管理工具分散的組織中。

介紹生成式人工智慧和數位孿生

生成式人工智慧和數位孿生技術的日益普及為人工智慧在網路運維市場擴大市場佔有率提供了巨大機會。生成式人工智慧能夠實現網路管理的自然語言介面和自動報告生成。數位孿生技術則提供網路的虛擬模型，用於模擬、測試和最佳化。這些技術提升了預測能力，並實現了更高水準的自動化。隨著生成式人工智慧和數位孿生技術的日益成熟，創新的網路運維解決方案正在不斷擴大市場佔有率。

人工智慧驅動決策的可靠性和可解釋性問題

人們對人工智慧驅動的網路決策的可靠性和可解釋性存在擔憂，這嚴重威脅著市場成長。網路維運團隊可能不願意信任那些在不了解人工智慧推理過程的情況下所做的自動化決策。黑盒人工智慧模式在重大網路事件發生時，難以明確界定責任。監管機構對可解釋人工智慧的要求可能會影響某些行業的採用。這些可靠性和可解釋性方面的擔憂可能會減緩人工智慧在網路維運領域的應用，尤其是在關鍵任務功能方面。

新型冠狀病毒（COVID-19）的影響：

新冠疫情大大加速了人工智慧在網路運維領域的應用。企業面臨前所未有的網路需求，同時也要在現場技術人員大幅減少的情況下維持營運。遠端網路管理和維運自動化從單純的效率提升工具轉變為不可或缺的功能，推動了AIOps平台的快速普及。遠距辦公、線上服務和數位協作的激增帶來了網路複雜性，而人工方式已無法應對。疫情凸顯了預測分析和自動化故障解決的價值。疫情後，在持續的數位轉型和混合辦公模式的推動下，市場仍保持強勁成長。

在預測期內，「網路監控和可觀測性」細分市場預計將佔據最大佔有率。

在預測期內，網路監控和可觀測性解決方案預計將佔據最大的市場佔有率，這主要得益於對網路效能、可用性和健康狀況的可見性這一基本需求。網路監控和可觀測性解決方案能夠提供網路行為的即時洞察，從而實現主動檢測和解決問題。此細分市場的優勢在於其廣泛的適用性，可應用於所有類型的網路和產業。隨著網路複雜性的增加，監控和可觀測性解決方案在解決方案類型細分市場中仍將保持最大的佔有率。

預計在預測期內，「通訊服務供應商」細分市場將呈現最高的複合年成長率。

在預測期內，通訊服務供應商細分市場預計將呈現最高的成長率，這主要得益於5G網路日益複雜化、對服務保障的需求不斷成長以及在提升客戶體驗的同時降低營運成本的迫切需求。營運商正在網路營運中採用人工智慧來管理日益複雜的基礎設施。該細分市場受益於5G部署和網路虛擬化。隨著營運商優先考慮自動化和效率，終端用戶細分市場正經歷最快的成長。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其對人工智慧的早期應用、先進的網路基礎設施以及主要供應商的存在。美國透過在網路自動化和人工智慧運維（AIOps）方面的大量投資，正推動著該地區的成長。人們對人工智慧優勢的高度認知正在促進其應用。成熟的技術生態系統為創新提供了支持。技術領先地位和早期應用將使北美保持其市場主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、日本、印度和東南亞地區5G的快速部署、雲端基礎設施的擴張以及網路複雜性的日益增加。該地區龐大且持續成長的電信市場正在催生對人工智慧網路營運的巨大需求。政府的數位化措施也在推動網路現代化。在網路複雜性不斷增加的背景下，亞太地區將經歷全球最快的市場成長。

免費客製化服務：

所有購買此報告的客戶均可從以下免費自訂選項中選擇一項：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶的興趣，我們將提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球網路營運人工智慧市場：按解決方案類型分類

  • 網路監控與可觀測性
  • 網路自動化與編配
  • AIOps平台
  • 網路效能管理
  • 網路安全和威脅偵測
  • 故障檢測和根本原因分析
  • 產能規劃與最佳化

第6章：全球網路營運人工智慧市場：依部署方式分類

  • 基於雲端的
  • 現場
  • 混合

第7章：全球網路營運人工智慧市場：按技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理
  • 人工智慧世代
  • 預測分析
  • 數位孿生
  • 強化學習

第8章：全球網路營運人工智慧市場：按應用領域分類

  • 網路故障管理
  • 效能最佳化
  • 預測性保護
  • 交通最佳化
  • 保全行動
  • 服務保固
  • 配置管理
  • 容量管理

第9章：全球網路營運人工智慧市場：依最終用戶分類

  • 電信服務供應商
  • 雲端服務供應商
  • 公司
  • 資料中心
  • 託管服務供應商
  • 政府/公共部門

第10章：全球網路營運人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • Cisco Systems, Inc.
  • IBM Corporation
  • Juniper Networks, Inc.
  • Hewlett Packard Enterprise
  • Nokia Corporation
  • Ericsson
  • Huawei Technologies Co., Ltd.
  • Broadcom Inc.
  • SolarWinds Corporation
  • Dynatrace, Inc.
  • Splunk LLC
  • ScienceLogic
  • New Relic, Inc.
  • LogicMonitor, Inc.
  • Zabbix LLC
Product Code: SMRC40019

According to Stratistics MRC, the Global AI in Network Operations Market is accounted for $19.3 billion in 2026 and is expected to reach $86.2 billion by 2034 growing at a CAGR of 20.6% during the forecast period. AI in network operations refers to the application of artificial intelligence and machine learning technologies to automate, optimize, and manage network infrastructure and services. The market encompasses solution types including network monitoring and observability, network automation and orchestration, AIOps platforms, network performance management, network security and threat detection, fault detection and root cause analysis, and capacity planning and optimization. These solutions are deployed across cloud-based, on-premises, and hybrid environments.

Market Dynamics:

Driver:

Growing network complexity and need for automated operations

The increasing complexity of modern networks, driven by cloud adoption, virtualization, 5G, and IoT, is a primary driver for AI in network operations. Traditional manual network management approaches cannot handle the scale and dynamism of modern networks. AI enables automated monitoring, anomaly detection, fault resolution, and performance optimization. Organizations are adopting AIOps to reduce operational costs, improve service quality, and free staff for strategic initiatives. As networks continue growing in complexity, demand for AI-powered network operations solutions expands, supporting sustained market growth.

Restraint:

Data quality challenges and integration complexity

Challenges related to data quality and integration complexity represent a major restraint for the market. AI models require large volumes of high-quality data for training and accurate predictions. Network data is often siloed across multiple tools and systems, creating integration challenges. Inconsistent data formats and incomplete data affect model performance. Integrating AI solutions with existing network management systems requires significant effort. These data and integration challenges may slow AI adoption in network operations, particularly among organizations with fragmented management tools.

Opportunity:

Adoption of generative AI and digital twins

The growing adoption of generative AI and digital twin technologies presents significant opportunities for AI in network operations market expansion. Generative AI enables natural language interfaces for network management and automated report generation. Digital twins provide virtual representations of networks for simulation, testing, and optimization. These technologies enhance predictive capabilities and enable more sophisticated automation. As generative AI and digital twins mature, innovative network operations solutions capture growing market share.

Threat:

Trust and explainability concerns in AI-driven decisions

Trust and explainability concerns regarding AI-driven network decisions pose significant threats to market growth. Network operations teams may be reluctant to trust automated decisions without understanding AI reasoning. Black-box AI models create accountability challenges for critical network incidents. Regulatory requirements for explainable AI may affect adoption in some industries. These trust and explainability concerns may slow AI adoption in network operations, particularly for mission-critical functions.

Covid-19 Impact:

The COVID-19 pandemic dramatically accelerated AI adoption in network operations as organizations faced unprecedented network demands while managing with significantly reduced on-site technical staff. Remote network management and automated operations transformed from optional efficiencies into essential capabilities, driving rapid deployment of AIOps platforms. Surging remote work, online services, and digital collaboration created network complexity that manual approaches could not address. The pandemic highlighted the value of predictive analytics and automated fault resolution. Post-pandemic, sustained digital transformation and hybrid work models have maintained strong market growth.

The Network Monitoring and Observability segment is expected to be the largest during the forecast period

The Network Monitoring and Observability segment is expected to account for the largest market share during the forecast period, driven by the fundamental need for visibility into network performance, availability, and health. Network monitoring and observability solutions provide real-time insights into network behavior, enabling proactive issue detection and resolution. The segment benefits from broad applicability across all network types and industries. As network complexity increases, monitoring and observability maintain the largest solution type segment share.

The Telecommunications Service Providers segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Telecommunications Service Providers segment is predicted to witness the highest growth rate, fueled by the complexity of 5G networks, growing demand for service assurance, and the need to reduce operational costs while improving customer experience. Telecom operators are deploying AI in network operations to manage increasingly complex infrastructure. The segment benefits from 5G deployment and network virtualization. As operators prioritize automation and efficiency, this end-user segment delivers the fastest growth.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by early AI adoption, advanced network infrastructure, and presence of major technology vendors. The United States leads regional growth with substantial investment in network automation and AIOps. High awareness of AI benefits drives adoption. Well-established technology ecosystem supports innovation. With technology leadership and early adoption, North America maintains its dominant market position.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by rapid 5G deployment, expanding cloud infrastructure, and increasing network complexity across China, Japan, India, and Southeast Asia. The region's large and growing telecommunications market creates substantial demand for AI network operations. Government digital initiatives support network modernization. As network complexity increases, Asia Pacific delivers the fastest market growth globally.

Key players in the market

Some of the key players in AI in Network Operations Market include Cisco Systems, Inc., IBM Corporation, Juniper Networks, Inc., Hewlett Packard Enterprise, Nokia Corporation, Ericsson, Huawei Technologies Co., Ltd., Broadcom Inc., SolarWinds Corporation, Dynatrace, Inc., Splunk LLC, ScienceLogic, New Relic, Inc., LogicMonitor, Inc., and Zabbix LLC.

Key Developments:

In September 2026, Cisco released its Impact of Agentic AI on Network Operations research, showing that over 80% of enterprise NetOps teams are transitioning toward autonomous "AgenticOps" operating models capable of making real-time closed-loop configuration and routing changes.

In September 2026, Hewlett Packard Enterprise expanded GreenLake with agentic AIOps and unified cloud operations software to manage and optimize enterprise AI clusters and distributed infrastructure.

In September 2026, Nokia partnered with Microsoft to integrate Nokia Data Suite with Microsoft Fabric's OneLake, enabling autonomous AI agents to perform real-time root-cause analysis, voice-over-new-radio assurance, and RAN diagnostics.

In July 2026, IBM advanced watsonx orchestration across its AIOps capabilities to automate hybrid cloud network monitoring, event correlation, and autonomous incident remediation.

Solution Types Covered:

  • Network Monitoring and Observability
  • Network Automation and Orchestration
  • AIOps Platforms
  • Network Performance Management
  • Network Security and Threat Detection
  • Fault Detection and Root Cause Analysis
  • Capacity Planning and Optimization

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Generative AI
  • Predictive Analytics
  • Digital Twins
  • Reinforcement Learning

Applications Covered:

  • Network Fault Management
  • Performance Optimization
  • Predictive Maintenance
  • Traffic Optimization
  • Security Operations
  • Service Assurance
  • Configuration Management
  • Capacity Management

End Users Covered:

  • Telecommunications Service Providers
  • Cloud Service Providers
  • Enterprises
  • Data Centers
  • Managed Service Providers
  • Government and Public Sector

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Network Operations Market, By Solution Type

  • 5.1 Network Monitoring and Observability
  • 5.2 Network Automation and Orchestration
  • 5.3 AIOps Platforms
  • 5.4 Network Performance Management
  • 5.5 Network Security and Threat Detection
  • 5.6 Fault Detection and Root Cause Analysis
  • 5.7 Capacity Planning and Optimization

6 Global AI in Network Operations Market, By Deployment

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

7 Global AI in Network Operations Market, By Technology

  • 7.1 Machine Learning
  • 7.2 Deep Learning
  • 7.3 Natural Language Processing
  • 7.4 Generative AI
  • 7.5 Predictive Analytics
  • 7.6 Digital Twins
  • 7.7 Reinforcement Learning

8 Global AI in Network Operations Market, By Application

  • 8.1 Network Fault Management
  • 8.2 Performance Optimization
  • 8.3 Predictive Maintenance
  • 8.4 Traffic Optimization
  • 8.5 Security Operations
  • 8.6 Service Assurance
  • 8.7 Configuration Management
  • 8.8 Capacity Management

9 Global AI in Network Operations Market, By End User

  • 9.1 Telecommunications Service Providers
  • 9.2 Cloud Service Providers
  • 9.3 Enterprises
  • 9.4 Data Centers
  • 9.5 Managed Service Providers
  • 9.6 Government and Public Sector

10 Global AI in Network Operations Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Cisco Systems, Inc.
  • 13.2 IBM Corporation
  • 13.3 Juniper Networks, Inc.
  • 13.4 Hewlett Packard Enterprise
  • 13.5 Nokia Corporation
  • 13.6 Ericsson
  • 13.7 Huawei Technologies Co., Ltd.
  • 13.8 Broadcom Inc.
  • 13.9 SolarWinds Corporation
  • 13.10 Dynatrace, Inc.
  • 13.11 Splunk LLC
  • 13.12 ScienceLogic
  • 13.13 New Relic, Inc.
  • 13.14 LogicMonitor, Inc.
  • 13.15 Zabbix LLC

List of Tables

  • Table 1 Global AI in Network Operations Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Network Operations Market Outlook, By Solution Type (2023-2034) ($MN)
  • Table 3 Global AI in Network Operations Market Outlook, By Network Monitoring and Observability (2023-2034) ($MN)
  • Table 4 Global AI in Network Operations Market Outlook, By Network Automation and Orchestration (2023-2034) ($MN)
  • Table 5 Global AI in Network Operations Market Outlook, By AIOps Platforms (2023-2034) ($MN)
  • Table 6 Global AI in Network Operations Market Outlook, By Network Performance Management (2023-2034) ($MN)
  • Table 7 Global AI in Network Operations Market Outlook, By Network Security and Threat Detection (2023-2034) ($MN)
  • Table 8 Global AI in Network Operations Market Outlook, By Fault Detection and Root Cause Analysis (2023-2034) ($MN)
  • Table 9 Global AI in Network Operations Market Outlook, By Capacity Planning and Optimization (2023-2034) ($MN)
  • Table 10 Global AI in Network Operations Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 11 Global AI in Network Operations Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 12 Global AI in Network Operations Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 13 Global AI in Network Operations Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 14 Global AI in Network Operations Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global AI in Network Operations Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 16 Global AI in Network Operations Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global AI in Network Operations Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 18 Global AI in Network Operations Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 19 Global AI in Network Operations Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 20 Global AI in Network Operations Market Outlook, By Digital Twins (2023-2034) ($MN)
  • Table 21 Global AI in Network Operations Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 22 Global AI in Network Operations Market Outlook, By Application (2023-2034) ($MN)
  • Table 23 Global AI in Network Operations Market Outlook, By Network Fault Management (2023-2034) ($MN)
  • Table 24 Global AI in Network Operations Market Outlook, By Performance Optimization (2023-2034) ($MN)
  • Table 25 Global AI in Network Operations Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 26 Global AI in Network Operations Market Outlook, By Traffic Optimization (2023-2034) ($MN)
  • Table 27 Global AI in Network Operations Market Outlook, By Security Operations (2023-2034) ($MN)
  • Table 28 Global AI in Network Operations Market Outlook, By Service Assurance (2023-2034) ($MN)
  • Table 29 Global AI in Network Operations Market Outlook, By Configuration Management (2023-2034) ($MN)
  • Table 30 Global AI in Network Operations Market Outlook, By Capacity Management (2023-2034) ($MN)
  • Table 31 Global AI in Network Operations Market Outlook, By End User (2023-2034) ($MN)
  • Table 32 Global AI in Network Operations Market Outlook, By Telecommunications Service Providers (2023-2034) ($MN)
  • Table 33 Global AI in Network Operations Market Outlook, By Cloud Service Providers (2023-2034) ($MN)
  • Table 34 Global AI in Network Operations Market Outlook, By Enterprises (2023-2034) ($MN)
  • Table 35 Global AI in Network Operations Market Outlook, By Data Centers (2023-2034) ($MN)
  • Table 36 Global AI in Network Operations Market Outlook, By Managed Service Providers (2023-2034) ($MN)
  • Table 37 Global AI in Network Operations Market Outlook, By Government and Public Sector (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.