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市场调查报告书
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1857042

全球人工智慧驱动的网路优化市场：预测至 2032 年——按组件、部署方式、技术、应用、最终用户和地区进行分析

AI-Driven Network Optimization Market Forecasts to 2032 - Global Analysis By Component (Software, Hardware, and Services), Deployment Mode (Cloud-Based, On-Premises, and Hybrid), 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个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧驱动的网路优化市场预计到 2025 年将达到 78 亿美元，到 2032 年将达到 279 亿美元，预测期内复合年增长率为 20.0%。

人工智慧驱动的网路优化解决方案利用人工智慧 (AI) 和机器学习 (ML) 技术，自主管理和优化通讯及企业网路。它透过分析即时流量资料来预测网路拥塞、动态分配资源并确保服务品质 (QoS)。最终形成一个能够主动解决问题、减少停机时间并提升使用者体验的自癒网络。这一市场的发展动力源于不断增长的数据消费量以及日益复杂的 5G 和物联网生态系统，这些都需要超越人类能力的智慧主动管理。

根据《麻省理工科技评论》报导，使用人工智慧驱动的网路优化技术的通讯业者报告称，数据吞吐量提高了 20-35%，终端用户的网路延迟降低了。

网路复杂性和资料流量呈指数级增长

网路复杂性和数据流量的指数级增长迫使通讯业者采用人工智慧驱动的最佳化技术来管理规模和效能。现代网路承载着异质工作负载，包括物联网遥测、高画质视讯、即时游戏和云端原生微服务，导致流量高峰难以预测，以及对延迟高度敏感的流量，而这些都无法透过人工调优来解决。人工智慧系统能够接收大量遥测数据，侦测模式，预测拥塞，并自主调整路由、服务品质 (QoS) 和资源分配。此外，自动化优化还能降低营运开销，使工程师能够专注于策略倡议，从而加速企业和服务供应商的网路现代化和服务差异化。

实施成本高且整合复杂

部署分析平台、大规模收集遥测资料以及训练模型需要前期投资大量资金。将人工智慧解决方案与传统路由器、各种厂商介面以及现有的OSS/BSS堆迭整合通常需要客製化开发和漫长的检验週期，从而增加计划风险。此外，对整体拥有成本和投资报酬率不确定性的担忧也会延缓采购核准，尤其对于小规模的业者而言。供应商和整合商必须证明其能够带来可衡量的营运成本节约、提供标准化的API以及分阶段部署模式，以降低门槛并加速推广应用程式。

与 SD-WAN 和网路虚拟化技术的集成

集中式 SD-WAN 策略控制、虚拟化网路功能和 AI 分析的结合，使营运商编配流量控制、动态路径选择和基于意图的策略。此外，NFV 和容器化服务使优化引擎能够更靠近工作负载，从而降低延迟并提高 SLA 遵守率。这些协同效应支援模组化、可获利的服务，透过自动化效能保证、自适应安全性和频宽优化，开闢新的收入来源，同时简化营运并加快客户价值实现速度。

与传统网路最佳化解决方案竞争

来自传统网路优化解决方案的竞争对以人工智慧为先导的供应商构成了重大威胁。企业和通讯业者通常更倾向于使用熟悉的基于规则的工具、硬体加速器以及供应商提供的、具有明确服务等级协定 (SLA) 和采购流程的优化器，并将人工智慧方法视为实验性技术。此外，现有供应商只需将基本的机器学习功能整合到现有产品中，即可降低差异化优势。

新冠疫情的影响：

新冠疫情加速了对人工智慧驱动的网路优化需求，远距办公和云端迁移导致流量激增。服务提供者和企业需要快速自动化以维持效能，这促使试点部署和供应商合作增加。然而，预算重新调整和计划延期导致一些组织削减支出并推迟大规模部署。整体而言，疫情验证了自主且高弹性网路的必要性，并提升了买方对支援分散式办公室和全球流量波动模式的云端原生人工智慧解决方案的兴趣。

预计在预测期内，软体板块将成为最大的板块。

预计在预测期内，软体领域将占据最大的市场份额，因为以软体为中心的AI解决方案能够实现快速部署、持续更新，并与多厂商网路环境广泛相容。软体平台提供分析、策略引擎和编配，无需立即升级硬件，从而降低了准入门槛。订阅许可和云端交付模式进一步推动了服务供应商和寻求营运敏捷性的企业的采用。此外，丰富的第三方整合、开发者工具炼和市场生态系统扩展了功能，使营运商能够在保护现有投资的同时逐步采用高级优化功能，从而加快价值实现速度并降低服务提供商的营运复杂性。

预计在预测期内，混合动力汽车细分市场将实现最高的复合年增长率。

预计在预测期内，混合云领域将保持最高的成长率，因为企业和通讯业者都在权衡效能、合规性和成本之间的关係。混合云解决方案透过在本地处理敏感流量并在云端环境中运行非关键分析，实现了最佳的平衡。此外，网路虚拟化和容器编配工具的出现，使得混合云部署变得切实可行且自动化。提供託管混合云套餐和清晰整合路径的服务供应商预计将加速客户迁移。这种技术可行性和商业模式的结合正在推动混合云的快速普及，尤其是在大规模营运商中，因为它使他们能够在不中断运作中服务和生态系统的情况下，对其传统设施进行现代化改造。

占比最大的地区：

由于北美拥有成熟的数位基础设施、高额的企业IT支出以及对自动化和人工智慧技术的早期应用，预计在预测期内，北美将占据最大的市场份额。主要云端服务供应商、通讯业者的集中以及供应商的大规模研发投入，共同打造了一个丰富的创新生态系统。此外，网路服务供应商和企业严格的效能服务等级协定 (SLA) 以及繁忙的流量状况，也推动了对高阶最佳化技术的需求。强大的专业服务、託管服务以及有利的创业融资，进一步加速了部署进程，使北美企业能够在商业试验、大规模部署和全球伙伴关係主导。

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，这主要得益于数位化的加速、行动宽频的普及以及大规模云端运算的采用，这些因素共同推动了对智慧网路优化的需求。各国政府和企业正在大力投资5G、边缘运算和智慧城市项目，这些项目正在建立复杂且分散的网络，而这些网络需要自动化。此外，充满活力的新兴企业生态系统和竞争激烈的供应商格局正在催生出针对区域需求的在地化、高性价比解决方案。价格优势、不断壮大的熟练劳动力以及快速增长市场中的跨境覆盖能力，进一步促进了这些解决方案的普及，使亚太地区成为未来成长的焦点。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 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
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧驱动网路优化市场（按组件划分）

  • 软体
    • 网路分析与效能监控平台
    • 基于人工智慧/机器学习的网路管理工具
    • 控制器编配软体
  • 硬体
    • 伺服器/AI加速器
    • 网路装置
    • 其他支援的硬体
  • 服务
    • 专业服务
    • 託管服务

6. 全球人工智慧驱动网路优化市场依部署方式划分

  • 云端基础的
  • 本地部署
  • 杂交种

7. 全球人工智慧驱动网路优化市场（按技术划分）

  • 机器学习（ML）
  • 深度学习（DL）
  • 自然语言处理（NLP）
  • 生成式人工智慧（GenAI）
  • 其他技术

8. 全球人工智慧驱动网路优化市场（按应用划分）

  • 流量管理与负载平衡
  • 资源分配与能力规划
  • 网路安全与异常检测
  • 故障预测与修復
  • 效能监控和诊断
  • 其他用途

9. 全球人工智慧驱动的网路优化市场（按最终用户划分）

  • 通讯
  • IT与资料中心
  • 银行、金融服务和保险业 (BFSI)
  • 製造业
  • 医疗保健
  • 政府/公共部门
  • 其他最终用户

第十章 由全球人工智慧驱动的网路最佳化市场（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
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 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：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • NVIDIA Corporation
  • Cisco Systems, Inc.
  • Juniper Networks, Inc.
  • Nokia Corporation
  • Telefonaktiebolaget LM Ericsson
  • Huawei Technologies Co., Ltd.
  • Arista Networks, Inc.
  • Ciena Corporation
  • Hewlett Packard Enterprise Development LP
  • IBM Corporation
  • VMware, Inc.
  • NetScout Systems, Inc.
  • Infovista SAS
  • NetBrain Technologies, Inc.
  • Amdocs Limited
  • Broadcom Inc.
  • Extreme Networks, Inc.
  • Fujitsu Limited
  • Dell Technologies Inc.
  • Forward Networks, Inc.
Product Code: SMRC31907

According to Stratistics MRC, the Global AI-Driven Network Optimization Market is accounted for $7.8 billion in 2025 and is expected to reach $27.9 billion by 2032 growing at a CAGR of 20.0% during the forecast period. AI-driven network optimization encompasses solutions using Artificial Intelligence (AI) and Machine Learning (ML) to autonomously manage and optimize telecommunications and enterprise networks. It analyzes real-time traffic data to predict congestion, dynamically allocate resources, and ensure Quality of Service (QoS). This leads to self-healing networks that preemptively resolve issues, reduce downtime, and enhance user experience. The market is driven by escalating data consumption and the complexity of 5G and IoT ecosystems, demanding proactive, intelligent management beyond human-scale capabilities.

According to MIT Technology Review, telecom operators using AI-driven network optimization have reported data throughput improvements of 20-35% and network latency reductions for end-users.

Market Dynamics:

Driver:

Exponential growth in network complexity and data traffic

Exponential growth in network complexity and data traffic has pushed operators to adopt AI-driven optimization to manage scale and performance. Modern networks carry heterogeneous workloads IoT telemetry, high-definition video, real-time gaming, and cloud-native microservices creating unpredictable traffic spikes and latency-sensitive flows that defy manual tuning. AI systems ingest vast telemetry, detect patterns, predict congestion, and autonomously adjust routing, QoS, and resource allocation, resulting in higher throughput and resilience. Furthermore, automated optimization reduces operational overhead and frees engineers to focus on strategic initiatives, accelerating network modernization and service differentiation across enterprises and service providers.

Restraint:

High implementation costs and integration complexity

Deploying analytics platforms, collecting scale telemetry, and training models require significant upfront investment in hardware, software, and skilled personnel. Integrating AI solutions with legacy routers, varied vendor interfaces, and existing OSS/BSS stacks often demands customization and lengthy validation cycles, raising project risk. Moreover, total cost of ownership concerns and uncertain ROI slow procurement approvals, particularly for smaller operators. Vendors and integrators must demonstrate measurable operational savings, standardized APIs, and phased deployment models to lower barriers and accelerate adoption.

Opportunity:

Integration with SD-WAN and network virtualization technologies

By combining centralized SD-WAN policy control, virtualized network functions, and AI analytics, operators can orchestrate traffic steering, dynamic path selection, and intent-based policies with minimal manual intervention. Additionally, NFV and containerized services allow optimization engines to be deployed closer to workloads, reducing latency and improving SLA adherence. Such synergy enables modular, monetizable services automated performance assurance, adaptive security, and bandwidth optimization opening new revenue streams while simplifying operations and accelerating time-to-value for buyers.

Threat:

Competition from traditional network optimization solutions

Competition from traditional network optimization solutions represents a significant threat to AI-first vendors, as established players offer proven, lower-risk alternatives. Enterprises and carriers often prefer familiar rule-based tools, hardware accelerators, and vendor-provided optimizers with clear SLAs and procurement paths, perceiving AI approaches as experimental. Moreover, incumbent vendors can incorporate basic machine learning features into existing suites, blunting differentiation.

Covid-19 Impact:

Covid-19 accelerated demand for AI-driven network optimization as traffic volumes surged with remote work and cloud migration. Service providers and enterprises needed rapid automation to maintain performance, prompting pilot deployments and increased vendor engagement. However, budget re-prioritization and project delays tempered spending in some organizations, slowing large-scale rollouts. Overall, the pandemic validated the need for autonomous, resilient networks and pushed buy-side interest toward cloud-native, AI-enabled solutions that support distributed workforces and fluctuating traffic patterns globally.

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 because software-centric AI solutions enable rapid deployment, continuous updates, and broad compatibility with multi-vendor network environments. Software platforms provide analytics, policy engines, and orchestration without requiring immediate hardware upgrades, lowering entry barriers. Subscription licensing and cloud delivery models further encourage adoption among service providers and enterprises seeking operational agility. Moreover, rich ecosystems of third-party integrations, developer toolchains, and marketplaces expand functionality, allowing operators to incrementally adopt advanced optimization capabilities while protecting existing investments and accelerating time-to-value and reducing operational complexity for providers.

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

Over the forecast period, the hybrid segment is predicted to witness the highest growth rate as enterprises and carriers balance performance, compliance, and cost considerations. Hybrid solutions permit sensitive traffic to be processed on-site while non-critical analytics run in cloud environments, delivering optimal trade-offs. Additionally, network virtualization and container orchestration tools make hybrid deployments practical and automatable. Service providers offering managed hybrid packages and clear integration paths will accelerate customer migrations. This combination of technical feasibility and commercial models drives rapid uptake, particularly among large operators modernizing legacy estates without disrupting live services and ecosystems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to mature digital infrastructure, high enterprise IT spending, and early adoption of automation and AI technologies. Major cloud providers, a dense telecom operator presence, and significant R&D investments from vendors create a rich innovation ecosystem. Additionally, stringent performance SLAs and busy traffic profiles among ISPs and enterprises drive demand for advanced optimization. Robust professional services, managed offerings, and favorable venture funding further accelerate deployments, enabling North American firms to lead commercial trials and large-scale rollouts and global partnerships.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as expanding digitalization, rising mobile broadband, and large-scale cloud adoption accelerate demand for intelligent network optimization. Governments and enterprises are investing in 5G, edge computing, and smart city initiatives that create complex, distributed networks requiring automation. Additionally, a vibrant startup ecosystem and competitive vendor landscape produce localized, cost-effective solutions tailored to regional needs. Affordability, increasing skilled talent, and cross-border deployments across rapidly growing markets further drive accelerated uptake, making Asia Pacific a focal point for future growth.

Key players in the market

Some of the key players in AI-Driven Network Optimization Market include NVIDIA Corporation, Cisco Systems, Inc., Juniper Networks, Inc., Nokia Corporation, Telefonaktiebolaget LM Ericsson, Huawei Technologies Co., Ltd., Arista Networks, Inc., Ciena Corporation, Hewlett Packard Enterprise Development LP, IBM Corporation, VMware, Inc., NetScout Systems, Inc., Infovista SAS, NetBrain Technologies, Inc., Amdocs Limited, Broadcom Inc., Extreme Networks, Inc., Fujitsu Limited, Dell Technologies Inc., and Forward Networks, Inc.

Key Developments:

In September 2025, NVIDIA introduced an AI Blueprint for telco network configuration, using customized LLMs trained on telco data to automate network parameter tuning and optimize performance. Additionally, NVIDIA partnered with OpenAI to deploy 10 gigawatts of AI systems, reinforcing its role in next-generation AI infrastructure.

In June 2025, Cisco unveiled a "secure network architecture to accelerate workplace AI transformation" which includes AI-powered management capabilities, high-capacity/low-latency devices and quantum-resistant security, to address AI-era network demands.

In February 2025, Juniper announced the EX4000 Series Switches with an "AI- and cloud-native architecture" for wired/wireless access, delivering up to 85 % lower OPEX, 90 % fewer trouble tickets and 85 % fewer truck rolls - clearly positioning AI-driven network operations.

Components Covered:

  • Software
  • Hardware
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning (DL)
  • Natural Language Processing (NLP)
  • Generative AI (GenAI)
  • Other Technologies

Applications Covered:

  • Traffic Management and Load Balancing
  • Resource Allocation and Capacity Planning
  • Network Security and Anomaly Detection
  • Fault Prediction and Remediation
  • Performance Monitoring and Diagnostics
  • Other Applications

End Users Covered:

  • Telecommunications
  • IT & Data Centers
  • BFSI (Banking, Financial Services, and Insurance)
  • Manufacturing
  • Healthcare
  • Government & Public Sector
  • Other End Users

Regions Covered:

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

What our report offers:

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

Free Customization Offerings:

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

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

Table of Contents

1 Executive Summary

2 Preface

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

3 Market Trend Analysis

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

4 Porters Five Force Analysis

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

5 Global AI-Driven Network Optimization Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Network Analytics and Performance Monitoring Platforms
    • 5.2.2 AI/ML-based Network Management Tools
    • 5.2.3 Controller and Orchestration Software
  • 5.3 Hardware
    • 5.3.1 Servers and AI Accelerators
    • 5.3.2 Network Equipment
    • 5.3.3 Other Supporting Hardware
  • 5.4 Services
    • 5.4.1 Professional Services
    • 5.4.2 Managed Services

6 Global AI-Driven Network Optimization Market, By Deployment Mode

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

7 Global AI-Driven Network Optimization Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning (ML)
  • 7.3 Deep Learning (DL)
  • 7.4 Natural Language Processing (NLP)
  • 7.5 Generative AI (GenAI)
  • 7.6 Other Technologies

8 Global AI-Driven Network Optimization Market, By Application

  • 8.1 Introduction
  • 8.2 Traffic Management and Load Balancing
  • 8.3 Resource Allocation and Capacity Planning
  • 8.4 Network Security and Anomaly Detection
  • 8.5 Fault Prediction and Remediation
  • 8.6 Performance Monitoring and Diagnostics
  • 8.7 Other Applications

9 Global AI-Driven Network Optimization Market, By End User

  • 9.1 Introduction
  • 9.2 Telecommunications
  • 9.3 IT & Data Centers
  • 9.4 BFSI (Banking, Financial Services, and Insurance)
  • 9.5 Manufacturing
  • 9.6 Healthcare
  • 9.7 Government & Public Sector
  • 9.8 Other End Users

10 Global AI-Driven Network Optimization 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 NVIDIA Corporation
  • 12.2 Cisco Systems, Inc.
  • 12.3 Juniper Networks, Inc.
  • 12.4 Nokia Corporation
  • 12.5 Telefonaktiebolaget LM Ericsson
  • 12.6 Huawei Technologies Co., Ltd.
  • 12.7 Arista Networks, Inc.
  • 12.8 Ciena Corporation
  • 12.9 Hewlett Packard Enterprise Development LP
  • 12.10 IBM Corporation
  • 12.11 VMware, Inc.
  • 12.12 NetScout Systems, Inc.
  • 12.13 Infovista SAS
  • 12.14 NetBrain Technologies, Inc.
  • 12.15 Amdocs Limited
  • 12.16 Broadcom Inc.
  • 12.17 Extreme Networks, Inc.
  • 12.18 Fujitsu Limited
  • 12.19 Dell Technologies Inc.
  • 12.20 Forward Networks, Inc.

List of Tables

  • Table 1 Global AI-Driven Network Optimization Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Driven Network Optimization Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Driven Network Optimization Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI-Driven Network Optimization Market Outlook, By Network Analytics and Performance Monitoring Platforms (2024-2032) ($MN)
  • Table 5 Global AI-Driven Network Optimization Market Outlook, By AI/ML-based Network Management Tools (2024-2032) ($MN)
  • Table 6 Global AI-Driven Network Optimization Market Outlook, By Controller and Orchestration Software (2024-2032) ($MN)
  • Table 7 Global AI-Driven Network Optimization Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 8 Global AI-Driven Network Optimization Market Outlook, By Servers and AI Accelerators (2024-2032) ($MN)
  • Table 9 Global AI-Driven Network Optimization Market Outlook, By Network Equipment (2024-2032) ($MN)
  • Table 10 Global AI-Driven Network Optimization Market Outlook, By Other Supporting Hardware (2024-2032) ($MN)
  • Table 11 Global AI-Driven Network Optimization Market Outlook, By Services (2024-2032) ($MN)
  • Table 12 Global AI-Driven Network Optimization Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 13 Global AI-Driven Network Optimization Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 14 Global AI-Driven Network Optimization Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 15 Global AI-Driven Network Optimization Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 16 Global AI-Driven Network Optimization Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 17 Global AI-Driven Network Optimization Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 18 Global AI-Driven Network Optimization Market Outlook, By Technology (2024-2032) ($MN)
  • Table 19 Global AI-Driven Network Optimization Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 20 Global AI-Driven Network Optimization Market Outlook, By Deep Learning (DL) (2024-2032) ($MN)
  • Table 21 Global AI-Driven Network Optimization Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 22 Global AI-Driven Network Optimization Market Outlook, By Generative AI (GenAI) (2024-2032) ($MN)
  • Table 23 Global AI-Driven Network Optimization Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 24 Global AI-Driven Network Optimization Market Outlook, By Application (2024-2032) ($MN)
  • Table 25 Global AI-Driven Network Optimization Market Outlook, By Traffic Management and Load Balancing (2024-2032) ($MN)
  • Table 26 Global AI-Driven Network Optimization Market Outlook, By Resource Allocation and Capacity Planning (2024-2032) ($MN)
  • Table 27 Global AI-Driven Network Optimization Market Outlook, By Network Security and Anomaly Detection (2024-2032) ($MN)
  • Table 28 Global AI-Driven Network Optimization Market Outlook, By Fault Prediction and Remediation (2024-2032) ($MN)
  • Table 29 Global AI-Driven Network Optimization Market Outlook, By Performance Monitoring and Diagnostics (2024-2032) ($MN)
  • Table 30 Global AI-Driven Network Optimization Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 31 Global AI-Driven Network Optimization Market Outlook, By End User (2024-2032) ($MN)
  • Table 32 Global AI-Driven Network Optimization Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 33 Global AI-Driven Network Optimization Market Outlook, By IT & Data Centers (2024-2032) ($MN)
  • Table 34 Global AI-Driven Network Optimization Market Outlook, By BFSI (Banking, Financial Services, and Insurance) (2024-2032) ($MN)
  • Table 35 Global AI-Driven Network Optimization Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 36 Global AI-Driven Network Optimization Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 37 Global AI-Driven Network Optimization Market Outlook, By Government & Public Sector (2024-2032) ($MN)
  • Table 38 Global AI-Driven Network Optimization Market Outlook, By Other End Users (2024-2032) ($MN)

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