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商品編碼

1857042

全球人工智慧驅動的網路最佳化市場：預測至 2032 年——按組件、部署方式、技術、應用、最終用戶和地區進行分析

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: 2025年10月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，全球人工智慧驅動的網路最佳化市場預計到 2025 年將達到 78 億美元，到 2032 年將達到 279 億美元，預測期內複合年成長率為 20.0%。

人工智慧驅動的網路最佳化解決方案利用人工智慧 (AI) 和機器學習 (ML) 技術，自主管理和最佳化通訊及企業網路。它透過分析即時流量資料來預測網路擁塞、動態分配資源並確保服務品質 (QoS)。最終形成一個能夠主動解決問題、減少停機時間並提升使用者體驗的自癒網路。這一市場的發展動力源於不斷成長的數據消費量以及日益複雜的 5G 和物聯網生態系統，這些都需要超越人類能力的智慧主動管理。

根據《麻省理工科技評論》報導，使用人工智慧驅動的網路最佳化技術的通訊業者報告稱，數據吞吐量提高了 20-35%，終端用戶的網路延遲降低了。

網路複雜性和資料流量呈指數級成長

網路複雜性和數據流量的指數級成長迫使通訊業者採用人工智慧驅動的最佳化技術來管理規模和效能。現代網路承載著異質工作負載，包括物聯網遙測、高畫質視訊、即時遊戲和雲端原生微服務，導致流量高峰難以預測，以及對延遲高度敏感的流量，而這些都無法透過人工調優來解決。人工智慧系統能夠接收大量遙測數據，偵測模式，預測擁塞，並自主調整路由、服務品質 (QoS) 和資源分配。此外，自動化最佳化還能降低營運開銷，使工程師能夠專注於策略舉措，從而加速企業和服務供應商的網路現代化和服務差異化。

實施成本高且整合複雜

部署分析平台、大規模收集遙測資料以及訓練模型需要前期投資大量資金。將人工智慧解決方案與傳統路由器、各種廠商介面以及現有的OSS/BSS堆疊整合通常需要客製化開發和漫長的檢驗週期，從而增加計劃風險。此外，對整體擁有成本和投資報酬率不確定性的擔憂也會延緩採購核准，尤其對於小規模的業者而言。供應商和整合商必須證明其能夠帶來可衡量的營運成本節約、提供標準化的API以及分階段部署模式，以降低門檻並加速推廣應用程式。

與 SD-WAN 和網路虛擬化技術的整合

集中式 SD-WAN 策略控制、虛擬化網路功能和 AI 分析的結合，使營運商編配流量控制、動態路徑選擇和基於意圖的策略。此外，NFV 和容器化服務使最佳化引擎能夠更靠近工作負載，從而降低延遲並提高 SLA 遵守率。這些協同效應支援模組化、可獲利的服務，透過自動化效能保證、自適應安全性和頻寬最佳化，開闢新的收入來源，同時簡化營運並加快客戶價值實現速度。

與傳統網路最佳化解決方案競爭

來自傳統網路最佳化解決方案的競爭對以人工智慧為先導的供應商構成了重大威脅。企業和通訊業者通常更傾向於使用熟悉的基於規則的工具、硬體加速器以及供應商提供的、具有明確服務等級協定 (SLA) 和採購流程的最佳化器，並將人工智慧方法視為實驗性技術。此外，現有供應商只需將基本的機器學習功能整合到現有產品中，即可降低差異化優勢。

新冠疫情的影響：

新冠疫情加速了對人工智慧驅動的網路最佳化需求，遠距辦公和雲端遷移導致流量激增。服務提供者和企業需要快速自動化以維持效能，這促使試點部署和供應商合作增加。然而，預算重新調整和計劃延期導致一些組織削減支出並推遲大規模部署。整體而言，疫情驗證了自主且高彈性網路的必要性，並提升了買方對支援分散式辦公室和全球流量波動模式的雲端原生人工智慧解決方案的興趣。

預計在預測期內，軟體板塊將成為最大的板塊。

預計在預測期內，軟體領域將佔據最大的市場佔有率，因為以軟體為中心的AI解決方案能夠實現快速部署、持續更新，並與多廠商網路環境廣泛相容。軟體平台提供分析、策略引擎和編配，無需立即升級硬體，從而降低了准入門檻。訂閱許可和雲端交付模式進一步推動了服務供應商和尋求營運敏捷性的企業的採用。此外，豐富的第三方整合、開發者工具鍊和市場生態系統擴展了功能，使營運商能夠在保護現有投資的同時逐步採用高級最佳化功能，從而加快價值實現速度並降低服務提供商的營運複雜性。

預計在預測期內，混合動力汽車細分市場將實現最高的複合年成長率。

預計在預測期內，混合雲領域將保持最高的成長率，因為企業和通訊業者都在權衡效能、合規性和成本之間的關係。混合雲解決方案透過在本地處理敏感流量並在雲端環境中運行非關鍵分析，實現了最佳的平衡。此外，網路虛擬化和容器編配工具的出現，使得混合雲部署變得切實可行且自動化。提供託管混合雲套餐和清晰整合路徑的服務供應商預計將加速客戶遷移。這種技術可行性和商業模式的結合正在推動混合雲的快速普及，尤其是在大規模營運商中，因為它使他們能夠在不中斷運作中服務和生態系統的情況下，對其傳統設施進行現代化改造。

佔比最大的地區：

由於北美擁有成熟的數位基礎設施、高額的企業IT支出以及對自動化和人工智慧技術的早期應用，預計在預測期內，北美將佔據最大的市場佔有率。主要雲端服務供應商、通訊業者的集中以及供應商的大規模研發投入，共同打造了一個豐富的創新生態系統。此外，網路服務供應商和企業嚴格的效能服務等級協定 (SLA) 以及繁忙的流量狀況，也推動了對高階最佳化技術的需求。強大的專業服務、託管服務以及有利的創業融資，進一步加速了部署進程，使北美企業能夠在商業試驗、大規模部署和全球夥伴關係主導。

複合年成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率，這主要得益於數位化的加速、行動寬頻的普及以及大規模雲端運算的採用，這些因素共同推動了對智慧網路最佳化的需求。各國政府和企業正在大力投資5G、邊緣運算和智慧城市項目，這些項目正在建立複雜且分散的網路，而這些網路需要自動化。此外，充滿活力的新興企業生態系統和競爭激烈的供應商格局正在催生出針對區域需求的在地化、具成本效益解決方案。價格優勢、不斷壯大的熟練勞動力以及快速成長市場中的跨境覆蓋能力，進一步促進了這些解決方案的普及，使亞太地區成為未來成長的焦點。

免費客製化服務

訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

公司簡介
- 對最多三家其他公司進行全面分析
- 對主要企業進行SWOT分析（最多3家公司）
區域分類
- 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
競爭基準化分析
- 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 亞太其他地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美洲
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第11章：主要趨勢

合約、商業夥伴關係和合資企業
企業合併（M&A）
新產品發布
業務拓展
其他關鍵策略

第12章：公司簡介

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.

簡介目錄圖表

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

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

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.

簡介目錄圖表

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)