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
Description
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
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
Table of Contents
200 Pages
- 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.
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