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市场调查报告书
商品编码
1932993

面向资料中心的AI驱动容量规划，全球市场预测至2034年：按组件、分析类型、解决方案类型、资料中心类型、部署模式、最终用户和地区划分

AI-Driven Capacity Planning for Data Centers Market Forecasts to 2034 - Global Analysis By Component, Analytics Type, Solution Type, Data Center Type, Deployment Model, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，全球资料中心人工智慧驱动容量规划市场预计将在 2026 年达到 45.3 亿美元，并在 2034 年达到 182.2 亿美元，在预测期内以 19% 的复合年增长率成长。

以资料中心为导向的AI驱动容量规划是一种利用人工智慧技术优化资源分配、预测未来需求并确保运算基础设施高效运作的方法。 AI模型透过分析历史效能资料、工作负载模式和环境因素，预测伺服器使用率、储存需求和网路频宽需求。这种主动式方法使资料中心能够避免资源过度分配或分配不足，降低能耗，并提高整体营运效率。 AI的整合实现了动态扩展、即时决策和自动调整，确保IT资源能够满足不断变化的业务需求，同时最大限度地降低成本并保持高服务可靠性。

对高效率资源利用的需求日益增长

云端运算、人工智慧和物联网带来的工作负载不断增长，推动了对智慧规划解决方案的需求。平台能够预测运算、储存和电力资源的分配，从而最大限度地减少浪费。供应商正在整合机器学习演算法以提高预测精度。银行、金融服务和保险 (BFSI)、电信和製造业等行业的公司正在采用人工智慧驱动的规划来提高营运效率。对优化利用率的需求最终推动了人工智慧容量规划的普及，使其成为建立弹性资料中心的重要策略驱动力。

人工智慧专业人才短缺

资料科学和人工智慧工程领域专业知识的匮乏正在减缓先进规划平台的普及。中小企业在人才招募和留任方面面临着特别严峻的挑战。培训和技能提升需要大量的投入和时间。为了弥补劳动力短缺，供应商面临简化介面和自动化流程的压力。持续的技能短缺最终限制了扩充性，并减缓了人工智慧驱动的产能规划解决方案的普及。

预测分析工具日益普及

预测平台能够实现异常检测、需求预测和动态资源分配。供应商正在整合人工智慧驱动的分析技术，以提高系统弹性并减少停机时间。企业正在利用预测洞察来调整基础设施，使其与业务成长保持一致。医疗保健、零售和物流等行业的应用正在迅速扩展。预测分析最终透过将人工智慧容量规划定位为资料中心营运的变革性力量，从而推动业务成长。

由于技术快速变革而导致的过时

营运负责人难以将现有规划平台适配到新技术上。频繁的升级会增加成本，并阻碍营运的连续性。供应商必须投入大量资金进行研发才能保持竞争力。小规模的供应商难以适应人工智慧生态系统的快速变化。持续存在的过时风险最终会抑制技术的普及，并减缓整体市场成长。

新冠疫情的感染疾病：

新冠疫情透过加速数位转型和增强对弹性基础设施的依赖，重塑了资料中心人工智慧驱动容量规划的市场格局。远距办公和线上活动的激增给资料中心带来了前所未有的压力。营运商纷纷采用人工智慧驱动的规划平台来维持服务连续性并优化资源配置。预算限制最初减缓了成本敏感型产业的采用速度。然而，随着对自动化和预测分析的日益重视，容量规划解决方案的投资也随之增加。最终，疫情再次凸显了人工智慧驱动规划作为提升营运弹性催化剂的战略重要性。

预计在预测期内，人工智慧规划平台细分市场将占据最大的市场份额。

在对智慧资源分配的需求驱动下，人工智慧规划平台预计将在预测期内占据最大的市场份额。这些平台能够提供对运算、储存和电力利用率的预测性洞察。营运商正在采用人工智慧规划工具来最大限度地减少浪费并提高效率。供应商正在整合机器学习演算法以扩大应用范围。大型企业正在推动对高阶规划框架的需求。人工智慧规划平台最终将主导容量规划解决方案的核心推动力。

预计在预测期内，预测性分析领域将实现最高的复合年增长率。

在对可执行洞察和先发制人决策的需求驱动下，预测性分析领域预计将在预测期内实现最高成长率。平台使营运商能够模拟各种场景并推荐最佳资源分配方案。供应商正在整合人工智慧驱动的预测性模型以提高扩充性。企业正在利用预测性分析来调整其基础设施以适应动态工作负载。在银行、金融和保险 (BFSI)、电信和製造业等行业，预测性分析的应用正在迅速成长。预测性分析最终透过赋能成长最快的领域——人工智慧驱动的容量规划——来推动成长。

占比最大的地区：

在预测期内，北美预计将保持最大的市场份额，这得益于其成熟的资料中心生态系统以及企业对人工智慧驱动型规划平台的广泛应用。美国在超大规模设施、银行、金融服务和保险（BFSI）基础设施以及云端原生营运方面投入巨资，主导趋势。加拿大则透过合规主导的倡议和政府支持的数位化计画来补充其成长。主要技术提供商的存在巩固了该地区的领先地位。对永续性和监管合规性日益增长的需求正在推动各行业的应用。北美最终加快了创新步伐，进一步巩固了其在人工智慧驱动型容量规划领域的领先地位。

年复合成长率最高的地区：

在预测期内，亚太地区预计将实现最高的复合年增长率，这主要得益于快速的数位化和不断扩展的资料中心生态系统。中国正在大力投资超大规模资料中心和人工智慧驱动的基础设施。印度则透过政府主导的数位化项目和金融科技的扩张来推动成长。日本和韩国则着力自动化和企业韧性的提升，积极推动相关技术的应用。该地区的电信、银行、金融和保险（BFSI）以及製造业正在推动对智慧规划平台的需求。亚太地区正在加速采用人工智慧驱动的容量规划技术，以巩固其作为成长最快中心的地位。

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    • 根据主要企业的产品系列、地理覆盖范围和策略联盟进行基准分析

目录

第一章执行摘要

第二章 前言

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

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 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
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球资料中心人工智慧驱动容量规划市场（按组件划分）

  • 软体
  • 平台和工具
  • 服务
  • 咨询和顾问服务
  • 整合和实施服务
  • 其他部件

6. 全球资料中心人工智慧驱动容量规划市场（依分析类型划分）

  • 预测分析
  • 预测分析
  • 说明分析

7. 全球资料中心人工智慧驱动容量规划市场（按解决方案类型划分）

  • 人工智慧规划平台
  • 资源优化工具
  • 工作负载和伺服器管理系统
  • 能源和冷却优化系统
  • 其他解决方案类型

8. 全球资料中心人工智慧驱动容量规划市场（按资料中心类型划分）

  • 超大规模资料中心
  • 託管资料中心
  • 企业资料中心
  • 边缘和微型资料中心
  • 其他资料中心类型

9. 全球资料中心人工智慧驱动容量规划市场（按部署模式划分）

  • 本地部署
  • 基于云端的

第十章 全球资料中心人工智慧驱动容量规划市场（依最终用户划分）

  • IT/通讯
  • 银行与金融服务业 (BFSI)
  • 医疗保健
  • 政府/国防
  • 能源与公共产业
  • 其他最终用户

第十一章 全球资料中心人工智慧驱动容量规划市场（按地区划分）

  • 北美洲
    • 我们
    • 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
    • 其他南美国家
  • 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

第十二章 重大进展

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

第十三章：企业概况

  • Schneider Electric SE
  • Eaton Corporation plc
  • ABB Ltd.
  • Siemens AG
  • Vertiv Holdings Co.
  • Huawei Technologies Co. Ltd.
  • Dell Technologies Inc.
  • Hewlett Packard Enterprise Company
  • Cisco Systems, Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • Google LLC
  • Oracle Corporation
  • NEC Corporation
Product Code: SMRC33561

According to Stratistics MRC, the Global AI-Driven Capacity Planning for Data Centers Market is accounted for $4.53 billion in 2026 and is expected to reach $18.22 billion by 2034 growing at a CAGR of 19% during the forecast period. AI-Driven Capacity Planning for Data Centers is the use of artificial intelligence technologies to optimize resource allocation, predict future demands, and ensure efficient operation of computing infrastructure. By analyzing historical performance data, workload patterns, and environmental factors, AI models can forecast server utilization, storage needs, and network bandwidth requirements. This proactive approach helps data centers prevent over-provisioning or under-provisioning, reduce energy consumption, and improve overall operational efficiency. Integrating AI enables dynamic scaling, real-time decision-making, and automated adjustments, ensuring that IT resources meet evolving business demands while minimizing costs and maintaining high service reliability.

Market Dynamics:

Driver:

Increasing demand for efficient resource utilization

Rising workloads from cloud computing, AI, and IoT intensify the need for intelligent planning solutions. Platforms enable predictive allocation of compute, storage, and power resources to minimize waste. Vendors are embedding machine learning algorithms to enhance forecasting accuracy. Enterprises across BFSI, telecom, and manufacturing are adopting AI-driven planning to improve operational efficiency. Demand for optimized utilization is ultimately amplifying adoption, positioning AI capacity planning as a strategic enabler of resilient data centers.

Restraint:

Lack of skilled AI professionals

Shortage of expertise in data science and AI engineering slows deployment of advanced planning platforms. Smaller enterprises face disproportionate challenges in recruiting and retaining talent. Training and reskilling initiatives require significant investment and time. Vendors are compelled to simplify interfaces and automate processes to offset workforce gaps. Persistent skill shortages are ultimately restricting scalability and delaying widespread adoption of AI-driven capacity planning solutions.

Opportunity:

Rising adoption of predictive analytics tools

Predictive platforms enable anomaly detection, demand forecasting, and dynamic resource allocation. Vendors are embedding AI-driven analytics to strengthen resilience and reduce downtime. Enterprises leverage predictive insights to align infrastructure with business growth. Adoption across industries such as healthcare, retail, and logistics is expanding rapidly. Predictive analytics is ultimately strengthening growth by positioning AI capacity planning as a transformative force in data center operations.

Threat:

Rapid technological changes causing obsolescence

Operators struggle to keep planning platforms aligned with new technologies. Frequent upgrades increase costs and disrupt operational continuity. Vendors must invest heavily in R&D to remain competitive. Smaller providers find it difficult to adapt to rapid shifts in AI ecosystems. Persistent obsolescence risks are ultimately constraining adoption and slowing overall market growth.

Covid-19 Impact:

The Covid-19 pandemic reshaped the AI-Driven Capacity Planning for Data Centers Market by accelerating digital transformation and intensifying reliance on resilient infrastructure. Remote work and surging online activity placed unprecedented strain on data centers. Operators deployed AI-driven planning platforms to maintain service continuity and optimize resources. Budget constraints initially slowed adoption in cost-sensitive industries. Growing emphasis on automation and predictive analytics encouraged stronger investments in capacity planning solutions. The pandemic ultimately reinforced the strategic importance of AI-driven planning as a catalyst for operational resilience.

The AI planning platforms segment is expected to be the largest during the forecast period

The AI planning platforms segment is expected to account for the largest market share during the forecast period, supported by demand for intelligent resource allocation. Platforms provide predictive insights into compute, storage, and power utilization. Operators deploy AI planning tools to minimize waste and enhance efficiency. Vendors are embedding machine learning algorithms to broaden adoption. Large-scale enterprises are driving demand for advanced planning frameworks. AI planning platforms are ultimately consolidating leadership by anchoring the backbone of capacity planning solutions.

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

Over the forecast period, the prescriptive analytics segment is predicted to witness the highest growth rate, supported by demand for actionable insights and proactive decision-making. Platforms enable operators to simulate scenarios and recommend optimal resource allocation. Vendors are embedding AI-driven prescriptive models to enhance scalability. Enterprises leverage prescriptive analytics to align infrastructure with dynamic workloads. Adoption across industries such as BFSI, telecom, and manufacturing is expanding rapidly. Prescriptive analytics is ultimately fueling growth by strengthening the fastest-growing segment of AI-driven capacity planning.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, anchored by mature data center ecosystems and strong enterprise adoption of AI-driven planning platforms. The United States leads with significant investments in hyperscale facilities, BFSI infrastructure, and cloud-native operations. Canada complements growth with compliance-driven initiatives and government-backed digital programs. Presence of major technology providers consolidates regional leadership. Rising demand for sustainability and regulatory compliance is shaping adoption across industries. North America is ultimately reinforcing innovation and strengthening its dominance in AI-driven capacity planning.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapid digitalization and expanding data center ecosystems. China is investing heavily in hyperscale facilities and AI-driven infrastructure. India is fostering growth through government-backed digitization programs and fintech expansion. Japan and South Korea are advancing adoption with strong emphasis on automation and enterprise resilience. Telecom, BFSI, and manufacturing sectors across the region are driving demand for intelligent planning platforms. Asia Pacific is ultimately fueling adoption and strengthening its position as the fastest-growing hub for AI-driven capacity planning.

Key players in the market

Some of the key players in AI-Driven Capacity Planning for Data Centers Market include Schneider Electric SE, Eaton Corporation plc, ABB Ltd., Siemens AG, Vertiv Holdings Co., Huawei Technologies Co., Ltd., Dell Technologies Inc., Hewlett Packard Enterprise Company, Cisco Systems, Inc., IBM Corporation, Microsoft Corporation, Amazon Web Services, Inc., Google LLC, Oracle Corporation and NEC Corporation.

Key Developments:

In January 2024, Siemens completed the acquisition of Belden's Hirschmann Automation and Control business, strengthening its industrial networking and edge computing portfolio. This enhances the real-time data infrastructure necessary for implementing robust AI-driven monitoring and control systems at the data center edge.

In March 2023, ABB launched the ABB Ability(TM) Energy and Asset Manager for data centers, a cloud-based platform that uses AI and data analytics to optimize energy consumption and predict maintenance needs. This product directly contributes to capacity planning by analyzing historical and real-time data to forecast power and cooling requirements, improving operational efficiency.

Components Covered:

  • Software
  • Platforms & Tools
  • Services
  • Consulting & Advisory Services
  • Integration & Implementation Services
  • Other Components

Analytics Types Covered:

  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics

Solution Types Covered:

  • AI Planning Platforms
  • Resource Optimization Tools
  • Workload & Server Management Systems
  • Energy & Cooling Optimization Systems
  • Other Solution Types

Data Center Types Covered:

  • Hyperscale Data Centers
  • Colocation Data Centers
  • Enterprise Data Centers
  • Edge & Micro Data Centers
  • Other Data Center Types

Deployment Models Covered:

  • On-Premise
  • Cloud-Based

End Users Covered:

  • IT & Telecom
  • BFSI (Banking & Financial Services)
  • Healthcare
  • Government & Defense
  • Energy & Utilities
  • 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 2023, 2024, 2025, 2026, 2028, 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

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 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

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

5 Global AI-Driven Capacity Planning for Data Centers Market, By Component

  • 5.1 Introduction
  • 5.2 Software
  • 5.3 Platforms & Tools
  • 5.4 Services
  • 5.5 Consulting & Advisory Services
  • 5.6 Integration & Implementation Services
  • 5.7 Other Components

6 Global AI-Driven Capacity Planning for Data Centers Market, By Analytics Type

  • 6.1 Introduction
  • 6.2 Predictive Analytics
  • 6.3 Prescriptive Analytics
  • 6.4 Descriptive Analytics

7 Global AI-Driven Capacity Planning for Data Centers Market, By Solution Type

  • 7.1 Introduction
  • 7.2 AI Planning Platforms
  • 7.3 Resource Optimization Tools
  • 7.4 Workload & Server Management Systems
  • 7.5 Energy & Cooling Optimization Systems
  • 7.6 Other Solution Types

8 Global AI-Driven Capacity Planning for Data Centers Market, By Data Center Type

  • 8.1 Introduction
  • 8.2 Hyperscale Data Centers
  • 8.3 Colocation Data Centers
  • 8.4 Enterprise Data Centers
  • 8.5 Edge & Micro Data Centers
  • 8.6 Other Data Center Types

9 Global AI-Driven Capacity Planning for Data Centers Market, By Deployment Model

  • 9.1 Introduction
  • 9.2 On-Premise
  • 9.3 Cloud-Based

10 Global AI-Driven Capacity Planning for Data Centers Market, By End User

  • 10.1 Introduction
  • 10.2 IT & Telecom
  • 10.3 BFSI (Banking & Financial Services)
  • 10.4 Healthcare
  • 10.5 Government & Defense
  • 10.6 Energy & Utilities
  • 10.7 Other End Users

11 Global AI-Driven Capacity Planning for Data Centers Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Schneider Electric SE
  • 13.2 Eaton Corporation plc
  • 13.3 ABB Ltd.
  • 13.4 Siemens AG
  • 13.5 Vertiv Holdings Co.
  • 13.6 Huawei Technologies Co. Ltd.
  • 13.7 Dell Technologies Inc.
  • 13.8 Hewlett Packard Enterprise Company
  • 13.9 Cisco Systems, Inc.
  • 13.10 IBM Corporation
  • 13.11 Microsoft Corporation
  • 13.12 Amazon Web Services, Inc.
  • 13.13 Google LLC
  • 13.14 Oracle Corporation
  • 13.15 NEC Corporation

List of Tables

  • Table 1 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Region (2025-2034) ($MN)
  • Table 2 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Component (2025-2034) ($MN)
  • Table 3 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Software (2025-2034) ($MN)
  • Table 4 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Platforms & Tools (2025-2034) ($MN)
  • Table 5 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Services (2025-2034) ($MN)
  • Table 6 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Consulting & Advisory Services (2025-2034) ($MN)
  • Table 7 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Integration & Implementation Services (2025-2034) ($MN)
  • Table 8 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Other Components (2025-2034) ($MN)
  • Table 9 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Analytics Type (2025-2034) ($MN)
  • Table 10 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Predictive Analytics (2025-2034) ($MN)
  • Table 11 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Prescriptive Analytics (2025-2034) ($MN)
  • Table 12 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Descriptive Analytics (2025-2034) ($MN)
  • Table 13 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Solution Type (2025-2034) ($MN)
  • Table 14 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By AI Planning Platforms (2025-2034) ($MN)
  • Table 15 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Resource Optimization Tools (2025-2034) ($MN)
  • Table 16 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Workload & Server Management Systems (2025-2034) ($MN)
  • Table 17 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Energy & Cooling Optimization Systems (2025-2034) ($MN)
  • Table 18 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Other Solution Types (2025-2034) ($MN)
  • Table 19 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Data Center Type (2025-2034) ($MN)
  • Table 20 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Hyperscale Data Centers (2025-2034) ($MN)
  • Table 21 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Colocation Data Centers (2025-2034) ($MN)
  • Table 22 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Enterprise Data Centers (2025-2034) ($MN)
  • Table 23 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Edge & Micro Data Centers (2025-2034) ($MN)
  • Table 24 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Other Data Center Types (2025-2034) ($MN)
  • Table 25 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Deployment Model (2025-2034) ($MN)
  • Table 26 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By On-Premise (2025-2034) ($MN)
  • Table 27 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Cloud-Based (2025-2034) ($MN)
  • Table 28 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By End User (2025-2034) ($MN)
  • Table 29 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By IT & Telecom (2025-2034) ($MN)
  • Table 30 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By BFSI (Banking & Financial Services) (2025-2034) ($MN)
  • Table 31 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Healthcare (2025-2034) ($MN)
  • Table 32 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Government & Defense (2025-2034) ($MN)
  • Table 33 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Energy & Utilities (2025-2034) ($MN)
  • Table 34 Global AI-Driven Capacity Planning for Data Centers Market Outlook, By Other End Users (2025-2034) ($MN)

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