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市場調查報告書
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1932993

面向資料中心的AI驅動容量規劃，全球市場預測至2034年：按組件、分析類型、解決方案類型、資料中心類型、部署模式、最終用戶和地區分類

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個工作天內

價格

根據 Stratistics MRC 的一項研究，全球資料中心人工智慧驅動容量規劃市場預計將在 2026 年達到 45.3 億美元，並在 2034 年達到 182.2 億美元，在預測期內以 19% 的複合年成長率成長。

以資料中心為導向的AI驅動容量規劃是一種利用人工智慧技術最佳化資源分配、預測未來需求並確保運算基礎設施高效運作的方法。 AI模型透過分析歷史效能資料、工作負載模式和環境因素，預測伺服器使用率、儲存需求和網路頻寬需求。這種主動式方法使資料中心能夠避免資源過度分配或分配不足，降低能耗，並提高整體營運效率。 AI的整合實現了動態擴展、即時決策和自動調整，確保IT資源能夠滿足不斷變化的業務需求，同時最大限度地降低成本並保持高服務可靠性。

對高效率資源利用的需求日益成長

雲端運算、人工智慧和物聯網帶來的工作負載不斷成長，推動了對智慧規劃解決方案的需求。平台能夠預測運算、儲存和電力資源的分配，從而最大限度地減少浪費。供應商正在整合機器學習演算法以提高預測精度。銀行、金融服務和保險 (BFSI)、電信和製造業等行業的公司正在採用人工智慧驅動的規劃來提高營運效率。對最佳化利用率的需求最終推動了人工智慧容量規劃的普及，使其成為建立彈性資料中心的重要策略驅動力。

人工智慧專業人才短缺

資料科學和人工智慧工程領域專業知識的匱乏正在減緩先進規劃平台的普及。中小企業在人才招募和留任方面面臨著特別嚴峻的挑戰。培訓和技能提升需要大量的投入和時間。為了彌補勞動力短缺，供應商面臨簡化介面和自動化流程的壓力。持續的技能短缺最終限制了擴充性，並減緩了人工智慧驅動的產能規劃解決方案的普及。

預測分析工具日益普及

預測平台能夠實現異常檢測、需求預測和動態資源分配。供應商正在整合人工智慧驅動的分析技術，以提高系統彈性並減少停機時間。企業正在利用預測洞察來調整基礎設施，使其與業務成長保持一致。醫療保健、零售和物流等行業的應用正在迅速擴展。預測分析最終透過將人工智慧容量規劃定位為資料中心營運的變革性力量，從而推動業務成長。

由於技術快速變革而導致的過時

營運負責人難以將現有規劃平台適配到新技術上。頻繁的升級會增加成本，並阻礙營運的連續性。供應商必須投入大量資金進行研發才能保持競爭力。小規模的供應商難以適應人工智慧生態系統的快速變化。持續存在的過時風險最終會抑制技術的普及，並減緩整體市場成長。

新冠疫情的感染疾病：

新冠疫情透過加速數位轉型和增強對彈性基礎設施的依賴，重塑了資料中心人工智慧驅動容量規劃的市場格局。遠距辦公和線上活動的激增給資料中心帶來了前所未有的壓力。營運商紛紛採用人工智慧驅動的規劃平台來維持服務連續性並最佳化資源配置。預算限制最初減緩了成本敏感型產業的採用速度。然而，隨著對自動化和預測分析的日益重視，容量規劃解決方案的投資也隨之增加。最終，疫情再次凸顯了人工智慧驅動規劃作為提升營運彈性催化劑的戰略重要性。

預計在預測期內，人工智慧規劃平台細分市場將佔據最大的市場佔有率。

在對智慧資源分配的需求驅動下，人工智慧規劃平台預計將在預測期內佔據最大的市場佔有率。這些平台能夠提供對運算、儲存和電力利用率的預測性洞察。營運商正在採用人工智慧規劃工具來最大限度地減少浪費並提高效率。供應商正在整合機器學習演算法以擴大應用範圍。大型企業正在推動對高階規劃框架的需求。人工智慧規劃平台最終將主導容量規劃解決方案的核心推動力。

預計在預測期內，預測性分析領域將實現最高的複合年成長率。

在對可執行洞察和先發制人決策的需求驅動下，預測性分析領域預計將在預測期內實現最高成長率。平台使營運商能夠模擬各種場景並推薦最佳資源分配方案。供應商正在整合人工智慧驅動的預測性模型以提高擴充性。企業正在利用預測性分析來調整其基礎設施以適應動態工作負載。在銀行、金融和保險 (BFSI)、電信和製造業等行業，預測性分析的應用正在迅速成長。預測性分析最終透過賦能成長最快的領域——人工智慧驅動的容量規劃——來推動成長。

佔比最大的地區：

在預測期內，北美預計將保持最大的市場佔有率，這得益於其成熟的資料中心生態系統以及企業對人工智慧驅動型規劃平台的廣泛應用。美國在超大規模設施、銀行、金融服務和保險（BFSI）基礎設施以及雲端原生營運方面投入巨資，主導趨勢。加拿大則透過合規主導的舉措和政府支持的數位化計畫來補充其成長。主要技術提供商的存在鞏固了該地區的領先地位。對永續性和監管合規性日益成長的需求正在推動各行業的應用。北美最終加快了創新步伐，進一步鞏固了其在人工智慧驅動型容量規劃領域的領先地位。

年複合成長率最高的地區：

在預測期內，亞太地區預計將實現最高的複合年成長率，這主要得益於快速的數位化和不斷擴展的資料中心生態系統。中國正在大力投資超大規模資料中心和人工智慧驅動的基礎設施。印度則透過政府主導的數位化項目和金融科技的擴張來推動成長。日本和韓國則著力自動化和企業韌性的提升，積極推動相關技術的應用。該地區的電信、銀行、金融和保險（BFSI）以及製造業正在推動對智慧規劃平台的需求。亞太地區正在加速採用人工智慧驅動的容量規劃技術，以鞏固其作為成長最快中心的地位。

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目錄

第1章執行摘要

第2章 前言

  • 概括
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球資料中心人工智慧驅動容量規劃市場（按組件分類）

  • 軟體
  • 平台和工具
  • 服務
  • 諮詢和顧問服務
  • 整合和實施服務
  • 其他部件

6. 全球資料中心人工智慧驅動容量規劃市場（依分析類型分類）

  • 預測分析
  • 預測分析
  • 說明分析

7. 全球資料中心人工智慧驅動容量規劃市場（按解決方案類型分類）

  • 人工智慧規劃平台
  • 資源最佳化工具
  • 工作負載和伺服器管理系統
  • 能源和冷卻最佳化系統
  • 其他解決方案類型

8. 全球資料中心人工智慧驅動容量規劃市場（按資料中心類型分類）

  • 超大規模資料中心
  • 託管資料中心
  • 企業資料中心
  • 邊緣和微型資料中心
  • 其他資料中心類型

9. 全球資料中心人工智慧驅動容量規劃市場（按部署模式分類）

  • 本地部署
  • 基於雲端的

第10章 全球資料中心人工智慧驅動容量規劃市場（依最終用戶分類）

  • IT/通訊
  • 銀行與金融服務業 (BFSI)
  • 醫療保健
  • 政府/國防
  • 能源與公共產業
  • 其他最終用戶

第11章 全球資料中心人工智慧驅動容量規劃市場（按地區分類）

  • 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
    • 其他南美國家
  • 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12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第13章：企業概況

  • 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.