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ITサポートにおけるAIと自動化の世界市場：2025年～2032年

Global AI and Automation in IT Support Market - 2025-2032

出版日
ページ情報
英文 204 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=144.06円
ITサポートにおけるAIと自動化の世界市場：2025年～2032年
出版日: 2025年02月13日
発行: DataM Intelligence
ページ情報: 英文 204 Pages
納期: 即日から翌営業日
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概要

ITサポートにおけるAIと自動化の世界市場は、2024年に263億8,000万米ドルに達し、2032年までには2,108億6,000万米ドルに達すると予測され、予測期間中の2025年から2032年のCAGRは29.67％で成長する見込みです。

ITサービスにおけるAIと自動化の世界市場は、IT運用を最適化するための機械学習アルゴリズムの導入拡大により、急速な変貌を遂げつつあります。AIによる自動化は、ソフトウェア・テスト、ネットワーク・モニタリング、システム・メンテナンスのような重要な業務を洗練させ、効率と精度を向上させながら、人間の関与を著しく減少させています。この移行により、IT専門家は戦略的目標に集中できるようになり、企業内のイノベーションが促進されます。

ジェネレーティブAIは業界成長の重要な原動力となりつつあり、企業は高度にカスタマイズされた体験を通じて顧客エンゲージメントを向上させることができます。ジェネレーティブAIは、カスタマイズされたマーケティング・キャンペーンやインタラクティブな製品推奨を通じて顧客との対話を変革し、その没入感と人間らしさを高めています。

顧客サービスに加え、AI主導の自動化は、デザイン、コンテンツ作成、製品開発の進歩を促進し、創造性とパーソナライゼーションの強化を促進します。AIによる自動化がITサービスに変革をもたらす中、こうした進歩を活用する企業は、業務効率、サービス品質、顧客体験において競争優位に立つことができます。

ダイナミクス

促進要因1-データセンターにおけるITインフラの成長

企業が高度なITシステムにますます依存するようになるにつれ、効率的で適応性のある管理の必要性が不可欠になっています。特に、クラウド・コンピューティングやデータ中心型サービスの出現により、インフラが複雑化した結果、データセンター環境の監視・管理にAIやロボットが広く活用されるようになりました。

リアルタイムで鋭い意思決定と予知保全を実現するAIの能力は、ダウンタイムを減らし、運用効率を高めています。自動化ツールは現在、エスカレーション前に起こりうる問題を検知する力をシステムに与え、企業はプロアクティブに問題に対処できるようになりました。

2024年9月、BlackRock、Global Infrastructure Partners（GIP）、Microsoft、MGXによる世界AIインフラ投資パートナーシップ（GAIIP）の設立は、AIの進展を促進するためのデータセンターへの多額の投資を強調しました。これらの投資は、AIのイノベーションを刺激するだけでなく、エネルギーインフラと冷却技術を改善し、増大する電力需要に対応します。

AI主導のロボットは、ネットワーク監視、セキュリティ評価、環境管理などの機能を自動化し、運用効率を高めてコストを削減する上で不可欠な存在になりつつあります。AIと自動化によって推進されるITインフラの進歩は、ITサポート業界の世界の拡大を刺激しています。

促進要因2-機械学習とAIの自動化によるITサポートの強化

ITサポート・スタッフは、機械学習アルゴリズムを活用して広範なデータ・セットを調査し、問題の発生を事前に察知して未然に防ぐことができるため、ダウンタイムや業務の中断を大幅に最小限に抑えることができます。このような予測能力は、継続的なソフトウェア更新と強力なセキュリティ・サービスが必要なクラウド環境では特に有益であり、鋭い監視と管理が必要となります。

企業がクラウド・ソリューションの導入を進めるにつれ、機械学習は自己学習機能によって継続的な機能強化を促進します。例えば、機械学習モデルは、システム・パフォーマンスのパターンを識別し、潜在的な脆弱性を突き止め、トラブルシューティング手順を自動化することができます。これにより、人的介入への依存度が低下し、IT専門家は事後的なメンテナンスではなく、戦略的な取り組みに集中できるようになります。

機械学習は、クラウド・サービスにおけるリソース割り当てを改善することでコスト削減を促進します。クラウド・サービスは多くの場合、従量課金モデルで運用されるため、企業は必要なリソースに対してのみ費用を負担することになります。この拡張性により、企業はさまざまなワークロードを効率的に管理できます。

一般データ保護規則（GDPR）とカリフォルニア州消費者プライバシー法（CCPA）は、機密データを保護するための厳格な方法の採用を企業に義務付けています。機械学習の手法は、異常や潜在的な脅威を検出し、規制基準の遵守を保証し、企業と消費者の両方のデータを保護することで、データ・セキュリティを向上させるために極めて重要です。

抑制要因：AIモデルの複雑性の課題がITサポートの進歩を妨げる

AIモデル、特にディープラーニングモデルは、高度なニューラルネットワーク設計に依存しており、効率的に動作させるためには、広範で多様かつ高品質なデータセットを必要とします。例えば、物体認識のモデルをトレーニングするには、最小限のデータセットでさえ誤った予測につながる可能性があるため、相当量のラベル付きデータが必要です。このようなモデルは、慎重な微調整と継続的なデータ更新を必要とするため、リソース集約的で継続が難しいです。

ITサポートの領域では、AIモデルは特定の組織要件に対応するためにカスタマイズが必要になることが多いです。クラウド・コンピューティングやサイバーセキュリティのモデルは、ハードウェア、ソフトウェア、セキュリティの仕様が異なるため、さまざまな運用環境に対応する必要があります。この適応プロセスは複雑で、新しいデータの種類や環境の変化に適応できる高度なアルゴリズムが必要となります。

欧州連合（EU）の一般データ保護規則（GDPR）は、特にデータプライバシーとユーザー同意に関する厳しい規制をAIアプリに課しているため、複雑なAIモデルの実装を妨げています。これらの要因と有能な労働者の不足が相まって、ITサポート・サービスにおけるAIの広範な導入が制限されています。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • データセンターにおけるITインフラの拡大
      • 機械学習とAI自動化によるITサポートの強化
    • 抑制要因
      • ITサポートの進歩を妨げているAIモデルの複雑課題
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • DMIの見解

第6章 コンポーネント別

  • ソリューション
  • サービス

第7章 展開モード別

  • オンプレミス
  • クラウドベース

第8章 技術別

  • 機械学習
  • 自然言語処理（NLP）
  • コンピュータビジョン
  • ロボティックプロセスオートメーション（RPA）
  • 生成AI
  • その他

第9章 用途別

  • ITヘルプデスクの自動化
  • ネットワーク監視と管理
  • インシデントの検出と解決
  • ソフトウェアテストと品質保証
  • IT資産と構成管理
  • セキュリティと脅威管理
  • その他

第10章 組織規模別

  • 中小企業
  • 大企業

第11章 エンドユーザー別

  • BFSI
  • IT・通信
  • ヘルスケア
  • 小売・eコマース
  • 製造業
  • 政府・公共部門
  • その他

第12章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他欧州地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋
  • 中東・アフリカ

第13章 競合情勢

  • 競合シナリオ
  • 市況・シェア分析
  • M&A分析

第14章 企業プロファイル

  • IBM Corporation
    • 会社概要
    • 製品ポートフォリオと概要
    • 財務概要
    • 主な発展
  • Microsoft Corporation
  • Google LLC
  • Oracle Corporation
  • Cisco Systems, Inc.
  • ServiceNow, Inc.
  • BMC Software, Inc.
  • Splunk Inc.
  • Capgemini SE
  • Cognizant Technology Solutions

第15章 付録

目次
Product Code: ICT9120

Global AI and Automation in IT Support Market reached US$ 26.38 billion in 2024 and is expected to reach US$ 210.86 billion by 2032, growing with a CAGR of 29.67% during the forecast period 2025-2032.

The global market for AI and automation in IT services is undergoing swift transformation, driven by the growing implementation of machine-learning algorithms to optimize IT operations. AI-driven automation is refining essential operations like software testing, network monitoring and system maintenance, markedly diminishing human involvement while improving efficiency and precision. The transition allows IT experts to concentrate on strategic objectives, promoting innovation within firms.

Generative AI is becoming a significant driver of industry growth, allowing businesses to improve customer engagement through highly tailored experiences. Generative AI is transforming client interactions through customized marketing campaigns and interactive product recommendations, enhancing their immersive and human-like qualities.

In addition to customer service, AI-driven automation is promoting progress in design, content creation and product development, facilitating enhanced creativity and personalization. As AI-driven automation transforms IT services, enterprises that utilize these advancements will have a competitive advantage in operational efficiency, service quality and customer experience.

Dynamics

Driver 1 - Growing IT infrastructure in data centres

As businesses increasingly depend on sophisticated IT systems, the necessity for efficient and adaptive management has become vital. The growing intricacy of infrastructure, particularly due to the emergence of cloud computing and data-centric services, has resulted in the extensive utilization of AI and robots for the oversight and administration of data center environments.

The capacity of AI to deliver real-time, astute decision-making and predictive maintenance has diminished downtime and enhanced operational efficiency. Automation tools now empower systems to detect possible issues prior to escalation, allowing enterprises to address problems proactively.

In September 2024, the establishment of the Global AI Infrastructure Investment Partnership (GAIIP) by BlackRock, Global Infrastructure Partners (GIP), Microsoft and MGX underscored the substantial investment directed towards data centers to facilitate AI progress. These investments will not only stimulate AI innovation but also improve energy infrastructure and cooling technologies, addressing increasing power demands.

AI-driven robots are becoming essential in automating functions like network surveillance, security assessments and environmental management, hence enhancing operational efficiency and reducing costs. The advancement of IT infrastructure, propelled by AI and automation, is stimulating the worldwide expansion of the IT support industry.

Driver 2 - Enhancing IT support with machine learning and AI automation

IT support staff can utilize machine learning algorithms to examine extensive data sets, enabling them to detect and prevent issues before their occurrence, thereby significantly minimizing downtime and operational disruptions. This predictive ability is especially beneficial in cloud environments, where continuous software updates and strong security services necessitate astute monitoring and administration.

As enterprises progressively embrace cloud solutions, machine learning facilitates ongoing enhancement via self-learning functionalities. For instance, machine learning models can discern patterns in system performance, pinpoint potential vulnerabilities and automate troubleshooting procedures. This diminishes reliance on human intervention, enabling IT professionals to concentrate on strategic initiatives instead of reactive maintenance.

Machine learning facilitates cost reduction by improving resource allocation in cloud services, ensuring that firms incur expenses solely for the resources they require, as cloud services often operate on a pay-as-you-go model. This scalability guarantees that enterprises can manage varying workloads effectively.

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) mandate enterprises to adopt rigorous methods for safeguarding sensitive data. Machine learning methods are crucial for improving data security by detecting abnormalities and potential threats, assuring adherence to regulatory standards and protecting both corporate and consumer data.

Restraint: Challenges in AI model complexity hindering IT support advancements

AI models, especially deep learning models, rely on sophisticated neural network designs that require extensive, varied and high-quality datasets to operate efficiently. For example, training a model for object recognition necessitates substantial labeled data, as even minimal datasets can result in erroneous predictions. These models require careful fine-tuning and ongoing data updates, rendering them resource-intensive and challenging to sustain.

In the realm of IT support, AI models frequently require customization to address particular organizational requirements. Models in cloud computing or cybersecurity must adjust to various operational settings, encompassing different hardware, software and security specifications. The adaptation process is intricate, necessitating sophisticated algorithms capable of adjusting to novel data kinds and changing environments.

The European Union's General Data Protection Regulation (GDPR) enforces stringent regulations on AI apps, particularly with data privacy and user consent, hence hampering the implementation of intricate AI models. The combination of these factors and the scarcity of competent workers restricts the extensive implementation of AI in IT support services.

Segment Analysis

The global AI and automation in IT support market is segmented based on component, deployment mode, technology, application, organization size, end-user and region.

Enhancing efficiency and customer satisfaction with IT helpdesk automation

Helpdesk automation use technology to optimize activities and procedures, including ticket prioritizing, routing and feedback collection, thereby improving operational efficiency. In contrast, helpdesk assistance concentrates on addressing customer concerns via many communication channels to guarantee satisfaction.

Automation enhances workflows and minimizes human labor, while support teams resolve particular user issues. Automation techniques like as AI-driven chatbots and automated ticket routing facilitate the management of substantial client interactions, delivering prompt and uniform responses while allowing support professionals to concentrate on more intricate duties.

Several companies are allocating resources to helpdesk automation to enhance productivity, decrease expenses and alleviate the burden on support workers. Automation empowers enterprises to manage an increased volume of client requests, offer round-the-clock self-service alternatives and optimize repetitive tasks.

By choosing appropriate technologies, establishing robust knowledge bases and automating high-volume processes organizations can markedly enhance their customer support operations, resulting in increased customer satisfaction and less employee burnout.

On October 31, 2023, Atlassian Pty Ltd. introduced a new virtual agent aimed at facilitating improved employee and client service with increased efficiency. It will assist teams in automating support interactions and providing rapid, continuous, conversational assistance using their preferred collaboration tools.

Geographical Penetration

Market insights and adoption trends in North America

North America, especially US and Canada, dominates the AI and automation in IT support market, propelled by technology innovations and a strong infrastructure. The region boasts a robust presence of prominent technology firms and startups focused on artificial intelligence, machine learning and automation, which have markedly expedited the integration of AI in optimizing IT support operations.

AI tools are predominantly employed to augment efficiency, automate repetitive processes such as ticket management and enhance service delivery. According to new research commissioned by IBM in 2024, around 42% of enterprise-scale enterprises (more than 1,000 people) questioned are actively using AI in their businesses. Early adopters are taking the lead, with 59% of responding firms already working with AI planning to accelerate and boost investment in the technology.

Competitive Landscape

The major Global players in the market include IBM Corporation, Microsoft Corporation, Google LLC oracle Corporation, Cisco Systems, Inc., ServiceNow, Inc., BMC Software, Inc., Splunk Inc., Capgemini SE and Cognizant Technology Solutions.

By Component

  • Solutions
  • Services

By Deployment Mode

  • On-Premises
  • Cloud-Based

By Technology

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Robotic Process Automation (RPA)
  • Generative AI

By Application

  • IT Helpdesk Automation
  • Network Monitoring & Management
  • Incident Detection & Resolution
  • Software Testing & Quality Assurance
  • IT Asset & Configuration Management
  • Security & Threat Management
  • Others

By Organization Size

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

By End-User

  • BFSI
  • IT & Telecom
  • Healthcare
  • Retail & E-commerce
  • Manufacturing
  • Government & Public Sector
  • Others

By Region

  • North America
  • South America
  • Europe
  • Asia-Pacific
  • Middle East and Africa

Key Developments

  • In October 2024, Singtel, a prominent telecommunications corporation headquartered in Singapore, officially introduced RE:AI, a novel AI cloud service designed to improve the scalability, accessibility and cost-effectiveness of AI for businesses and the public sector. Leveraging Singtel's proprietary 5G MEC orchestration platform, RE:AI allows users to seamlessly build, operate and scale AI applications, thus promoting more efficient AI integration across diverse industries.
  • In April 2024, Intel introduced the Gaudi 3 accelerator, engineered to enhance AI performance and scalability. The Gaudi 3 possesses advanced networking capabilities with 200 Gbps Ethernet connections, enabling scalability to clusters of 8,192 accelerators.

Why Purchase the Report?

  • To visualize the global AI and Automation in IT Support market segmentation based on offering, component, network deployment, frequency band, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of the AI and Automation in IT Support market with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The Global AI and Automation in IT Support market report would provide approximately 86 tables, 90 figures and 204 pages.

Target Audience 2025

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet By Component
  • 3.2. Snippet By Deployment Mode
  • 3.3. Snippet By Technology
  • 3.4. Snippet By Application
  • 3.5. Snippet By Organization Size
  • 3.6. Snippet By End-User
  • 3.7. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Growing IT infrastructure in data centres
      • 4.1.1.2. Enhancing IT support with machine learning and AI automation
    • 4.1.2. Restraints
      • 4.1.2.1. Challenges in AI model complexity hindering IT support advancements
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. DMI Opinion

6. By Component

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 6.1.2. Market Attractiveness Index, By Component
  • 6.2. Solutions*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Service

7. By Deployment Mode

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 7.1.2. Market Attractiveness Index, By Deployment Mode
  • 7.2. On-Premises*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Cloud-Based

8. By Technology

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 8.1.2. Market Attractiveness Index, By Technology
  • 8.2. Machine Learning*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Natural Language Processing (NLP)
  • 8.4. Computer Vision
  • 8.5. Robotic Process Automation (RPA)
  • 8.6. Generative AI
  • 8.7. Others

9. By Application

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.1.2. Market Attractiveness Index, By Application
  • 9.2. IT Helpdesk Automation*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Network Monitoring & Management
  • 9.4. Incident Detection & Resolution
  • 9.5. Software Testing & Quality Assurance
  • 9.6. IT Asset & Configuration Management
  • 9.7. Security & Threat Management
  • 9.8. Others

10. By Organization Size

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.1.2. Market Attractiveness Index, By Organization Size
  • 10.2. Small & Medium Enterprises (SMEs)*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Large Enterprises

11. By End-User

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.1.2. Market Attractiveness Index, By End-User
  • 11.2. BFSI*
    • 11.2.1. Introduction
    • 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 11.3. IT & Telecom
  • 11.4. Healthcare
  • 11.5. Retail & E-commerce
  • 11.6. Manufacturing
  • 11.7. Government & Public Sector
  • 11.8. Others

12. By Region

  • 12.1. Introduction
    • 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 12.1.2. Market Attractiveness Index, By Region
  • 12.2. North America
    • 12.2.1. Introduction
    • 12.2.2. Key Region-Specific Dynamics
    • 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.2.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.2.9.1. US
      • 12.2.9.2. Canada
      • 12.2.9.3. Mexico
  • 12.3. Europe
    • 12.3.1. Introduction
    • 12.3.2. Key Region-Specific Dynamics
    • 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.3.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.3.9.1. Germany
      • 12.3.9.2. UK
      • 12.3.9.3. France
      • 12.3.9.4. Italy
      • 12.3.9.5. Spain
      • 12.3.9.6. Rest of Europe
  • 12.4. South America
    • 12.4.1. Introduction
    • 12.4.2. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.4.8.1. Brazil
      • 12.4.8.2. Argentina
      • 12.4.8.3. Rest of South America
  • 12.5. Asia-Pacific
    • 12.5.1. Introduction
    • 12.5.2. Key Region-Specific Dynamics
    • 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 12.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.5.9.1. China
      • 12.5.9.2. India
      • 12.5.9.3. Japan
      • 12.5.9.4. Australia
      • 12.5.9.5. Rest of Asia-Pacific
  • 12.6. Middle East and Africa
    • 12.6.1. Introduction
    • 12.6.2. Key Region-Specific Dynamics
    • 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 12.6.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

  • 13.1. Competitive Scenario
  • 13.2. Market Positioning/Share Analysis
  • 13.3. Mergers and Acquisitions Analysis

14. Company Profiles

  • 14.1. IBM Corporation*
    • 14.1.1. Company Overview
    • 14.1.2. Product Portfolio and Description
    • 14.1.3. Financial Overview
    • 14.1.4. Key Developments
  • 14.2. Microsoft Corporation
  • 14.3. Google LLC
  • 14.4. Oracle Corporation
  • 14.5. Cisco Systems, Inc.
  • 14.6. ServiceNow, Inc.
  • 14.7. BMC Software, Inc.
  • 14.8. Splunk Inc.
  • 14.9. Capgemini SE
  • 14.10. Cognizant Technology Solutions

LIST NOT EXHAUSTIVE

15. Appendix

  • 15.1. About Us and Services
  • 15.2. Contact Us