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1654697

全球 IT 支援市場中的人工智慧和自動化 - 2025 至 2032 年

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

出版日期: 2025年02月13日 | 出版商:

DataM Intelligence | 英文 204 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球 IT 支援市場中的人工智慧和自動化達到 263.8 億美元，預計到 2032 年將達到 2,108.6 億美元，在 2025-2032 年預測期內的複合年成長率為 29.67%。

隨著機器學習演算法擴大應用於最佳化 IT 營運，全球 IT 服務領域的人工智慧和自動化市場正在經歷快速轉型。人工智慧驅動的自動化正在改善軟體測試、網路監控和系統維護等基本操作，顯著減少人工參與，同時提高效率和精度。這種轉變使 IT 專家能夠專注於策略目標，促進公司內部的創新。

生成式人工智慧正在成為產業成長的重要驅動力，使企業能夠透過高度客製化的體驗來提高客戶參與度。生成式人工智慧正在透過客製化行銷活動和互動式產品推薦來改變客戶互動，增強其沉浸感和人性化特質。

除了客戶服務之外，人工智慧驅動的自動化還在推動設計、內容創作和產品開發的進步，促進創造力和個人化的增強。隨著人工智慧驅動的自動化改變 IT 服務，利用這些進步的企業將在營運效率、服務品質和客戶體驗方面具有競爭優勢。

動力學

促進因素 1：資料中心 IT 基礎架構不斷成長

隨著企業越來越依賴複雜的 IT 系統，高效率、適應性強的管理變得至關重要。基礎設施日益複雜，特別是由於雲端運算和以資料為中心的服務的出現，導致人工智慧和機器人被廣泛用於資料中心環境的監督和管理。

人工智慧提供即時、明智的決策和預測性維護的能力減少了停機時間並提高了營運效率。自動化工具現在使系統能夠在問題升級之前檢測到可能的問題，使企業能夠主動解決問題。

2024 年 9 月，貝萊德、全球基礎設施合作夥伴 (GIP)、微軟和 MGX 成立全球人工智慧基礎設施投資夥伴關係 (GAIIP)，強調對資料中心進行大量投資以促進人工智慧進步。這些投資不僅將刺激人工智慧創新，還將改善能源基礎設施和冷卻技術，滿足日益成長的電力需求。

人工智慧機器人在網路監控、安全評估和環境管理等自動化功能中變得越來越重要，從而提高了營運效率並降低了成本。在人工智慧和自動化的推動下，IT基礎設施的進步正在刺激IT支援產業在全球的擴張。

促進因素 2：利用機器學習和人工智慧自動化增強 IT 支持

IT 支援人員可以利用機器學習演算法來檢查大量資料集，使他們能夠在問題發生之前檢測並預防問題，從而顯著減少停機時間和營運中斷。這種預測能力在雲端環境中尤其有益，因為持續的軟體更新和強大的安全服務需要精明的監控和管理。

隨著企業逐步採用雲端解決方案，機器學習透過自學習功能促進持續改善。例如，機器學習模型可以辨別系統效能模式、找出潛在漏洞並自動執行故障排除程序。這減少了對人工干預的依賴，使 IT 專業人員能夠專注於策略計劃而不是被動維護。

機器學習透過改善雲端服務中的資源分配來降低成本，確保公司僅為其所需的資源承擔費用，因為雲端服務通常採用現收現付模式運作。這種可擴展性保證企業能夠有效管理不同的工作負載。

《一般資料保護規範》（GDPR）和《加州消費者隱私法案》（CCPA）要求企業採用嚴格的方法來保護敏感資料。機器學習方法對於偵測異常和潛在威脅、確保遵守監管標準以及保護企業和消費者資料至關重要，從而提高資料安全性。

限制：人工智慧模型複雜性的挑戰阻礙了 IT 支援的發展

人工智慧模型，尤其是深度學習模型，依賴複雜的神經網路設計，需要廣泛、多樣且高品質的資料集才能有效運作。例如，訓練一個物體識別模型需要大量標記資料，因為即使是最少的資料集也可能導致錯誤的預測。這些模型需要仔細微調和持續的資料更新，因此需要大量資源且難以維持。

在 IT 支援領域，AI 模型經常需要客製化以滿足特定的組織要求。雲端運算或網路安全中的模型必須適應各種操作設置，涵蓋不同的硬體、軟體和安全規範。適應過程非常複雜，需要能夠適應新資料類型和不斷變化的環境的複雜演算法。

歐盟的《一般資料保護規範》（GDPR）對AI應用實施了嚴格的監管，特別是在資料隱私和使用者同意方面，從而阻礙了複雜AI模型的實施。這些因素的結合，加上高素質勞動力的稀缺，限制了人工智慧在IT支援服務領域的廣泛應用。

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

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