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全球 IT 支援市场中的人工智慧和自动化 - 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个工作天内

价格

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简介目录

2024 年全球 IT 支援市场中的人工智慧和自动化达到 263.8 亿美元，预计到 2032 年将达到 2,108.6 亿美元，在 2025-2032 年预测期内的复合年增长率为 29.67%。

随着机器学习演算法越来越多地应用于优化 IT 运营，全球 IT 服务领域的人工智慧和自动化市场正在经历快速转型。人工智慧驱动的自动化正在改善软体测试、网路监控和系统维护等基本操作，显着减少人工参与，同时提高效率和精度。这种转变使 IT 专家能够专注于策略目标，促进公司内部的创新。

生成式人工智慧正在成为产业成长的重要驱动力，使企业能够透过高度客製化的体验来提高客户参与度。生成式人工智慧正在透过客製化行销活动和互动式产品推荐来改变客户互动，增强其沉浸感和人性化特质。

除了客户服务之外，人工智慧驱动的自动化还在推动设计、内容创作和产品开发的进步，促进创造力和个人化的增强。随着人工智慧驱动的自动化改变 IT 服务，利用这些进步的企业将在营运效率、服务品质和客户体验方面具有竞争优势。

动力学

驱动因素 1：资料中心 IT 基础架构不断成长

随着企业越来越依赖复杂的 IT 系统，高效率、适应性强的管理变得至关重要。基础设施日益复杂，特别是由于云端运算和以资料为中心的服务的出现，导致人工智慧和机器人被广泛用于资料中心环境的监督和管理。

人工智慧提供即时、明智的决策和预测性维护的能力减少了停机时间并提高了营运效率。自动化工具现在使系统能够在问题升级之前检测到可能的问题，使企业能够主动解决问题。

2024 年 9 月，贝莱德、全球基础设施合作伙伴 (GIP)、微软和 MGX 成立全球人工智慧基础设施投资伙伴关係 (GAIIP)，强调对资料中心进行大量投资以促进人工智慧进步。这些投资不仅将刺激人工智慧创新，还将改善能源基础设施和冷却技术，满足日益增长的电力需求。

人工智慧机器人在网路监控、安全评估和环境管理等自动化功能中变得越来越重要，从而提高了营运效率并降低了成本。在人工智慧和自动化的推动下，IT基础设施的进步正在刺激IT支援产业在全球的扩张。

驱动因素 2：利用机器学习和人工智慧自动化增强 IT 支持

IT 支援人员可以利用机器学习演算法来检查大量资料集，使他们能够在问题发生之前检测并预防问题，从而显着减少停机时间和营运中断。这种预测能力在云端环境中尤其有益，因为持续的软体更新和强大的安全服务需要精明的监控和管理。

随着企业逐步采用云端解决方案，机器学习透过自学习功能促进持续改善。例如，机器学习模型可以辨别系统效能模式、找出潜在漏洞并自动执行故障排除程序。这减少了对人工干预的依赖，使 IT 专业人员能够专注于策略计划而不是被动维护。

机器学习透过改善云端服务中的资源分配来降低成本，确保公司仅为其所需的资源承担费用，因为云端服务通常采用现收现付模式运作。这种可扩展性保证企业能够有效管理不同的工作负载。

《一般资料保护规范》（GDPR）和《加州消费者隐私法案》（CCPA）要求企业采用严格的方法来保护敏感资料。机器学习方法对于侦测异常和潜在威胁、确保遵守监管标准以及保护企业和消费者资料至关重要，从而提高资料安全性。

限制：人工智慧模型复杂性的挑战阻碍了 IT 支援的发展

人工智慧模型，尤其是深度学习模型，依赖复杂的神经网路设计，需要广泛、多样且高品质的资料集才能有效运作。例如，训练一个物体识别模型需要大量标记资料，因为即使是最少的数据集也可能导致错误的预测。这些模型需要仔细微调和持续的资料更新，因此需要大量资源且难以维持。

在 IT 支援领域，AI 模型经常需要客製化以满足特定的组织要求。云端运算或网路安全中的模型必须适应各种操作设置，涵盖不同的硬体、软体和安全规范。适应过程非常复杂，需要能够适应新资料类型和不断变化的环境的复杂演算法。

欧盟的《一般资料保护规范》（GDPR）对AI应用实施了严格的监管，特别是在资料隐私和使用者同意方面，从而阻碍了复杂AI模型的实施。这些因素的结合，加上高素质劳动力的稀缺，限制了人工智慧在IT支援服务领域的广泛应用。

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

15. Appendix

15.1. About Us and Services
15.2. Contact Us

02-2729-4219

+886-2-2729-4219

License/价格

全球 IT 支援市场中的人工智慧和自动化 - 2025 至 2032 年

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

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

第五章：产业分析

第 6 章：按组件

第 7 章：按部署模式

第 8 章：按技术

第九章：按应用

第 10 章：依组织规模

第 11 章：按最终用户

第 12 章：按地区

第 13 章：竞争格局

第 14 章：公司简介

第 15 章：附录