Summary

Overview
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

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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