AI infrastructure encompasses almost every stage of the machine learning workflow. It enables data scientist, data engineers, software engineers and DevOps teams to access and manage the computing resources to test, train and deploy AI algorithms. The world of corporate data becomes streamlined and well-optimized through AI infrastructure. Furthermore, proper AI infrastructure is required to train machine learning algorithms that run through databases and message queuing systems to provide data delivery flow.
Moreover, AI infrastructure is the key that enables the whole machine learning process from start to finish. With a proper AI infrastructure, data scientists, operators, and programmers can access the data, deploy machine learning algorithms, and manage the hardware’s computing resources. It is the crucial tool that enables a particular workflow, then divide the workflow into steps. Furthermore, the technological renaissance is brought on by artificial intelligence. Data strategies can become sophisticated through AI infrastructure. Data manipulation and management works both ways. Artificial intelligence can make data strategies far more complex and intricate, and more sophisticated data strategies can improve the overall quality of artificial intelligence. Such advantageous provide lucrative opportunities for the market growth during the forecast period.
On the contrary, artificial intelligence-based technology has a widespread variety of applications ranging from self-driving cars to recommendation systems that are influencing many lifestyle choices. Such applications are empowering the growth of the AI infrastructure market.
Furthermore, AI infrastructure rapidly powers mobility information and insights, altering transportation planning by making vital data collection and understanding more accessible, faster, cheaper, and safer. Cities, transit agencies, transportation departments, and other entities increasingly turn to AI infrastructure to solve challenges, prioritize investments, and gain stakeholder support, thus providing lucrative opportunities for the market growth in the upcoming years. Furthermore, private, and public sectors are adopting AI infrastructure due to surge in demand for digitization. This factor creates lucrative growth opportunities in the market.
Factors such as rise in artificial intelligence maturity in the modern business enterprises and everyday lifestyle of people are signaling significant growth opportunities for the future of global market. In addition, surge in digital and internet penetration around the world is positively impacting the growth of the market. However, lack of trained AI experts and high implementation cost of AI-based technology hampers the market growth. On the contrary, increase in government initiatives and automation trends are expected to offer remunerative opportunities for expansion of the AI infrastructure industry during the forecast period.
The AI infrastructure market is segmented on the basis of component, deployment mode, technology, application, end user, and region. By component, it is divided into hardware, software and services. By deployment mode, it is classified into on-premise, hybrid and cloud. On the basis of technology, it is bifurcated into machine learning and deep learning. By application, the market is classified into AI training, inferencing and others. By end user, the market is categorized into enterprises, government and cloud service providers (CSPs). Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The market players operating in the AI infrastructure market include Alphabet Inc., Amazon.com, Inc., IBM Corporation, Intel Corporation, Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, Samsung and Toshiba Corporation. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the AI infrastructure market globally.
Moreover, AI infrastructure is the key that enables the whole machine learning process from start to finish. With a proper AI infrastructure, data scientists, operators, and programmers can access the data, deploy machine learning algorithms, and manage the hardware’s computing resources. It is the crucial tool that enables a particular workflow, then divide the workflow into steps. Furthermore, the technological renaissance is brought on by artificial intelligence. Data strategies can become sophisticated through AI infrastructure. Data manipulation and management works both ways. Artificial intelligence can make data strategies far more complex and intricate, and more sophisticated data strategies can improve the overall quality of artificial intelligence. Such advantageous provide lucrative opportunities for the market growth during the forecast period.
On the contrary, artificial intelligence-based technology has a widespread variety of applications ranging from self-driving cars to recommendation systems that are influencing many lifestyle choices. Such applications are empowering the growth of the AI infrastructure market.
Furthermore, AI infrastructure rapidly powers mobility information and insights, altering transportation planning by making vital data collection and understanding more accessible, faster, cheaper, and safer. Cities, transit agencies, transportation departments, and other entities increasingly turn to AI infrastructure to solve challenges, prioritize investments, and gain stakeholder support, thus providing lucrative opportunities for the market growth in the upcoming years. Furthermore, private, and public sectors are adopting AI infrastructure due to surge in demand for digitization. This factor creates lucrative growth opportunities in the market.
Factors such as rise in artificial intelligence maturity in the modern business enterprises and everyday lifestyle of people are signaling significant growth opportunities for the future of global market. In addition, surge in digital and internet penetration around the world is positively impacting the growth of the market. However, lack of trained AI experts and high implementation cost of AI-based technology hampers the market growth. On the contrary, increase in government initiatives and automation trends are expected to offer remunerative opportunities for expansion of the AI infrastructure industry during the forecast period.
The AI infrastructure market is segmented on the basis of component, deployment mode, technology, application, end user, and region. By component, it is divided into hardware, software and services. By deployment mode, it is classified into on-premise, hybrid and cloud. On the basis of technology, it is bifurcated into machine learning and deep learning. By application, the market is classified into AI training, inferencing and others. By end user, the market is categorized into enterprises, government and cloud service providers (CSPs). Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The market players operating in the AI infrastructure market include Alphabet Inc., Amazon.com, Inc., IBM Corporation, Intel Corporation, Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, Samsung and Toshiba Corporation. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the AI infrastructure market globally.
Key Benefits For Stakeholders
- The study provides an in-depth analysis of the global AI infrastructure market forecast along with the current and future trends to explain the imminent investment pockets.
- Information about key drivers, restraints, and opportunities and their impact analysis on global AI infrastructure market trends is provided in the report.
- The Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
- The quantitative analysis of the global AI infrastructure market from 2022 to 2031 is provided to determine the market potential.
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- Senario Analysis & Growth Trend Comparision
- Technology Trend Analysis
- Regulatory Guidelines
- Strategic Recommedations
- Additional company profiles with specific to client's interest
- Expanded list for Company Profiles
- SWOT Analysis
Key Market Segments
By Deployment Mode
- On-Premise
- Cloud
- Hybrid
By Technology
- Machine Learning
- Deep Learning
By End-Users
- Enterprises
- Governments
- Cloud Service Providers (CSPs)
By Application
- AI Training
- Inferencing
- Others
By Component
- Hardware
- Software
- Services
By Region
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
- Key Market Players
- NVIDIA Corporation
- Microsoft Corporation
- Alphabet Inc.
- Oracle Corporation
- Samsung Electronics Co., Ltd.
- IBM Corporation
- Intel Corporation
- Toshiba Corporation
- Micron Technology, Inc.
- Amazon.com, Inc.
Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: AI INFRASTRUCTURE MARKET, BY COMPONENT
CHAPTER 5: AI INFRASTRUCTURE MARKET, BY DEPLOYMENT MODE
CHAPTER 6: AI INFRASTRUCTURE MARKET, BY TECHNOLOGY
CHAPTER 7: AI INFRASTRUCTURE MARKET, BY END-USERS
CHAPTER 8: AI INFRASTRUCTURE MARKET, BY APPLICATION
CHAPTER 9: AI INFRASTRUCTURE MARKET, BY REGION
CHAPTER 10: COMPETITIVE LANDSCAPE
CHAPTER 11: COMPANY PROFILES
List of Tables
List of Figures
Executive Summary
According to the report, the AI infrastructure market was valued at $23.5 billion in 2021, and is estimated to reach $309.4 billion by 2031, growing at a CAGR of 29.8% from 2022 to 2031.Artificial intelligence makes it possible for machines to learn from previous experiences and adjust to new inputs to perform tasks like humans. Such capabilities of AI-based technology enable machines to automate much more complex tasks such as driving cars (self-driving cars), holding conversations (conversational bots), filtering e-mail (spam and fraud detectors), and other things. Such factors drive the AI infrastructure market forecast. Furthermore, growing demand to improve operational efficiency and the rising cost of manual labor. In addition, increasing digital dependence and implementation of industry 4.0 trends are expected to offer remunerative opportunities for expansion of the AI infrastructure market during the forecast period.
On the contrary, big data analytics is heavily used in supply chain management to evaluate operational hazards, improve communication, secure proprietary data, and improve supply chain accessibility. This data is used by industries in a variety of ways, including predictive analytics and the creation of more efficient cloud-based platforms. Data mining, statistics, and machine learning are used in predictive analytics to assess future supply demands, inventory, and customer behavior. Companies use predictive analytics and machine learning to forecast future physical hazards in the supply chain and financial, customer, and other operational risks, thus propel the growth of the market. Moreover, AI can involve handling sensitive data such as patient records, financial information and personal data, so it is imperative the infrastructure is secured end-to-end with state-of-the-art technology. As companies increase their use of AI, they will place heavier burdens on the network, server and storage infrastructures. Businesses need to make careful choices and identify IaaS providers that can offer cost-effective dedicated servers as a means to boost performance and to enable them to continue investing in AI without increasing their budget. Such factors will help to enhance the market growth during the forecast period.
On the basis of deployment mode, the on-premise segment captured the largest AI infrastructure market size in 2021 and is expected to continue this trend throughout the forecast period. This is attributed to the numerous advantages offered by the on-premise deployment such as a high level of data security and safety. Industries prefer on-premise model owing to high data security and less data breach as compared to cloud based deployment models, which further drive the demand for on-premise deployment model within the sectors. However, the cloud segment is expected to exhibit highest growth during the forecast period. Factors such as rise in the adoption of cloud-based AI infrastructure due to low cost and easier maintenance drives the growth of the market. In addition, it provides flexibility & scalability to boost business process, which propels the growth of the AI infrastructure industry.
Region-wise, North America dominated the market share in 2021 for the AI infrastructure market. Adoption of AI infrastructure growing steadily to meet increasing demands from today’s businesses to enhance their business process and improve the customer experience will provide lucrative opportunities for the market in this region. However, Asia-Pacific is expected to exhibit highest growth during the forecast period. This is attributed to increase in penetration of advanced technology and higher adoption of cloud-based solution and services, and artificial intelligence are particularly fueling regional market growth.
The outbreak of COVID-19 is anticipated to provide numerous opportunities for the market to grow during the forecast period. This is attributed to significant investment in advanced technologies such as cloud technology, big data, artificial intelligence, and machine learning, due to rise in adoption of cloud-based solution & services across the globe. In addition, various companies are seeking for cost-effective solutions to boost their productivity to attract consumers toward cloud-based solution and to increase their financial benefits.
Furthermore, the pandemic brought big challenges in the manufacturing sector from supply chain disruptions and drop in workforce availability to raw material shortages. Thus, manufacturers had to rely on the power of their data and analytics to stay competitive and innovate ahead. Such factors prompted the demand for AI infrastructure solutions in the manufacturing sector.
With augmented analytics, manufacturers will be able to analyze key data such as the production capabilities of the production lines, shipping timings, schedules of their workforce, and their warehousing space availability. In addition, with the insights gained from the analyzed data, manufacturers will be able to make a calculated pivot to start manufacturing products, such as face masks during the pandemic, that are in demand to bring in much-needed revenue to sustain, which is expected to provide lucrative growth opportunities for the AI infrastructure market in the upcoming year.
KEY FINDINGS OF THE STUDY
By component, the hardware segment accounted for the largest AI infrastructure market share in 2021.By deployment mode, the on-premise segment accounted for the largest AI infrastructure market share in 2021.
By technology, the machine learning segment accounted for the largest AI infrastructure market share in 2021.
On the basis of end-users, the enterprise segment accounted for the largest AI infrastructure market share in 2021.
Based on application, the AI training segment accounted for the largest AI infrastructure market share in 2021.
Region wise, North America generated highest revenue in 2021.
The key players that operate in the AI infrastructure market analysis Alphabet Inc., Amazon.com, Inc., IBM Corporation, Intel Corporation, Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, Samsung and Toshiba Corporation. These players have adopted various strategies to increase their market penetration and strengthen their position in the AI infrastructure industry.
Companies Mentioned
- NVIDIA Corporation
- Microsoft Corporation
- Alphabet Inc.
- Oracle Corporation
- Samsung Electronics Co., Ltd.
- IBM Corporation
- Intel Corporation
- Toshiba Corporation
- Micron Technology, Inc.
- Amazon.com, Inc.
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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