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Global AI-powered Storage Market to reach USD XX million by 2028.

Global AI-powered Storage Market Size study, By Offering (Hardware, Software), By Storage System (Direct-attached Storage (DAS), Network-attached Storage (NAS), Storage Area Network (SAN)), By Storage Architecture (File- and Object-Based Storage, Object Storage), By End-User (Enterprises, Government Bodies, Cloud Service Providers, Telecom Companies), and Regional Forecasts 2022-2028

Product Code: EPPGS-29858403
Publish Date: 17-05-2022
Page: 200

Global AI-powered Storage Market is valued at approximately USDXX million in 2021 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2022-2028. AI-powered storage is a smart storage system that employs artificial intelligence to continuously learn and adapt to its hybrid cloud environment in order to effectively manage and serve data. It can be installed as a virtual appliance, hardware, or cloud service. Surging demand for cloud-based services, rising adoption of AI in HPC data centers, thriving growth in data volumes, and rise in data volumes are the several key factors that are bolstering the market demand across the globe. For instance, according to Statista, the high-performance computing-based artificial intelligence generates the revenue of USD 667 million in machine learning, USD 209 million in deep learning, and other AI in HPC USD 42 million. Also, the amount is projected to grow and reached USD 1569 million in machine learning, USD 1133 million in deep learning, and other AI in HPC USD 204 million. Thereby, the surging adoption of AI in HPC data centers is fueling the demand for AI-powered storage, which, in turn, accelerates the market growth worldwide. However, the lack of data security in the cloud- and server-based services and the dearth of AI Hardware professionals impede the growth of the market over the forecast period of 2022-2028. Also, the rising number of cross-industry partnerships and collaborations and the growing availability and rapid development of useful data analysis tools are anticipated to act as a catalyzing factor for the market demand during the forecast period.

The key regions considered for the global AI-powered Storage Market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America is the leading region across the world in terms of market share owing to the rising acceptance of the emerging technologies and growing adoption of cloud-based services. Whereas, Asia-Pacific is anticipated to exhibit the highest CAGR over the forecast period 2022-2028. Factors such as the increasing government policies for promoting the adoption of AI-powered storage systems, as well as the surging adoption of connected devices, would create lucrative growth prospects for the AI-powered Storage Market across the Asia-Pacific region.
Major market players included in this report are:
Intel Corporation
NVIDIA Corporation
IBM Corporation
Samsung Electronics Co. Ltd.
Micron Technology
Dell Technologies

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Offering:
By Storage System:
Direct-attached Storage (DAS)
Network-attached Storage (NAS)
Storage Area Network (SAN)
By Storage Architecture:
File- and Object-Based Storage
Object Storage
By End-User:
Government Bodies
Cloud Service Providers
Telecom Companies
By Region:
North America

Asia Pacific
South Korea
Latin America
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019, 2020
Base year – 2021
Forecast period – 2022 to 2028

Target Audience of the Global AI-powered Storage Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2028 (USD Million)
1.2.1. AI-powered Storage Market, by Region, 2020-2028 (USD Million)
1.2.2. AI-powered Storage Market, by Offering, 2020-2028 (USD Million)
1.2.3. AI-powered Storage Market, by Storage System, 2020-2028 (USD Million)
1.2.4. AI-powered Storage Market, by Storage Architecture, 2020-2028 (USD Million)
1.2.5. AI-powered Storage Market, by End-User, 2020-2028 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global AI-powered Storage Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global AI-powered Storage Market Dynamics
3.1. AI-powered Storage Market Impact Analysis (2020-2028)
3.1.1. Market Drivers Surging demand for cloud-based services Rising adoption of AI in HPC data centers
3.1.2. Market Challenges Lack of data security in cloud- and server-based services Dearth of AI Hardware professionals
3.1.3. Market Opportunities Rising number of cross-industry partnerships and collaborations Growing availability and rapid development of useful data analysis tools
Chapter 4. Global AI-powered Storage Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2028)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 market scenario
Chapter 6. Global AI-powered Storage Market, by Offering
6.1. Market Snapshot
6.2. Global AI-powered Storage Market by Offering, Performance – Potential Analysis
6.3. Global AI-powered Storage Market Estimates & Forecasts by Offering, 2018-2028 (USD Million)
6.4. AI-powered Storage Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software
Chapter 7. Global AI-powered Storage Market, by Storage System
7.1. Market Snapshot
7.2. Global AI-powered Storage Market by Storage System, Performance – Potential Analysis
7.3. Global AI-powered Storage Market Estimates & Forecasts by Storage System, 2018-2028 (USD Million)
7.4. AI-powered Storage Market, Sub Segment Analysis
7.4.1. Direct-attached Storage (DAS)
7.4.2. Network-attached Storage (NAS)
7.4.3. Storage Area Network (SAN)
Chapter 8. Global AI-powered Storage Market, by Storage Architecture
8.1. Market Snapshot
8.2. Global AI-powered Storage Market by Storage Architecture, Performance – Potential Analysis
8.3. Global AI-powered Storage Market Estimates & Forecasts by Storage Architecture, 2018-2028 (USD Million)
8.4. AI-powered Storage Market, Sub Segment Analysis
8.4.1. File- and Object-Based Storage
8.4.2. Object Storage
Chapter 9. Global AI-powered Storage Market, by End-User
9.1. Market Snapshot
9.2. Global AI-powered Storage Market by End-User, Performance – Potential Analysis
9.3. Global AI-powered Storage Market Estimates & Forecasts by End-User, 2018-2028 (USD Million)
9.4. AI-powered Storage Market, Sub Segment Analysis
9.4.1. Enterprises
9.4.2. Government Bodies
9.4.3. Cloud Service Providers
9.4.4. Telecom Companies
Chapter 10. Global AI-powered Storage Market, Regional Analysis
10.1. AI-powered Storage Market, Regional Market Snapshot
10.2. North America AI-powered Storage Market
10.2.1. U.S. AI-powered Storage Market Offering breakdown estimates & forecasts, 2018-2028 Storage System breakdown estimates & forecasts, 2018-2028 Storage Architecture breakdown estimates & forecasts, 2018-2028 End-User breakdown estimates & forecasts, 2018-2028
10.2.2. Canada AI-powered Storage Market
10.3. Europe AI-powered Storage Market Snapshot
10.3.1. U.K. AI-powered Storage Market
10.3.2. Germany AI-powered Storage Market
10.3.3. France AI-powered Storage Market
10.3.4. Spain AI-powered Storage Market
10.3.5. Italy AI-powered Storage Market
10.3.6. Rest of Europe AI-powered Storage Market
10.4. Asia-Pacific AI-powered Storage Market Snapshot
10.4.1. China AI-powered Storage Market
10.4.2. India AI-powered Storage Market
10.4.3. Japan AI-powered Storage Market
10.4.4. Australia AI-powered Storage Market
10.4.5. South Korea AI-powered Storage Market
10.4.6. Rest of Asia Pacific AI-powered Storage Market
10.5. Latin America AI-powered Storage Market Snapshot
10.5.1. Brazil AI-powered Storage Market
10.5.2. Mexico AI-powered Storage Market
10.6. Rest of The World AI-powered Storage Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Intel Corporation Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
11.2.2. NVIDIA Corporation
11.2.3. IBM Corporation
11.2.4. Samsung Electronics Co. Ltd.
11.2.5. NetApp
11.2.6. Micron Technology
11.2.7. CISCO
11.2.8. Toshiba
11.2.9. Lenovo
11.2.10. Dell Technologies
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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