AI in Fashion Market Overview
Artificial Intelligence has the potential to revolutionize the fashion industry. The industry is one of the world’s largest. Furthermore, AI has the potential to transform the fashion sector in terms of manufacturing, design, sales, and consumer use. Today's AI technology promises to help manufacturers create higher-quality items while also reducing errors in forecasting the next big fashion trends. In the near future, increased investment in the industry is likely to propel the AI in fashion market to significant growth.
Because of the numerous AI applications in the fashion sector, the global AI in fashion market is predicted to grow rapidly in the near future. Customization, enhanced material procurements, reduced returns, and automation in operations, inventory management, and product discovery are all likely to benefit from AI. These are crucial issues for change in the fashion sector, which has traditionally relied on family-based business models that promote a particular mode of growth. Growth is predicted to be propelled by increased global competition, global convergence, and a desire for more individualized preferences in the fashion industry.
| Report Metric | Details |
| Market size available for years | 2023–2030 |
| Base year considered | 2023 |
| Forecast period | 2024–2030 |
| Forecast unit | Value (USD Million) |
| Segments covered | Components, Applications, Deployment Mode, Category, End-User, and Region. |
| Regions covered | North America (the U.S. and Canada), Europe (UK, Germany, France, Italy, Spain, Russia, Rest of Europe), Asia-Pacific (China, India, Japan, Australia, South East Asia, Rest of Asia Pacific), Latin America and the Middle East and Africa (Brazil, Saudi Arabia, UAE, Rest of LAMEA) |
| Companies covered | Microsoft, IBM, Google, AWS, SAP, Facebook, Adobe, Oracle, Catchoom, Huawei, Vue.ai, Heuritech, Wide Eyes, FINDMINE, Intelistyle, Lily AI, Pttrns.ai, Syte, mode.ai, Stitch Fix. |
Covid-19 Impact on AI in Fashion Market
Like many other industries, COVID-19 badly knocked the electronic and semiconductor industries. This unprecedented event has impacted nearly 230 countries in just a few weeks, resulting in the forced shutdown of manufacturing and transportation activities within and across the countries. This has directly affected the overall sector's growth. It is estimated that COVID-19 to leave more than USD 30 billion impacts on the electronics and semiconductor industry. The sector is majorly affected due to transport restrictions on major electronics and semiconductor raw material providers. However, the emerging need for semiconductors in several industries will offer rapid market recovery over the future period.
AI in Fashion Market Segment Overview
According to Components, the Solution segment will have a bigger market share during the projected period. This is due to a higher rate of adoption. Fashion businesses are increasingly turning to modern technologies to help them improve their operations and attract new customers. Furthermore, understanding current trends through numerous social media platforms and catering to the needs of customers has become difficult for businesses. As a result, fashion retailers and brands are increasingly implementing AI-based solutions, such as platform and software tools including SDKs, APIs, and machine learning models.
AI in Fashion Market, By Components
- Solution
- Software Tools
- Platforms
- Services
- Training and Consulting
- System Integration and Testing
- Support and Maintenance
AI in Fashion Market, By Applications
· Product Recommendation
· Product Search and Discovery
· Supply Chain Management and Demand Forecasting
· Creative Designing and Trend Forecasting
· Customer Relationship Management
· Virtual Assistants
· Others (Fraud detection, fabric waste reduction, and price optimization)
AI in Fashion Market, By Deployment Mode
· Cloud
· On-premises
AI in Fashion Market, By Category
· Apparel
· Accessories
· Footwear
· Beauty and Cosmetics
· Jewelry and Watches
· Others (eyewear, home decor)
AI in Fashion Market, By End-User
· Fashion Designers
· Fashion Stores
AI in Fashion Regional Overview
Region-Wise, in North America, the global AI in fashion market is expected to rise rapidly. Big fashion brands are projected to boost their investments in startups, as well as their use of AI in major retail chains and their investment in innovation. Furthermore, the AI in fashion market is predicted to increase speedily in the Asia Pacific region. Because of the significant expansion of the IT sector, changing lifestyle habits, and increased disposable income, the region is seeing a massive influx of startups.
AI in Fashion Market, By Geography
· North America (US & Canada)
· Europe (UK, Germany, France, Italy, Spain, Russia & Rest of Europe)
· Asia-Pacific (Japan, China, India, Australia, & South Korea, & Rest of Asia-Pacific)
· LAMEA (Brazil, Saudi Arabia, UAE & Rest of LAMEA)
AI in Fashion Market, Key Players
· Microsoft
· IBM
· Google
· AWS
· SAP
· Facebook
· Adobe
· Oracle
· Catchoom
· Huawei
· Vue.ai
· Heuritech
· Wide Eyes
· FINDMINE
· Intelistyle
· Lily AI
· Pttrns.ai
· Syte
· mode.ai
· Stitch Fix
Frequently Asked Questions (FAQ) :
Q1. What are the driving factors for the AI in Fashion market?
Because of the numerous AI applications in the fashion sector, the global AI in fashion market is predicted to grow rapidly in the near future
Q2. Which Segments are covered in the AI in Fashion Market report?
Components, Applications, Deployment Mode, Category, End-User, and Region. these segments are covered in the AI in Fashion market report
Q3. Which segment is projected to hold the largest share in the AI in Fashion Market?
The solution segment is projected to hold the largest share in the AI in Fashion Market.
Q4. Which region holds the largest share in the AI in Fashion market?
North America held the largest share in the global AI in Fashion market
Q5. Which are the prominent players in the AI in Fashion Market?
Microsoft, IBM, Google, AWS, SAP, Facebook, Adobe, Oracle, Catchoom, Huawei, Vue.ai, Heuritech, Wide Eyes, FINDMINE, Intelistyle, Lily AI, Pttrns.ai, Syte, mode.ai, Stitch Fix. are some key players in the AI in Fashion Market.
1. Introduction
- 1.1. Research Scope
- 1.2. Market Segmentation
- 1.3. Research Methodology
- 1.4. Definitions and Assumptions
2. Executive Summary
3. Market Dynamics
- 3.1. Market Drivers
- 3.2. Market Restraints
- 3.3. Market Opportunities
4. Key Insights
- 4.1. Key Emerging Trends – For Major Countries
- 4.2. Latest Technological Advancement
- 4.3. Regulatory Landscape
- 4.4. Industry SWOT Analysis
- 4.5. Porters Five Forces Analysis
5. Global AI in Fashion Market Analysis (USD Billion), Insights and Forecast, 2020-2027
- 5.1. Key Findings / Summary
- 5.2. Market Analysis, Insights and Forecast – By Components
- 5.2.1. Solution
- 5.2.1.1. Software Tools
- 5.2.1.2. Platforms
- 5.2.2. Services
- 5.2.2.1. Training and Consulting
- 5.2.2.2. System Integration and Testing
- 5.2.2.3. Support and Maintenance
- 5.2.1. Solution
- 5.3.1. Product Recommendation
- 5.3.2. Product Search and Discovery
- 5.3.3. Supply Chain Management and Demand Forecasting
- 5.3.4. Creative Designing and Trend Forecasting
- 5.3.5. Customer Relationship Management
- 5.3.6. Virtual Assistants
- 5.3.7. Others (Fraud detection, fabric waste reduction, and price optimization)
- 5.4.1. Cloud
- 5.4.2. On-premises
- 5.5.1. Apparel
- 5.5.2. Accessories
- 5.5.3. Footwear
- 5.5.4. Beauty and Cosmetics
- 5.5.5. Jewelry and Watches
- 5.5.6. Others (eyewear, home decor)
- 5.6.1. Fashion Designers
- 5.6.2. Fashion Stores
- 5.7.1. North America
- 5.7.2. Europe
- 5.7.3. Asia Pacific
- 5.7.4. Latin America, Middle East and Africa
6. North America AI in Fashion Market Analysis (USD Billion), Insights and Forecast, 2020-2027
- 6.1. Key Findings / Summary
- 6.2. Market Analysis, Insights and Forecast – By Components
- 6.2.1. Solution
- 6.2.1.1. Software Tools
- 6.2.1.2. Platforms
- 6.2.2. Services
- 6.2.2.1. Training and Consulting
- 6.2.2.2. System Integration and Testing
- 6.2.2.3. Support and Maintenance
- 6.2.1. Solution
- 6.3.1. Product Recommendation
- 6.3.2. Product Search and Discovery
- 6.3.3. Supply Chain Management and Demand Forecasting
- 6.3.4. Creative Designing and Trend Forecasting
- 6.3.5. Customer Relationship Management
- 6.3.6. Virtual Assistants
- 6.3.7. Others (Fraud detection, fabric waste reduction, and price optimization)
- 6.4.1. Cloud
- 6.4.2. On-premises
- 6.5.1. Apparel
- 6.5.2. Accessories
- 6.5.3. Footwear
- 6.5.4. Beauty and Cosmetics
- 6.5.5. Jewelry and Watches
- 6.5.6. Others (eyewear, home decor)
- 6.6.1. Fashion Designers
- 6.6.2. Fashion Stores
- 6.7.1. U.S.
- 6.7.2. Canada
7. Europe AI in Fashion Market Analysis (USD Billion), Insights and Forecast, 2020-2027
- 7.1. Key Findings / Summary
- 7.2. Market Analysis, Insights and Forecast – By Components
- 7.2.1. Solution
- 7.2.1.1. Software Tools
- 7.2.1.2. Platforms
- 7.2.2. Services
- 7.2.2.1. Training and Consulting
- 7.2.2.2. System Integration and Testing
- 7.2.2.3. Support and Maintenance
- 7.2.1. Solution
- 7.3.1. Product Recommendation
- 7.3.2. Product Search and Discovery
- 7.3.3. Supply Chain Management and Demand Forecasting
- 7.3.4. Creative Designing and Trend Forecasting
- 7.3.5. Customer Relationship Management
- 7.3.6. Virtual Assistants
- 7.3.7. Others (Fraud detection, fabric waste reduction, and price optimization)
- 7.4.1. Cloud
- 7.4.2. On-premises
- 7.5.1. Apparel
- 7.5.2. Accessories
- 7.5.3. Footwear
- 7.5.4. Beauty and Cosmetics
- 7.5.5. Jewelry and Watches
- 7.5.6. Others (eyewear, home decor)
- 7.6.1. Fashion Designers
- 7.6.2. Fashion Stores
- 7.7.1. UK
- 7.7.2. Germany
- 7.7.3. France
- 7.7.4. Italy
- 7.7.5. Spain
- 7.7.6. Russia
- 7.7.7. Rest of Europe
8. Asia Pacific AI in Fashion Market Analysis (USD Billion), Insights and Forecast, 2020-2027
- 8.1. Key Findings / Summary
- 8.2. Market Analysis, Insights and Forecast – By Components
- 8.2.1. Solution
- 8.2.1.1. Software Tools
- 8.2.1.2. Platforms
- 8.2.2. Services
- 8.2.2.1. Training and Consulting
- 8.2.2.2. System Integration and Testing
- 8.2.2.3. Support and Maintenance
- 8.2.1. Solution
- 8.3.1. Product Recommendation
- 8.3.2. Product Search and Discovery
- 8.3.3. Supply Chain Management and Demand Forecasting
- 8.3.4. Creative Designing and Trend Forecasting
- 8.3.5. Customer Relationship Management
- 8.3.6. Virtual Assistants
- 8.3.7. Others (Fraud detection, fabric waste reduction, and price optimization)
- 8.4.1. Cloud
- 8.4.2. On-premises
- 8.5.1. Apparel
- 8.5.2. Accessories
- 8.5.3. Footwear
- 8.5.4. Beauty and Cosmetics
- 8.5.5. Jewelry and Watches
- 8.5.6. Others (eyewear, home decor)
- 8.6.1. Fashion Designers
- 8.6.2. Fashion Stores
- 8.7.1. China
- 8.7.2. India
- 8.7.3. Japan
- 8.7.4. Australia
- 8.7.5. South East Asia
- 8.7.6. Rest of Asia Pacific
9. Latin America, Middle East and Africa AI in Fashion Market Analysis (USD Billion), Insights and Forecast, 2020-2027
- 9.1. Key Findings / Summary
- 9.2. Market Analysis, Insights and Forecast – By Components
- 9.2.1. Solution
- 9.2.1.1. Software Tools
- 9.2.1.2. Platforms
- 9.2.2. Services
- 9.2.2.1. Training and Consulting
- 9.2.2.2. System Integration and Testing
- 9.2.2.3. Support and Maintenance
- 9.2.1. Solution
- 9.3.1. Product Recommendation
- 9.3.2. Product Search and Discovery
- 9.3.3. Supply Chain Management and Demand Forecasting
- 9.3.4. Creative Designing and Trend Forecasting
- 9.3.5. Customer Relationship Management
- 9.3.6. Virtual Assistants
- 9.3.7. Others (Fraud detection, fabric waste reduction, and price optimization)
- 9.4.1. Cloud
- 9.4.2. On-premises
- 9.5.1. Apparel
- 9.5.2. Accessories
- 9.5.3. Footwear
- 9.5.4. Beauty and Cosmetics
- 9.5.5. Jewelry and Watches
- 9.5.6. Others (eyewear, home decor)
- 9.6.1. Fashion Designers
- 9.6.2. Fashion Stores
- 9.7.1. Brazil
- 9.7.2. Saudi Arabia
- 9.7.3. UAE
- 9.7.4. Rest of LAMEA
10. Competitive Analysis
- 10.1. Company Market Share Analysis, 2018
- 10.2. Key Industry Developments
- 10.3. Company Profile
- 10.4. Microsoft
- 10.4.1. Business Overview
- 10.4.2. Segment 1 & Service Offering
- 10.4.3. Overall Revenue
- 10.4.4. Geographic Presence
- 10.4.5. Recent Development
- 10.5. IBM
- 10.6. Google
- 10.7. AWS
- 10.8. SAP
- 10.9. Facebook
- 10.10. Adobe
- 10.11. Oracle
- 10.12. Catchoom
- 10.13. Huawei
- 10.14. Vue.ai
- 10.15. Heuritech
- 10.16. Wide Eyes
- 10.17. FINDMINE
- 10.18. Intelistyle
- 10.19. Lily AI
- 10.20. Pttrns.ai
- 10.21. Syte
- 10.22. mode.ai
Data Library Research are conducted by industry experts who offer insight on industry structure, market segmentations technology assessment and competitive landscape (CL), and penetration, as well as on emerging trends. Their analysis is based on primary interviews (~ 80%) and secondary research (~ 20%) as well as years of professional expertise in their respective industries. Adding to this, by analysing historical trends and current market positions, our analysts predict where the market will be headed for the next five years. Furthermore, the varying trends of segment & categories geographically presented are also studied and the estimated based on the primary & secondary research.
In this particular report from the supply side Data Library Research has conducted primary surveys (interviews) with the key level executives (VP, CEO’s, Marketing Director, Business Development Manager and SOFT) of the companies that active & prominent as well as the midsized organization
FIGURE 1: DLR RESEARH PROCESS
Extensive primary research was conducted to gain a deeper insight of the market and industry performance. The analysis is based on both primary and secondary research as well as years of professional expertise in the respective industries.
In addition to analysing current and historical trends, our analysts predict where the market is headed over the next five years.
It varies by segment for these categories geographically presented in the list of market tables. Speaking about this particular report we have conducted primary surveys (interviews) with the key level executives (VP, CEO’s, Marketing Director, Business Development Manager and many more) of the major players active in the market.
Secondary ResearchSecondary research was mainly used to collect and identify information useful for the extensive, technical, market-oriented, and Friend’s study of the Global Extra Neutral Alcohol. It was also used to obtain key information about major players, market classification and segmentation according to the industry trends, geographical markets, and developments related to the market and technology perspectives. For this study, analysts have gathered information from various credible sources, such as annual reports, sec filings, journals, white papers, SOFT presentations, and company web sites.
Market Size EstimationBoth, top-down and bottom-up approaches were used to estimate and validate the size of the Global market and to estimate the size of various other dependent submarkets in the overall Extra Neutral Alcohol. The key players in the market were identified through secondary research and their market contributions in the respective geographies were determined through primary and secondary research.
Forecast Model
