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Global Multimodal AI Market to reach USD 11.45 billion by the end of 2030

Global Multimodal AI Market Size Study & Forecast, by Component (Software, Service), by Modality (Image Data, Text Data, Speech & Voice Data, Video & Audio Data), by Enterprise Size (Large Enterprise, SMEs), by End-use (Media & Entertainment, BFSI, IT & Telecommunication, Healthcare, Automotive & Transportation, Gaming, Others), and Regional Analysis, 2023-2030

Product Code: ICTNGT-69707148
Publish Date: 10-03-2024
Page: 200

Global Multimodal AI Market is valued at approximately USD 0.99 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 35.8% during the forecast period 2023-2030. Multimodal AI refers to artificial intelligence systems capable of processing and understanding information from diverse sources such as text, images, audio, and video. By integrating data from multiple modalities, these systems achieve a deeper understanding of complex information, akin to human perception. Utilizing advanced machine learning algorithms, such as deep learning models, multimodal AI enables tasks such as image recognition, speech recognition, and natural language understanding. Its applications range from healthcare and autonomous driving to virtual assistants and multimedia content analysis. Multimodal AI enhances the intelligence and context-awareness of systems, enabling them to make more accurate decisions across various domains. The growth of the Multimodal AI Market is driven by several factors, including the increasing demand for analyzing unstructured data across various formats, the ability of multimodal AI to address intricate tasks and provide holistic problem-solving solutions, the rapid development of the multimodal ecosystem fueled by Generative AI techniques, and the availability of large-scale machine learning models that facilitate multimodal support.

Additionally, the rise in the adoption of smartphones, smart devices, and the increasing availability of high-quality data is acting as a catalyzing factor for the market demand across the globe. According to Statista, in 2022, it was assessed that approximately 104,7.22 million mobiles have subscribed to 5G around the world. Also, it is anticipated that the figure is likely to rise and reach nearly 2021.2 million by 2025. Thus, these aforementioned factors are primarily attributed to the global market growth. Moreover, the rising demand for customized and industry-specific solutions, as well as the enhanced adaptability to unseen data types to propel multimodal AI forward presents various lucrative opportunities over the forecast years. However, the susceptibility to bias in multimodal models and the limitations in transferability are hindering the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Multimodal AI Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the emergence of technologies and a growing preference for advanced, human-like interactions between machines and users. The widespread adoption of smartphones and smart devices, along with the increasing abundance of high-quality data have also contributed to the regional market expansion. The region’s focus on innovation fosters an environment conducive to the advancement of multimodal AI. Whereas, Asia Pacific is expected to grow at the highest CAGR over the forecast years. The rapid adoption and integration of advanced technologies across diverse industries. Countries such as China, Japan, South Korea, and India have experienced robust economic growth, prompting substantial investments in AI, which are significantly propelling the market demand across the region. Furthermore, businesses and governments in the region are increasingly prioritizing digital transformation initiatives, thereby accelerating the deployment of multimodal AI solutions across various industries in Asia Pacific.

Major market players included in this report are:
Amazon Web Services, Inc.
Google LLC
International Business Machines (IBM) Corporation
Jina AI GmbH
Microsoft Corporation
OpenAI, L.L.C.
Twelve Labs Inc.
Uniphore Technologies Inc.

Recent Developments in the Market:
Ø In December 2023, Meta announced its intention to integrate multimodal AI capabilities into its offerings, including smart glasses. These functionalities leverage data captured by the device’s cameras and microphones to provide users with information about their surroundings. Through a simple command—”Hey Meta”—users wearing Ray-Ban smart glasses can activate a virtual assistant that seamlessly combines visual and auditory inputs to perceive events in their immediate environment.
Ø In December 2023, Alphabet Inc., a prominent American multinational technology conglomerate, introduced the initial phase of its cutting-edge AI model, Gemini. This pioneering model marks the first instance of outperforming human experts in MMLU (Massive Multitask Language Understanding), a renowned benchmark for assessing language model capabilities.
Ø In October 2023, Reka AI, Inc. introduced Yasa-1, an innovative multimodal AI assistant designed to expand its comprehension beyond text to encompass images, short videos, and audio snippets. Yasa-1 provides enterprises with the flexibility to customize their abilities to private datasets of different modalities, facilitating the development of unique experiences for various use cases. With proficiency in 20 languages, the assistant is equipped to provide contextually relevant responses sourced from the internet, manage extensive contextual documents, and execute code as needed.

Global Multimodal AI Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Modality, Enterprise Size, End-use, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

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 years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential 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 Component:

By Modality:
Image Data
Text Data
Speech & Voice Data
Video & Audio Data

By Enterprise Size:
Large Enterprise

By End-use:
Media & Entertainment
IT & Telecommunication
Automotive & Transportation

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Multimodal AI Market, by Region, 2020-2030 (USD Billion)
1.2.2. Multimodal AI Market, by Component, 2020-2030 (USD Billion)
1.2.3. Multimodal AI Market, by Modality, 2020-2030 (USD Billion)
1.2.4. Multimodal AI Market, by Enterprise Size, 2020-2030 (USD Billion)
1.2.5. Multimodal AI Market, by End-use, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Multimodal AI Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Multimodal AI Market Dynamics
3.1. Multimodal AI Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Rapid development of the multimodal ecosystem fuelled by Generative AI techniques Rising in adoption of smartphones
3.1.2. Market Challenges Susceptibility to bias in multimodal models Limitations in transferability
3.1.3. Market Opportunities Rising demand for customized and industry-specific solutions Enhanced adaptability to unseen data types to propel multimodal AI forward
Chapter 4. Global Multimodal AI 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
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Multimodal AI Market, by Component
5.1. Market Snapshot
5.2. Global Multimodal AI Market by Component, Performance – Potential Analysis
5.3. Global Multimodal AI Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. Multimodal AI Market, Sub Segment Analysis
5.4.1. Software
5.4.2. Service
Chapter 6. Global Multimodal AI Market, by Modality
6.1. Market Snapshot
6.2. Global Multimodal AI Market by Modality, Performance – Potential Analysis
6.3. Global Multimodal AI Market Estimates & Forecasts by Modality 2020-2030 (USD Billion)
6.4. Multimodal AI Market, Sub Segment Analysis
6.4.1. Image Data
6.4.2. Text Data
6.4.3. Speech & Voice Data
6.4.4. Video & Audio Data
Chapter 7. Global Multimodal AI Market, by Enterprise Size
7.1. Market Snapshot
7.2. Global Multimodal AI Market by Enterprise Size, Performance – Potential Analysis
7.3. Global Multimodal AI Market Estimates & Forecasts by Enterprise Size 2020-2030 (USD Billion)
7.4. Multimodal AI Market, Sub Segment Analysis
7.4.1. Large Enterprise
7.4.2. SMEs
Chapter 8. Multimodal AI Market, by End-use
8.1. Market Snapshot
8.2. Global Multimodal AI Market by End-use, Performance – Potential Analysis
8.3. Global Multimodal AI Market Estimates & Forecasts by End-use 2020-2030 (USD Billion)
8.4. Multimodal AI Market, Sub Segment Analysis
8.4.1. Media & Entertainment
8.4.2. BFSI
8.4.3. IT & Telecommunication
8.4.4. Healthcare
8.4.5. Automotive & Transportation
8.4.6. Gaming
8.4.7. Others
Chapter 9. Global Multimodal AI Market, Regional Analysis
9.1. Top Leading Countries
9.2. Top Emerging Countries
9.3. Multimodal AI Market, Regional Market Snapshot
9.4. North America Multimodal AI Market
9.4.1. U.S. Multimodal AI Market Component breakdown estimates & forecasts, 2020-2030 Modality breakdown estimates & forecasts, 2020-2030 Enterprise Size breakdown estimates & forecasts, 2020-2030 End-use breakdown estimates & forecasts, 2020-2030
9.4.2. Canada Multimodal AI Market
9.5. Europe Multimodal AI Market Snapshot
9.5.1. U.K. Multimodal AI Market
9.5.2. Germany Multimodal AI Market
9.5.3. France Multimodal AI Market
9.5.4. Spain Multimodal AI Market
9.5.5. Italy Multimodal AI Market
9.5.6. Rest of Europe Multimodal AI Market
9.6. Asia-Pacific Multimodal AI Market Snapshot
9.6.1. China Multimodal AI Market
9.6.2. India Multimodal AI Market
9.6.3. Japan Multimodal AI Market
9.6.4. Australia Multimodal AI Market
9.6.5. South Korea Multimodal AI Market
9.6.6. Rest of Asia Pacific Multimodal AI Market
9.7. Latin America Multimodal AI Market Snapshot
9.7.1. Brazil Multimodal AI Market
9.7.2. Mexico Multimodal AI Market
9.8. Middle East & Africa Multimodal AI Market
9.8.1. Saudi Arabia Multimodal AI Market
9.8.2. South Africa Multimodal AI Market
9.8.3. Rest of Middle East & Africa Multimodal AI Market

Chapter 10. Competitive Intelligence
10.1. Key Company SWOT Analysis
10.1.1. Company 1
10.1.2. Company 2
10.1.3. Company 3
10.2. Top Market Strategies
10.3. Company Profiles
10.3.1. Aimesoft Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.3.2. Amazon Web Services, Inc.
10.3.3. Google LLC
10.3.4. International Business Machines (IBM) Corporation
10.3.5. Jina AI GmbH
10.3.6. Meta.
10.3.7. Microsoft Corporation
10.3.8. OpenAI, L.L.C.
10.3.9. Twelve Labs Inc.
10.3.10. Uniphore Technologies Inc.
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. 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.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
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:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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