Multimodal AI Market by Offering (Solutions & Services), Data Modality (Image, Audio), Technology (ML, NLP, Computer Vision, Context Awareness, IoT), Type (Generative, Translative, Explanatory, Interactive), Vertical and Region - Global Forecast to 2028
[380 Pages Report] The global Multimodal AI Market is projected to grow from USD 1.0 billion in 2023 to USD 4.5 billion by 2028, at a CAGR of 35.0% during the forecast period. The factors that propel the multimodal AI market includes the demand for analyzing unstructured data in multiple formats, the capacity of multimodal AI to tackle complex tasks and offer a comprehensive problem-solving approach, the acceleration of multimodal ecosystem development through Generative AI techniques, and the accessibility of large-scale machine learning models that support multimodality.
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Market Dynamics
Driver: Generative AI techniques to accelerate multimodal ecosystem development
Generative AI is like the creative powerhouse of the AI world, capable of producing new content such as text, images, or even entire videos. It can create content that combines multiple data formats. For instance, it can generate detailed written descriptions for images, create realistic images from textual descriptions, or even produce videos with a nuanced understanding of the content. This blending of data formats is where Generative AI and multimodal AI synergize. As Generative AI advances, it not only enhances the creative aspects of multimodal AI but also paves the way for more sophisticated, integrated systems. This is revolutionary because it enables the development of AI applications that can understand, interpret, and produce content across various data types seamlessly. One striking example is in the space of content creation: a multimodal AI system driven by Generative AI can automatically generate marketing materials that combine text, images, and videos for a more compelling and personalized user experience. It can craft interactive educational content that caters to individual learning styles, enhancing engagement and comprehension. Moreover, it can automate the creation of multimedia presentations, making them more impactful and informative.
Restraint: Susceptibility to bias in multimodal models
Multimodal AI models, like their unimodal counterparts, are vulnerable to bias, and this bias often originates from the very data they are trained on. Training datasets, comprising text, images, videos, and more, may inadvertently reflect societal or cultural biases present in the data sources. These biases can manifest in numerous ways, such as gender or racial bias in image recognition, or linguistic and contextual bias in natural language processing tasks. When multimodal AI models are trained on such data, they inevitably inherit and perpetuate these biases, which can lead to inaccurate or unfair results when making predictions or decisions. This bias in AI models is not only a technical challenge but also an ethical concern, as it can contribute to discriminatory practices, reinforce stereotypes, and exacerbate inequalities. Addressing bias in multimodal AI models requires vigilant data curation, diversity in data sources, and sophisticated debiasing techniques. It also necessitates an ongoing commitment to ethical AI development and the responsible use of these technologies, ensuring that AI systems are not only technically proficient but also aligned with ethical and societal values.
Opportunity: Rising demand for customized and industry-specific solutions
As AI technologies continue to advance, there is a growing recognition that the application of multimodal AI can be highly tailored to address specific industry needs and challenges. From healthcare and finance to education and entertainment, each sector has its unique data characteristics and demands. multimodal AI is well-positioned to provide customized solutions by harnessing the power of multiple data modalities. For instance, in healthcare, multimodal AI can be utilized to analyze medical images, textual patient records, and even audio recordings of doctor-patient interactions to offer comprehensive diagnostic insights, revolutionizing patient care and medical research. In the automotive sector, multimodal AI is being employed to create advanced driver-assistance systems, combining visual data from cameras with textual data from sensors and audio data from in-car voice assistants to enhance road safety and the driving experience. This industry-specific approach is paving the way for a new era of innovation, where the unique challenges and opportunities of each sector are addressed with tailor-made multimodal AI solutions.
Challenge: Limitations in transferability pose challenges for multimodal AI adaptation to diverse data types
Limited transferability highlights a fundamental constraint in the versatility and adaptability of these AI systems. Just as a conductor trained in classical music might encounter challenges when orchestrating a jazz ensemble, multimodal AI models trained on one type of data might not seamlessly adapt or perform effectively when presented with a different type of data. This limitation in transferability underscores the need for careful consideration, especially when deploying these models in dynamic and diverse real-world scenarios. The challenge lies in the fact that the knowledge acquired during training is inherently tied to the specific data modalities, patterns, and characteristics within that training dataset. When faced with new or different types of data, such as transitioning from textual data to image data, or from structured data to unstructured data, these models often struggle to make accurate predictions or extract meaningful insights.
Multimodal AI Market Ecosystem
The multimodal AI market ecosystem is a dynamic landscape consisting of various key components, each playing a distinct role in advancing the field of AI and software development. These components include multimodal AI solution providers, service providers, end users, and regulatory bodies.
By Vertical, BFSI segment accounts for the largest market size during the forecast period
Multimodal AI in the BFSI segment is revolutionizing operations by integrating various data types and AI capabilities. In this sector, multimodal AI is applied to enhance customer experiences, streamline processes, and mitigate risks. For instance, in customer interactions, it utilizes natural language processing (NLP) for text and speech analysis, facial recognition for authentication, and even sentiment analysis to gauge customer satisfaction. In fraud detection, multimodal AI combines transaction data, images, and patterns of behavior to identify anomalies more effectively. Additionally, it plays a crucial role in automating document processing, where it interprets both text and visual information from documents, improving accuracy and efficiency. The adoption of multimodal AI in BFSI not only enhances security and fraud prevention but also contributes to personalized customer services and efficient backend operations, ultimately shaping a more advanced and responsive financial landscape.
By Type, Generative Multimodal AI segment is projected to grow at the highest CAGR during the forecast period
Generative Multimodal AI has the unique ability to generate new content across multiple modalities, such as text, images, and even audio simultaneously. This type of AI is trained to comprehend the relationships between different data types and can generate coherent and contextually relevant content across these modalities. For example, it can produce captions for images, translate visual scenes into descriptive text, or even generate realistic images based on textual descriptions. The strength of Generative Multimodal AI lies in its capacity to create a unified and nuanced understanding of data, enabling more advanced applications in content creation, storytelling, and problem-solving across a variety of industries, including entertainment, design, and communication.
North America to account for the largest market size during the forecast period
The multimodal AI market in North America stands as a global powerhouse, shaped by the innovation and technological ability of both the US and Canada. The region is experiencing robust growth, driven by a convergence of technologies and a surge in demand for more sophisticated and human-like interactions between machines and users. One of the key driving factors is the widespread adoption of smartphones, smart devices, and the increasing availability of high-quality data. The region’s focus on innovation, particularly in Silicon Valley, fosters a conducive environment for multimodal AI advancements. North American companies are at the forefront of developing and implementing multimodal AI solutions, reflecting the region's commitment to driving technological advancements and pushing the boundaries of artificial intelligence for enhanced user engagement and problem-solving.
Key Market Players
The major multimodal AI and service providers include Google (US), Microsoft (US), OpenAI (US), Meta (US), AWS (US), IBM (US), Twelve Labs (US), Aimesoft (US), Jina AI (Germany), Uniphore (US), Reka AI (US), Runway (US), Jiva.ai (UK), Vidrovr (US), Mobius Labs (US), Newsbridge (France), OpenStream.ai (US), Habana Labs (US), Modality.AI (US), Perceiv AI (Canada), Multimodal (US), Neuraptic AI (Spain), Inworld AI (US), Aiberry (US), One AI (US), Beewant (France), Owlbot.AI (US), Hoppr (US), Archetype AI (US), Stability AI (England). These companies have used both organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the multimodal AI market.
Scope of the Report
Report Metrics |
Details |
Market size available for years |
2017–2028 |
Base year considered |
2022 |
Forecast period |
2023–2028 |
Forecast units |
USD Billion |
Segments covered |
Offering (Solutions & Services), Data Modality (Image, Audio), Technology (ML, NLP, Computer Vision, Context Awareness, IoT), Type (Generative, Translative, Explanatory, Interactive), Vertical, and Region. |
Geographies covered |
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America |
Companies covered |
Google (US), Microsoft (US), OpenAI (US), Meta (US), AWS (US), IBM (US), Twelve Labs (US), Aimesoft (US), Jina AI (Germany), Uniphore (US), Reka AI (US), Runway (US), Jiva.ai (UK), Vidrovr (US), Mobius Labs (US), Newsbridge (France), OpenStream.ai (US), Habana Labs (US), Modality.AI (US), Perceiv AI (Canada), Multimodal (US), Neuraptic AI (Spain), Inworld AI (US), Aiberry (US), One AI (US), Beewant (France), Owlbot.AI (US), Hoppr (US), Archetype AI (US), Stability AI (England). |
This research report categorizes the multimodal AI market based on Offering, Data Modality, Technology, Type, Vertical, and Region.
By Offering:
-
Solutions
- Framework
- Platform
- Software
-
Solutions by Deployment Mode
-
- Cloud
- On Premises
-
-
Services
-
Professional Services
- Consulting
- Training & Workshops
- Multimodal Data Integration
- Custom Multimodal AI Development
- Multimodal Data Annotation
- Support & Maintenance
- Managed Services
-
Professional Services
By Data Modality:
- Text Data
- Speech and Voice Data
- Image Data
- Video Data
- Audio Data
By Technology:
- Machine Learning
- Natural Language Processing
- Computer Vision
- Context Awareness
- Internet of Things
By Type:
- Generative Multimodal AI
- Translative Multimodal AI
- Explanatory Multimodal AI
- Interactive Multimodal AI
By Vertical:
- BFSI
- Retail & eCommerce
- Telecommunications
- Government & Public Sector
- Healthcare & Life Sciences
- Manufacturing
- Automotive, Transportation & Logistics
- Media & Entertainment
- Other Verticals
By Region:
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Recent Developments:
- In November 2023, Open AI’s GPT-4 Turbo introduces the capability to accept images as inputs within the Chat Completions API. This enhancement opens up various use cases, including generating image captions, conducting detailed analysis of real-world images, and processing documents that contain figures. Additionally, developers can seamlessly integrate DALL·E 3 into their applications and products by specifying "dall-e-3" as the model when using the Images API, extending the creative potential of multimodal AI.
- In August 2023, Meta introduced SeamlessM4T, a groundbreaking AI translation model that stands as the first to offer comprehensive multimodal and multilingual capabilities. This innovative model empowers individuals to communicate across languages through both speech and text effortlessly.
- In July 2023, Meta announced the release of Llama 2, the next iteration of its open-source large language model. This development is part of an expanded partnership between Microsoft and Meta, with Microsoft being designated as the preferred partner for Llama 2.
- In June 2023, Microsoft introduced Kosmos-2, a Multimodal Large Language Model (MLLM) that enhances its abilities to understand object descriptions, including bounding boxes, and connect text with the visual domain. In addition to the typical MLLM functions, like processing various modalities, following instructions, and adapting in-context, Kosmos-2 brings the grounding capability into play within downstream applications, broadening its scope in the realm of multimodal AI.
- In February 2023, Uniphore acquired Hexagone, a company that combines voice, visual, and text data to gain insights through AI. This addition strengthens Uniphore's X Platform, making it even better at understanding human behavior. With these improvements, Uniphore aimed to enhance the accuracy and empathy in resolving customer conversations and inquiries.
Frequently Asked Questions (FAQ):
What are multimodal AI?
Multimodal AI refers to AI systems that can process and understand information from multiple modalities or sources, such as text, images, speech, and more. It involves the integration of diverse data types to enhance the accuracy and richness of AI models.
Which region is expected to hold the highest share in the multimodal AI market?
North America is expected to dominate the multimodal AI market in 2028. North America is at the forefront of multimodal AI development and adoption, with a thriving ecosystem of startups, established tech giants, and innovative enterprises actively leveraging these tools.
Which are key end users adopting multimodal AI solutions and services?
Key end users adopting Multimodal AI solutions and services include BFSI, Healthcare & Life Sciences, Retail & eCommerce, Manufacturing, Telecommunications, Media & Entertainment, Government & Public Sector And Other Verticals.
Which are the key drivers supporting the market growth for multimodal AI?
The key drivers supporting the market growth for multimodal AI include the need to analyze unstructured data in multiple formats drives the multimodal AI market, the ability of multimodal AI to handle complex tasks and provide a holistic approach to problem-solving, Generative AI techniques to accelerate multimodal ecosystem development and the availability of large-scale machine learning models that support multimodality.
Who are the key vendors in the market for multimodal AI?
The key vendors in the global multimodal AI market include are Google (US), Microsoft (US), OpenAI (US), Meta (US), AWS (US), IBM (US), Twelve Labs (US), Aimesoft (US), Jina AI (Germany), Uniphore (US), Reka AI (US), Runway (US), Jiva.ai (UK), Vidrovr (US), Mobius Labs (US), Newsbridge (France), OpenStream.ai (US), Habana Labs (US), Modality.AI (US), Perceiv AI (Canada), Multimodal (US), Neuraptic AI (Spain), Inworld AI (US), Aiberry (US), One AI (US), Beewant (France), Owlbot.AI (US), Hoppr (US), Archetype AI (US), Stability AI (England).
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The research study for the multimodal AI market involved extensive secondary sources, directories, and several journals. Primary sources were mainly industry experts from the core and related industries, preferred multimodal AI solution providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information, and assess the market’s prospects.
Secondary Research
The market size of companies offering multimodal AI solutions and services was arrived at based on secondary data available through paid and unpaid sources. It was also arrived at by analyzing the product portfolios of major companies and rating the companies based on their performance and quality.
In the secondary research process, various sources were referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendor websites. Additionally, multimodal AI spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on solutions, services, market classification, and segmentation according to offerings of major players, industry trends related to solutions, services, deployment modes, functionality, applications, verticals, and regions, and key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and multimodal AI expertise; related key executives from multimodal AI solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using multimodal AI, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of multimodal AI solutions and services, which would impact the overall multimodal AI market
The following is the breakup of primary profiles:
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Market Size Estimation
Multiple approaches were adopted for estimating and forecasting the multimodal AI market. The first approach involves estimating the market size by summation of companies’ revenue generated through the sale of solutions and services.
Market Size Estimation Methodology-Top-down approach
In the top-down approach, an exhaustive list of all the vendors offering solutions and services in the Multimodal AI market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor’s offerings were evaluated based on the breadth of solutions and services, deployment modes, applications, and verticals. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets repository for validation.
Market Size Estimation Methodology-Bottom-up approach
In the bottom-up approach, the adoption rate of multimodal AI solutions and services among different end users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of multimodal AI solutions and services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the multimodal AI market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major multimodal AI solution providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall multimodal AI market size and segments’ size were determined and confirmed using the study
Top-down and Bottom-up approaches
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Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Market Definition
According to Twelve Labs, Multimodal AI is a rapidly evolving field that focuses on understanding and leveraging multiple modalities to build more comprehensive and accurate AI models.
According to Aimesoft, Multimodal AI is a new AI paradigm, in which various data types (image, text, speech, numerical data) are combined with multiple intelligence processing algorithms to achieve higher performances. Multimodal AI often outperforms single modal AI in many real-world problems.
Stakeholders
- Multimodal AI solution vendors
- Managed service providers
- Support and maintenance service providers
- System Integrators (SIs)/migration service providers
- Value-added resellers (VARs) and distributors
- Distributors and value-added resellers (VARs)
- System integrators (SIs)
- Independent software vendors (ISV)
- Third-party providers
- Technology providers
Report Objectives
- To define, describe, and predict the multimodal AI market by offering (solutions and services) data modality, technology, type, vertical, and region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
- To profile key players and comprehensively analyze their market rankings and core competencies
- To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the multimodal AI market.
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product matrix provides a detailed comparison of the product portfolio of each company
Geographic Analysis as per Feasibility
- Further breakup of the North American Multimodal AI Market
- Further breakup of the European Market
- Further breakup of the Asia Pacific Market
- Further breakup of the Middle East & Africa Market
- Further breakup of the Latin American Multimodal AI Market
Company Information
- Detailed analysis and profiling of additional market players (up to five)
Growth opportunities and latent adjacency in Multimodal AI Market