CHICAGO, Nov. 20, 2023 /PRNewswire/ — Expected to increase and diversify over the next years, the multimodal AI market will focus on improving AI systems’ ability to comprehend and interact with the world more like humans, with applications spanning multiple industries. In the upcoming years, innovation and new opportunities should be brought about by multimodal AI’s cooperation with other cutting-edge technologies.

The Multimodal AI Market is estimated 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, according to MarketsandMarkets. Multimodal AI refers to artificial intelligence that leverages a variety of data types, such as video, audio, speech, images, text, and conventional numerical datasets, to enhance its ability to make more precise predictions, draw insightful conclusions, and provide accurate solutions to real-world challenges. This approach involves training AI systems to synthesize and process diverse data sources concurrently, enabling them to better understand content and context, a significant improvement compared to earlier AI models.

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Scope of the Report

Services segment to account for higher CAGR during the forecast period

Multimodal AI services encompass a comprehensive range of offerings that caters diverse needs in the professional and managed services domains. Professional services include expert consulting, offering strategic guidance on implementing multimodal AI solutions, as well as specialized training and workshops to equip teams with the necessary skills. Multimodal data integration services facilitate the seamless combination of various data types, optimizing information utilization. Custom multimodal AI development ensures tailored solutions to meet specific business requirements, while multimodal data annotation enhances model accuracy through meticulous labeling. Ongoing support and maintenance services guarantee the sustained performance and evolution of multimodal AI applications. In the managed services, comprehensive solutions are provided, handling the end-to-end management of multimodal AI systems. This includes infrastructure management, continuous improvement, and ensuring optimal performance, allowing organizations to leverage the benefits of multimodal AI without the complexities of day-to-day management, fostering efficiency and innovation.

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Cloud segment is expected to hold the largest market size for the year 2023

Multimodal AI in the cloud deployment mode harnesses the power of diverse data types and computational resources available in cloud environments. In a cloud deployment mode, multimodal AI systems utilize remote servers and computing resources to process and analyze data from various sources simultaneously. This allows for the seamless integration of different data modalities, such as text, images, audio, and video, in a centralized cloud environment. Cloud-based multimodal AI provides the advantage of scalability, enabling organizations to easily scale their computational resources based on demand. This deployment mode facilitates accessibility and collaboration, allowing users to access and interact with multimodal AI systems from different locations. It also promotes efficient resource utilization as the processing power required for complex multimodal tasks can be dynamically allocated in the cloud.

The healthcare and life sciences vertical is projected to grow at the highest CAGR during the forecast period

Multimodal AI in the Healthcare and Life Sciences vertical offers transformative benefits by enhancing medical imaging analysis, disease diagnosis, and personalized treatment planning. By merging medical images with patient records and genetic data, healthcare providers can achieve a more precise understanding of individual patient health, allowing for tailored treatment plans and ultimately leading to improved patient outcomes and operational efficiency in healthcare. This technology holds significant promise for diagnostics, leveraging diverse data types such as medical images, electronic health records, lab results, and voice data. The integration of image data from CT scans or X-rays with textual information from patient records enables more accurate diagnoses, detecting patterns that may elude human analysis or unimodal AI systems. Additionally, multimodal AI supports remote patient monitoring by analyzing data from various sensors and wearables, tracking vital signs, physical activity, and even speech patterns to predict potential health issues, marking a notable advancement in healthcare capabilities.

Top Key Companies in Multimodal Al Market:

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.

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.

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Multimodal Al Market Advantages:

• Systems that use multimodal AI are able to comprehend and analyse data simultaneously from several sources. This can result in a more thorough and sophisticated interpretation of the data since it considers multiple modalities that offer distinct viewpoints.
• Integrating data from various modalities can improve AI systems’ precision and resilience. For object recognition, for instance, a system that combines text and image data may be more accurate and adaptable than one that only employs one modality.
• Interacting with robots can become more organic and human-like thanks to multimodal AI. Interactions become more natural and user-friendly when users may speak, text, and image together with the system.
• Multimodal AI is useful in security applications where integrating data from several sources—like audio, video, and biometric data—can improve threat detection, face recognition, and surveillance system accuracy.
• Through content analysis and comprehension across multiple modalities, multimodal AI can enhance information retrieval. For example, content management is one area where this is especially helpful because users may use many kinds of queries to get information.
• More effective learning procedures may result from multimodal AI systems’ capacity to transfer knowledge from one modality to another. A system that has been trained on photographs, for instance, might make better use of that expertise to comprehend associated textual material.
• Education, entertainment, the automotive industry, and manufacturing are just a few of the domains where multimodal AI creates new opportunities for innovation. New applications and solutions can be developed thanks to the capacity to handle a variety of data kinds.

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.

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