Multimodal AI Market Growth, Opportunities Business Scenario, Share, Growth Size, Scope, Key Segments and Forecast to 2028

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Multimodal AI Market Growth, Opportunities Business Scenario, Share, Growth Size, Scope, Key Segments and Forecast to 2028

November 28
21:02 2023
Multimodal AI Market Growth, Opportunities Business Scenario, Share, Growth Size, Scope, Key Segments and Forecast to 2028
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), (UK), Vidrovr (US), Mobius Labs (US), Newsbridge (France), (US), Habana Labs (US).
Multimodal Al 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

The Multimodal AI Market is expected to grow from USD 1.0 billion in 2023 to USD 4.5 billion in 2028, at a CAGR of 35.0%  during the forecast period. The multimodal AI market is driven by various factors, such as 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.

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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.

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.

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Unique Features in the Multimodal AI Market

Integration of Different Data Types: Multimodal AI combines multiple modalities to process and analyse data from multiple sources, including text, images, and audio, enabling thorough insights.

Knowledge Transfer across Modalities: Multimodal artificial intelligence (AI) facilitates cross-modal learning, in which insights from one modality (like images) can be used to another to enhance comprehension and forecasts (like text).

Holistic Understanding of Content: Multimodal AI enhances context understanding by handling numerous modalities at once. For example, total comprehension is improved when spoken language is understood in the context of visual information.

Correlating Semantic Information: To extract deeper meaning and insights from the combined data, multimodal AI correlates semantic information across modalities in addition to identifying patterns in individual modalities.

Improved Model Robustness: Models are more resilient when data from several modalities is combined. Because multimodal AI can rely on information from other modality, it is frequently more robust against noise or missing data in one modality.

Rich User Interaction: Multimodal AI facilitates more realistic and immersive user interfaces by processing and reacting to a variety of inputs, including voice commands, gestures, and visual signals.

Major Highlights of the Multimodal AI Market

Consumer electronics have become more and more reliant on multimodal AI, as speech, image, and gesture recognition has become commonplace in smart products and virtual assistants.

Notable developments in multimodal AI’s natural language processing that allow for a more complex comprehension of contextual information in textual material and enhance user-AI system interaction.

By combining several modalities, including speech, text, and visual components, virtual assistants and AI-powered chatbots improved their ability to deliver a smooth and customised user experience.

Enhanced in-car experiences with voice commands, gesture recognition, and visual context awareness for apps like infotainment and driver assistance due to a greater integration of multimodal AI in the automotive industry.

Interactive and customised e-learning experiences are made possible by multimodal AI applications in education. Systems with speech recognition, handwriting recognition, and visual content interpretation capabilities were among those covered in this.

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Top Key Companies in the Multimodal AI Market

Key players operating in the multimodal AI market across the globe are Alphabet Inc. (Google), Microsoft Corporation (Microsoft), OpenAI, Inc. (OpenAI), Meta Platforms, Inc. (Meta), Amazon Web Services, Inc. (AWS), IBM Corporation (IBM),  Twelve Labs Inc. (Twelve Labs), Aimesoft (Aimesoft), (Jina AI GmBH) Jina AI, Uniphore Technologies Inc. (Uniphore), Reka AI, Inc. (Reka AI), Runway AI, Inc. (Runway), Ltd (, Vidrovr (Vidrovr), Mobius Labs GmbH (Mobius Labs), Newsbridge (Newsbridge), Openstream Inc. (, Habana Labs, Ltd. (Habana Labs), Modality.AI, Inc (Modality.AI), Perceiv Research Inc. (Perceiv AI), Multimodal, Inc. (Multimodal), Neuraptic AI (Neuraptic AI), Theai, Inc. (Inworld AI), Aiberry (Aiberry), One AI Inc. (One AI), Beewant (Beewant), Owlbot (Owlbot.AI), IntellixAI Inc. (Hoppr), Archetype AI (Archetype AI), Stability AI Ltd. (Stability AI). These companies employ various approaches, both organic and inorganic, including introducing new products, forming strategic partnerships and collaborations, and engaging in mergers and acquisitions, to broaden their presence and offerings within the multimodal AI market.

Google has been a driving force in AI research for almost two decades, making many important breakthroughs in artificial intelligence, including the development of AI transformers and the BERT language model. Google has also made significant contributions to reinforcement learning, a methodology that enhances AI by utilizing human feedback to improve model performance. Google Cloud has launched Vertex AI Multimodal Embeddings as General Availability, which uses the VLM called Contrastive Captioner (CoCa) developed by the Google Research team. It is a vision model augmented with LLM intelligence that can look at either images or text and understand their meaning. Google has also launched a range of products that infuse generative AI into its offerings, empowering developers to responsibly build with enterprise-level safety, security, and privacy. Google’s next-generation foundation model, Gemini, is still in training. Gemini was created from the ground up to be multimodal, highly efficient at tool and API integrations, and built to enable future innovations, like memory and planning. Gato is a deep neural network created by researchers of DeepMind, a subsidiary of Google. It is a transformer-based model that exhibits multimodality and can perform a range of complex tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more.

Open AI is a company dedicated to researching and deploying AI systems that are beneficial to humanity. They recognize the immense power of AI and prioritize developing systems that are safe, aligned with human values, and more important than profits. OpenAI is a leading force in the multimodal AI market, offering a range of innovative products and solutions including models such as GPT-4, DALL·E 2, and CLIP. GPT-4 is a powerful language model capable of processing both text and images, enabling versatile applications in text generation and image understanding. DALL·E 2 is an innovative AI system that creates images from textual descriptions, allowing for creative visual synthesis. CLIP efficiently learns visual concepts from natural language guidance, enabling various visual recognition tasks. These solutions collectively demonstrate OpenAI’s expertise in integrating different modalities, offering advanced capabilities in understanding and generating content across text, images, and more.

Twelve Labs is a renowned company in the field of multimodal AI, specializing in video understanding and data management. The company’s core expertise lies in extracting a wealth of insights from videos, spanning aspects like motion analysis, object and human recognition, audio comprehension, text recognition from screens, and speech transcription. These remarkable functionalities are built on top of the platform’s state-of-the-art multimodal foundation model designed specifically for video content. Twelve Labs helps add rich, contextual video understanding to the applications by offering developer-friendly APIs. Some of its notable offerings include the Video-to-Text API suite, the AI Playground, and their advanced video-language foundation model, Pegasus-1. Their latest advancements include launching cloud-native APIs for lightning-fast video search and introducing a first-of-its-kind video-language foundation model. These innovations position Twelve Labs as a significant player in the rapidly evolving multimodal AI landscape.

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