查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1856974

全球多模态人工智慧市场：未来预测（至2032年）—按组件、模态、多模态人工智慧类型、技术、最终使用者和地区进行分析

Multimodal AI Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Modality (Text Data, Speech & Voice Data, Image Data and Other Modalities), Multimodal AI Type, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球多模态人工智慧市场预计到 2025 年将达到 24 亿美元，到 2032 年将达到 238 亿美元，预测期内复合年增长率为 38.8%。

多模态人工智慧是指能够同时处理、理解和产生多种类型资料（包括文字、图像、音讯和影片）资讯的人工智慧系统。与专注于单一模态的传统人工智慧模型不同，多模态人工智慧整合了这些不同的资料来源，从而产生更丰富、更具上下文感知能力的洞察。这种能力支持影像描述、视讯分析、语音助理和跨模态搜寻等应用。结合不同的模态可以提高准确性、推理能力和类人理解能力。多模态人工智慧是迈向更通用、更智慧的系统的重要一步，这些系统能够无缝地解读复杂的现实世界资讯。

提高了准确性和稳健性

跨模态模型融合了文字、图像、音讯和感测器数据，以提升上下文理解能力和预测可靠性。在情绪侦测、目标追踪和对话反应生成等任务中，多模态系统优于单模态模型。与边缘设备和云端平台的整合支援分散式环境下的即时推理和自适应学习。企业利用多模态人工智慧来增强决策能力、自动化工作流程并实现个人化使用者体验。这些功能推动了平台创新，并提升了关键任务型应用的营运效率。

高运算需求

训练和推理需要藉助先进的GPU和针对跨模态融合与对齐优化的流程，才能完成大型资料集的训练和推理。对于即时应用而言，模型复杂性和延迟要求会增加基础设施成本。小型公司和学术实验室在获取运算资源以及管理跨边缘和云端环境的部署方面面临挑战。能源消耗和碳排放仍然是大型多模态系统需要关注的问题。

自然互动的进展

语音、手势和脸部辨识能够实现数位和实体环境的直觉式介面和身临其境型使用者体验。人工智慧代理人利用多模态线索，能够更准确、更快速地解读使用者的情感和脉络。与扩增实境/虚拟实境机器人和智慧型装置的集成，拓展了其在消费品产业和医疗保健领域的应用场景。多语言人群、神经病变和老年人群体对类人互动和包容性设计的需求日益增长。这些趋势正在推动多模态使用者体验对话式人工智慧以及整个辅助技术生态系统的发展。

监管和隐私挑战

多种数据收集方式引发了公共和私营部门对知情同意、监控和生物识别安全性的担忧。脸部辨识、语音资料和行为追踪的法律规范因司法管辖区和应用场景而异。模型决策缺乏透明度，使得审核、课责和伦理监督变得更加复杂。公众对偏见操纵和虚假资讯的关注，加大了供应商和开发商的压力。这些风险持续限制敏感产业和受监管环境中平台的普及应用。

新冠疫情的影响：

疫情加速了人们对多模态人工智慧的兴趣，推动了医疗零售、教育和公共服务等领域远距互动和数位参与的激增。医院利用多模态平台进行远端医疗诊断和病患监测，以提升对情境的感知能力。零售商在行动和网路通路上应用人工智慧技术，实现虚拟试穿、语音购物和情绪分析。教育机构部署多模态工具，用于远距学习评估和无障碍支援。在疫情封锁和恢復阶段，大众对人工智慧驱动的互动和自动化技术的认知度显着提高。后疫情时代的策略已将多模态人工智慧作为数位转型中提升营运韧性和用户参与的核心支柱。

预计在预测期内，影像资料区段将是最大的资料部分。

由于影像资料在电脑视觉人脸脸部辨识和多模态平台中的目标侦测方面发挥基础性作用，预计在预测期内，影像资料区段将占据最大的市场份额。与文字转语音和感测器输入的整合可以提高即时应用中的场景理解、上下文分析和决策准确性。基于影像的模型支援医疗保健、成像、自主导航、零售分析和监控系统等应用场景。工业、消费和政府部门对可扩展的高解析度影像处理的需求正在不断增长。供应商提供模组化流程和预训练模型，以实现快速部署和客製化。

预计在预测期内，自然语言处理（NLP）将以最高的复合年增长率成长。

预计在预测期内，自然语言处理 (NLP) 领域将迎来最高的成长率，这主要得益于多模态平台在对话式人工智慧内容产生和情绪分析领域的扩展。 NLP 模型整合了影像、语音和手势数据，以提升情境反应的准确性和情绪智慧。其应用领域包括行动、桌面和嵌入式环境中的虚拟助理、客户支援、教育工具和辅助功能平台。全球市场和不同用户群体对多语言、情感感知和特定领域的 NLP 的需求正在不断增长。供应商提供基于变压器 的架构以及针对特定任务和产业的精细化模型。

占比最大的地区：

在预测期内，北美预计将占据最大的市场份额，这得益于其先进的人工智慧基础设施研究生态系统以及在医疗保健、国防、零售和媒体等行业的企业级应用。美国和加拿大的公司正在诊断、自动驾驶系统、客户体验和公共应用领域部署多模态平台。对生成式人工智慧边缘运算和云端原生架构的投资，有助于在法规环境中实现可扩展性、高效能和合规性。领先的人工智慧研究实验室、大学和科技公司的存在，推动了模型开发的标准化和商业化。监管机构透过沙盒计画、伦理框架和创新津贴方式支持人工智慧的发展。

复合年增长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率，这主要得益于行动技术的普及、数位创新以及政府支持的人工智慧计画在智慧城市、教育、医疗和公共服务领域的融合发展。中国、印度、日本和韩国等国家正在城市基础设施、农村服务和工业自动化领域扩展多模态平台。当地企业推出针对区域用例和合规规范量身定制的多语言模型。对边缘人工智慧机器人和即时互动的投资将支援平台在消费者业务和政府领域的扩展。城市中心的製造业园区和欠发达地区对扩充性、低成本的多模态解决方案的需求正在成长。这些趋势正在推动多模态人工智慧生态系统和创新丛集在区域内的整体成长。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球多模态人工智慧市场（按组件划分）

  • 软体
  • 服务

6. 全球多模态人工智慧市场（以模态划分）

  • 文字数据
  • 音讯数据
  • 影像资料
  • 影片数据
  • 感测器和数值资料
  • 其他方式

7. 全球多模态人工智慧市场（以多模态人工智慧类型划分）

  • 生成式多模态人工智慧
  • 互动式多模态人工智慧
  • 基于概览的多模态人工智慧
  • 转化型多模态人工智慧
  • 其他类型的多模态人工智慧

8. 全球多模态人工智慧市场（依技术划分）

  • 自然语言处理（NLP）
  • 电脑视觉
  • 机器学习
  • 情境感知
  • 物联网 (IoT)
  • 其他技术

9. 全球多模态人工智慧市场（以最终用户划分）

  • 媒体与娱乐
  • 银行、金融服务和保险（BFSI）
  • 卫生保健
  • 零售与电子商务
  • 汽车与运输
  • 製造业
  • 政府/国防
  • 通讯
  • 教育
  • 其他最终用户

第十章：全球多模态人工智慧市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • Google
  • OpenAI
  • Twelve Labs
  • Microsoft
  • IBM
  • Amazon Web Services（AWS）
  • Meta Platforms
  • Apple
  • Anthropic
  • Hugging Face
  • Runway
  • Adept AI
  • DeepMind
  • Stability AI
  • Rephrase.ai
Product Code: SMRC31838

According to Stratistics MRC, the Global Multimodal AI Market is accounted for $2.40 billion in 2025 and is expected to reach $23.8 billion by 2032 growing at a CAGR of 38.8% during the forecast period. Multimodal AI refers to artificial intelligence systems designed to process, understand, and generate information from multiple types of data simultaneously, such as text, images, audio, and video. Unlike traditional AI models that specialize in a single modality, multimodal AI integrates these diverse data sources to create richer and more context-aware insights. This capability enables applications like image captioning, video analysis, voice-activated assistants, and cross-modal search. By combining different modalities, it can improve accuracy, reasoning, and human-like understanding. Multimodal AI represents a step toward more versatile and intelligent systems capable of interpreting complex, real-world information seamlessly.

Market Dynamics:

Driver:

Improved accuracy and robustness

Cross-modal models combine text image audio and sensor data to improve contextual understanding and prediction reliability. Multimodal systems outperform single-modality models in tasks such as emotion detection object tracking and conversational response generation. Integration with edge devices and cloud platforms supports real-time inference and adaptive learning across distributed environments. Enterprises use multimodal AI to enhance decision-making automates workflows and personalize user experiences. These capabilities are driving platform innovation and operational efficiency across mission-critical applications.

Restraint:

High computational demands

Training and inference require advanced GPUs large datasets and optimized pipelines for cross-modal fusion and alignment. Infrastructure costs increase with model complexity and latency requirements across real-time applications. Smaller firms and academic labs face challenges in accessing compute resources and managing deployment across edge and cloud environments. Energy consumption and carbon footprint remain concerns for large-scale multimodal systems.

Opportunity:

Advancements in natural interaction

Voice gesture and facial recognition enable intuitive interfaces and immersive user experiences across digital and physical environments. AI agents use multimodal cues to interpret intent emotion and context with higher precision and responsiveness. Integration with AR VR robotics and smart devices expands use cases across consumer industrial and healthcare domains. Demand for human-like interaction and inclusive design is rising across multilingual neurodiverse and aging populations. These trends are fostering growth across multimodal UX conversational AI and assistive technology ecosystems.

Threat:

Regulatory and privacy challenges

Data collection from multiple modalities raises concerns around consent surveillance and biometric security across public and private sectors. Regulatory frameworks for facial recognition voice data and behavioral tracking vary across jurisdictions and use cases. Lack of transparency in model decision-making complicates auditability accountability and ethical oversight. Public scrutiny around bias manipulation and misinformation increases pressure on vendors and developers. These risks continue to constrain platform adoption across sensitive industries and regulated environments.

Covid-19 Impact:

The pandemic accelerated interest in multimodal AI as remote interaction and digital engagement surged across healthcare retail education and public services. Hospitals used multimodal platforms for telemedicine diagnostics and patient monitoring with improved contextual awareness. Retailers adopted AI for virtual try-ons voice commerce and sentiment analysis across mobile and web channels. Educational institutions deployed multimodal tools for remote learning assessment and accessibility support. Public awareness of AI-driven interaction and automation increased during lockdowns and recovery phases. Post-pandemic strategies now include multimodal AI as a core pillar of digital transformation operational resilience and user engagement.

The image data segment is expected to be the largest during the forecast period

The image data segment is expected to account for the largest market share during the forecast period due to its foundational role in computer vision facial recognition and object detection across multimodal platforms. Integration with text audio and sensor inputs improves scene understanding contextual analysis and decision accuracy across real-time applications. Image-based models support use cases in healthcare imaging autonomous navigation retail analytics and surveillance systems. Demand for scalable high-resolution image processing is rising across industrial consumer and government domains. Vendors offer modular pipelines and pretrained models for rapid deployment and customization.

The natural language processing (NLP) segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the natural language processing (NLP) segment is predicted to witness the highest growth rate as multimodal platforms scale across conversational AI content generation and sentiment analysis. NLP models integrate with image audio and gesture data to enhance contextual understanding response accuracy and emotional intelligence. Applications include virtual assistants customer support educational tools and accessibility platforms across mobile desktop and embedded environments. Demand for multilingual emotion-aware and domain-specific NLP is rising across global markets and diverse user segments. Vendors offer transformer-based architectures and fine-tuned models for specialized tasks and industries.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its advanced AI infrastructure research ecosystem and enterprise adoption across healthcare defense retail and media sectors. U.S. and Canadian firms deploy multimodal platforms across diagnostics autonomous systems customer experience and public safety applications. Investment in generative AI edge computing and cloud-native architecture supports scalability performance and compliance across regulated environments. Presence of leading AI labs universities and technology firms drives model development standardization and commercialization. Regulatory bodies support AI through sandbox programs ethical frameworks and innovation grants.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as mobile penetration digital innovation and government-backed AI programs converge across smart cities education healthcare and public services. Countries like China India Japan and South Korea scale multimodal platforms across urban infrastructure rural outreach and industrial automation. Local firms launch multilingual culturally adapted models tailored to regional use cases and compliance norms. Investment in edge AI robotics and real-time interaction supports platform expansion across consumer enterprise and government domains. Demand for scalable low-cost multimodal solutions rises across urban centers manufacturing zones and underserved populations. These trends are accelerating regional growth across multimodal AI ecosystems and innovation clusters.

Key players in the market

Some of the key players in Multimodal AI Market include Google, OpenAI, Twelve Labs, Microsoft, IBM, Amazon Web Services (AWS), Meta Platforms, Apple, Anthropic, Hugging Face, Runway, Adept AI, DeepMind, Stability AI and Rephrase.ai.

Key Developments:

In May 2025, OpenAI launched GPT-4o, a fully multimodal model capable of processing text, image, voice, and code in real time. Integrated into ChatGPT Enterprise and API endpoints, GPT-4o supports sensory fusion and agentic reasoning, enabling dynamic applications across customer support, education, and creative industries.

In March 2025, Google DeepMind launched Gemini 2.5, its most advanced multimodal AI model capable of processing text, image, video, and audio simultaneously. Gemini 2.5 introduced improved reasoning and cross-format understanding, enabling businesses to deploy richer customer insights, creative generation, and operational analytics across diverse media inputs.

Components Covered:

  • Software
  • Services

Modalities Covered:

  • Text Data
  • Speech & Voice Data
  • Image Data
  • Video Data
  • Sensor & Numerical Data
  • Other Modalities

Multimodal AI Types Covered:

  • Generative Multimodal AI
  • Interactive Multimodal AI
  • Explanatory Multimodal AI
  • Translative Multimodal AI
  • Other Multimodal AI Types

Technologies Covered:

  • Natural Language Processing (NLP)
  • Computer Vision
  • Machine Learning
  • Context Awareness
  • Internet of Things (IoT)
  • Other Technologies

End Users Covered:

  • Media & Entertainment
  • Banking, Financial Services & Insurance (BFSI)
  • Healthcare
  • Retail & E-Commerce
  • Automotive & Transportation
  • Manufacturing
  • Government & Defense
  • Telecommunications
  • Education
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Multimodal AI Market, By Component

  • 5.1 Introduction
  • 5.2 Software
  • 5.3 Services

6 Global Multimodal AI Market, By Modality

  • 6.1 Introduction
  • 6.2 Text Data
  • 6.3 Speech & Voice Data
  • 6.4 Image Data
  • 6.5 Video Data
  • 6.6 Sensor & Numerical Data
  • 6.7 Other Modalities

7 Global Multimodal AI Market, By Multimodal AI Type

  • 7.1 Introduction
  • 7.2 Generative Multimodal AI
  • 7.3 Interactive Multimodal AI
  • 7.4 Explanatory Multimodal AI
  • 7.5 Translative Multimodal AI
  • 7.6 Other Multimodal AI Types

8 Global Multimodal AI Market, By Technology

  • 8.1 Introduction
  • 8.2 Natural Language Processing (NLP)
  • 8.3 Computer Vision
  • 8.4 Machine Learning
  • 8.5 Context Awareness
  • 8.6 Internet of Things (IoT)
  • 8.7 Other Technologies

9 Global Multimodal AI Market, By End User

  • 9.1 Introduction
  • 9.2 Media & Entertainment
  • 9.3 Banking, Financial Services & Insurance (BFSI)
  • 9.4 Healthcare
  • 9.5 Retail & E-Commerce
  • 9.6 Automotive & Transportation
  • 9.7 Manufacturing
  • 9.8 Government & Defense
  • 9.9 Telecommunications
  • 9.10 Education
  • 9.11 Other End Users

10 Global Multimodal AI Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Google
  • 12.2 OpenAI
  • 12.3 Twelve Labs
  • 12.4 Microsoft
  • 12.5 IBM
  • 12.6 Amazon Web Services (AWS)
  • 12.7 Meta Platforms
  • 12.8 Apple
  • 12.9 Anthropic
  • 12.10 Hugging Face
  • 12.11 Runway
  • 12.12 Adept AI
  • 12.13 DeepMind
  • 12.14 Stability AI
  • 12.15 Rephrase.ai

List of Tables

  • Table 1 Global Multimodal AI Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Multimodal AI Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Multimodal AI Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Multimodal AI Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global Multimodal AI Market Outlook, By Modality (2024-2032) ($MN)
  • Table 6 Global Multimodal AI Market Outlook, By Text Data (2024-2032) ($MN)
  • Table 7 Global Multimodal AI Market Outlook, By Speech & Voice Data (2024-2032) ($MN)
  • Table 8 Global Multimodal AI Market Outlook, By Image Data (2024-2032) ($MN)
  • Table 9 Global Multimodal AI Market Outlook, By Video Data (2024-2032) ($MN)
  • Table 10 Global Multimodal AI Market Outlook, By Sensor & Numerical Data (2024-2032) ($MN)
  • Table 11 Global Multimodal AI Market Outlook, By Other Modalities (2024-2032) ($MN)
  • Table 12 Global Multimodal AI Market Outlook, By Multimodal AI Type (2024-2032) ($MN)
  • Table 13 Global Multimodal AI Market Outlook, By Generative Multimodal AI (2024-2032) ($MN)
  • Table 14 Global Multimodal AI Market Outlook, By Interactive Multimodal AI (2024-2032) ($MN)
  • Table 15 Global Multimodal AI Market Outlook, By Explanatory Multimodal AI (2024-2032) ($MN)
  • Table 16 Global Multimodal AI Market Outlook, By Translative Multimodal AI (2024-2032) ($MN)
  • Table 17 Global Multimodal AI Market Outlook, By Other Multimodal AI Types (2024-2032) ($MN)
  • Table 18 Global Multimodal AI Market Outlook, By Technology (2024-2032) ($MN)
  • Table 19 Global Multimodal AI Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 20 Global Multimodal AI Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 21 Global Multimodal AI Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 22 Global Multimodal AI Market Outlook, By Context Awareness (2024-2032) ($MN)
  • Table 23 Global Multimodal AI Market Outlook, By Internet of Things (IoT) (2024-2032) ($MN)
  • Table 24 Global Multimodal AI Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 25 Global Multimodal AI Market Outlook, By End User (2024-2032) ($MN)
  • Table 26 Global Multimodal AI Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 27 Global Multimodal AI Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2024-2032) ($MN)
  • Table 28 Global Multimodal AI Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 29 Global Multimodal AI Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 30 Global Multimodal AI Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 31 Global Multimodal AI Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 32 Global Multimodal AI Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 33 Global Multimodal AI Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 34 Global Multimodal AI Market Outlook, By Education (2024-2032) ($MN)
  • Table 35 Global Multimodal AI Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.