查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1549918

生成式人工智慧：市场占有率分析、产业趋势/统计、成长预测（2024-2029）

Generative AI - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

生成式人工智慧市场规模预计到 2024 年为 360.6 亿美元，预计到 2029 年将达到 2,819 亿美元，在预测期内（2024-2029 年）复合年增长率为 50.87%。

生成式人工智慧市场

主要亮点

  • 该市场主要由不断扩大的资讯技术 (IT) 行业以及越来越多地使用人工智慧整合系统来提高生产力和敏捷性所推动。除此之外，辅助聊天机器人进行有效对话并提高消费者满意度的生成式人工智慧的日益普及也推动了市场的成长。生成式人工智慧可以根据个人选择和行动建立个人化推荐、客製化广告和客製化产品。此外，该市场还受到生成式人工智慧的日益使用的推动，例如在元宇宙中构建虚拟世界、使用基于文字的说明创建数位艺术作品以及生成独特和创新的内容。此外，这个市场吸引了老牌公司和创业投资的大量投资和资金。
  • 生成式人工智慧能够实现模型的多模态演化，可以同时处理图像和文字等多种模态，从而扩大了应用领域并提高了多功能性。生成式人工智慧透过利用自然语言而不是程式语言来加强世界上人类和电脑之间的联繫。生成式人工智慧可以改变您的业务，同时为自动化、创新和个人化开闢新的机会，从而降低成本并改善客户体验。例如，2023 年 3 月，提供基于 AI 的写作助理的 Grammarly 公司宣布推出 GrammarlyGo，这是一种生成式 AI 功能，可让用户创建、编辑和个性化自己的写作。
  • GAN已经在许多领域和公司得到应用，适应性很强。它用于图像合成、风格传输、图像到图像转换、文字生成、影片生成、资料增强等。 GAN 开发新的、多样化的、现实的模型的能力使其成为各种生成任务的理想选择，并且是市场开拓的驱动力。此外，开放原始码实作和预训练 GAN 模型的可用性正在推动 GAN 的采用和使用。这些资源与 PyTorch 和 TensorFlow 等易于使用的库和框架集成，降低了设计人员和研究人员的进入门槛，使他们能够在应用程式中利用 GAN，而无需从头开始。
  • 医学研究依赖于获取有关各种健康状况的大量资料。这项资料需要改进，特别是对于罕见疾病。此类资料也很昂贵，隐私法规范其使用和共用。医疗保健领域的生成式人工智慧可以产生合成资料样本，以增强现实世界的健康资料集。由于医疗保健资料不属于特定个人，因此这些样本不受隐私法规的约束。人工智慧可以开发 EHR资料、扫描等。例如，一个德国研究团队建立了一个名为 GANerAid 的人工智慧模型，用于产生用于临床试验的合成患者资料。该模型基于 GAN 程序，即使训练资料集集有限，也能够创建具有所需特征的医疗资料。
  • 生成式人工智慧解决方案使组织能够更好地了解合规性相关问题和资料管理。软体工具使人工智慧解决方案能够及时提取大量资料并创建准确完整的资料。大量公司越来越多地转向基于人工智慧的解决方案。此外，多个行业的新兴企业数量显着增加。这些新参与企业非常愿意采用人工智慧来实现业务自动化和规模化。基于人工智慧的解决方案主要因其成本和时间效率、改善的用户体验、易用性以及先进技术的新功能而被采用。
  • 相反，人工智慧有潜力解决常见的业务挑战。儘管如此，随着公司将消费者和供应商资料输入复杂的人工智慧演算法以创建新的敏感讯息，隐私问题正在出现。安全和隐私问题是数位转型市场的主要挑战之一。随着公司越来越依赖数位技术，他们收集和储存大量敏感资料，使他们更容易受到网路攻击和资料外洩。此外，随着连接设备数量的增加，骇客的攻击面也在扩大，使保护这些设备及其收集和传输的资料变得更加困难。

生成式人工智慧市场趋势

BFSI 预计将占据较大市场份额

  • 生成式人工智慧有潜力在未来几年彻底改变银行的风险管理。这是从以任务为导向的活动转向与企业部门合作进行策略性风险预防和新消费者旅程早期的控制过程，通常被称为「左移」方法。因此，风险专业人士可以为公司提供新产品开发和策略性企业决策的建议，探索新出现的风险趋势和情景，增强抵御能力，并主动加强风险和控制流程。
  • 此外，生成式人工智慧模型使银行能够透过分析历史资料模式和市场趋势来识别风险领域并保持盈利。透过模拟各种经济场景，GAN 使银行能够评估和降低信用风险、市场风险和操作风险等风险。例如，万事达卡最近宣布了一种新的生成人工智慧模型，以帮助银行更好地检测其网路上的可疑交易。根据万事达卡称，这项技术可以让银行将诈欺侦测率提高 20%，在某些情况下高达 300%。
  • 客户服务永远是成功的基石。然而，如何有效地满足消费者的多样化需求是一项挑战。这就是配备生成式人工智慧的聊天机器人发挥作用的地方。人工智慧驱动的聊天机器人可以与消费者进行自然的、类似人类的对话，并提供 24/7 即时帮助。这些机器人不仅仅是基于规则的；它们理解上下文、情感和语言的细微差别，以创建无缝、个性化的互动。当消费者提出问题或需要帮助时，聊天机器人会使用生成式人工智慧来检查询问并提供适当的答案和解决方案。
  • 同样，资产管理在业务中非常重要，客户依靠金融机构来开拓和保护他们的资产。生成式人工智慧对于改善资产管理和投资组合优化流程至关重要。生成式人工智慧模型可以解释财务资料、经济指标、市场趋势和客户檔案。人工智慧可以利用这些资料产生预测模型，提案最佳的资产配置和投资策略。根据不断变化的市场状况和新机会，这些模型可以即时调整投资组合。这种动态资产管理方法使银行能够在有效管理风险的同时实现最大回报。
  • 根据 NVIDIA Research 2023 的数据，资料分析是 2023 年金融服务业中最常用的人工智慧应用。调查显示，43%的受访者使用AI进行生成，69%的受访者使用AI进行资料分析，其次是资料处理和资料处理。其他常见的人工智慧使用案例包括自然语言处理和大规模语言模型。从2022年起，人工智慧在金融业务中的采用将大幅增加，并且预计未来将进一步扩大。人工智慧在金融领域的大规模采用预计将推动所研究市场的需求。

预计北美将占据很大的市场份额

  • 北美拥有功能齐全的人工智慧研究社区，杰出的机构和研究人员推动着生成式人工智慧的进步。该地区领先的研究中心和大学开展高级研究，发表重要论文，并为产生人工智慧方法的发展做出贡献。该地区庞大的人口、高消费支出和先进的技术基础设施也为生成式人工智慧解决方案的采用和商业化创造了有利的环境。此外，北美预计将在人工智慧研究和开发方面处于领先地位，Google、微软和IBM等大公司在生成式人工智慧技术上投入大量资金。此外，该地区先进的基础设施、有利的政府倡议以及人工智慧在医疗保健、金融和汽车等领域的早期采用也有助于该地区的市场主导地位。
  • 美国国家人工智慧安全委员会的最终报告呼吁，到 2026 财年，美国人工智慧的联邦研发资金每年增加一倍，达到 320 亿美元。在2023年预算中，政府决定将联邦研发预算在2021年授权水准基础上增加28%，达到超过2,040亿美元。国家人工智慧研究机构，无论是新的还是现有的，都准备好获得部分资金。这些机构将商业部门、协会、学术机构以及联邦、州和地方当局聚集在一起，共同应对人工智慧研究和劳动力发展的挑战。这些政府措施预计将为生成人工智慧市场创造成长机会。
  • 根据史丹佛人工智慧指数报告，到 2023 年，美国机构将出现 61 个着名的人工智慧模型，远远领先欧盟的 21 个和中国的 15 个。 2023年以及过去五年，美国人工智慧相关法规数量大幅增加。到 2023 年，人工智慧相关法规将达到 25 项，而 2016 年只有 1 项。 2023年，人工智慧相关法规数量将增加56.3%。此外，新的人工智慧指数研究表明，可靠的人工智慧报告严重缺乏标准化。主要开发商，包括 OpenAI、Google 和 Anthropic，主要根据各种负责任的 AI 基准测试他们的模型。
  • IBM委託的一项新市场研究显示，加拿大企业越来越多地采用和部署人工智慧 (AI)，大约 37% 的企业规模组织（1,000 名或更多员工）表示这是其业务的一部分。使用人工智慧。儘管全球人工智慧采用率保持稳定（自 2023 年 4 月以来为 42%），但在加拿大，实施人工智慧的公司数量从 2023 年 4 月的 34% 增加到 2023 年 11 月的 37%。早期采用者处于领先地位，35% 的公司已经致力于人工智慧，并打算加速或扩大对该技术的投资。人工智慧在该国的大规模采用预计将为该地区的市场公司创造成长机会。
  • 产生人工智慧市场的公司正在共同努力，以满足加拿大企业的需求。例如，2024 年 4 月，IBM 宣布在魁北克省蒙特娄创建新的云多区域 (MZR)。它将旨在帮助客户满足不断变化的监管要求，并在安全的企业云端平台中利用生成式人工智慧等技术。在 2021 年启用 IBM Cloud 多伦多 MZR 和蒙特娄现有资料中心的基础上，新的蒙特娄 MZR 计划于 2025 年上半年投入使用。 IBM 在加拿大扩大业务预计将有助于加拿大各地的客户管理新兴的监管要求和现有法规，例如主权的地理要求，同时推动创新。

生成式人工智慧产业概述

全球生成式人工智慧市场由少数知名公司半垄断，包括Google公司、IBM公司、微软公司、Adobe公司和亚马逊网路服务公司。为了增加市场占有率，公司持续投资于策略联盟、收购以及解决方案和服务的开拓。例如

  • 2024 年 5 月，Sainsbury’s 与微软达成了为期五年的合作关係，将生成式人工智慧纳入超级市场的业务中。这家英国零售巨头将利用微软的生成式人工智慧网路购物和人工智慧客户支援中「改善客户的搜寻体验」。 Sainsbury’s 也表示，将利用微软的人工智慧来提高店内员工的效率，人工智慧将引导员工到需要补货的货架。
  • 2024 年 5 月，IBM 和 SAP SE 宣布了他们对下一个合作时代的愿景，包括新的生成式 AI 功能和产业专用的云端解决方案，以帮助客户释放业务价值。两家公司正在与 SAP 一起为 RISE 建立新的生成式 AI 功能，并探索将 AI 跨垂直云端解决方案和业务应用程式线引入 SAP 业务流程的机会。最初，IBM 计划在 SAP 业务技术平台 (SAP BTP) 的支援下，在 SAP 的云端解决方案和应用程式中扩展 AI 功能。

其他好处

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第一章简介

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章市场洞察

  • 市场概况
  • 市场生态系分析
  • 产业吸引力－波特五力分析
    • 买方议价能力
    • 供应商的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争公司之间的敌对关係
  • 宏观经济因素对市场的影响
  • 案例研究分析

第五章市场动态

  • 市场驱动因素
    • 跨多个行业更多地使用人工智慧整合系统
    • 客製化和个人化需求日益增长
  • 市场限制
    • 隐私和道德问题
  • 科技的影响
    • 产生互惠网路 (GAN)
    • 变压器
    • 变分自动编码器 (VAE)
    • 扩散网络

第六章 市场细分

  • 按成分
    • 软体
    • 服务
  • 按最终用户
    • BFSI
    • 卫生保健
    • 资讯科技/通讯
    • 政府机构
    • 零售/消费品
    • 其他最终用户产业
  • 按地区
    • 北美洲
    • 欧洲
    • 亚洲
    • 澳洲/纽西兰
    • 拉丁美洲
    • 中东/非洲

第七章 竞争格局

  • 公司简介
    • Google LLC
    • IBM Corporation
    • Microsoft Corporation
    • Adobe Inc
    • Amazon Web Services
    • Cohere
    • Nvidia Corporation
    • SAP SE
    • Rephrase AI
    • Konverge AI

第八章投资分析

第九章市场展望及未来性

简介目录
Product Code: 50002560

The Generative AI Market size is estimated at USD 36.06 billion in 2024, and is expected to reach USD 281.90 billion by 2029, growing at a CAGR of 50.87% during the forecast period (2024-2029).

Generative AI - Market

Key Highlights

  • The market is primarily propelled by the expanding information technology (IT) sector and the growing use of AI-integrated systems for improving productivity and agility. Besides this, generative AI's ever-increasing popularity for aiding chatbots in conducting effective conversations and enhancing consumer satisfaction also propels the market's growth. Generative AI can construct personalized recommendations, tailored advertisements, and customized products based on individual choices and behavior. Moreover, the increasing utilization of generative AI for making virtual worlds in the metaverse, producing digital artworks using text-based descriptions, and generating unique and innovative content is also pushing the market forward. Furthermore, the market has drawn significant investments and funding from established businesses and venture capitalists.
  • Generative AI allows models to evolve multimodal, which implies they can process multiple modalities simultaneously, such as images and text, widening their application areas and improving their versatility. Generative AI enhances the connection to the globe where humans communicate with computers, utilizing natural language rather than programming languages. Generative AI can transform businesses by opening new opportunities for automation, innovation, and personalization, all while lowering costs and improving customer experience. For instance, in March 2023, Grammarly, Inc., an AI-based writing assistant, announced the launch of GrammarlyGo, a feature of generative AI that allows users to compose writing, edit, and personalize text.
  • GANs have found applications in numerous fields and enterprises, making them highly adaptable. They are utilized in image synthesis, style transfer, image-to-image translation, text generation, video generation, data augmentation, and more. The ability of GANs to develop new, diverse, and realistic models has made them a go-to choice for various generative tasks, driving the market studied. Moreover, the availability of open-source implementations and pre-trained GAN models have facilitated the adoption and usage of GANs. These resources, integrated with easy-to-use libraries and frameworks like PyTorch and TensorFlow, have lowered the obstacle to entry for designers and researchers, letting them leverage GANs for their applications without starting from scratch.
  • Medical research depends on accessing vast amounts of data on different health conditions. This data needs to be improved, especially regarding rare diseases. Such data is also expensive, and privacy laws govern its usage and sharing. Generative AI in medicine can produce synthetic data samples that augment real-life health datasets. These samples are not subject to privacy regulations, as healthcare data does not belong to particular individuals. Artificial intelligence can develop EHR data, scans, etc. For example, a team of German researchers built an AI-powered model, GANerAid, to generate synthetic patient data for clinical trials. This model is based on the GAN procedure and can create medical data with the desired properties even if the training dataset is limited.
  • Generative AI solutions allow organizations to understand their compliance-related issues and data management better. Software tools enable AI-enabled solutions to extract a large amount of data on time and produce accurate and complete data. There is a surge in the trend where a significant number of companies are increasingly demanding AI-based solutions. Moreover, multiple industries are witnessing a considerable increase in startups. These new players are highly attracted to adopting AI to automate and expand their businesses. AI-based solutions are mainly deployed due to their cost and time efficiency, improved user experience, ease of use, and new features with advanced technology.
  • On the contrary, artificial intelligence can potentially solve common business challenges. Still, privacy concerns are popping up, and firms feed consumer and vendor data into advanced, AI-fueled algorithms to create new sensitive information. Security and privacy concerns are among the key challenges in the digital transformation market. As firms rely more on digital technology, they collect and store enormous volumes of sensitive data, making them vulnerable to cyberattacks and data breaches. Furthermore, as the number of connected devices rises, hackers' attack surface expands, making it more challenging to secure these devices and the data they collect and transmit.

Generative AI Market Trends

BFSI is Expected to Hold a Significant Share of the Market

  • Generative AI can revolutionize banks' risk management over the next few years. It could permit processes to move away from task-oriented activities toward partnering with company lines on strategic risk prevention and having controls at the outset in new consumer journeys, often referred to as a "shift left" approach. That, in turn, would free up risk professionals to advise companies on new product development and strategic corporation decisions, explore emerging risk trends and scenarios, strengthen resilience, and enhance risk and control processes proactively.
  • Furthermore, generative AI models can enable banks to identify risk areas and preserve profitability by analyzing historical data patterns and market trends. By simulating different economic scenarios, GANs can allow banks to assess and mitigate risks, such as credit, market, and operational risks. For instance, Mastercard recently launched a new generative AI model to enable banks to better detect suspicious transactions on its network. According to Mastercard, the technology is poised to allow banks to improve their fraud detection rate by 20%, with rates reaching as much as 300% in some cases.
  • Customer service has always been a cornerstone of success. However, serving consumers' diverse requirements efficiently can be challenging. This is where generative AI-powered chatbots step in. AI-driven chatbots can engage consumers in natural, human-like conversations, providing instant assistance 24/7. These bots are not just rule-based; they understand context, sentiment, and nuances in language, making exchanges seamless and personalized. When a consumer has a query or needs assistance, the chatbot uses generative AI to examine the inquiry and provide relevant responses or solutions.
  • Similarly, wealth management is critical in banking, where customers entrust financial institutions to develop and safeguard their assets. Generative AI is pivotal in improving wealth management and portfolio optimization processes. Generative AI models can interpret financial data, economic indicators, market trends, and customer profiles. AI can utilize this data to generate predictive models that suggest optimal asset allocations and investment strategies. Based on varying market conditions and emerging opportunities, these models can adjust portfolios in real time. This dynamic method of wealth management allows banks to maximize returns while managing risk effectively.
  • According to Nvidia survey 2023, Data analytics was the most used AI-enabled application in the financial services industry in 2023. Based on the study, 43% of the respondents used AI for generative AI, and 69% of the respondents used AI for data analytics, followed by data processing and data processing. Other common AI use cases were natural language processing and large language models. The adoption of AI in financial businesses increased significantly since 2022, and it is anticipated to grow even further in the coming years. Such massive adoption of AI in the finance sector is expected to drive the demand for the market studied.

North America is Expected to Hold Significant Share of the Market

  • North America has a functional AI research community, with eminent institutions and researchers propelling advancements in generative AI. The region's foremost research centers and universities conduct advanced research, publish significant papers, and contribute to developing generative AI methods. The region's large population, high consumer spending, and advanced technology infrastructure also create a favorable environment for the adoption and commercialization of generative AI solutions. In addition, North America is expected to lead in AI research and development, with major players like Google, Microsoft, and IBM investing heavily in generative AI technologies. Moreover, the region's advanced infrastructure, favorable government initiatives, and early adoption of AI in sectors such as healthcare, finance, and automotive contribute to its market dominance.
  • The final report of the National Security Commission on Artificial Intelligence recommended increasing federal R&D funding for AI in the United States by a factor of two annually, up to USD 32 billion in fiscal 2026. The government decided to increase the federal R&D budget by 28% from FY 2021 authorized levels to more than USD 204 billion under the fiscal 2023 budget plan. The national AI research institutes, both new and established, were poised to get some of those funds. To address the difficulties of AI research and workforce development, these institutes bring together the commercial sector, organizations, academics, and federal, state, and municipal authorities. Such government initiatives are expected to create opportunities for the generative AI market to grow.
  • According to the Stanford AI Index Report, in 2023, 61 notable AI models originated from US-based institutions, far outpacing the European Union's 21 and China's 15. The number of AI-related regulations in the United States has risen significantly in 2023 and over the last five years. In 2023, there were 25 AI-related regulations, up from just one in 2016. In 2023, the number of AI-related regulations increased by 56.3%. Moreover, new research from the AI Index indicates a substantial lack of standardization in reliable AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against various responsible AI benchmarks.
  • New market research commissioned by IBM reports that Canadian companies are increasingly adopting and deploying artificial intelligence (AI), with about 37% of enterprise-scale organizations (over 1,000 employees) saying their company uses it as part of their business operations. While AI adoption remained steady globally (42% since April 2023), Canada saw an uptick in enterprises deploying AI from 34% in April 2023 to 37% in November 2023. Early adopters are leading the way, with 35% of the enterprises already working with AI intending to accelerate and expand investment in the technology. Such a huge adoption of AI in the country is expected to create opportunities for growth for the market players in the region.
  • The players in the generative AI market are collaborating with Canadian enterprises to cater to their needs. For instance, in April 2024, IBM announced its new Cloud Multizone Region (MZR) in Montreal, Quebec. It will be designed to help clients address their evolving regulatory requirements and leverage technology such as generative AI with a secured enterprise cloud platform. Building on the opening of IBM Cloud's Toronto MZR in 2021 and existing data centers in Montreal, the new Montreal MZR is planned for the first half of 2025. IBM's expanded presence in Canada is expected to help clients throughout the country manage their emerging and existing regulatory demands, including geographic requirements around sovereignty, while driving innovation.

Generative AI Industry Overview

The global generative AI market is semi-consolidated, with a few prominent players, such as Google LLC, IBM Corporation, Microsoft Corporation, Adobe Inc., Amazon Web Services, and others. To increase market share, corporations continually spend on strategic partnerships or acquisitions and solution and services development. For instance,

  • In May 2024, Sainsbury's agreed a five-year partnership deal with Microsoft to incorporate generative AI into the supermarket chain's operations. The British retail giant will use Microsoft generative AI to "improve customers' search experience" for online shopping and AI customer support. Sainsbury's said it will also use Microsoft AI to improve the efficiency of in-store staff, with AI guiding workers to shelves that need restocking.
  • In May 2024, IBM and SAP SE announced their vision for the next era of their collaboration, which includes new generative AI capabilities and industry-specific cloud solutions that can assist clients in unlocking business value. The companies are exploring opportunities to build new generative AI capabilities for RISE with SAP and infuse AI into SAP business processes across industry-specific cloud solutions and lines of business applications. Initially, IBM plans to extend AI capabilities across SAP's cloud solutions and applications, all underpinned by the SAP Business Technology Platform (SAP BTP).

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Market Ecosystem Analysis
  • 4.3 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.3.1 Bargaining Power of Buyers
    • 4.3.2 Bargaining Power of Suppliers
    • 4.3.3 Threat of New Entrants
    • 4.3.4 Threat of Substitutes
    • 4.3.5 Intensity of Competitive Rivalry
  • 4.4 Impact of Macro Economic Factors on the Market
  • 4.5 Case Study Analysis

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Use of AI-Integrated System across Multiple Industries
    • 5.1.2 Increase in Demand for Customization and Personalization Needs
  • 5.2 Market Restrains
    • 5.2.1 Privacy and Ethical Concerns
  • 5.3 Impact of Technologies
    • 5.3.1 Generative Adversarial Network (GANs)
    • 5.3.2 Transformer
    • 5.3.3 Variational Autoencoder (VAE)
    • 5.3.4 Diffusion Networks

6 MARKET SEGMENTATION

  • 6.1 By Component
    • 6.1.1 Software
    • 6.1.2 Services
  • 6.2 By End User
    • 6.2.1 BFSI
    • 6.2.2 Healthcare
    • 6.2.3 IT and Telecommunication
    • 6.2.4 Government
    • 6.2.5 Retail and Consumer Goods
    • 6.2.6 Other End-user Industries
  • 6.3 By Geography
    • 6.3.1 North America
    • 6.3.2 Europe
    • 6.3.3 Asia
    • 6.3.4 Australia and New Zealand
    • 6.3.5 Latin America
    • 6.3.6 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Google LLC
    • 7.1.2 IBM Corporation
    • 7.1.3 Microsoft Corporation
    • 7.1.4 Adobe Inc
    • 7.1.5 Amazon Web Services
    • 7.1.6 Cohere
    • 7.1.7 Nvidia Corporation
    • 7.1.8 SAP SE
    • 7.1.9 Rephrase AI
    • 7.1.10 Konverge AI

8 INVESTMENT ANALYSIS

9 MARKET OUTLOOK AND FUTURE OF THE MARKET