查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1521713

可解釋的人工智慧- 市場佔有率分析、產業趨勢/統計、成長預測（2024-2029）

Explainable 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個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

可解釋的人工智慧市場規模預計到 2024 年為 86.3 億美元，預計到 2029 年將達到 211.9 億美元，在預測期內（2024-2029 年）複合年成長率為 19.69%。

可解釋的人工智慧市場

主要亮點

  • 可解釋的人工智慧是指人工智慧（AI）系統的發展，可以為決策流程提供連貫且透明的解釋。人工智慧模型在各個領域都取得了無與倫比的性能。可解釋的人工智慧旨在增強人工智慧系統的信任、責任和可解釋性。這與技術的普遍成長和進步密切相關。隨著技術的不斷發展和增強，可解釋的人工智慧將使得開發和執行更加複雜和透明的人工智慧系統成為可能。
  • 數位轉型和進步技術進步（通常稱為工業 4.0）是可解釋人工智慧 (XAI) 需求背後的驅動趨勢。這一發展使不同行業能夠透過融入數位技術來成功適應。將 XAI 方法與工業 4.0 技術相整合，可實現準確和高品質的應用，使公司更加敏捷並以客戶為中心。工業 4.0 利用人工智慧進行預測性維護和故障檢測，以減少非計劃性停機。 XAI 使營運商能夠了解 AI 預測和建議背後的推理。這種透明度對於維護人員來說至關重要，可讓他們了解何時以及如何執行維護活動。
  • 由於金融、零售和醫療保健等各行業提供公平、責任和道德使用的監管和合規要求不斷提高，全球可解釋人工智慧市場正在不斷成長。各國和監管機構正在認知到人工智慧系統中透明度和課責的重要性，以確保道德服務並防止偏見和偏見結果。因此，歐洲《一般資料保護規範》（GDPR）的序言以及金融穩定委員會（FSB）等組織的各種指導方針都強調了人工智慧演算法可解釋性的必要性，為市場帶來了廣闊的前景。 。此外，越來越多的公司正在採用符合這些法規和準則的可解釋的人工智慧解決方案，以提供透明和可解釋的人工智慧系統，這對市場成長產生了積極影響。
  • 詐欺偵測是可解釋人工智慧的一個主要用途部分，用於預測詐欺攻擊並確定哪些攻擊具有更高的威脅。網路安全越來越受到企業和政府的關注。網路安全解決方案供應商擴大利用人工智慧，解釋人工智慧演算法的見解有幾個好處，包括增強對系統的信心和更好地理解營運。可解釋的人工智慧解決方案被用於網路安全的多個領域，並正在促進市場成長。 XAI 諮詢服務專注於協助您部署和實施透明、可解釋和可解釋的 AI 解決方案。
  • 雲端基礎的解決方案是當今數位環境的重要組成部分。對雲端基礎的智慧服務不斷成長的需求以及多重雲端營運的成​​長趨勢正在推動所研究市場的需求。最新的XAI技術為雲端運算付加了獨特的價值，提升了其價值。這方面不僅對於提高整體製程可行性是必要的，而且對於融入新技術也是必要的。可解釋的人工智慧軟體還可以幫助縮小雲端運算和最新突破之間的差距。它還有助於滿足新企業和新興企業的需求。
  • 相反，業務諮詢、研發、運算能力和建構最小可行產品的成本是在實施之前產生的。自動資料準備、基礎設施提供、處理以及系統和員工的實用化等因素構成了總效能成本。實施成本很高，並且根據行業規模而有所不同。市場的一個關鍵挑戰是用人工智慧取代人類勞動力。人工智慧技術是最大限度地提高生產力的下一步，用工廠生產線取代個人製程。

可解釋的人工智慧市場趨勢

BFSI 細分市場預計將佔據主要市場佔有率

  • XAI 的新興業務將使銀行能夠解決此類透明度和信任問題，並讓人工智慧管治更加清晰。糟糕的客戶引導流程給金融機構造成了數百萬美元的損失。對於許多銀行來說，申請貸款和評估您的健康狀況將很困難。可解釋的人工智慧描述了在保持透明度的同時進行合格檢查和風險管理的系統。 XAI 預測追蹤銀行績效的關鍵見解。例如，Akira AI 提供準確、動態、自動化的預測，幫助在供應鏈管理和客戶流失做出更好的決策。
  • XAI正在為BFSI產業帶來前所未有的變化。這些技術透過自動化日常業務、減少錯誤、提高準確性和提高效率，正在徹底改變會計。根據 ICAEW 報告，人工智慧可以將財務職能的總成本降低 16%，88% 的會計專業人士認為人工智慧將在未來幾年改善他們的工作生活。詐欺檢測和預防也是人工智慧和技術將改變會計的一個領域。傳統的審核技術依賴手動採樣和測試，既耗時又容易出錯。人工智慧驅動的審核工具可以快速準確地分析大量資料並識別異常和可疑交易。這使得審核能夠專注於高風險領域和潛在的詐欺，降低公司財務損失和聲譽受損的風險。
  • 人工智慧透過預測貸款需求、付款速度和 ATM 要求來改善銀行的現金管理。銀行使用歷史現金資料來建立預測現金可用性的模型。這些見解使銀行能夠在需要的時間和地點提供適量的資金。可以使用人工智慧工具監控每個地區自動提款機（ATM）的運作狀態，讓金融機構知道哪些ATM機現金用完並重新啟動，而不會給客戶帶來不便。例如，根據日本銀行家協會的數據，截至 2023 年 9 月，地區銀行在日本各地安裝了超過 28,500 台 ATM 和自動提款機(CD)。日本郵政銀行有近 31,500 台 ATM 和 CD 的記錄。
  • 許多銀行和金融機構都引進了XAI，以便為客戶提供更好的服務。例如，2023 年 9 月，Temenos 宣布了一款生成式人工智慧解決方案，可自動對銀行交易進行分類。該技術使銀行能夠提供個人化見解、創造獨特的數位銀行體驗並提供相關產品。該公司表示，Temenos 處於業務人工智慧的前沿。將第一個真正可解釋的人工智慧引入金融服務業，金融機構現在可以用簡單的商業語言向客戶及其交易對手解釋基於人工智慧的決策。
  • 根據 Nvidia Research 2023 的數據，資料分析是 2023 年金融服務業中最常使用的人工智慧應用程式。調查顯示，69%的受訪者使用人工智慧進行資料分析，其次是資料處理。其他常見的人工智慧使用案例包括自然語言處理和大規模語言模型。預計從 2022 年起，金融業務中人工智慧的採用將大幅增加，並在未來幾年進一步增加。人工智慧在此類金融領域的大量採用預計將推動市場成長。

預計北美將佔據很大的市場佔有率

  • 除了聯邦政府對先進技術的戰略投資外，北美還擁有由世界各地領先科學家和企業家以及知名研究中心支持的強大基礎設施，加速了該地區人工智慧的發展。該產業預計將受益於美國政府多項與人工智慧相關的舉措。例如，美國國家科學基金會與美國農業部、美國國防安全保障部、科學技術局、美國標準與技術研究所、美國食品和農業研究所以及美國農業部合作。智慧創新”·第二部分”計劃已經啟動。
  • 國家安全委員會關於人工智慧的最終報告提案國會每年將人工智慧的聯邦研發預算增加一倍，預計到 2026 財政年度總計達到 320 億美元。拜登政府的2023會計年度預算提案將增加聯邦研發支出超過2,040億美元，比2021會計年度授權水準增加28%。國家人工智慧研究機構，無論是新的還是現有的，都將獲得這些資金的一部分。這些機構將商業部門、組織、學術機構以及聯邦、州和地方當局聚集在一起，共同應對人工智慧研究和勞動力發展的挑戰。這些政府針對人工智慧發展的措施被認為將為所研究的市場的成長創造機會。
  • 此外，與許多其他國家統計機構一樣，加拿大統計局正在採用機器學習和人工智慧，並擴大利用替代資料來源來增強和現代化其許多統計系統。由於這些新資料來源的數量和速度，經常需要機器學習技術來利用它們。人工智慧推動加拿大的經濟發展和優質就業，因此加拿大政府致力於資助加速人工智慧在經濟和社會中採用的措施。例如，聯邦政府最近宣布將為泛加拿大人工智慧戰略第二階段投資4.43億美元。泛加拿大人工智慧策略第二階段將有助於最大限度地發揮人工智慧的潛力，造福加拿大人，加速可靠的技術開發，並促進人工智慧社群內的多樣性和協作。
  • 北美地區的各種零售公司正在實施人工智慧，以更好地服務客戶。例如，線上委託零售商 ThredUp 推出了 Goody Boxes，其中包含根據每位客戶的個人風格量身定做的各種二手服飾。顧客保留他們想要的東西，付款，然後退回他們不需要的東西。 AI演算法會記住每位顧客的喜好，以便未來的盒子將更適合他們的口味。客戶更喜歡非訂閱盒，因為他們可以看到單獨的零件。特斯拉的自動駕駛汽車是人工智慧和物聯網如何協同工作的例子之一。透過結合人工智慧，自動駕駛汽車可以預測汽車和行人在各種情況下的行為。例如，它判斷路況、天氣、最佳速度等，每次開車都變得更聰明。
  • 大多數製造商與能夠提供全面服務以支援大規模 XAI 解決方案的公司合作。像微軟這樣的供應商正在提供人工智慧來幫助製造業。製造組織中擴大採用人工智慧將提高缺陷檢測、品質保證、組裝整合、組裝最佳化和生成設計的效率。由人工智慧和深度學習驅動的新電腦視覺技術正在開發中，以實現視覺檢查自動化，以滿足不斷成長的全球需求。

可解釋的人工智慧產業概述

XAI 市場是半靜態的，有一些著名的參與企業，包括： IBM Corporation、Microsoft Corporation、Amelia US LLC、Google LLC 和 Arthur.ai 不斷在策略合作夥伴關係或收購以及解決方案和服務開發上投入資金。

  • 2024 年 3 月，蘋果收購了 DarwinAI，這是一家人工智慧視覺品質保證Start-Ups，提供端到端解決方案，在提高產品品質的同時提高生產效率。 Darwin AI的取得專利的XAI平台被許多財富500強公司使用。蘋果收購 DarwinAI 符合蘋果長期以來小心地將創新科技公司融入其生態系統的做法。
  • 2023年7月，富士通有限公司宣布與Informa D&B達成策略協議，透過將XAI引入商業和金融資訊產業來創造新價值。此次合作將透過結合可解釋的人工智慧技術，開創決策的新時代。透過採用這項技術，富士通和 Informa 致力於為產業帶來變革性創新。這將使 Informa 在西班牙的 450 萬用戶能夠以敏捷、高效的方式存取高度複雜的資料，從而顯著提高商業資訊解決方案的品質。

其他福利

  • Excel 格式的市場預測 (ME) 表
  • 3 個月分析師支持

目錄

第1章簡介

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行概述

第4章市場洞察

  • 市場概況
  • 產業價值鏈分析
  • 產業吸引力－波特五力分析
    • 供應商的議價能力
    • 消費者議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭公司之間的敵對關係

第5章市場動態

  • 市場促進因素
    • 人工智慧系統對課責和透明度的需求日益成長
    • 更多地利用最尖端科技進行創新
  • 市場限制因素
    • 可解釋人工智慧的實施成本高昂
    • 缺乏熟練的人工智慧工程師
  • 對科技的影響
    • 機器學習演算法
    • 深度學習
    • 神經網路
  • 使用案例分析
    • AI模型效能監控
    • 監理與合規風險管理
    • 在混合雲中部署人工智慧計劃
  • 案例研究分析

第6章 市場細分

  • 按服務
    • 解決方案
    • 按服務
  • 按發展
    • 雲
    • 本地
  • 按最終用戶產業
    • BFSI
    • 醫療保健
    • 製造業
    • 零售
    • 資訊科技/通訊
    • 其他
  • 按地區*
    • 北美洲
    • 歐洲
    • 亞洲
    • 澳洲/紐西蘭
    • 拉丁美洲
    • 中東/非洲

第7章 競爭格局

  • 公司簡介
    • IBM Corporation
    • Microsoft Corporation
    • Amelia US LLC
    • Google LLC
    • Arthur.ai
    • Ditto.ai
    • Intel
    • AWS
    • NVIDIA
    • Mphasis
    • Alteryx

第8章投資分析

第9章市場的未來

簡介目錄
Product Code: 50002195

The Explainable AI Market size is estimated at USD 8.63 billion in 2024, and is expected to reach USD 21.19 billion by 2029, growing at a CAGR of 19.69% during the forecast period (2024-2029).

Explainable AI - Market

Key Highlights

  • Explainable AI refers to the growth of artificial intelligence (AI) systems that can provide coherent and transparent explanations for their decision-making processes. AI models have achieved unparalleled performance in various domains. Explainable AI seeks to enhance trust, responsibility, and interpretability in AI systems. It is closely tied to more general growth and advancements in technology. As technology continues to evolve and enhance, explainable AI allows for the development and execution of more refined and transparent AI systems.
  • The advancements in digital transformation and progressive technologies, often referred to as Industry 4.0, are a driving trend behind the demand for explainable AI (XAI). This development has led to the successful adaptation of diverse industries by embracing digital technologies. Integrating XAI methods with Industry 4.0 technologies allows precise and high-quality applications, making firms more agile and customer-focused. Industry 4.0 leverages Al for predictive maintenance and fault detection, lowering unplanned downtime. With XAI, operators can understand the reason behind Al's predictions and recommendations. This transparency is crucial for maintenance personnel, permitting them to make informed findings about when and how to conduct maintenance activities.
  • The global explainable AI market is growing due to the rising regulatory and compliance requirements in various industries, such as finance, retail, and healthcare, to provide fairness, responsibility, and ethical use. Nations and regulatory bodies have recognized the significance of transparency and accountability in AI systems to assure ethical service and prevent biases or prejudiced outcomes. As a result, the preface of the General Data Protection Regulation (GDPR) in Europe and different guidelines from organizations, such as the Financial Stability Board (FSB), underline the need for explainability in AI algorithms, thus creating a promising outlook for the market. Moreover, a growing number of firms are adopting explainable AI solutions in adherence with these regulations and guidelines to provide transparent and interpretable AI systems, which, in turn, is positively influencing the market growth.
  • Fraud detection is a primary application area of explainable AI where it predicts fraudulent attacks and determines which attack has a more elevated threat. Cybersecurity is an increasing concern for companies and governments. Vendors of cybersecurity solutions are increasingly utilizing AI and explaining an AI algorithm's findings brings several benefits, including greater confidence in the system and a better understanding of its operation. Explainable AI solutions are being used in several areas of cybersecurity, enhancing the market growth. XAI consulting services specialize in assisting institutions adopt and implement AI solutions that are transparent, interpretable, and accountable.
  • Cloud-based solutions are an essential component of the present digital environment. The expanding trend of multi-cloud operation, as well as the growing need for cloud-based intelligence services, drives the demand in the market under study. The latest XAI technologies add unique and increased value to cloud computing. This aspect not only improves overall process viability but is also necessary for incorporating new technology. Explainable AI software can also help bridge the gap between cloud computing and modern breakthroughs. It also assists in satisfying the needs of new enterprises and startups.
  • On the contrary, the costs of business consulting, research and development, computing power, and cost to build minimal viable products are incurred before implementation. Factors such as automatic data preparation, delivering the infrastructure, processing, and making it actionable for systems and employees constitute the total cost for performance. The implementation cost is high and depends on the size of the industry. A significant challenge in the market is replacing the human workforce with AI. AI technology is the next step in maximum productivity, replacing individual craftsmanship with the factory production line.

Explainable AI Market Trends

BFSI Segment is Expected to Hold Significant Share of the Market

  • The emerging field of XAI can enable banks to navigate such transparency and trust issues and provide greater clarity on AI governance. Due to insufficient customer onboarding processes, financial institutions lose millions of dollars. It becomes difficult for many banks to evaluate their health by applying for a loan. Explainable AI provides a system for eligibility checks and risk management while maintaining transparency. XAI forecasts the key insights to track the banks' performance. For example, Akira AI provides accurate, dynamic, and automated predictions, helping it to make better decisions for supply chain management and customer churn.
  • XAI is unprecedentedly transforming the BFSI industry. These technologies are revolutionizing accounting by automating routine tasks, reducing errors, improving accuracy, and improving efficiency. According to a report by ICAEW, AI can save 16% of the total cost of the finance function, and 88% of accounting experts believe AI will enhance their working lives in the next few years. Deception detection and prevention are another area where AI and technology transform accounting. Traditional auditing methods depend on manual sampling and testing, which can be time-consuming and prone to errors. AI-powered auditing tools can analyze large amounts of data quickly and accurately, identifying anomalies and questionable transactions. This enables auditors to focus on high-risk areas and potential frauds, lowering the risk of financial loss and reputational damage for businesses.
  • AI improves the cash management of banks by predicting loan demand, payment speed, and ATM requirements. Banks are using historical cash data to build models that predict cash availability. These insights give banks the right amount of money where and when anyone needs it. The operations of automated teller machines (ATMs) in various regions can be monitored by AI tools and financial institutions can know which ATMs have cash shortage and can be refilled again without causing any inconvenience to the customer. For instance, according to Japanese Bankers Association, as of September 2023, regional banks had installed over 28.5 thousand ATMs and cash dispensers (CDs) across Japan. The Japan Post Bank recorded almost 31.5 thousand ATMs and CDs.
  • There are various banks and financial institutions that are incorporating XAI to provide better services to their customers. For instance, in September 2023, Temenos unveiled a generative AI solution that automatically categorizes banking transactions. The technology empowers banks to offer personalized insights, create unique digital banking experiences, and provide relevant products. The company stated that Temenos is at the forefront of AI in banking. The first to bring true explainable AI to the financial services industry, which helps financial institutions explain in simple business language to customers and their clients alike how AI-based decisions are taken.
  • According to Nvidia survey 2023, data analytics was the most used AI-enabled application in the financial services industry in 2023. Based on the survey, 69% of the respondents used AI for data analytics, followed by 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 increase even further in the coming years. Such huge adoption of AI in finance sector would drive the growth of the market.

North America is Expected to Hold Significant Share of the Market

  • North America has a robust innovation ecosystem supported by strategic federal investments in advanced technology, in addition to the presence of forward-thinking scientists and entrepreneurs who come together from around the world and renowned research centers that have accelerated the development of AI in the North American region. The industry is anticipated to benefit from many US government initiatives related to AI. For instance, the Expanding AI Innovation through Capacity Building and Part II program was launched by the US National Science Foundation in coordination with the US Department of Agriculture, the US Department of Homeland Security, the Science and Technology Directorate, the National Institute of Standards and Technology, National Institute of Food and Agriculture, and the US Department of Defense.
  • The National Security Commission on Artificial Intelligence's final report proposed that Congress is expected to increase federal R&D funding for AI by a factor of two annually, up to a total of USD32 billion by fiscal year 2026. The federal R&D budget will be increased by 28% from FY 2021 authorized levels to more than USD 204 billion under the Biden administration's fiscal 2023 budget plan. The National AI Research Institutes, both new and established, would 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 for the development of AI will create an opportunity for the market studied to grow.
  • In addition, Statistics Canada, like many other national statistical agencies, has embraced machine learning and artificial intelligence and is increasingly utilizing alternative data sources to enhance and modernize its many statistical systems. Machine learning techniques are frequently needed to exploit these new data sources because of their volume and speed. Since AI is promoting economic development and high-quality employment in Canada, the government of Canada is dedicated to funding initiatives to accelerate the adoption of AI throughout the economy and society. For instance, the federal government announced an investment of USD 443 million recently in the Pan-Canadian Artificial Intelligence Strategy's second phase. The Pan-Canadian Artificial Intelligence Strategy's second phase will assist in maximizing AI's potential for Canadians' benefit, speed up reliable technology development, and promote diversity and collaboration within the AI community.
  • Various retail firms in the North American region are adopting AI to provide better services to customers. For example, ThredUp, an online consignment business, introduced Goody Boxes, comprising different used apparel items tailored to each customer's style. Customers keep and pay for the things they want while returning the ones they do not want. An AI algorithm recalls each customer's preferences so that future boxes are more tailored to their interests. Customers prefer non-subscription boxes overlooking individual parts. Tesla's self-driving cars are one of the examples of AI and IoT working in tandem. With the incorporation of AI, self-driving cars predict the behavior of cars and pedestrians in various circumstances. For instance, they can determine road conditions, weather, optimal speed and get smarter with each trip.
  • Most manufacturers partner with firms that can provide complete services to support a large-scale XAI solution. Vendors like Microsoft are helping manufacturing organizations with their AI offerings. The increasing adoption of AI within manufacturing institutions enables increased efficiencies in defect detection, quality assurance, assembly line integration, assembly line optimization, and generative design. New computer vision technologies are being developed, powered by AI and deep learning, making it possible to automate visual inspection to match the increasing global demand.

Explainable AI Industry Overview

The XAI market is semi-consolidated, with a few prominent players such as IBM Corporation, Microsoft Corporation, Amelia US LLC, Google LLC, and Arthur.ai. To increase market share, corporations continually spend on strategic partnerships or acquisitions and solution and services development. The following are some recent market developments:

  • In March 2024, Apple Inc. acquired DarwinAI, an AI visual quality assurance startup that provides an end-to-end solution for improving product quality while increasing production efficiency. Darwin AI's patented XAI platform has been adopted by numerous Fortune 500 companies. Apple's acquisition of DarwinAI aligns with its longstanding practice of discreetly assimilating innovative technology firms into its ecosystem.
  • In July 2023, Fujitsu Limited announced a strategic agreement with Informa D&B to deliver new value by bringing XAI to the business and financial information industry. This collaboration brings with it a new era of decision-making through the incorporation of explainable AI technology. Fujitsu and Informa are committed to bringing transformative innovation to the industry through the introduction of this technology, which will allow Informa's 4.5 million users in Spain access to highly sophisticated data in an agile and efficient manner, significantly improving the quality of business information solutions.

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 Industry Value Chain Analysis
  • 4.3 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.3.1 Bargaining Power of Suppliers
    • 4.3.2 Bargaining Power of Consumers
    • 4.3.3 Threat of New Entrants
    • 4.3.4 Threat of Substitutes
    • 4.3.5 Intensity of Competitive Rivalry

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Growing Need for Accountability and Transparency in AI Systems
    • 5.1.2 Increasing Use of Cutting-edge Technologies for Innovation
  • 5.2 Market Restraints
    • 5.2.1 High Implementation Cost of Explainable AI
    • 5.2.2 Lack of Skilled and Expert AI Technicians
  • 5.3 Technology Impact
    • 5.3.1 Machine Learning Algorithms
    • 5.3.2 Deep Learning
    • 5.3.3 Neural Network
  • 5.4 Use Cases Analysis
    • 5.4.1 Monitoring of AI Model Performance
    • 5.4.2 Managing Regulatory, Compliance Risk
    • 5.4.3 Deployment of AI Projects across Hybrid Cloud
  • 5.5 Case Study Analysis

6 MARKET SEGMENTATION

  • 6.1 By Offering
    • 6.1.1 Solution
    • 6.1.2 Services
  • 6.2 By Deployment
    • 6.2.1 Cloud
    • 6.2.2 On-premise
  • 6.3 By End-user Industry
    • 6.3.1 BFSI
    • 6.3.2 Healthcare
    • 6.3.3 Manufacturing
    • 6.3.4 Retail
    • 6.3.5 IT and Telecommunication
    • 6.3.6 Other End-user Industries
  • 6.4 By Geography***
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia
    • 6.4.4 Australia and New Zealand
    • 6.4.5 Latin America
    • 6.4.6 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles*
    • 7.1.1 IBM Corporation
    • 7.1.2 Microsoft Corporation
    • 7.1.3 Amelia US LLC
    • 7.1.4 Google LLC
    • 7.1.5 Arthur.ai
    • 7.1.6 Ditto.ai
    • 7.1.7 Intel
    • 7.1.8 AWS
    • 7.1.9 NVIDIA
    • 7.1.10 Mphasis
    • 7.1.11 Alteryx

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET