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ESGとサステナビリティにおけるAIの世界市場：2025年～2032年

Global AI in ESG & Sustainability Market - 2025-2032

出版日
ページ情報
英文 203 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
ESGとサステナビリティにおけるAIの世界市場：2025年～2032年
出版日: 2025年02月13日
発行: DataM Intelligence
ページ情報: 英文 203 Pages
納期: 即日から翌営業日
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  • 目次
概要

ESGとサステナビリティにおけるAIの世界市場は、2024年に1,823億4,000万米ドルに達し、2032年までには8,467億5,000万米ドルに達すると予測され、予測期間中の2025年から2032年のCAGRは21.16%で成長する見込みです。

環境・社会・ガバナンス（ESG）戦略への人工知能（AI）の活用は、持続可能性と倫理的慣行に対する企業のアプローチに革命をもたらしています。ジェネレーティブAIは、ビッグデータセットの分析、パフォーマンスリスクの特定、目標達成のためのカスタマイズされた提案の提供を通じて、ESGチームが広範な機会を活用できるようにします。このシステムは、ESG戦略の策定、目標の設定、実施、報告といったデータ中心の複雑な手順を合理化します。

AIは、複数のESGの側面に不可欠です。環境管理では、AIは消費と廃棄物管理のイニシアチブを改善し、炭素削減と包括的な報告を促進します。AIは、多様性、公平性、インクルージョン対策、サプライチェーン調達、社会・ガバナンス要因に関する洞察を提供します。アプリケーションは透明性を高め、利害関係者の信頼を醸成します。ある世論調査によると、知識労働者の95％が、より透明性の高いESG報告によって企業の持続可能性への取り組みに対する信頼が高まると主張しています。

AIは、消費と廃棄を最小限に抑える戦略をピンポイントで示すことで、コストと生態系への影響を削減し、財務的・環境的な利点を提供します。ネット・ゼロ・クラウドのようなESG管理ツールは、AIを統合して企業の環境影響の計算と報告の精度を高めています。さらに、AIは企業がESGの枠組みの中でイノベーションを起こす力を与え、新たな機会を創出し、ブランド評価を向上させます。ESGにおけるAIの応用は、進歩を促進するだけでなく、市場の差別化を高めます。

ダイナミクス

促進要因1-炭素削減と持続可能なビジネス慣行のためのAIの活用

環境・社会・ガバナンス（ESG）プロジェクトに人工知能（AI）を組み込むことで、持続可能な取り組みが顕著に進展しています。膨大なデータセットの分析を通じて持続可能性評価を自動化するAIの能力は、この革命の重要な推進力です。GPTを含む大規模言語モデル（LLM）は、地球温暖化の影響を評価し、持続可能な戦略を提案することで、企業が強化すべき分野を特定することに成功しています。

輸送やエネルギー使用など、多くのソースからのデータを評価するAIの能力は、組織が正確なカーボンフットプリントを決定することを可能にし、それによって精度と効率の両方を向上させ、運用経費を削減します。人工知能は、エネルギー消費と物流の最適化を通じてカーボンフットプリントの最小化に大きく貢献します。予測分析を含むAI主導の技術は、企業が最も持続可能な配送ルートを決定するのを支援し、それによって温室効果ガス排出量を大幅に削減します。

エネルギー消費をリアルタイムで監視することで、企業は動的な修正を実施し、大幅なエネルギー節約と二酸化炭素排出量の削減を実現できます。AIは、可視性を高め、経路を最適化し、無駄を削減することで、持続可能なサプライチェーン管理を強化します。機械学習アルゴリズムは環境基準に従ってサプライヤーを評価し、倫理的な調達と透明性を促進します。企業の評判を高め、拡大するESG法の遵守を保証すると同時に、法的リスクを軽減します。AIを活用することで、企業はイノベーションを促進し、環境規制を遵守しながら持続可能な目標を達成することができます。

促進要因2-ESGにおけるAI導入を促進する規制状況

世界の政府や規制機関は、より厳格なESG情報開示を義務付けており、企業は報告能力を向上させる必要があります。組織が広範なESG情報を効果的に評価し、コンプライアンスを保証し、透明性を高めるためには、AI主導のソリューションがますます不可欠になっています。

欧州連合の企業持続可能性報告指令（CSRD）は、より広範な企業の包括的な持続可能性開示を要求しており、グローバルなベンチマークを確立しています。国際サステナビリティ基準審議会（ISSB）は、サステナビリティ関連開示のための一貫した枠組みを開発し、投資家にESGリスクと可能性に関する一貫した情報を提供しています。IFRS財団の国・地域別適用指針は、世界な規制の一貫性を促進し、国・地域間で統一された持続可能性報告を保証しています。

国レベルでの規制の枠組みは様々です。英国は2025年までに気候変動関連の財務情報開示を義務付ける予定ですが、米国では州レベルでESGを推進する法律と反対する法律が混在しており、世界企業にとっては複雑なコンプライアンス環境となっています。規制の厳しさが増す中、コンプライアンスを自動化し、報告義務を軽減し、企業の持続可能性計画を強化するためには、AIを活用したESGソリューションが不可欠となります。ESGコンプライアンスにAIを活用する企業は、透明性の向上、規制リスクの軽減、投資家の信頼強化によって競争優位性を獲得できると思われます。

抑制要因：サイバーセキュリティとデータ・プライバシーのリスク

環境、社会、ガバナンス指標を含む重要な機密ESGデータを扱うAIシステムは、サイバー攻撃を受けやすいです。Global Reporting Initiative（GRI）やSustainability Accounting Standards Board（SASB）のようなESG報告フレームワークにAIが組み込まれたことで、サイバーセキュリティが重要な問題として浮き彫りになりました。

サイバー攻撃はESGに関連する重大な懸念をもたらします。例えば、2021年にはハッカーがフロリダの水処理施設に侵入し、遠隔操作で化学物質の濃度を操作しました。近年ではドイツの鉄鋼会社に対するサイバー攻撃で高炉の操業停止を余儀なくされ、作業員の安全が脅かされました。その1年前には、米国FDAがセキュリティの欠陥により50万台のペースメーカーを回収し、2020年にドイツで発生したランサムウェア攻撃では、病院の救急部門が閉鎖され、患者の死亡につながりました。

サイバーセキュリティの人材不足は状況をさらに悪化させ、企業が効果的な保護対策を確立する妨げとなっています。サイバー攻撃が発電所や水処理施設などの重要なインフラにますます焦点を当てるようになっているため、規制当局の監視が強化され、ESG計画にAIを組み込むことが課題になると予想されます。こうした危険は市場の成長を妨げ、より強固なサイバーセキュリティ基準が必要となります。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • AIを活用した炭素削減と持続可能なビジネス慣行
      • ESGにおけるAI導入を推進する規制状況
    • 抑制要因
      • サイバーセキュリティとデータプライバシーのリスク
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • DMIの見解

第6章 技術別

  • 機械学習（ML）
  • 自然言語処理（NLP）
  • ディープラーニング
  • 予測分析
  • 生成AI
  • その他

第7章 展開別

  • クラウドベースソリューション
  • オンプレミスソリューション

第8章 組織規模別

  • 中小企業
  • 大企業

第9章 エンドユーザー別

  • エネルギー・公益事業
  • 製造業
  • 小売り
  • 金融サービス
  • ヘルスケア
  • 情報技術
  • 消費財
  • 政府・公共部門
  • その他

第10章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他欧州地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋
  • 中東・アフリカ

第11章 競合情勢

  • 競合シナリオ
  • 市況・シェア分析
  • M&A分析

第12章 企業プロファイル

  • Salesforce
    • 会社概要
    • 製品ポートフォリオと概要
    • 財務概要
    • 主な発展
  • Microsoft
  • IBM
  • Google Cloud
  • SAP
  • Oracle
  • Accenture
  • PwC
  • C3.ai
  • Honeywell

第13章 付録

目次
Product Code: ICT9118

Global AI in ESG & Sustainability Market reached US$ 182.34 billion in 2024 and is expected to reach US$ 846.75 billion by 2032, growing with a CAGR of 21.16% during the forecast period 2025-2032.

The use of Artificial Intelligence (AI) into Environmental, Social and Governance (ESG) strategies is revolutionizing corporate approaches to sustainability and ethical practices. Generative AI empowers ESG teams to capitalize on extensive opportunities through the analysis of big datasets, the identification of performance risks and the provision of customized suggestions for target attainment. This system streamlines the intricate, data-centric procedure of ESG strategy formulation, objective establishment, implementation and reporting.

AI is integral to multiple ESG dimensions. In environmental management, AI improves consumption and waste management initiatives while facilitating carbon reduction and comprehensive reporting. AI provides insights on diversity, equity and inclusion measures, along with supply chain sourcing and social and governance factors. The applications enhance transparency, fostering confidence among stakeholders. A poll indicated that 95% of knowledge workers assert that more transparent ESG reporting enhances trust in a company's sustainability initiatives.

AI offers financial and environmental advantages by pinpointing strategies to minimize consumption and waste, thereby reducing costs and ecological impacts. ESG management tools, such as Net Zero Cloud, have integrated AI to enhance the accuracy of firms' calculations and reporting of their environmental impact. Furthermore, AI empowers firms to innovate within ESG frameworks, creating new opportunities and improving brand reputation. The application of AI in ESG not only expedites advancement but also enhances market differentiation.

Dynamics

Driver 1 - Leveraging AI for carbon reduction and sustainable business practices

The incorporation of Artificial Intelligence (AI) into Environmental, Social and Governance (ESG) projects is propelling notable progress in sustainability endeavors. The capacity of AI to automate sustainability evaluations through the analysis of extensive datasets is a significant driver of this revolution. Large language models (LLMs), including GPTs, evaluate the effects of global warming and propose sustainable strategies, allowing companies to successfully identify areas for enhancement.

AI's ability to evaluate data from many sources, such as transportation and energy use, enables organizations to determine accurate carbon footprints, thereby improving both precision and efficiency while lowering operational expenses. Artificial intelligence significantly contributes to minimizing carbon footprints through the optimization of energy consumption and logistics. AI-driven technologies, including predictive analytics, assist organizations in determining the most sustainable delivery routes, thereby substantially reducing greenhouse gas emissions.

Real-time monitoring of energy consumption enables companies to implement dynamic modifications, resulting in significant energy savings and a decrease in carbon emissions. AI augments sustainable supply chain management by enhancing visibility, optimizing routing and reducing waste. Machine learning algorithms evaluate suppliers according to environmental standards, facilitating ethical sourcing and transparency. It enhances a company's reputation and assures adherence to growing ESG laws, while reducing legal risks. Through the utilization of AI organizations can foster innovation and attain enduring sustainability objectives while complying with environmental regulations.

Driver 2 - Regulatory landscape driving AI adoption in ESG

Global governments and regulatory agencies are enacting more stringent ESG disclosure mandates, necessitating firms to improve their reporting proficiency. AI-driven solutions are increasingly vital for organizations to effectively evaluate extensive ESG information, guarantee compliance and enhance transparency.

The European Union's Corporate Sustainability Reporting Directive (CSRD) requires comprehensive sustainability disclosures from a wider array of corporations, establishing a global benchmark. The International Sustainability Standards Board (ISSB) is developing a cohesive framework for sustainability-related disclosures, offering investors consistent information regarding ESG risks and possibilities. The IFRS Foundation's jurisdictional adoption guide facilitates global regulatory coherence, guaranteeing uniform sustainability reporting across jurisdictions.

The regulatory framework at the national level is varied. The UK will mandate climate-related financial disclosures by 2025, whereas the US is witnessing a combination of pro- and anti-ESG legislation at the state level, resulting in a convoluted compliance landscape for global firms. With the increasing stringency of regulations, AI-driven ESG solutions will be essential for automating compliance, alleviating reporting obligations and enhancing corporate sustainability plans. Organizations utilizing AI for ESG compliance will acquire a competitive advantage by improving transparency, reducing regulatory risks and bolstering investor trust.

Restraint: Cybersecurity and data privacy risks

AI systems handling significant sensitive ESG data, including environmental, social and governance indicators, are more susceptible to cyber attacks. The incorporation of AI in ESG reporting frameworks like the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) has underscored cybersecurity as a significant issue.

Cyberattacks pose substantial ESG-related concerns. For instance, In 2021, hackers breached a Florida water treatment facility, manipulating chemical concentrations remotely and in recent years, a cyberattack on a German steel company compelled the shutdown of a blast furnace, endangering worker safety. A year prior, the US FDA withdrew 500,000 pacemakers owing to security flaws, while a 2020 ransomware assault in Germany resulted in the closure of a hospital emergency department, leading to a patient's mortality.

The shortage of cybersecurity personnel intensifies the situation, hindering firms' ability to establish effective protection measures. As cyberattacks increasingly focus on vital infrastructure, including power plants and water treatment facilities, regulatory oversight is anticipated to intensify, hence challenging the integration of AI into ESG plans. These dangers impede market growth and require more robust cybersecurity standards.

Segment Analysis

The global AI in ESG & sustainability market is segmented based on technology, deployment, organization size, end-user and region.

AI-Driven Sustainability in Energy & Utility Sector

The energy and utility sector is a major consumer of AI in ESG and sustainability, utilizing AI-driven solutions for carbon footprint reduction, energy efficiency, water conservation and system modernization. Artificial Intelligence facilitates real-time surveillance, predictive analysis and automated reporting, assisting utilities in achieving ESG objectives while enhancing resource management efficiency. The incorporation of AI in renewable energy forecasts, smart grids and advanced metering infrastructure (AMI) improves operational efficiency and sustainability initiatives.

Regulatory frameworks, such the EU's Corporate Sustainability Reporting Directive (CSRD) and the US Securities and Exchange Commission (SEC) climate disclosure regulations, impose rigorous ESG reporting requirements on energy corporations. AI-driven technologies assist utilities in adhering to rules by automating data acquisition and guaranteeing precise sustainability reporting. AI is essential in enhancing ESG initiatives within the energy industry, driven by the emergence of microgrids, IoT, blockchain and carbon capture technologies. The advances promote efficiency, diminish environmental impact and improve regulatory compliance, cultivating a sustainable future.

Geographical Penetration

North America's AI Role in advancing ESG & sustainability goals

North America leads in AI adoption for ESG and sustainability, driven by major technology firms and rising regulatory focus on sustainable practices. ESG software platforms like as Enablon, Intelex and Sphera provide real-time tracking and reporting of sustainability parameters, consolidating data from multiple sources for an integrated assessment of performance. These platforms are essential for optimizing data collection, analysis and reporting through customisable templates, hence assisting firms in effectively achieving ESG objectives.

Cloud-based data management solutions from Microsoft Azure and Google Cloud have enhanced this industry by providing scalable and effective platforms for the storage and management of extensive ESG datasets. These technologies enable firms, particularly those with intricate supply chains, to automate data entry and swiftly discern trends, hence improving decision-making and transparency with stakeholders.

Artificial intelligence and machine learning tools are helpful in evaluating vast datasets to forecast and enhance variables such as carbon emissions and energy consumption. For example, Microsoft's AI-powered technologies monitor carbon emissions to assist in achieving its carbon-negative objective by 2030. Blockchain technology is increasingly being adopted, exemplified by Unilever's implementation to enhance supply chain transparency, foster trust among stakeholders and validate sustainability assertions.

Competitive Landscape

The major Global players in the market include Algotec Green Technology, Gross-Wen Technologies (GWT), Liqoflux, Agromorph, Xylem Inc., Valicor Environmental Services, Algenuity originClear Inc., Evodos B.V. and MicroBio Engineering Inc.

By Technology

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Deep Learning
  • Predictive Analytics
  • Generative AI
  • Others

By Deployment

  • Cloud-based Solutions
  • On-premises Solutions

By Organization Size

  • Small and Medium Enterprises (SMEs)
  • Large Enterprises

By End-User

  • Energy & Utilities
  • Manufacturing
  • Retail
  • Financial Services
  • Healthcare
  • Information Technology
  • Consumer Goods
  • Government & Public Sector
  • Others

By Region

  • North America
  • South America
  • Europe
  • Asia-Pacific
  • Middle East and Africa

Key Developments

  • In January 14, 2024, the Capgemini Research Institute's released their paper on the sustainability of generative AI, titled 'Developing Sustainable Gen AI', indicates that generative AI has a substantial and increasing adverse environmental impact. As enterprises evaluate the capacity of generative AI to enhance company growth in relation to the technology's environmental impact, the paper delineates strategies for formulating a responsible and sustainable generative AI approach.

Why Purchase the Report?

  • To visualize the global AI in ESG & sustainability market segmentation based on technology, deployment, organization size, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of the AI in ESG & Sustainability market with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global AI in ESG & sustainability market report would provide approximately 62 tables, 54 figures and 203 pages.

Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Technology
  • 3.2. Snippet by Deployment
  • 3.3. Snippet by Organization Size
  • 3.4. Snippet by End-User
  • 3.5. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Leveraging AI for carbon reduction and sustainable business practices
      • 4.1.1.2. Regulatory landscape driving AI adoption in ESG
    • 4.1.2. Restraints
      • 4.1.2.1. Cybersecurity and data privacy risks
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. DMI Opinion

6. By Technology

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 6.1.2. Market Attractiveness Index, By Technology
  • 6.2. Machine Learning (ML)*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Natural Language Processing (NLP)
  • 6.4. Deep Learning
  • 6.5. Predictive Analytics
  • 6.6. Generative AI
  • 6.7. Others

7. By Deployment

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 7.1.2. Market Attractiveness Index, By Deployment
  • 7.2. Cloud-based Solutions*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. On-premises Solutions

8. By Organization Size

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 8.1.2. Market Attractiveness Index, By Organization Size
  • 8.2. Small and Medium Enterprises (SMEs)*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Large Enterprises

9. By End-User

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 9.1.2. Market Attractiveness Index, By End-User
  • 9.2. Energy & Utilities*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Manufacturing
  • 9.4. Retail
  • 9.5. Financial Services
  • 9.6. Healthcare
  • 9.7. Information Technology
  • 9.8. Consumer Goods
  • 9.9. Government & Public Sector
  • 9.10. Others

10. By Region

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 10.1.2. Market Attractiveness Index, By Region
  • 10.2. North America
    • 10.2.1. Introduction
    • 10.2.2. Key Region-Specific Dynamics
    • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.2.7.1. US
      • 10.2.7.2. Canada
      • 10.2.7.3. Mexico
  • 10.3. Europe
    • 10.3.1. Introduction
    • 10.3.2. Key Region-Specific Dynamics
    • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.3.7.1. Germany
      • 10.3.7.2. UK
      • 10.3.7.3. France
      • 10.3.7.4. Italy
      • 10.3.7.5. Spain
      • 10.3.7.6. Rest of Europe
  • 10.4. South America
    • 10.4.1. Introduction
    • 10.4.2. Key Region-Specific Dynamics
    • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.4.7.1. Brazil
      • 10.4.7.2. Argentina
      • 10.4.7.3. Rest of South America
  • 10.5. Asia-Pacific
    • 10.5.1. Introduction
    • 10.5.2. Key Region-Specific Dynamics
    • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.5.7.1. China
      • 10.5.7.2. India
      • 10.5.7.3. Japan
      • 10.5.7.4. Australia
      • 10.5.7.5. Rest of Asia-Pacific
  • 10.6. Middle East and Africa
    • 10.6.1. Introduction
    • 10.6.2. Key Region-Specific Dynamics
    • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

  • 11.1. Competitive Scenario
  • 11.2. Market Positioning/Share Analysis
  • 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

  • 12.1. Salesforce*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. Microsoft
  • 12.3. IBM
  • 12.4. Google Cloud
  • 12.5. SAP
  • 12.6. Oracle
  • 12.7. Accenture
  • 12.8. PwC
  • 12.9. C3.ai
  • 12.10. Honeywell

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us