Summary

Overview
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

ページTOPに戻る

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