How AI in ESG is Transforming Reporting and Sustainability Practices?

With demand for real-time, verifiable Environmental, Social, and Governance (ESG) data rising sharply, around 63% of companies are already using, or planning to use, AI for ESG-related data collection, analysis, and reporting. This rapid adoption shows how AI in ESG is evolving from a “good-to-have” tool into a strategic necessity at the boardroom level.

This article explores how AI is reshaping ESG frameworks, streamlining compliance, generating predictive insights, and enabling executives to manage sustainability with greater accountability.

As ESG expectations intensify, AI has shifted from an experimental tool to a strategic cornerstone. In 2024, 78% of organisations reported using AI in at least one business function—up from 55% the year before. This reflects the accelerating integration of advanced analytics and automation across enterprise operations.

The AI-in-ESG and sustainability market mirrors this momentum. Valued at USD 1.24 billion in 2024, it is projected to grow to USD 14.87 billion by 2034, at a CAGR of 28.2%. AI now supports ESG workflows ranging from data aggregation and real-time monitoring to predictive modelling. These capabilities streamline compliance and provide forward-looking insights for boards and investors.

Looking ahead, AI will not only support ESG functions but also transform them into predictive, decision-shaping engines, critical as regulations tighten and stakeholder scrutiny deepens.

AI is reshaping ESG by automating data ingestion, standardising formats, and identifying anomalies through machine learning. It draws from diverse sources, ERPs, supplier systems, news feeds, and third-party databases, to deliver rapid, reliable, and standardised insights.

Beyond data integration, AI enables:

• Predictive forecasting –anticipating sustainability risks and trends.

• Anomaly detection – flagging inconsistencies or non-compliance issues.

• Real-time monitoring – ensuring continuous visibility across ESG metrics.

Together, these functions turn ESG from a retrospective reporting exercise into a forward-looking management tool. Notably, over 69% of institutional investors now use, or are considering the adoption of AI tools to enhance ESG analysis.

The speed of change in sustainability and governance expectations has outpaced traditional reporting frameworks. Static, retrospective disclosures are inadequate in today’s environment, where stakeholders demand transparency, immediacy, and anticipatory reporting. Innovation is now the foundation of credibility, competitive advantage, and resilience in ESG performance.

• Challenges in Traditional ESG Reporting

  Traditional ESG reports are often produced through manual, paper-based processes, fragmented systems, and inconsistent methodologies. The result is data that lacks comparability across frameworks, is prone to blind spots, and increases compliance risk.

  Without automation, companies struggle to capture the full scope of their environmental and social impacts. These inefficiencies make ESG disclosures reactive rather than strategic, limiting their value for boards and investors in decision-making.

• Demand for Real-Time, Reliable ESG Data

  Stakeholders increasingly expect ESG metrics to resemble financial reporting in their reliability and timeliness. Investors, regulators, and customers want real-time visibility into emissions, diversity initiatives, supply-chain risks, and governance practices—rather than waiting for static annual reports.

  By adopting AI systems, companies can deliver accurate, real-time ESG insights, manage risks proactively, and enhance strategic agility. This shift enables organisations to prepare for challenges in advance instead of merely responding to them.

The regulatory landscape for ESG is tightening at a pace that demands executive attention. From Europe’s Corporate Sustainability Reporting Directive (CSRD) to the U.S. SEC’s climate disclosure rules, mandatory frameworks are shifting ESG from a “soft” disclosure exercise into a compliance requirement. Alongside these, voluntary standards continue to shape market expectations, creating a dual challenge for companies: meeting legal obligations while also realising the reputational and competitive benefits of going beyond baseline requirements.

• Mandatory frameworks such as the EU’s CSRD and the SEC’s proposed climate rules establish legally enforceable benchmarks for data quality and comparability.

• Voluntary frameworks like the Global Reporting Initiative (GRI), Sustainability Accounting Standards Board (SASB), and the Task Force on Climate-Related Financial Disclosures (TCFD) act as industry best practices. While not legally binding, they build investor confidence and strengthen market positioning.

Together, these frameworks create a layered disclosure environment in which companies must demonstrate both compliance and leadership.

As ESG rules become more prescriptive and data-intensive, reliance on AI is accelerating. Manual processes cannot keep pace with granular reporting demands, cross-border compliance, or the need for audit-ready disclosures. AI tools:

• Automate alignment with multiple frameworks,

• Detect inconsistencies across datasets, and

• Generate compliance-ready outputs with audit trails.

In this way, regulation acts as a catalyst, pushing organisations to adopt technology that turns ESG reporting into a strategic business asset.

AI is redefining ESG reporting by shifting it from periodic, manual exercises to high-velocity, strategic processes. Innovations include:

• Automated carbon accounting (including Scope 3 emissions, which often represent up to 90% of a company’s footprint),

• Real-time risk surveillance across supply chains and operations, and

• Advanced scoring models that support investor-grade ESG ratings.

AI platforms are addressing some of the most labour-intensive ESG tasks. For example, Climatiq, an AI-powered platform, automates carbon emissions data collection—including Scope 3 emissions—making reporting more accurate, scalable, and actionable.

Machine learning (ML), combined with big data, is enabling real-time insights and predictive foresight. A 2025 MDPI study highlights that approximately 73% of AI’s financial performance impact comes from improved ESG disclosure quality. This underscores not only the value of transparency for stakeholder trust but also how central AI has become to modern ESG strategies.

AI’s role in ESG reporting can be summarised across three critical dimensions:

• Automated ESG Data Collection: Consolidates inputs from sensors, ledgers, supply chain platforms, and newsfeeds into standardised outputs, ensuring comprehensive coverage and higher accuracy.

• Real-Time Monitoring and Risk Analysis: Flags anomalies, compliance breaches, or reputational risks instantly, enabling proactive executive interventions.

• Predictive Analytics: Simulates potential risks such as climate disruptions, supply shocks, or regulatory tightening, helping companies adjust ESG strategies in advance.

AI-driven ESG reporting offers tangible benefits:

• Improved Accuracy and Data Integrity: Minimises human error, validates inputs against benchmarks, and ensures consistent disclosures across frameworks.

• Enhanced Efficiency and Cost Savings:Automates data-heavy processes, reducing costs and freeing teams to focus on strategic priorities.

• Faster Reporting Cycles and Real-Time Updates:Provides instant dashboards and compliance-ready outputs, ensuring stakeholders have timely, actionable insights.

AI is actively driving real-world transformations in ESG reporting, enhancing oversight, accelerating materiality determinations, and delivering sharper analytical depth across sustainability domains.

1. Datamaran: AI-Powered Double Materiality

   Datamaran’s Smart ESG platform incorporates an AI module that automates double materiality assessments. It identifies, prioritises, and categorises ESG impacts, risks, and opportunities across operations and value chains. By scanning regulations, news, and peer reports in real time, it surfaces material insights that help firms such as Barclays, Cisco, Kraft Heinz, and Marathon Petroleum make defensible, audit-ready decisions faster.

2. Project Gaia by BIS: AI-Driven Climate Risk Detection

   In a proof-of-concept led by the Bank for International Settlements, Project Gaia used large language models (LLMs) to analyse more than 2,300 documents from 187 financial institutions. The system achieved 98% accuracy in detecting the absence of climate risks and 80% accuracy in categorisation, demonstrating AI’s potential to streamline complex climate risk discovery processes.

3. GaiaLens: ESG Ratings and Scoring

   Platforms such as GaiaLens apply real-time analysis of global media and public data to generate dynamic ESG and controversy scores. Updated continuously, these AI-driven scores help counter greenwashing by providing explainable, sentiment-based assessments powered by LLM reasoning, enhancing transparency for both companies and investors.

4. RepRisk: ESG Risk Surveillance

   RepRisk applies AI combined with human analysis to review millions of documents daily across 23 languages. Its natural language processing (NLP) models identify ESG-related incidents and generate contextual summaries alongside quantitative risk indices. This approach delivers risk monitoring at an unmatched level of granularity.

AI is shifting ESG from a compliance exercise to a core driver of strategy, resilience, and investor confidence.

• Better Decision-Making: Provides evidence-based insights to strengthen investment strategies, capital allocation, and corporate planning.

• Scalable Supply Chain Oversight: Extends ESG accountability across global supply chains, enhancing transparency, resilience, and compliance.

• Actionable Intelligence:Empowers companies to anticipate risks, adapt quickly, and maintain leadership in sustainability performance.

While the potential is significant, adoption of AI in ESG comes with technical, ethical, and operational hurdles that must be addressed strategically.

• Data Quality and Accessibility:ESG data is often unstructured or inconsistent, making it difficult to generate reliable insights. Standardised, verifiable datasets across global supply chains remain scarce.

• Transparency and Algorithmic Bias: Black-box models risk embedding bias into ESG scoring. Without explainability, stakeholder trust may erode.

• Regulatory and Ethical Compliance: AI systems must align with fast-evolving ESG disclosure rules, data privacy requirements, and ethical standards, or face penalties and reputational damage.

• Skills and Infrastructure Gaps:Effective deployment requires specialised expertise and significant investment—resources often out of reach for smaller organisations.

• Stakeholder Expectations: Stakeholders increasingly demand clear disclosure of how AI is applied in ESG reporting. Without transparency, companies risk scepticism about the credibility of their disclosures.

Embedding AI into ESG requires adherence to globally recognised ethical frameworks that ensure fairness, accountability, and sustainability. Beyond compliance, these frameworks guide companies toward responsible innovation and long-term resilience.

• Responsible AI Governance Models

  International initiatives such as the OECD’s AI Principles and UNESCO’s AI Ethics Recommendation provide governance templates for ethical deployment of AI in ESG contexts. These models emphasise:

  1. Human-centred design,

  2. Transparency, and

  3. Accountability throughout the AI lifecycle.

  Adopting such frameworks helps companies mitigate risks of misuse while positioning ESG activities within globally trusted standards. By doing so, organisations strengthen investor confidence and enhance credibility in ethical decision-making.

• AI Ethics for ESG Risk Assessment

  Ethical AI practices are essential for fair and accurate ESG risk modelling. Prioritising accountability, inclusivity, and explainability ensures that algorithms do not disproportionately disadvantage vulnerable communities or sectors. With regulatory scrutiny intensifying, integrating ethical principles into ESG risk assessment offers firms two key advantages:

  1. Compliance assurance with evolving standards, and

  2. Competitive differentiation as investors increasingly reward ethical, transparent practices.

• Sustainability-Aligned Technology Guidelines

  New technology standards are embedding sustainability directly into AI development. Examples include green computing, energy-efficient infrastructure, and the EU’s AI Act sustainability provisions. These initiatives encourage companies to design AI systems with:

  1. Reduced carbon emissions, and

  2. Lifecycle accountability for energy and resource usage.

In ESG, such alignment ensures that technology used to measure sustainability outcomes does not itself create contradictions through excessive environmental impact. This dual focus creates opportunities for companies to use AI responsibly while enabling long-term, evidence-based environmental and social actions.

To adopt AI in ESG reporting responsibly and effectively, organisations should:

• Engage Key ESG Stakeholders Early: Involve investors, regulators, and communities to build alignment and trust.

• Prioritise Transparent AI Methodologies: Use explainable AI to provide clear audit trails and boost confidence in reported outcomes.

• Continuously Evaluate Model Performance: Regularly monitor accuracy, relevance, and ethical compliance.

• Invest in ESG Talent and Digital Infrastructure: Build skilled teams and scalable systems to sustain responsible AI integration.

AI is redefining how organisations demonstrate resilience, accountability, and long-term value. For business leaders, the opportunity lies in deploying intelligent ESG systems that extend beyond compliance into strategic foresight.

Unlocking this potential requires trusted insights and reliable data. Partnering with Dun & Bradstreet empowers businesses to harness AI-driven ESG intelligence with confidence, supporting stronger governance, sharper decision-making, and sustainable growth.