How GenAI is accelerating sustainability in organizations—starting with reporting

While GenAI is making waves across industries, one of its most impactful applications lies in sustainability. For organizations, sustainability isn’t just about making eco-friendly products—it is embedded in everything, from operational efficiency to long-term business strategy.

But let’s start with something fundamental. Sustainability teams today are tasked with one of the most complex and time-consuming processes—sustainability reporting. It is not just about crunching numbers; it involves navigating multiple frameworks, gathering accurate data, engaging stakeholders, and ensuring compliance with evolving regulations.

So, how can GenAI step in and make a difference? Let’s break it down, step by step.

The Sustainability Reporting Process

Sustainability reporting is an end-to-end journey that involves multiple steps as shown below:

Steps in Sustainability Reporting

Now, add to this complexity the multiple reporting frameworks—GRI, SASB, TCFD, CSRD, and more. Each has different requirements, and organizations must determine which apply to them.

This is where GenAI can be a game-changer. Let’s break it down step by step.

How GenAI Enhances Each Step of Sustainability Reporting

1. Identifying Purpose & Scope

Before diving in, organizations need to define their sustainability goals, choose the right reporting standards (GRI, SASB, TCFD, CSRD, etc.), and set clear boundaries for what’s being measured.

How GenAI Helps:

• Analyzes past reports, industry standards, and regulations to suggest the most relevant reporting frameworks. 

• Benchmarks against competitors’ sustainability reports to highlight best practices.

The Impact: Cuts down research time. Ensures compliance accuracy. Helps select the right frameworks efficiently.

Is AI Necessary? Nice to Have—can be done manually, but AI speeds it up significantly.

Best solution -Retrieval-Augmented Generation (RAG)

Why ? A standard LLM like ChatGPT won't have real-time regulatory updates.

RAG can pull up-to-date regulatory information and past reports to suggest applicable frameworks.

For example Nasdaq's ESG AI Assistant, within the "Nasdaq Sustainable Lens" platform, uses AI to streamline ESG reporting and analysis. It enables companies to efficiently research, benchmark, and gain insights from vast amounts of ESG data.

2. Engaging Stakeholders

Sustainability reporting isn’t done in isolation—it’s about capturing insights from investors, employees, customers, and regulators to shape an organization’s ESG strategy.

How GenAI Helps:

• Uses sentiment analysis to scan surveys, social media, and reports, identifying key stakeholder concerns.

• AI chatbots facilitate real-time engagement and gather feedback efficiently.

The Impact: Reduces manual effort. Helps prioritise the most critical sustainability issues and Improves responsiveness and communication.

Is AI Necessary? Good to Have—traditional engagement works, but AI makes it faster and more insightful.

Best AI Solution: Standard LLM (like ChatGPT) + Sentiment Analysis Model

Why? A general-purpose LLM can manage chatbot interactions. Sentiment analysis tools help interpret stakeholder feedback.

3. Identifying Material Issues

Which ESG issues actually matter? This step ensures reports focus on what’s most relevant—both now and in the future.

How GenAI Helps:

• AI scans industry reports, regulations, and stakeholder feedback to surface emerging material issues.

• Predicts sustainability risks and opportunities based on historical data.

The Impact: Keeps reports relevant and credible. Improves strategic decision-making. Speeds up materiality assessments.

Is AI Necessary? Must Have—AI dramatically improves accuracy and efficiency.

Best AI Solution: Fine-Tuned LLM + RAG

Why? A fine-tuned model understands industry-specific ESG risks. RAG ensures real-time alignment with current trends.

Example: Baker Hughes used AI and LLMs to speed up ESG materiality assessments, allowing their team to focus on broader stakeholder needs. This approach involved analyzing stakeholder documents and training NLP and LLMs to identify ESG-related information, saving around 30,000 hours in the assessment process.

4. Collecting Data & Measuring KPIs

ESG data comes from everywhere—IoT sensors, supply chains, financial reports, employee surveys, and more.

How GenAI Helps:

• Automates data collection from multiple sources.

• Detects anomalies and inconsistencies in ESG metrics.

The Impact: Reduces manual work and human errors. Enables real-time tracking. Ensures accurate and reliable ESG data.

Is AI Necessary? Must Have—manual tracking at scale is nearly impossible.

Best AI Solution: Custom Fine-Tuned Model or AI-Enhanced Data Pipeline.

Why? Structured data from sensors, ERP systems, and APIs require a custom AI model to analyze trends. A fine-tuned model on sustainability metrics ensures accurate KPI tracking.

5. Analysing & Validating Data

Bad data leads to bad output. This step ensures ESG reports are accurate, reliable, and audit-ready.

How GenAI Helps:

• AI flags inconsistencies, missing values, and potential data manipulation.

• Predicts trends and validates reported data.

The Impact: Increases report credibility. Reduces errors and compliance risks. Speeds up validation.

Is AI Necessary? Must Have—ensures reporting accuracy and trust.

Best AI Solution: AI-Powered Data Validation + Standard LLM for Explanation

Why? AI tools can automate anomaly detection and validate ESG data. A standard LLM (like ChatGPT) can explain anomalies or suggest corrective actions.

6. Preparing the Sustainability Report

This is where all the data, insights, and analysis come together into a structured, readable report.

How GenAI Helps:

• Auto-generates drafts based on key findings.

• Tailors reports for different audiences—investors, regulators, and customers.

The Impact: Faster report creation. Consistent, structured formatting. Customization for different stakeholders.

Is AI Necessary? Good to Have—not essential, but saves time.

Best AI Solution: Standard LLM (like ChatGPT) + Fine-Tuned Model for Formatting

Why? LLMs like ChatGPT are excellent at drafting reports, but it may not format them to match sustainability reporting frameworks. A fine-tuned LLM (trained on past sustainability reports) can auto-format reports for CSRD, ISSB, etc.

7. External Assurance (Optional Step)

Some organizations go the extra mile to have their sustainability reports externally verified. It is a necessary step for multiple frameworks like CSRD.

How GenAI Helps:

• AI-powered anomaly detection assists auditors in verifying data accuracy.

• AI simulates audit scenarios to test sustainability claims.

The Impact: Increases credibility. Speeds up the assurance process.

Is AI Necessary? Nice to Have—helpful but not mandatory.

Best AI Solution: AI-Powered Anomaly Detection Model + RAG for Best Practices

Why? An AI-driven anomaly detection tool can identify reporting errors before external audits. RAG can fetch industry best practices to improve report credibility.

8. Publishing & Communicating the Report

Reports need to be shared with stakeholders, investors, and the public in a way that’s clear and engaging.

How GenAI Helps:

• AI creates executive summaries, press releases, and social media content.

• Chatbots answer stakeholder queries about the report.

The Impact: Simplifies content creation. Improves stakeholder engagement.

Is AI Necessary? Good to Have—AI helps, but human oversight is key.

Best AI Solution: Standard LLM (like ChatGPT) + AI Summarisation Model 

Why? LLM's like ChatGPT can generate executive summaries, social media posts, and blog content. AI-powered summarisation tools can create tailored content for investors vs. customers.

9. Monitoring & Continuous Improvement

Sustainability isn’t a one-time effort—it’s an ongoing process.

How GenAI Helps:

• AI tracks ESG performance in real time.

• Predictive analytics identifies future risks.

The Impact: Ensures continuous improvement. Helps organizations stay ahead of ESG risks.

Is AI Necessary? Must Have—proactive risk management is essential.

Best AI Solution: Predictive AI Model + RAG for Regulatory Changes 

Why? Predictive AI models can forecast sustainability risks based on market trends. RAG ensures compliance by fetching real-time regulatory updates.

Final Thoughts: The Future of AI-Driven Sustainability

Sustainability reporting is complex, but GenAI could be a game changer. It doesn’t just automate tasks—it enhances accuracy, drives efficiency, and provides deeper insights. By automating data collection, analysis, and report generation, AI solutions are enabling organizations to meet regulatory requirements more efficiently and focus on strategic sustainability initiatives. That said, not every step needs a custom AI model. In many cases, fine-tuning an existing model or using RAG  is enough.

As the landscape of sustainability reporting continues to evolve, integrating GenAI into these processes will likely become increasingly essential. Organizations that embrace these technologies today will be better positioned to navigate the complexities of tomorrow's sustainability challenges.

The key takeaway? AI isn’t just a “nice to have” for sustainability—it’s becoming a necessity. The reality? AI in sustainability is no longer optional—it’s quickly becoming a necessity.

Your thoughts? Have you explored AI-driven sustainability reporting? Let’s discuss!