Introduction

Generative AI excels in tasks related to swiftly handling large amounts of text and performing various analyses on data. It can answer questions, draft summaries, classify information, and analyze data against different parameters. Environmental, Social, and Governance (ESG) reporting involves extensive documentation and complex compliance requirements. Leveraging generative AI can significantly enhance productivity and accuracy in ESG reporting processes. Below, I’ve outlined five key ways to help you be highly productive and achieve excellent results when using generative AI for ESG reporting.

1. Data Preparation

When starting your ESG reporting work, it’s crucial to have a complete data set of ESG-related documents readily available. This data might be consolidated in a public report or spread across multiple documents, especially if different topics are separated into different files. Ensure all relevant documents are organized and accessible before you begin analyzing them to achieve comprehensive results.

In some cases, you may be required to audit your supply chain and contractors. Traditionally, this involves sending out questionnaires and manually reviewing responses to align them with your requirements—a time-consuming process. Embracing automation and generative AI can streamline this task, making the auditing process more efficient and less labor-intensive.

2. Select Your Reporting Framework

When evaluating compliance, you need to select the reporting framework you are working with. Various ESG reporting standards exist, such as the European Sustainability Reporting Standards (ESRS), Sustainability Accounting Standards Board (SASB), and Global Reporting Initiative (GRI). Clearly stating and separating the requirements of your chosen framework is essential when using AI for compliance evaluation.

Using AI requires that the requirements are explicitly defined and structured. If you input the entire framework documentation without proper organization, the current resolution of large language models (LLMs) may be insufficient to handle all the information effectively. This can lead to partial and unreliable results. Therefore, prepare a concise and well-structured set of requirements for the AI to process.

3. Choose Your Solution

For detailed analysis of your documents, you need a specialized solution. You can either develop a custom tool using available frameworks and LLMs or opt for an off-the-shelf product designed for detailed analysis of ESG-related data. Domain-specific solutions like Briink or ESGFlow.ai provide structured analyses, identifying key gaps in your data compared to disclosure requirements.

Developing your own solution allows for customization to fit specific needs but requires significant time and resources. Off-the-shelf products, on the other hand, are ready to use and often come with support and updates, making them a practical choice for many organizations. Consider your organization’s needs, resources, and expertise when choosing between a custom solution and a commercial product.

4. Manage the Improvements

After obtaining a detailed analysis, you need to manage the implementation of suggested improvements effectively. This involves using a task management solution where you can create tasks, assign deadlines, and describe the expected outcomes to align with reporting framework requirements.

Generative AI can assist in this process by automating task creation and providing detailed instructions. You can prompt the AI with your analysis reports and integrate the results into task management software. Alternatively, utilize the end-to-end processes provided by ESG data analysis platforms. For example, at ESGFlow.ai, we’ve implemented a one-click solution to create tasks from your analysis report, complete with detailed instructions on how to improve your data set to achieve compliance. This automation can save you considerable time and ensure that improvements are systematically addressed.

5. Utilize AI Chatbots for Custom Queries

Often, your organization or team members will have questions regarding ESG-related topics. These inquiries might come from clients, financial institutions, shareholders, or other stakeholders. Finding answers within extensive ESG documentation can be time-consuming.

In such cases, a context-aware generative AI chatbot or questionnaire app can be a great assistant. By adding your ESG documents to a knowledge base, users can ask questions and receive detailed answers based on your data, complete with citations to specific files and pages. This simplifies interaction with the data and helps users understand what certain information means in your organizational context. The AI can also summarize data and provide real-life examples, enhancing comprehension of complex concepts like double materiality.

Conclusion

Increasing reporting demands and shorter reporting periods require either expanding the size of your ESG team or improving productivity. Implementing generative AI into your ESG reporting and data management processes can increase productivity tenfold. I’m excited to see what the future brings in this area, with more detailed and real-time information management. The winners will be companies that utilize AI for their ESG processes, gaining a competitive advantage through efficiency and compliance excellence.

By embracing generative AI, organizations can not only meet the growing demands of ESG reporting but also enhance the quality and reliability of their disclosures. Now is the time to integrate AI into your ESG processes and stay ahead in the evolving landscape of sustainability reporting.