Takeaways
- ESG reporting for the Corporate Sustainability Reporting Directive (CSRD) and EU Taxonomy requires gathering complex, scattered data from multiple departments and document formats.
- AI, particularly Retrieval Augmented Generation (RAG), can streamline ESG data collection by integrating and structuring data from diverse sources.
- RAG solutions use semantic search and Large Language Models (LLMs) to retrieve relevant information and generate structured responses for sustainability reports.
- While effective, RAG has limitations, such as potential data gaps and context loss, which require human validation for accuracy.
- AI-powered platforms like Greenomy integrate RAG to improve ESG reporting efficiency, enhance data extraction, and assist with regulatory compliance.
Summary
Preparing ESG disclosure reports for the Corporate Sustainability Reporting Directive (CSRD) and EU Taxonomy is a complex task due to the vast amount of scattered data across multiple departments and document formats. AI, particularly Retrieval Augmented Generation (RAG), offers a solution by streamlining data collection, improving accuracy, and ensuring compliance with sustainability regulations. Unlike standalone Large Language Models (LLMs), which suffer from outdated knowledge and hallucinations, RAG retrieves relevant data from external sources before generating responses.
A RAG system consists of two major components: a document retrieval system using semantic search and an LLM for generating structured answers. Semantic search identifies relevant documents based on meaning rather than keywords, transforming text into numerical embeddings for more accurate matching. Once relevant data is retrieved, the LLM processes it according to predefined instructions, ensuring coherence and language consistency.
Despite its advantages, RAG has limitations, including the risk of retrieving irrelevant information, missing crucial context, or failing to recognize incomplete data. These risks can be mitigated through improved document retrieval methods and human validation. AI-powered ESG platforms, such as Greenomy, integrate RAG solutions to help businesses efficiently extract required data points, navigate sustainability regulations, and compare their ESG strategies with industry peers.
