Title: AI in Data Collection for CSRD/EU Taxonomy Reporting Resource URL: https://www.greenomy.io/blog/ai-as-a-helping-hand-in-data-collection-for-csrd-eu-taxonomy-reporting Publication Date: 2024-02-09 Format Type: Blog Post Reading Time: 12 minutes Contributors: Aleksandra Vercauteren; Source: Greenomy Keywords: [Artificial Intelligence, Sustainability, ESG Reporting, Retrieval Augmented Generation, EU Taxonomy] Job Profiles: Data Architect;Data Scientist;Chief Sustainability Officer (CSO);Machine Learning Engineer;Artificial Intelligence Engineer; Synopsis: In this article, Aleksandra M.W. Vercauteren, Senior Machine Learning Engineer at Greenomy, discusses how Retrieval Augmented Generation (RAG), an AI-powered technique, can assist companies in collecting and structuring data for ESG reporting under the CSRD and the EU Taxonomy. 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 Unlike standalone Large Language Models (LLMs), which suffer from outdated knowledge and hallucinations, RAG retrieves relevant data from external sources before generating responses.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. Content: ## Introduction Preparing a comprehensive disclosure report for the EU Taxonomy or the Corporate Sustainability Reporting Directive (CSRD) presents significant challenges. The regulatory framework is intricate, the required data points are numerous, and relevant information is often dispersed across multiple departments and document formats. This article examines how Retrieval Augmented Generation (RAG), an advanced AI technique, can streamline the data-collection phase of the ESG (Environmental, Social, and Governance) reporting process. ## The Complexity of ESG Reporting under the EU Green Deal Organizations seeking to comply with the ESG requirements of the EU Green Deal must navigate several hurdles: ### Regulatory Complexity • Detailed rules and technical screening criteria govern activities and disclosures under both CSRD and the EU Taxonomy. • Compliance necessitates an understanding of environmental, social, and governance metrics across various operational domains. ### Dispersed Data Sources • Essential information resides in documents managed by distinct departments (e.g., HR, finance, legal, supply chain). • Data formats vary widely—PDFs, spreadsheets, free-text reports—making manual aggregation labor-intensive. • No central repository exists to house all required data points for streamlined access. ## Leveraging AI to Enhance ESG Data Collection Artificial intelligence can automate and accelerate each phase of ESG reporting, particularly the data-collection stage. Key advantages include: ### Efficiency and Automation • AI tools can ingest and process vast volumes of documentation, reducing manual effort and freeing teams to focus on analysis and strategy. ### Data Integration • Advanced AI pipelines can extract, clean, standardize, and centralize both quantitative and qualitative data from disparate sources into a unified ESG data model. ### Accuracy and Traceability • Automated extraction minimizes human error and ensures end-to-end traceability of data points. • When combined with human validation, AI output fosters stakeholder confidence in report reliability. ### Scalability • AI systems can be adapted year over year, accommodating evolving regulatory requirements without rebuilding from scratch. ## Role of Large Language Models (LLMs) in ESG Reporting ### Capabilities of LLMs • Generative pre-trained transformer models can process and summarize unstructured text in multiple languages. • These models can infer implicit relationships—for instance, reconstructing a table from a linear text sequence by identifying column–row associations. • Example: When supplied with a string of numbers and labels extracted from a table, an LLM can accurately reformat it into a structured representation. ### Limitations of LLMs • Knowledge cutoff: LLMs only reflect publicly available information up to their last training date. • Lack of private data access: They cannot consult proprietary corporate sources unless explicitly ingested. • Risk of hallucination: LLMs may produce plausible but inaccurate or fabricated content, which is unacceptable in compliance reporting. ## Retrieval Augmented Generation: Overcoming LLM Shortcomings Retrieval Augmented Generation addresses these limitations by combining a semantic search engine with an LLM. The process unfolds in two primary stages: ### 1. Document Retrieval via Semantic Search • Documents and data sources are converted into numerical vector representations (embeddings) that capture semantic meaning across hundreds of dimensions. • Semantic similarity measures (e.g., cosine similarity) identify the most relevant document segments for a given query, irrespective of exact keyword matches. ### 2. Question Answering with an LLM • Retrieved passages are presented to the LLM alongside precise instructions on format, language, and regulatory context. • The model generates accurate, well-structured responses grounded strictly in the retrieved sources. ## Risks and Mitigation Strategies in RAG Deployments ### Potential Pitfalls • Irrelevant retrievals can lead to verbose or partially incorrect answers. • Chunking documents for embedding may sever critical contextual links, resulting in misinterpretation. ### Countermeasures • Enrich document metadata and retrieve surrounding context (e.g., adjacent paragraphs) to preserve coherence. • Implement a human-in-the-loop validation step to verify completeness and correctness before finalizing disclosures. ## AI-Powered Platform for ESG Reporting An AI-driven platform integrates RAG and additional tools to support end-to-end sustainability reporting: ### RAG-Based Data Extraction • Users upload corporate documents, and curated queries—developed by sustainability and legal experts—automatically extract required data points for CSRD/EU Taxonomy compliance. ### AI Legal Advisor • When certain data remain unavailable or regulations prove difficult to interpret, an AI-powered legal assistant offers plain-language explanations of relevant requirements. ### Peer Benchmarking and Strategy Discovery • Through RAG, companies can explore industry best practices and compare their ESG strategies against those of peers to uncover improvement opportunities. --- By combining RAG with rigorous validation processes and specialized AI assistants, organizations can transform the daunting task of ESG reporting into a manageable, future-proof workflow—ensuring both compliance and strategic insight.