_Summary
ESG data extraction: what it is and why it’s useful to structure data with AI
ESG data extraction is the use of AI to read unstructured documents and capture the environmental, social and governance values inside them as structured, traceable data.
ESG data is rarely delivered as stand-alone data. Picture a ninety-page sustainability report. Somewhere inside it are the emissions figures, the workforce numbers and the targets your reporting needs. Before any report can be written, someone reads many documents, finds the values and copies them into a spreadsheet. Then the same figure is eventually entered again for CSRD, again for a customer questionnaire and again for a due diligence request. The process is slow, and every re-entry is another chance for an error.
What does “structured ESG data” look like?
Structured ESG data fits defined fields: a KPI table with values, units and reporting periods. Semi-structured ESG data has some organizing logic but no rigid schema, like supplier questionnaires and tagged forms. Unstructured ESG data has no predefined model at all: the narrative report, the scanned certificate, the contract clause, in all these examples information is sitting in free-form text, images, tables with no consistent shape.
The gap between the last two categories is where AI earns its place. It classifies the document, extracts the values that matter and places them into a schema that a database, a report or an auditor can consume.
Can AI extract ESG data reliably?
Before sharing our own experience at Dydon AI, we’d like to let some recent research talk first on this topic. The research “ESGReveal: An LLM-based approach for extracting structured data from ESG reports” published in the Journal of Cleaner Production, developed a quantitative framework for assessing corporate ESG performance based on large language models (LLM) techniques, evaluating the performance of various models, including GPT-3.5, GPT-4, ChatGLM, and QWEN.
The study also provides an accurate measurement of the gap. When tested on reports from 166 listed companies, the model with the best performance reached 76.9% accuracy in data extraction. This proves that the approach works. However, there is still room for improvement, as we see in our daily experience of working with our clients at Dydon AI. This can be achieved by strengthening domain knowledge for a specific set of data.
Why does domain specialisation decide the accuracy?
Because ESG data is not general text. A general-purpose model reads words by their most common meaning, while ESG reporting runs on specialised ones. “Material” has a precise meaning under double materiality. Scope 3 has defined boundaries. The same emissions figure can appear in tonnes, in kilotonnes or as an intensity ratio. These errors are predictable, not random: retrieval built on general training data leans toward a word’s statistically dominant meaning, so it keeps missing the regulatory one.
Research on financial AI points the same way.
In a study by AWS researchers on the FinanceBench benchmark, a standard retrieval pipeline reached 52.4% factual correctness.
Building domain expertise into how the system retrieves, calculates and verifies lifted it to 94.7%. The lesson carries over: in regulated settings, accuracy comes from the domain layer built around the model.
In practice, domain specialisation means 3 things:
- The domain defines the structure. Data is categorised by semantic domain (ESG, financial, regulatory), and the target schema follows the regulation, not a generic template.
- The system is calibrated to your domain. Terminology and definitions are configured into the extraction, it can be build around the needs of your organisation.
- Humans close the loop. Experts validate values against the exact place in the source, and that feedback improves the next extraction.
Your team already has the domain expertise. What is usually missing is the AI engineering, governance and maintenance needed to turn it into a system that runs reliably every day.
How does structured ESG data answer questionnaires automatically?
Once ESG data is extracted and validated, it stops being a one-off output and becomes a reusable asset. Companies answer multiple questionnaires from the same or similar source documents. With structured data behind them, AI can suggest an answer for each question, cite the source it came from and leave the final decision to your expert.
That is the difference between extracting once and re-reading every time a new questionnaire arrives. The bottleneck is not the questionnaire itself. It is the repeated manual work of finding the right figures in the documents.
Which other documents can be turned into structured data?
ESG is one possible use case, not the exception. IDC estimates that 80–90% of enterprise data is unstructured, and the pattern is always the same: a person opens a document, works out what it is and re-enters a handful of values into a system.
4 concrete examples:
Banks: financing files for EU Taxonomy checks
- Documents in: building permits, energy performance certificates, technical specifications and company reports attached to a loan or project.
- Structured out: the economic activity, the data points behind each screening criterion and the alignment result, each linked to its source page.
- Feeds: the credit check and the audit-ready Article 8 report, project by project.
Financial institutions: ICT contracts for DORA
- Documents in: service agreements and contracts with ICT providers.
- Structured out: provider, services covered, start and end dates, notice periods, subcontractors and data locations.
- Feeds: the register of information and third-party risk reviews, without someone re-reading every contract when a rule changes.
Manufacturers and importers: supplier certificates and declarations
- Documents in: supplier certificates, sustainability declarations and technical data sheets, often scanned and in several languages.
- Structured out: certificate validity and expiry, product or material, origin and emissions data.
- Feeds: supply chain due diligence (LkSG, CSDDD), CBAM and EUDR obligations, Scope 3 figures and customer questionnaires, from one dataset.
Any organisation: due diligence questionnaires
- Documents in: internal policies, certifications, contracts, annual reports and previously answered questionnaires.
- Structured out: the facts a questionnaire asks for, each with its source document.
- Feeds: pre-filled answers with cited sources, which your experts validate instead of writing from scratch.
Wealth Management: Financial data structured into portfolio management
The same logic already applies for financial documents. In our article on family offices, contract notes and account statements from dozens of custodian banks become one validated dataset per client. What changes from case to case is the schema and the domain calibration. Capture, validation and traceability stay the same.
Why is structure a governance topic, not just a data topic?
Regulators increasingly ask not only what an AI system concluded, but how it got there. For high-risk AI systems, the EU AI Act requires automatic event logging (Article 12) and effective human oversight (Article 14). Whether a use case counts as high-risk depends on how it is used, but the direction is clear. The most useful enterprise AI is the most secure and auditable one.
What does Dydon AI offer for regulatory intelligence?
Dydon AI provides a secure AI platform for regulatory intelligence. It captures and structures data from thousands of documents, stores it in client-isolated infrastructure and uses it to generate traceable, explainable answers, always under human control.
Intelligent Document Processing: data extraction from any document
Intelligent Document Processing reads PDFs, Word and Excel files and scanned reports, in bulk or via API. It understands the content semantically, categorises it by domain (ESG, financial, regulatory) and attaches audit-ready evidence to every extracted value. Your experts validate the results, and their feedback improves quality over time.
Automatic Questionnaire Answering: from structured data to answers
Automatic Questionnaire Answering uses that validated dataset to pre-fill ESG, due diligence and other compliance questionnaires. The AI proposes answers with cited sources, and humans validate, adjust or override every one. Where data is missing, the built-in collaboration tools request it from internal teams or counterparties, so there is no chasing by email.
One platform, more than ESG
The same structured data also powers Compliance Gap Monitoring, which highlights what changed in a regulation and compares internal policies against the updated requirements. It supports EU Taxonomy reporting through TAXO TOOL, developed in partnership with the Association of German Public Banks (VÖB) and used by 100+ banks, and a secure internal regulatory chatbot for your team.
Built for control
Everything runs inside an environment you control, on your own servers or in an isolated EU/Swiss data centre. Each client is a fully separate processing environment, and there is no training on client data.
Want to see how much of your document work could be captured automatically?
Dydon AI offers a free AI-potential analysis to identify where document-heavy ESG and compliance work can be automated, where human validation should stay in the process, and what a secure, API-connected setup could look like for your team.
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_In this article
- What is ESG data extraction, and why is it needed?
- What does structured ESG data look like?
- Can AI extract ESG data reliably?
- Why does domain specialisation decide the accuracy?
- How does structured ESG data answer questionnaires automatically?
- Which other documents can be turned into structured data?
- Why is structure a governance topic, not just a data topic?
- What does Dydon AI offer for regulatory intelligence?
- Want to see how much of your document work could be captured automatically?