AI and Digital Tagging in Sustainability Reporting: From Magic to Reality

By Donato Calace, in collaboration with John Turner (CEO at XBRL International) and Jérôme Basdevant (CTO and co-founder at Datamaran)

As corporate sustainability reporting requirements have evolved, digital tagging and AI are sometimes portrayed as competing solutions. However, this dichotomy is misleading. Digital tagging provides structured, transparent, and comparable data, while AI enhances analysis and interpretation. By integrating these technologies in combination, organizations can move beyond compliance and gain strategic insights from ESG-related data. 

I recently sat down with John Turner, the CEO of XBRL International, and Jérôme Basdevant, co-founder and CTO at Datamaran, to discuss the differences between digital tagging and AI and the advantages of combining the two. 

Understanding Digital Tagging: The Backbone of Transparency

Regulatory developments such as the EU’s Corporate Sustainability Reporting Directive (CSRD) and the IFRS sustainability standards are mandating digital disclosures. As John Turner, CEO of XBRL International, explains, digital tagging using a structured "dictionary" allows software to interpret disclosures consistently. This ensures:

• Clarity – Data is unambiguously categorized, reducing the risk of misinterpretation.
• Comparability – Investors and stakeholders can analyze information consistently across companies.
• Traceability – Structured reports allow data to be traced directly to original disclosures, improving reliability and accountability.

While digital tagging is well established in financial reporting globally, sustainability reporting remains fragmented. Without digital disclosures, AI-driven ESG analysis requires AI models to first extract and interpret unstructured data, leading to inconsistencies and inaccuracies. Turner emphasizes the need for structure, stating, “There’s an assumption that you can give your annual report to ten different people, and they will come up with exactly the same view about your performance. But actually, what you'll get is ten different versions of the truth.” This is why digital tagging with oversight from corporate management, providing a single, authoritative, and analysis-ready version of events, is crucial for maintaining transparency and consistency in corporate sustainability reporting.

The Role of AI: Unlocking Deeper Insights

Datamaran has pioneered AI-driven ESG analysis for over a decade. Jerome Basdevant, CTO and co-founder of Datamaran, highlights AI’s role in:

• Extracting insights – AI can process vast amounts of disclosures to identify key themes and risks.
• Benchmarking disclosures – AI enables companies to compare their ESG reporting against industry peers.
• Materiality analysis – AI helps organizations determine which ESG issues are most relevant based on evolving regulatory and market expectations.

However, AI is not a substitute for digital tagging. AI excels at making sense of data, but without structured tagging, interpretations may vary, leading to potential discrepancies in analysis. Basdevant reflects on this challenge: “We ended up developing our own digital tagging because there was no standard around that at the time. We had to create our own ontology — a dictionary of ESG issues — so that we could provide comparability. Structured data is the foundation for AI-driven insights.”

The Debate: Does AI Make Digital Tagging Redundant?

Recent policy discussions suggest a shift from "digital from the outset" to skepticism about digital mandates. Some argue that AI can automate the extraction and structuring of ESG data, making digital tagging unnecessary. However, this assumption is flawed.

As Basdevant explains, using AI to "recreate structure" where tagging should already exist is inefficient and inaccurate. AI models require substantial computational resources and can struggle with consistency when processing unstructured data. Instead, AI should build upon structured tagging, not replace it. “Why use AI to solve a problem that digital tagging already addresses? That’s like using a chainsaw to cut an omelet — it’s an unnecessary and inefficient application of the technology.” AI should be leveraged for insight generation rather than to compensate for missing foundational structure.

Turner adds that structured digital reports ensure "a single version of the truth." Without digital tagging, multiple AI providers will produce slightly different interpretations of the same data, reducing trust in ESG analytics. “With structured tagging, companies remain accountable for their own disclosures. Without it, you end up in a hall of mirrors, faced with multiple distorted reflections of reality —  which is exactly what regulators and investors are trying to avoid,” he cautions. Digital-first reporting should be seen as essential. “We don’t take photos with a camera, print them, and then digitize them. We take digital photos from the outset. ESG reporting should follow the same logic.”

Moving Forward: A Combined Approach for Effective ESG Reporting

Rather than "AI vs. digital tagging," the real opportunity lies in their integration:

• AI enhances digital tagging – AI can assist in the tagging process, helping companies to minimize the effort.
• Digital tagging improves AI’s accuracy – Structured data allows AI to perform higher-quality analysis without ambiguity.
• Regulatory clarity – Policymakers should ensure that structured disclosures remain a core part of ESG reporting requirements to promote consistency.

As Basdevant concludes, "If companies can reduce their effort in acquiring and standardizing data, they can focus on deriving insights that bring real business value." Turner agrees, adding, "The future of ESG reporting and analysis is not about choosing between AI and digital tagging — it’s about recognizing how they complement each other to create transparency and trust.”

Conclusion

Digital tagging and AI are not competing technologies; they are complementary tools that, when combined, provide transparency, comparability, and actionable insights. In an era where sustainability reporting is becoming a core part of corporate strategy, businesses must embrace both to stay ahead.

For regulators, businesses, and investors alike, the path forward is clear: digital-first, AI-enhanced sustainability reporting is the key to reliable, insightful, and decision-ready disclosures and data.

XBRL International is a global not-for-profit standards development organisation, operating in the public interest to improve the accountability and transparency of business performance. It develops and maintains the XBRL standard, the digital language for consistent, comparable, and traceable business data exchange worldwide. Find out more at xbrl.org.