AI and data analytics applied to ESG.

Probability: High

Financial Impact: Some Profit Creation

Technological innovation in ESG, particularly integrating artificial intelligence (AI) and data analytics, is expected to play a significant role in 2024. The European Financial Reporting Advisory Group (EFRAG) is working on developing an XBRL taxonomy draft specifically for the European Sustainability Reporting Standard. It’s a preliminary version of the standardized set of tags that will be used specifically for reporting sustainability information in Europe, starting in the financial year 2024¹. This reflects the effort to create a common language and structure for companies to use when reporting their sustainability data in a machine-readable format and making data comparable and accessible, benefiting companies and those using the reported information for analysis and decision-making.

Artificial Intelligence (AI) also holds a significant promise for revolutionizing data collection in the ESG industry. Its ability to simulate scenarios, generate synthetic datasets, and aid in reporting processes can enhance the accuracy, efficiency, and privacy of ESG-related analysis ². We predict machine learning and AI will have more practical applications in addressing social problems, especially those affecting business, such as talent shortage. Hence, algorithms are most helpful when applied to complex problems where there is not only an extensive history of past cases to learn from, but also a clear outcome that can be measured since measuring the outcome concretely is a prerequisite to predicting ³. However, careful implementation, validation, and consideration of ethical implications, as well as understanding the complex linkages between social problems to select the appropriate outcome, are essential to harness the full potential of generative AI in the ESG domain, leading to more assertive strategies⁴.

Companies should prepare for these changes by:

• Familiarizing with reporting standards such as XBRL taxonomy drafts developed by organizations like EFRAG and understanding the requirements and implications of these standards for reporting sustainability data in a machine-readable format.
• Developing cross-functional expertise within the organization to understand both the technical and business aspects of AI in ESG. This may involve training employees, hiring data science experts, and fostering collaboration between IT, sustainability, and other relevant departments.
• Exploring the use of machine learning to address complex social problems affecting your company.
• Investing in data infrastructure to facilitate the integration of AI and data analytics. 
• Approaching AI implementation with careful consideration of ethical implications. 
• Engaging with industry peers, regulatory bodies, and experts to share best practices in applying AI for ESG. 

Sources:

1. https://amana.de/en/article/efrag-publishes-esrs-poc-xbrl-taxonomy.html ↩︎
2. https://www.biia.com/using-ai-with-data-collection-in-the-esg-industry/ ↩︎
3. https://hbr.org/2016/12/a-guide-to-solving-social-problems-with-machine-learning ↩︎
4. https://iabac.org/blog/ai-for-good-tackling-social-issues-with-machine-learning ↩︎

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