Navigating the complex landscape of ESG frameworks
Firstly, ESG professionals must contend with an ever-changing global map of ESG frameworks, standards, and regulations. The relevance of each will vary for individual companies based on where both they and their investors are based, the revenue and headcount of the company, the services or technologies they offer, and the sustainability claims they make (or do not make). Therefore, the first step in determining what data should be collected is a task in itself! AI can make sense of large datasets and may be able to provide guidance on what is relevant for your company.
Reducing the reporting burden
Once you know what data needs to be collected, AI's ability to extract key data points from documents will significantly reduce the reporting burden. The workload of gathering and scanning through document after document could be replaced by querying a group of documents and sense-checking the outputs. Such applications are not limited to just regulatory reporting and could also be applied to annual ESG questionnaires and transaction due diligence.
Demonstrating the substance of ESG claims
Finally, as ESG requirements become stricter, the ability to demonstrate the substance of your claims will become even more important from both reputational and legal perspectives. AI will assist in ensuring that the sources underlying ESG claims are tracked and can be easily reviewed.
Limitations of AI in ESG data collection
However, AI can only support the collection of ESG data that is stored in documents, which may not be the case for companies with immature ESG approaches, especially smaller companies. Therefore, ESG data collection will still require guidance from ESG specialists and humans that know a company well to confirm the absence of information and processes.
The role of human reporting in ESG metrics
Additionally, ESG metrics may never be contained in documents, regardless of a company's size or the maturity of its ESG approach. AI will not replace the need for employees to self-report DEI data, nor will it eliminate the requirement for a human to share relevant policies and approaches with the AI model for assessment. Furthermore, some information is too critical for AI to attempt to identify from documents, such as the quantification of regulatory breaches and violations, or the exposure of investments to controversial sectors and supply chains.
The necessity of human involvement in high-stakes scenarios
Finally, AI tools will not eliminate the need for human involvement, as a deep understanding of ESG metrics and frameworks is required to guide these tools in retrieving the correct information and confirming that the appropriate approach is taken in high-stakes scenarios, such as regulatory reporting and transaction due diligence.
Therefore, the AI revolution will have a significant impact on ESG, an already rapidly evolving industry. However, it will serve as a tool to reduce time spent on repetitive tasks, unlocking more time for interesting and impactful work.
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