Gen AI tools can play a particularly important role in facilitating ESG data measurement, especially in areas such as emissions tracking or assessing social aspects like gender diversity. The current lack of unified data standards is an obstacle in the path of quantifying such information. Gen AI solutions can autonomously harvest and catalogue data dictionaries and metadata of internal IT systems, and then map it to an ESG data model. This is also useful in discovering data from siloed IT systems as well as various unstructured data formats.
Globally, ESG policies specific to countries, sectors and even organizations are evolving. Meanwhile, reporting rules differ widely, which can be a challenge, dedicated LLMs provide insights and guidance on ESG regulations specific to regions, which helps companies understand different ESG policies. In addition, LLMs can assist in reporting as well. For instance, as EU’s regulations deem certain standards as essential for exporters, organizations must be first, aware of the regulations and second, know how to navigate these nuanced requirements.
It is important to remember in this era of consumer- and investor-led ESG movements that companies must comprehend and address public perception. Using Gen AI solutions that offer social listening and natural language processing capabilities, companies can track public sentiment and identify emerging trends and act on identified issues. This ability to monitor public sentiment and adapt accordingly is crucial for maintaining trust and meeting stakeholder expectations.
Use with caution
Like any technology, Gen AI tools have limitations and potential risks. Training these tools on inaccurate or skewed data will lead to generating false or biased insights or content. Therefore, it is necessary to have human oversight and stringent source data governance to mitigate incidents caused due to false or unverified data used for training, as that can occasionally produce plausible but wrong information.
Intellectual property concerns are an ongoing debate, and the outputs of Gen AI may not always align with company norms and values on an individual level.
Safeguarding the privacy and security of sensitive data during training and deployment poses a critical challenge. In addition, upholding user trust requires preventing unauthorized access to Gen AI systems.
To ensure the responsible and effective use of Gen AI tools, it is essential to address these concerns. Policymakers in various countries are preparing regulations for AI with China, Canada and the EU already in the process of passing laws, while India and the UK are looking to instead use existing regulations to address the potential problems of AI. In India, industry body NASSCOM has published a self-regulation guidance and tools to implement responsible AI practices. Till the time more comprehensive regulations are formed, there is a need to apply rigorous data verification, maintain human oversight and prioritize privacy and security.
Either way, Gen AI’s myriad benefits hold significant transformative potential for organizations as they navigate the still largely uncharted and rapidly evolving territory of ESG and sustainability.