How Generative AI can build an organization’s ESG roadmap

Gen AI offers transformative opportunities to organizations in their sustainability journey.

In brief 

• Enterprise Gen AI platforms can provide a coherent way of ESG reporting across geographies. 
• Companies can use Gen AI to get ESG compliance insights, operational efficiency improvements, and to track public sentiments.
• Intellectual property concerns and the misalignment of Gen AI outputs with company norms and values can pose challenge.

In India, SEBI mandates top 1,000 listed companies to disclose their ESG data under its Business Responsibility and Sustainability Reporting (BRSR) framework. The process requires the companies to answer 140 questions. However, most of these companies lack confidence when it comes to meeting their ESG requirements, even though many of them have been following BRSR since 2021. Globally, over 50,000 organizations are now required to follow the mandatory Corporate Sustainability Reporting Directive (CSRD) of the European Union. For EU’s CSRD too, the companies face similar challenges. The reasons include the absence of a standardized reporting framework and reliable data. 

To address these challenges, many organizations turn to Artificial Intelligence (AI) solutions. However, effective ESG integration and disclosure is still an evolving landscape.

Enter Generative AI powered by Large Language Models (LLMs), Gen AI tools can excel traditional AI applications in tasks such as recognizing images, processing text, audio, and video, and more. As a result, they can transform the way companies track, measure, and perform on ESG parameters. Enterprise Gen AI-based ESG platforms, trained on sector-specific data, will not only consolidate, analyze, and summarize business information but also provide a coherent way of ESG reporting across geographies.  

How can Gen AI help? Here are two examples. First, a company needs to build a manufacturing unit but lacks location information and ecological impact data. The company can use specific Gen AI tools that can collect available aerial footage and analyze it with geospatial and open data and extract insight. Understanding biodiversity, ecosystem sensitivity and air/water quality allows the company to make informed decisions.

Next, a multinational retailer wants to streamline its process of collecting Greenhouse gases (GHGs) Scope 3 upstream emission data. Gen AI tools can help the MNC analyze the data and derive insights to improve supplier selection and ratings. Here, Gen AI can automate and personalize guidance for supply chain partners to improve their ESG-wide areas.

Creating an ESG data repository

A company’s ESG strategy is predicated on available and accurate granular data. To make it work, it needs an ESG data repository. Currently, most companies have scattered ESG data and standards, making filing, compliance, and stakeholder engagement difficult. Using advanced natural language processing techniques, Gen AI goes beyond simply matching keywords; as a reasoning engine, it can go deeper and analyze the query’s objective. This leads to more relevant and contextual search results, which enhance the overall search experience itself.

Another benefit is that advanced analytics and complementary capabilities democratize ESG data. When Gen AI tools are integrated with business intelligence capabilities and applications, any employee can extract meaningful insights from company data using simple natural language queries like data on energy usage, sustainability practices, or emission reduction strategies to take informed decisions in line with the company’s ESG objectives. 

Gen AI can also provide sector-specific understanding as trained co-pilots navigate ESG nuances and offer compliance insights and operational efficiency improvements. 

Measuring sustainability

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.