How Generative AI Help an Organization’s ESG & Sustainability Roadmap?

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Being Environmental, Social, and Governance (ESG) oriented has become the “industry standard”. Consumers show a preference for brands that actively address ESG issues in alignment with their personal values. Generative AI (GenAI) is already dominating all industry segments and, ESG is not an exception. This new technology has potential to change the way we do environmental impact assessments. Generative AI offers innovative solutions for sustainability to optimize resource usage and reduce carbon footprints across various industries.

Businesses can use GenAI to build an ESG roadmap too. Let’s see how.

GenAI powered by Large Language Models (LLMs) surpasses traditional AI applications in various tasks, including image recognition, text processing, audio and video analysis, and much more. As a result, they have the potential to revolutionize the way companies track, measure, and perform on ESG parameters. Enterprise GenAI-based ESG platforms, trained on sector-specific data, serve not only to consolidate, analyze, and summarize business information but also to provide a consistent approach to ESG reporting across different geographic regions.

In this blog, we are discussing the key sustainability problems and how GenAI can help to resolve these.

Key Sustainability Problems Companies Are Facing

Crafting a sustainable business strategy is far from a one-size-fits-all endeavor. The investments made by a company in sustainability are influenced by the unique challenges and opportunities inherent in its supply chain, customer base, stakeholder relationships, and industry landscape.

Here are five critical issues that companies should prioritize to drive both social and environmental impact, as well as foster enterprise growth:

Supply Chain Transparency and Traceability

Achieving true sustainability in business is nearly impossible without comprehensive supply chain transparency and traceability. To address this, companies must map the entire journey of their products, from raw materials to the point of sale. This ensures environmental responsibility and facilitates actions against modern-day slavery and fair labor practices. The demand for accurate and inclusive data is growing, and initiatives like the Global Partnership for Sustainable Development Data aim to meet this need.

Diversity, Equity, and Inclusion (DEI) Across the Business and Value Chain

DEI principles should be integrated across all facets of a business, from HR to community engagement and sales. Inclusive innovation and inclusive data are gaining traction to address corporate sustainability issues around DEI.

Net Zero and Scope 3 Emissions

Companies are under increasing pressure to back up net-zero commitments with transparent and actionable strategies. This involves setting quantifiable, time-bound, science-based targets and accounting for scope 3 emissions, which are indirect emissions generated across a product’s life cycle or from business activities. Leading companies are forming partnerships with suppliers and exploring cross-sector collaborations to advance more climate-friendly value chains.

Circular Economy Principles

Building circular economy principles into business models is a strategic approach to address scope 3 emissions and other value chain impacts. Circular economy models aim to eliminate waste and pollution, keep materials in use, and regenerate natural systems. Leading companies are integrating social impact elements into circular initiatives, rewriting their relationships with sustainability challenges, including climate change, plastic waste, and soil degradation.

Nature-Positive Approaches and Regenerative Agriculture

Embracing a nature-positive economy requires businesses to minimize harm and actively boost the resilience and health of natural systems. Companies can achieve this by investing in ecosystem restoration within and beyond their value chains, surpassing “no-deforestation” commitments. Additionally, adopting regenerative farming practices contributes to creating agricultural systems that restore soils, safeguard biodiversity, and sequester carbon, aligning with global goals for climate action and ecosystem restoration.

Generative AI In Sustainability: Six Key Benefits

According to Ernst and Young, the following are the key benefits of Generative AI in sustainability:

ESG Data Repository

Building an ESG data repository is crucial for any company’s ESG strategy. Currently, most organizations have scattered ESG data and standards, challenging filing, compliance, and stakeholder engagement. GenAI, with advanced natural language processing techniques, goes beyond simple keyword matching and offers a deeper analysis of queries. This results in more relevant and contextual search results, improving the overall search experience.

Democratize ESG Data

GenAI’s advanced analytics and complementary capabilities democratize ESG data. GenAI allows any employee to extract meaningful insights from company data using natural language queries, making informed decisions aligned with the company’s ESG objectives when integrated with business intelligence tools and applications.

ESG Insights

GenAI also excels in providing sector-specific understanding. Trained co-pilots can navigate ESG nuances, offer compliance insights, and suggest operational efficiency improvements.

ESG Data Measurement

GenAI tools are handy for ESG data measurement, especially in areas like emissions tracking or assessing social aspects such as gender diversity. The need for unified data standards poses a challenge in quantifying this information. GenAI solutions can autonomously collect and catalog data dictionaries and metadata from internal IT systems, mapping it to an ESG data model. This approach helps discover data from siloed IT systems and various unstructured data formats.

Guidance on ESG Regulations and Reporting

ESG policies, both globally and within specific countries, sectors, and organizations, are continually evolving. Reporting rules also vary widely, posing a challenge. Dedicated Large Language Models (LLMs) offer insights and guidance on ESG regulations specific to regions, helping companies understand different ESG policies and assisting in reporting.

Monitor Public Sentiments with Social Listening

In the era of consumer- and investor-led ESG movements, companies must comprehend and address public perception. GenAI solutions, equipped with social listening and natural language processing capabilities, enable companies to monitor public sentiment, identify emerging trends, and take action on identified issues. This ability to adapt according to public sentiment is crucial for maintaining trust and meeting stakeholder expectations.

How Generative AI Supports ESG Audits?

As per Forbes, overcoming the complexities associated with ESG audits necessitates auditors to refine specialized skills, collaborate with relevant stakeholders, promote transparency and accountability, and advocate for establishing robust ESG reporting frameworks.

In this journey, generative AI emerges to enhance auditor efficiency and expedite the audit process. It can offer benefits such as:

Agility in Research and Planning of ESG Audits

Generative AI assists auditors in automating the intricate information-mining processes during the research and planning stage of an ESG audit. This includes automatically identifying scoping entities like GRI/SASB ESG Standards, Topics, Disclosures, and Requirements by contextual comparisons with ESG-mandated standard documents.

Productive Client Interactions and On-site Audit Actions

AI-powered document intelligence automates data validation and document review processes. Auditors can extract valuable information from images using OCR capabilities. GenAI also supports client interactions by transcribing meetings, summarizing key action points, and generating plans for follow-up meetings.

Streamlined End-to-End Data Collection and Analysis

GenAI empowers auditors to interrogate documents for specific insights, ensuring correct, consistent, and complete ESG data.

Enhanced Efficiency in Stakeholder Management Processes

GenAI identifies and maps the right stakeholders, gathers contextual understanding of stakeholder discussions, and compares data for deeper analysis. It records and analyzes stakeholder sentiments, improving overall stakeholder management.

Improved Audit Assessment Quality and Intelligent Recommendations

Generative AI’s predictive analytics anticipates materiality decisions, highlights current and future audit risks based on trends, and automatically generates effective recommendations for clients.

Proactive Risk Management and Compliance Assurance

Generative AI aids auditors in quickly identifying qualitative and quantitative risks from documents like CSR reports and 10-K reports. This is particularly challenging given the diverse nature of risk charts for different organizations based on industry type and region.

Comprehensive ESG Reporting and Effective Stakeholder Communication

Generative AI, with its ability to ingest and process large document volumes in minutes, suggests reporting scopes and accelerates decision-making.

Example Of Companies Using GenAI-Powered ESG Report Automation

A European bank has harnessed the power of generative AI to create a virtual expert specializing in environmental, social, and governance (ESG) matters. This innovative system synthesizes and extracts information from lengthy documents containing unstructured data.

The virtual expert adeptly responds to intricate queries, pinpointing the origin of each answer and extracting relevant details from images and tables.

Challenges Of Implementing GenAI For ESG Initiatives

However, like any technology, GenAI tools have limitations and potential risks. Training these tools on inaccurate or biased data can lead to the generation of false or biased insights or content. Human oversight and stringent source data governance are necessary to mitigate incidents arising from false or unverified data used for training.

Intellectual property concerns are an ongoing debate, and the outputs of GenAI may not always align with individual company norms and values.

Ensuring the privacy and security of sensitive data during training and deployment is a critical challenge. Upholding user trust requires preventing unauthorized access to GenAI systems.

Integrate GenAI Into Your ESG Journey With Gramener

As a data science company and an early GenAI implementor, Gramener is here to help you with the following solutions to build a sustainable ESG roadmap:

ESG Disclosure Guide

Streamline your team’s workload by summarizing information from various ESG disclosure documents, questionnaire responses, and performance scorecards. Implement a GenAI model trained on the company’s current ESG disclosure, policies, standards, and past responses to inquiries from ESG rating agencies.

Supplier Sustainability Guide

Utilize Conversational AI to handle inquiries related to supply chain sustainability, supported by thorough training on guidance documents, ESG disclosure standards, and more. Train the Conversational AI interface for suppliers based on the company’s supplier requirements, GHG accounting guidance, ESG disclosure guidance, and frequently asked questions (FAQs).

OCR & Data Handler

Implement an OCR module for interpreting digital format (scanned) documents and leverage LLMs to extract specific details from diverse documents, such as electricity consumption data from utility bills and readings from lab reports.

Interested in GenAI solutions? Book a Discovery call with us.