ESG: Why it matters and how to get started

Environmental, Social, and Governance (ESG) has evolved over the past few years to become more than just a trendy term. It is now a hot topic across industries and essential to communicating risk-averse and climate-conscious ideals to important stakeholders. ESG investment has increased dramatically since the pandemic, from $5 billion in 2018 to over $50 billion in 2020 and to about $70 billion in 2021.

ESG is a framework created to be included into an organization's enterprise value strategy, and it has grown in significance as a component of the investment analysis process.

ESG concerns are receiving more attention globally, and investors are using these criteria to assess companies on non-financial issues, including risk management and growth potential. Beyond a company's carbon footprint, the ESG standards offer a comprehensive view of a company.

Why is ESG important to organisations?

Executives should prioritise exceeding stakeholder expectations, managing sustainability-related risks, and seizing commercial opportunities as more companies and their investors realise the importance of integrating ESG as a key element of overall business strategy.

Numerous company studies have shown a favourable association between ESG and financial success, despite inflation and supply chain disruption brought on by the epidemic, as investors were compelled to keep an eye on their risk management tactics.

By increasing stakeholder participation, loyalty, and competitive advantage through the disclosure of ESG actions, long-term value that goes beyond financial metrics is finally delivered.

ESG is now being given significant weight by financial stakeholders, as evidenced by research from analyst firm Gartner that estimates 85% of investors in 2020 took ESG considerations into account when making investment decisions. The research shows that lowering investment risks is the main factor motivating investors to give ESG a high priority ranking. They accomplish this by taking into account a wide range of variables, including, among others, regulatory involvement, competitive positioning, supply chain dependability, corporate reputation, consumer preferences, and business ethics.

To quantify ESG and the effects of business on society and its stakeholders, however, still presents numerous difficulties.

For instance, the lack of global standards for ESG measures makes it challenging to make unbiased judgments. No ESG data repository or management method can filter, analyse, and extract insightful data from firm portfolios. For businesses, this creates problems with data accessibility, traceability, and quality. ESG scores still need to be viewed with a certain amount of scepticism, despite corporations disclosing a growing amount of ESG data.

The solution

Artificial intelligence (AI) can help in this situation. By automating data collecting and indicators to compute critical elements automatically, using Computer Vision (CV) and Natural Language Processing, AI could help solve issues brought on by data processing and the erroneous sharing of information (NLP).

AI data analytics could then increase openness and accountability for business data by assisting investors in identifying dangers and opportunities. Artificial intelligence (AI) and data analytics have the capacity to provide more dependable and accurate ESG performance reports by utilising algorithms and minimising human bias.

Applying data analytics to ESG data is still in its early stages, and more work needs to be done to make it a workable solution that will boost trust in the ESG rating system. In the future, real-time research to understand a company's social impact will be made possible by the automation of ESG reporting and scoring, clearly revealing success factors, hazards, and possibilities.

How does AI in practise look like?

Automate data collection - Using Computer Vision (CV) and Natural Language Processing (NLP), you may turn messy, unstructured data into valuable information, group each category according to a topic, and then automatically generate the main ESG indicators.

Data analytics will begin bringing more openness, accountability, and quality - Organizations can uncover risks and opportunities for ESG investments by using alternative data that may exist outside of a company's established ESG and NLP improved extraction capabilities. Sentiment analysis techniques will be used to extract the tone of articles and NLP and machine learning will be used to convert textual data into numerical data, producing useful potential predictive indicators for determining long-term dangers.

IoT capabilities, optimization, and forecasting- The energy use of an organization's assets can be monitored and optimized using IoT sensors connected, real-time data streaming capabilities, and alarms.

Intelligent monitoring and stress testing - AI might be used to build an internal ESG Rating model to make sure the impact of your project is integrated into your company. An end-to-end strategy can be developed to support an AI-driven scoring system that has the capacity to handle all of the crucial ESG factors, including metrics and targets, strategy, risk management, and governance, in order to drive the decision-making process. You might be able to comprehend the elements that affect your ESG score without having to wait for the upcoming report by automating your ESG reporting and performing real-time research.

ESG is increasingly in the spotlight, and investors' and firms' sustainability journeys are fundamentally impacted by AI and data analytics.