AI Offers Actionable Insights To Achieve Sustainability

By Naveen Kamat June 26, 2023

With ESG achievements increasingly factoring into consumer decisions and regulatory requirements, companies can leverage AI to gain an early advantage

AI Offers Actionable Insights To Achieve Sustainability
Multiple and varied ESG standards/ frameworks, lack of historical data, inconsistencies in existing data, lack of a common taxonomy for data classification are some of the common challenges that block the pitch for adoption of AI. DepositPhotos

Until some time back, corporate initiatives around sustainability or social responsibility were mostly a cursory tick-in-the-box to meet the company's regulatory obligations or even a low priority focus area to supplement business strategies. However, there has been a paradigm shift in the general mindset in the recent past due to various reasons, and today, we live in a world increasingly cognizant of its environmental, social and governance (ESG) responsibilities.

This shift in mindset has been fuelled by multiple factors out of which two are at the forefront — consumption and displacement. Our planet’s resources are finite, and their unchecked human consumption have drastically reduced its availability and total capacity. This has created a cascading effect, where disasters — both man-made and natural — have led to the displacement of millions of people around the world.

For India, the world's most populous nation and one of the most powerful economies, this poses an equal measure of threat and responsibility. As the nation’s technological prowess progresses, so does the toll it takes to keep up a steady supply of energy and resources. It’s high time to take collective responsibility and act to mitigate the danger.  
Data, AI and ESG 

The realm of artificial intelligence (AI) today encompasses a vast subset of technologies that have helped businesses leverage machines like never before. Due to neural networks, machine learning and natural language processing, AI can see, read, hear and understand patterns in a business faster and more accurately than any human could. By being able to do so, it can offer actionable insights.

In their current state, most corporate ESG initiatives need to make substantial changes at a fast-enough pace as was highlighted in the agenda of the World Economic Forum (WEF) in Davos earlier this year. It is here that the inherent quality of AI comes into effect.

Even in order to accurately monitor or forecast achievement of ESG-related KPIs, detect any anomalies or outliers or run AI-based simulations, it becomes important to have timely and reliable data, which most likely reside in different source systems and in many different formats from different vendors.  Ensuring availability of good, curated data thus becomes critical to achieving ESG targets across the enterprise.

Challenges: AI's adoption in ESG

Multiple and varied ESG standards/ frameworks, lack of historical data, inconsistencies in existing data, lack of a common taxonomy for data classification are some of the common challenges that block the pitch for adoption of AI.    
It's important for organizations to align with one or two ESG standards/frameworks and identify the data needed to support those standards/ frameworks.  Based on the data availability, it then becomes easier to prioritize the use cases that they would wish to target – in the environmental, social and governance areas. 

With the right data foundation, and with the acceleration provided by all the advances now in technologies such as generative AI, many newer possibilities begin to emerge.For example, we see consumers are increasingly veering to products and brands that go above and beyond to ensure they are meeting ESG standards by dramatically increasing transparency in their supply chains.  With its ability to analyse vast amounts of data — including supplier contracts, certifications and audits — generative AI can identify potential risks and provide recommendations on how to mitigate them. This helps brands ensure transparency into their supply chains and meeting the sustainability expectations of their customers.

Role of India in promoting AI for the world's ESG goals

In May 2021, regulatory body Securities and Exchange Board of India (SEBI) announced that the top 1,000 publicly listed firms, with a net worth of over INR 500 crore (over $ 61 million), will be mandatorily required to publish a standalone Business Responsibility and Sustainability Report (BRSR), starting FY23. In March earlier this year, SEBI also issued guidelines towards corporate ESG ratings to make sustainability efforts and spending account for more than just a regulatory obligation. This regulatory guidance is the initial step in India's pursuit of achieving net-zero emissions by 2070, as declared at COP26.

Sustainability agenda is becoming central to large enterprises not only from a regulatory compliance perspective, but also to ensure capital flows and brand differentiation. Early movers on sustainability, dominated by digital-first sectors such as food, hospitality, retail, tend to outperform on top line growth and value creation. Given the massive pool of skills and talent around data engineering and data science, and IT and digital transformation in general, India has a natural advantage in terms of pioneering and innovative solutions that can help organisations, both in India and globally, leapfrog towards the attainment of their ESG targets.    

(Naveen Kamat is vice president and chief technology officer, Data and AI Services, Kyndryl India.)