How AI is Helping Businesses in their ESG and Sustainability Journey

A Morgan Stanley survey states, “85% of US investors are interested in sustainable investing.”
In the present age of sustainable investing, ESG and sustainability are the two widely discussed topics. Modern-day investors are far more aware and conscious about investing in companies that are sustainable in their operations and aligned to the fundamentals of ESG – the environmental, social, and governance factors.

The COVID-19 pandemic has been a significant accelerator in the growth of ESG. The market disruptions and uncertainties during the pandemic triggered investors to look for ESG funds for increasing resiliency.

  • A report by Bank of America Global Research found that ESG funds saw a record $51.2 billion in inflows in 2020, a more than four-fold increase from the previous year.
  • According to data from BlackRock, ESG-themed ETFs saw inflows of $20.6 billion in 2020, an increase of 70% compared to the previous year.
Investors as well as policymakers consider the pandemic as a wake-up call and believe that it will have a continued impact on ESG investing not only in the near future but for many more years to come. A survey by J.P. Morgan across 50 global institutions has revealed “71% of respondents believe it is “rather likely,” “likely,” or “very likely” that the occurrence of a low probability/high impact risk, such as COVID-19, would increase awareness and actions globally to tackle high impact/high probability risks such as those related to climate change and biodiversity losses.”
To match the growing ESG expectations of investors and ensure they are better equipped to mitigate such risks in the future, companies are becoming more agile in their approach toward ESG adherence, accurate ESG analysis, and reporting.
This article covers how companies today align their business goals with ESG strategies to ensure the in-flow of funds and long-run sustainability. Also, the challenges that they are facing in establishing a positive brand reputation and trust amongst the investors, and how Artificial Intelligence (AI) is helping them in their ESG readiness.

Challenges in Achieving ESG Goals

A survey by the US Chamber of Commerce reveals that 92% of large US companies are now incorporating ESG factors into their business strategies, up from 66% in 2018.
Most organizations in the US are now including the fundamental ESG principles in their corporate vision and strategies. However, in doing so, the top management and key decision-makers of these companies face a common situation — there’s a gap between what the company aspires for or promises to achieve and the actual situation.
– Lack of ESG expertise has emerged as one of the main challenges that organizations face today. Creating robust ESG and sustainability strategies and carrying out risk and opportunity assessments requires ESG proficiency and there seems to be a dearth of this skill set.
– The leaders of several companies have also voiced their concerns over the absence of uniform global reporting metrics for ESG compliance and accurate disclosure – another major challenge.
– Moreover, the challenges related to collecting the right data and ensuring the correctness of data, managing data for reliable insights, etc. are slowing down organizations in their processes of embracing ESG principles and integrating impactful ESG strategies in their business.
And there are many more such barriers. But there’s no business without any challenges. Challenges will be there. It’s about figuring out how to find your way around them and moving forward to meet your ESG targets intelligently.

Accomplishing ESG Goals with Data Analytics and AI

To overcome these challenges and accomplish ESG goals, companies today are adopting next-generation technologies. Leveraging data analytics and AI technologies, companies are streamlining their key processes — minimizing/eliminating redundant legacy methods and automating existing workflows — bringing in new levels of operational efficiencies to achieve ESG goals and increase business resilience.
Data-driven insights obtained using these advanced technologies are helping companies in forecasting impending ESG risks. Businesses are able to address these risks early on and make informed decisions to ensure ESG compliance.
Most importantly, AI is enabling companies to meet the expected investor-grade ESG-disclosure standards and maintain transparency with their stakeholders – the key to ESG success.
• Achieving a Single View of ESG Data with AI for Informed Decision-making
AI-powered robust data platforms are breaking data silos and enabling businesses to bring all ESG data within the enterprise into one place over a single dashboard.
Such a comprehensive view of the ESG initiatives is helping organizations track their ESG progress more efficiently and identify core competencies and weaker performing sections that need special attention.
The single view of ESG data is allowing companies to assess the risks and identify opportunities better and build a successful ESG roadmap.
• Ensuring ESG Transparency through Accurate ESG Reporting Using AI
While considering investing in a particular company, investors need full visibility into that company’s ESG progress as well as its ESG goals. Businesses that fail to deliver data transparency and accuracy in ESG reporting face the risk of losing out on potential investors and also retaining existing stakeholders.
Leveraging data analytics and AI enables organizations to drive ESG transparency. They can report their ESG data efficiently to the government agencies such as U.S. SEC (Security and Exchange Commission) as required.
Accurate reporting of ESG data brings in the needed transparency for investors and gives them an understanding of a company’s ESG direction and progression. For example, a company might not be carbon-positive today but maybe making significant efforts toward this goal. Such inferences can be made by investors with ESG transparency.
ESG transparency also eliminates communication barriers and improves the trustworthiness of the business among all its stakeholders. This in turn creates a stable and sustainable relationship among all of them.

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A Few More Use Cases of AI in ESG
Here are a few potential use cases of AI in transforming ESG processes and outcomes. While we have just touched upon them in this article, we will discuss each of these use cases in more detail in our forthcoming blog posts. Let’s check them out –
1. Achieving Accuracy and Efficiency in ESG Audits
Practitioners in audit firms can use an AI-powered ESG audit solution to automate the process of reviewing large volumes of CSR documents in ESG audits.
The AI-powered document intelligence capability of the solution can make data collection and analysis much easier for auditors.
The audit solution allows early identification of ESG risks and proactive remediation, ensures accurate ESG compliance scoring and enhanced ESG performance, and the firm can easily strengthen its clients’ reputation to attract sustainable investing.
2. Enabling Banks to Determine the Pricing for Sustainability Loans
When an enterprise seeks a sustainability-linked loan (SLL) from a bank, its ESG performance is evaluated and rated against sustainability performance targets (SPTs). The better the rating the cheaper the cost of capital and vice versa. It means the financial terms of a loan are measured by key performance indicators (KPIs) against the predetermined SPTs.
In the lending process, tracking multiple activities with complex pricing structures against ESG targets requires considerable effort and can limit a bank’s scope in sustainability lending, also introducing risks associated with manual processes.
An AI-powered automated solution can help banks evaluate an enterprise’s ESG key performance indicators (KPIs) and subsequent pricing changes. It can capture all the sustainability data, analyze and bring actionable insights to all relevant parties. Such insights help banks in offering loans with a margin ratchet – the lending concept that focuses on incentivizing borrowers to improve their financial health over the life of a loan and encouraging them (businesses) to perform against their ESG KPIs perspective. In sustainability lending, upward and downward margin ratchets are now a common scene.
3. Identifying Potential Greenwashing Instances
Governments and regulatory bodies may also use data analytics & AI to monitor greenwashing activity and investigate potential violations of environmental regulations. AI algorithms can be used to provide unbiased assessments of their accuracy and bring out any false or misleading claims.
4. Tracking ESG Metrics in Healthcare
In healthcare, AI-powered algorithms can be used to track ESG metrics such as patient safety, quality of care, or environmental sustainability. AI helps generate reports for insights into areas of improvement and detect potential ESG risks.

Final Thoughts

Industry leaders must act now to embrace new digital tools and data analytics & AI solutions that can transform large volumes of unstructured ESG data into structured data for meaningful and actionable insights. And, when data is transformed into a well-structured and easily accessible asset, it can bring teams, resources, and insights together, creating the opportunity to collaborate and drive real improvements in ESG performance.
Empowered with the right data, the right technology, and the right tools, a company can meet the gold standard in ESG governance, risk management, and compliance. It can demonstrate honest, noticeable ESG and sustainability efforts to strike the right chords with the new-gen “socially responsible” investors.
Want to know more about how companies across industry segments are leveraging data analytics and AI in their ESG journeys? Reach out to us.

Anand Subramaniam

Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.

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