Due to climate change, and the state of the world, companies are under considerable pressure from investors and customers to improve their ESG performance. While companies and governments work hard to develop and implement ESG principles, there is still much work in this field and many pieces that need to be added to build reliable ESG mechanisms.
One of the most significant challenges global businesses face today is setting ESG goals and effectively monitoring and progressing toward them. The complexity and fragmentation of incoming data sources often result in incomplete analyses, inconsistent reporting, and unmet commitments.
This is precisely why Artificial intelligence (AI) has the potential to significantly impact ESG efforts and, ultimately, contribute to addressing climate change. By providing comprehensive ESG solutions, reporting capabilities, and emission insights, AI can facilitate progress toward ESG goals for all businesses, regardless of size. Additionally, according to Oracle’s study, 96 percent of business leaders admit human bias and emotion often distract from the ESG goals, and 89 percent believe organizations that use technology to help drive sustainable business practices will be the ones that succeed in the long run. In comparison, an astounding 93 percent of business leaders would trust a bot over a human to make sustainability and social decisions.
ESG Data Analyses and ESG Reporting
The first obstacle to proper ESG reporting is collecting relevant data. The ESG-relevant data is often fragmented, complex, and generally very hard and time-consuming to gather. The data is scattered around different sectors, such as the company’s production, various business streamlines, HR, internal and official reports, etc. On top of this, other sectors produce different types of data with different structures, different meanings, and different quantification. Thus, collecting them all in one ESG report uniformly is hard. Hence, companies must implement internal reporting rules and data collection tools to produce a consistent and trustworthy ESG report.
When it comes to gathering data, the benefits of using AI are apparent:
In addition to enhancing the efficiency and accuracy of ESG data collection, AI has the potential to transform how companies report on their ESG performance. With advanced natural language generation (“NLG“) models, companies can automatically generate detailed reports without requiring manual report writing or hiring professional writers. This streamlines the reporting process, saves time, and reduces the likelihood of human errors, such as typos or misinterpretation of data. Furthermore, NLG models enable companies to generate reports in multiple languages, thus improving accessibility for global audiences. By leveraging these capabilities, companies can accelerate the production of high-quality, comprehensive reports while reducing costs associated with manual labor.
Some companies already use AI to analyze their own ESG performance (such as Microsoft and Walmart), and others that use AI to analyze the ESG performance of other companies (such as BlackRock, Goldman Sachs, and Sustainalytics).
ESG Advantages and Drawbacks
Not only can AI help with ESG reporting, but AI can also impact ESG performance.
E
According to the World Economic Forum, digital solutions can reduce global emissions by 20 percent by 2050. There are several ways in which AI can help do just that:
While AI algorithms that can predict energy consumption are already in place, there is room for improvement to ensure that they can accommodate various sources of energy production that exist today and meet evolving regulatory and measurement requirements. Complex algorithmic features also require refinement to respond to changing trends or behaviors and extend beyond the industrial scale to cater to individual and household demands.
Although AI can play a vital role in reducing emissions, it is essential to note that the carbon footprint associated with training a single AI is approximately 300 tons. This is equivalent to the total electricity usage of 60 homes for a year. As AI continues to develop and become more advanced, the carbon footprint associated with its training is likely to increase too.
S
When it comes to social elements, AI is a double-edged sword. For example, AI can help in the hiring process, mitigate unconscious bias, and broaden the scope of potential candidates. By leveraging AI and modern datasets, companies can analyze job listings to identify opportunities for inclusivity and appeal to a more diverse set of applicants. However, there is a risk of potential bias within the AI algorithms, bias in data selection, and bias within the humans training the AI, which can reverse the positive effects of AI on these social factors.
AI can also have significant implications for human rights, including the right to privacy, non-discrimination, and freedom of expression. The European Parliament’s Committee on Human Rights has emphasized ensuring that AI systems are developed and used by international human rights standards. They have called for the development of clear guidelines for using AI in law enforcement and measures to prevent the use of AI for mass surveillance and other human rights violations.
Furthermore, AI automation is a vast job market risk. As AI becomes more advanced, more people risk losing their jobs. Goldman Sachs even estimates that AI will replace 300 million full-time jobs.
G
In addition to ESG reporting, a wide range of possible AI applications regarding governance factors exist. For example, Next-generation risk modeling can provide corporate boards with valuable insights and enable them to make informed and ethical decisions while analyzing market trends and identifying potential risks. Also, it can improve the transparency and accuracy of accounting methods.
Although AI benefits corporate governance, there are also some risks to consider. A critical threat is the lack of transparency and accountability in AI decision-making processes. It is, therefore, essential to establish clear guidelines and protocols for AI decision-making, including transparency and accountability measures, to mitigate these risks and ensure that AI is used responsibly in corporate governance.
Finally, using AI may lead to a loss of human oversight in a company’s decision-making, resulting in a lack of empathy or understanding of complex social or ethical issues in its decisions.
As AI becomes more ubiquitous, it is increasingly essential for businesses and investors to consider the potential ESG implications of these technologies. From workplace regulations to human rights concerns to ethical considerations and investment risks, various challenges and opportunities are associated with AI and ESG. By carefully evaluating these risks and opportunities and engaging with companies to promote responsible AI use, businesses and investors can ensure that AI helps and boosts ESG values’ implementation.
