Artificial Intelligence (AI) is the hottest topic of 2024 and, no matter what your stance on it is, we can all agree that it’s a game-changer.
ChatGPT can consolidate data in seconds, DALL-E can produce detailed images from a quick prompt, and Sora is set to revolutionise video content production. Take a look at this YouTube video to see what OpenAI can do with video creation already.
We’ve seen many businesses across a range of industries incorporate generative AI with varying degrees of success, but at KEY ESG, we’ve found that a blended approach to new technologies is always the best way forward. Today, we’re taking a closer look at how AI will impact ESG reporting, shedding light on how KEY ESG plans to leverage new innovations in machine learning to improve efficiency over the coming years.
How AI can help with ESG monitoring
At the heart of ESG and sustainability reporting lies the need for deep, accurate, and timely data. AI technologies, including machine learning and natural language processing, can sift through vast amounts of information, from emissions reports to time sheets, identifying data trends, risks, and opportunities that human analysts might overlook.
MSCI, S&P Global, Refinitiv/LSEG, and many other large corporations are already successfully using AI to crawl ESG reports and extract insights. AI can standardise and validate data across multiple sources, providing a more consistent and transparent view of ESG performance. And it’s fast. By using AI in their processes, these firms have been able to streamline their work, reviewing huge quantities of data in a fraction of the time it would usually take. However, AI isn’t perfect, and results must always be checked and verified by experts.
The potential pitfalls of AI in ESG reporting
AI is excellent at scanning data, but it’s crucial that investors and regulatory bodies remain cautious of the many risks posed by its use:
Bias and data quality
One of the most significant challenges in using AI for ESG and sustainability investing is the risk of bias and poor-quality data. AI systems are only as good as the data fed into them. If the data is biased or of poor quality, the AI's analysis and recommendations will be skewed.
AI doesn’t know what’s true or false – it assumes all information presented to it is correct. In the context of ESG investing, where subjective judgement and nuanced understanding of sustainability issues are crucial, reliance on biased AI algorithms can lead to misinformed investment decisions.
Out-of-date insights
The ESG regulatory landscape is constantly changing. With so much information out there, AI is not guaranteed to be working from the most up-to-date data. The consequences of working from incorrect or outdated regulatory guidance could be severe and could lead to legal ramifications or fines.
Regulatory and ethical considerations
The evolving nature of AI technology and its applications in finance often outpaces the development of regulatory frameworks. This gap can lead to ethical dilemmas, such as the potential for AI to exacerbate existing inequalities or to prioritise financial gains over genuine sustainability outcomes.
Security and privacy concerns
The risk of data breaches or unauthorised access to investment insights could have severe implications for investors. Robust cybersecurity measures and data privacy protocols must be in place to protect sensitive information.
How KEY ESG uses AI
KEY ESG currently uses AI to streamline internal workflows. We have a taskforce in place to explore and develop AI initiatives that enable our team to work more efficiently.
We’ve found AI to be invaluable in improving accuracy for our developers, and we often use it to crawl large datasets. However, ESG challenges require human solutions, and there are many aspects of our work that AI could never replace.
There will always be a knowledgeable person on the other end of the phone whenever a client calls customer support. Our software will always be updated and monitored by highly trained ESG experts who are committed to tracking and monitoring the ESG landscape. And our creative approach will never change – only a human being can come up with human solutions for human problems.
KEY ESG will continue to hand over some of the more time-consuming (and slightly boring!) ESG data-related tasks to AI, giving our team more time to innovate in other areas. We’re also working on a system by which AI can be used to instantly generate tailored visual reports and graphs based on detailed prompts.
The future of sustainability reporting
For KEY ESG, integrating AI into our systems represents a commitment to innovation, sustainability, and client success – three things which were foundational to our ethos long before the rise of AI technology.
As the ESG landscape continues to evolve, we anticipate that AI will increasingly shape sustainable investing practices. While AI can process and analyse data at a scale and speed unattainable by humans, it should complement rather than replace human judgement.
Overdependence on AI could lead to a lack of critical oversight, making the investment process vulnerable to errors or manipulation. Reliability is key in ESG monitoring, so our team will always use generative AI as a tool, not a solution.
Navigation
Navigation
Section
Artificial Intelligence (AI) is the hottest topic of 2024 and, no matter what your stance on it is, we can all agree that it’s a game-changer.
ChatGPT can consolidate data in seconds, DALL-E can produce detailed images from a quick prompt, and Sora is set to revolutionise video content production. Take a look at this YouTube video to see what OpenAI can do with video creation already.
We’ve seen many businesses across a range of industries incorporate generative AI with varying degrees of success, but at KEY ESG, we’ve found that a blended approach to new technologies is always the best way forward. Today, we’re taking a closer look at how AI will impact ESG reporting, shedding light on how KEY ESG plans to leverage new innovations in machine learning to improve efficiency over the coming years.
How AI can help with ESG monitoring
At the heart of ESG and sustainability reporting lies the need for deep, accurate, and timely data. AI technologies, including machine learning and natural language processing, can sift through vast amounts of information, from emissions reports to time sheets, identifying data trends, risks, and opportunities that human analysts might overlook.
MSCI, S&P Global, Refinitiv/LSEG, and many other large corporations are already successfully using AI to crawl ESG reports and extract insights. AI can standardise and validate data across multiple sources, providing a more consistent and transparent view of ESG performance. And it’s fast. By using AI in their processes, these firms have been able to streamline their work, reviewing huge quantities of data in a fraction of the time it would usually take. However, AI isn’t perfect, and results must always be checked and verified by experts.
The potential pitfalls of AI in ESG reporting
AI is excellent at scanning data, but it’s crucial that investors and regulatory bodies remain cautious of the many risks posed by its use:
Bias and data quality
One of the most significant challenges in using AI for ESG and sustainability investing is the risk of bias and poor-quality data. AI systems are only as good as the data fed into them. If the data is biased or of poor quality, the AI's analysis and recommendations will be skewed.
AI doesn’t know what’s true or false – it assumes all information presented to it is correct. In the context of ESG investing, where subjective judgement and nuanced understanding of sustainability issues are crucial, reliance on biased AI algorithms can lead to misinformed investment decisions.
Out-of-date insights
The ESG regulatory landscape is constantly changing. With so much information out there, AI is not guaranteed to be working from the most up-to-date data. The consequences of working from incorrect or outdated regulatory guidance could be severe and could lead to legal ramifications or fines.
Regulatory and ethical considerations
The evolving nature of AI technology and its applications in finance often outpaces the development of regulatory frameworks. This gap can lead to ethical dilemmas, such as the potential for AI to exacerbate existing inequalities or to prioritise financial gains over genuine sustainability outcomes.
Security and privacy concerns
The risk of data breaches or unauthorised access to investment insights could have severe implications for investors. Robust cybersecurity measures and data privacy protocols must be in place to protect sensitive information.
How KEY ESG uses AI
KEY ESG currently uses AI to streamline internal workflows. We have a taskforce in place to explore and develop AI initiatives that enable our team to work more efficiently.
We’ve found AI to be invaluable in improving accuracy for our developers, and we often use it to crawl large datasets. However, ESG challenges require human solutions, and there are many aspects of our work that AI could never replace.
There will always be a knowledgeable person on the other end of the phone whenever a client calls customer support. Our software will always be updated and monitored by highly trained ESG experts who are committed to tracking and monitoring the ESG landscape. And our creative approach will never change – only a human being can come up with human solutions for human problems.
KEY ESG will continue to hand over some of the more time-consuming (and slightly boring!) ESG data-related tasks to AI, giving our team more time to innovate in other areas. We’re also working on a system by which AI can be used to instantly generate tailored visual reports and graphs based on detailed prompts.
The future of sustainability reporting
For KEY ESG, integrating AI into our systems represents a commitment to innovation, sustainability, and client success – three things which were foundational to our ethos long before the rise of AI technology.
As the ESG landscape continues to evolve, we anticipate that AI will increasingly shape sustainable investing practices. While AI can process and analyse data at a scale and speed unattainable by humans, it should complement rather than replace human judgement.
Overdependence on AI could lead to a lack of critical oversight, making the investment process vulnerable to errors or manipulation. Reliability is key in ESG monitoring, so our team will always use generative AI as a tool, not a solution.
