Appeal of AI in ESG Analysis Grows
Use of AI could rise amid falling costs, as industry embraces technology to tackle data and reporting challenges.
The bountiful benefits of using AI in ESG analysis are becoming increasingly recognised by investors and the financial services sector, heightening demand for tools and services powered by the technology.
AI has been credited with facilitating more accurate, efficient, and real-time data collection, analysis, and ESG progress reporting. It has been used to consolidate and enhance ESG data and can support investors in understanding the requirements imposed by new rules. AI can also reduce the costs of and time spent doing ESG analysis at a time when finances are tight for many firms.
“ESG teams are not growing exponentially in terms of budgets and resources, so people are having to inevitably do things in smarter ways,” James Phare, CEO at sustainable fintech Neural Alpha, told ESG Investor. “AI is the only answer to the particular automation challenges that we have within the ESG space.”
The AI landscape is rapidly evolving, with several large tech companies including Alphabet, Anthropic, Apple, Microsoft and OpenAI continually developing AI tools and services. The release of Elon Musk’s Grok-3 AI chatbot earlier this week has been stressed as a signal to investors that innovation in AI is not slowing down.
The cost of AI itself appears to be falling, too. According to comments from OpenAI CEO Sam Altman earlier this month, it may be decreasing by as much as ten times per year.
“As a solution for doing more with less and reducing costs while being able to bring new scale to analysis, there’s really kind of no substitute for AI capabilities, so I think people will continue to increasingly adopt it,” said Phare.
Increased adoption
AI can play a key role in consolidating and enhancing ESG data, with the volume of ESG data ever expanding. ESG regulations are a key driver behind the growth in sustainability-related data from companies.
A report from business management consultancy Teneo analysing 250 sustainability reports from S&P 500 companies published between 1 January and 30 July 2024 found that the length of reports had increased for a third consecutive year, averaging 83 pages (up 20% from an average of 70 pages in 2021).
Large language models (LLMs) can analyse huge tracts of text across multiple documents at scale. This can significantly reduce the time taken for big data gathering exercises, allowing investors to directly access relevant company disclosures at scale, rather than relying on external data provision.
“We are in the ESG 3.0 phase, meaning the use of technology, data, AI, is becoming more common in ESG analysis,” said Gordon Tveito-Duncan, CEO and Co-founder at AI-powered ESG analytics firm GaiaLens. “That’s a good thing, because if we can set it up in the right way we can use AI to get better data as a first step, which will lead to better analysis. Using LLMs in the right way will improve sustainable transparency and explainability.”
GaiaLens uses AI in its GL Chat and GL Report tools. The former offers users access to the GaiaLens PDF GenAI Chatbot which can be used for ESG analysis in extracting the latest carbon emissions numbers or analysing trends over time. The latter offers an end-to-end sustainability reporting solution, which can generate reports for all major sustainability frameworks.
The company also has a Controversy Detection System, which includes a LLM that generates four controversies scores, E, S, G and greenwashing, with explanations.
Neural Alpha’s Responsible Capital is an AI platform used by asset owners and asset managers to enhance ESG data analysis, which utilises an LLM. It also offers news on controversies and exclusions, as well as screening and peer comparison.
Ben Wilmot, Co-founder and Managing Director at UK-based consultancy Canbury Insights, said that there was an “understandable hesitation” to overly commit to AI in ESG analysis given the need for “due consideration around accuracy and any potential impact on existing team structure”.
“We believe that more and more organisations will realise that the use of AI is a necessary step to deal with increasing regulation and focus without a corresponding increase in costs,” he added. “We think AI will become more prevalent, and we think this is a good thing – freeing up sustainability professionals to focus on the value-add elements of their roles.”
Agentic AI expansion
The next significant expansion of AI’s use in ESG analysis is expected to be agentic AI. Agentic AI combines newer types of AI, such as LLMs, traditional AI like machine learning, and enterprise automation, creating independent AI agents which can analyse data, set goals, and take actions with reduced human oversight.
For ESG analysis, agentic AI holds the potential to automating complex, knowledge-intensive tasks at scale. AI-focused software company Briink has spotlighted success in incrementally integrating agentic approaches for specific tasks, but noted that developing deep ESG domain knowledge is essential to have a meaningful impact.
Last month, OpenAI released agentic AI platform Operator, with less expensive agentic platforms expected to follow.
“The implementation of AI agents in the space will the next big development in the AI space,” said Tveito-Duncan. “It’s already been released on the consumer side through OpenAI, but its widespread use for businesses is going to take longer, particularly when it comes to financial services.
“It raises a security risks and there are other factors for investors or financial services companies to consider, but it could be hugely exciting,” he added. “It automates workflows in a smart and scalable way, particularly for asset owners, empowering them to increase engagement with asset managers.”
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