AI and ESG – Part One: Opportunities
Lorenzo Saa, Chief Sustainability Officer of Clarity AI, considers the possibilities when the power of long-term capital meets the power of technology.
AI is already revolutionising how we live, work, and play, and it’s set to radically transform global progress on sustainability. It is no surprise therefore that business leaders such as Google CEO Sundar Pichai predict that its impact on human society will be as profound as the discovery of fire.
But like fire, the transformative power of AI comes with risks too. So, the central question for investors right now is how to harness the potential of AI, without getting burned.
The way institutional investors in particular respond to this challenge could shape all our futures.
Institutional investors are large-scale players that manage huge pools of capital including pension and insurance schemes. Unlike other participants in global markets, they tend to take a long-term view – investing in a way that aims to produce sustainable returns 25 years or more from now. This makes sustainability a key consideration for institutional investors, as evidenced by the fact that over half of the world’s institutional assets are now managed by signatories to the UN Principles for Responsible Investment.
All of this makes institutional investors critical to shaping how AI gets embedded across industries in the years ahead. Currently, they are looking at AI to drive sustainability in two key ways:
1) Investing in the AI sustainability revolution: They see how investing directly or indirectly in companies that provide or deploy AI can help catalyse the transition to a low-carbon, sustainable economy. This could help them both to manage the long-term risks linked to climate change and nature loss and to grasp new opportunities to invest in the sustainability winners of tomorrow.
2) Empowering investment decisions with AI: Investors are looking at AI as a tool to enhance the speed, quality, depth, and scope of their investment decision-making processes.
AI and sustainability revolution: An investment opportunity
AI has the potential to revolutionise fields as diverse as energy, agriculture, healthcare, and ocean conservation, helping us monitor, optimise, and predict (MOP) progress toward global sustainability goals. I like to think of investors as using AI to ‘MOP up’ sustainability challenges. AI-driven solutions can help track deforestation, emissions, and biodiversity loss, optimise energy use, agriculture, and finance, and predict climate threats and health risks – empowering businesses, governments, and investors to make informed, impactful decisions.
For example, Global Forest Watch has used AI to reduce deforestation rates by 50%, Siemens’ AI-powered wind turbines have improved efficiency by 10%, and IBM’s Green Horizon project has helped cut air pollution by 20% through better urban planning.
By channelling capital into AI-driven innovations, investors not only accelerate progress toward key global targets, such as the Paris Agreement, the UN Sustainable Development Goals, and the 30×30 goal of the Global Biodiversity Framework, but also unlock potential opportunities for better risk-adjusted returns. In this dual role, they support a more sustainable, low-carbon economy, while enhancing their own resilience in a rapidly changing market.
From data to action: How AI is shaping investment decisions
The second approach to sustainability institutional investors are taking with AI is to enhance the speed, quality, depth, and scope of their investment decision-making processes.
Institutional investors diversify their portfolios across thousands of global companies and securities creating an ecosystem of millions of data points. This comes with persistent challenges over the quality and coverage of that data and information, and how to turn it into valuable knowledge, wisdom, and action. Enter AI, with the potential to fundamentally reshape all parts of the knowledge pyramid behind each investment decision (See Figure 1).
For data collection, AI and machine learning are invaluable for capturing information from diverse sources and formats (such as text, tables, and graphs) and improving data reliability. This quality control is not a marginal issue. Our research found that across three data providers that offered clients the same reported emission data points, the data was different 13% of the time and showed a discrepancy of over 20%.
AI can also help close data gaps when corporate self-reported data is not available, making more accurate estimations beyond industry averages by using comparable and alternative sets of information like news or geospatial data. For example, AI tools can now overlay corporate human rights disclosures with information pulled from global news and independent sources to see if they match up.
To transform data into actionable insights, AI assistant solutions now allow institutional investors to converse intelligently with their portfolios on sustainability topics. They can inquire about data sources, methodologies, performance, and recommendations for improving scores. Additionally, AI is being used to provide forward-looking insights. For example, we used AI to analyse the decarbonisation plans of the world’s 400 largest emitters, finding that only 40% have credible transition plans.
Finally, AI is already helping investors to report back to their many stakeholders in different formats, styles and languages. Perhaps most importantly for investors today, AI can offer ways to optimise compliance reporting, reducing the reporting burden, and allowing responsible investors to focus on making investment decisions.
Figure 1: The Knowledge Pyramid
Part two of this article will look at the risks and governance implications arising from the growth of AI.
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