How can AI help us understand corporate transition performance?

This New York Climate Week, in collaboration with The Patrick J. McGovern Foundation, we held a discussion with experts, practitioners, and funders to explore how Artificial Intelligence can help us understand corporate transition performance.

Written by Madeleine Lewis

Understanding the transition performance of companies will allow financial practitioners to align their decisions with real-world climate impact. But today, only a few hundred companies are analyzed in depth by science-aligned climate initiatives. 

Working with partners, we want to expand this work to cover much more of the market, and across more transition metrics. But we can’t get there without innovation. 

We’ve been engaging with leading climate and AI organizations over the past few months to identify solutions in this area. This New York Climate Week, in collaboration with The Patrick J. McGovern Foundation, we furthered this dialogue, convening experts, practitioners, and funders to explore these opportunities.

Here are some of the main takeaways:

  • Philanthropy: There's a compelling case for philanthropic involvement in this crucial space, but the boundary between public good and commercial ventures must be carefully defined. 
  • Trust: To embrace AI, we need confidence in transition datasets and will need to establish robust safeguards to ensure responsible and ethical AI implementation. 
  • Unlocking expertise: It's not a choice between AI and climate analysts. AI can empower climate organizations by freeing them from cumbersome tasks and amplifying their expertise. AI tools will need to be tailored to their organizational contexts and specific expertise.  
  • Building common tools and architectures: Collaboration and coordination are key to developing standardized tools and architectures that prevent fragmentation, ensuring efficiency and effectiveness. 
  • Beyond LLMs: While Large Language Models offer important advantages, other AI tools, including Machine Learning and smaller language models still have a role in analyzing climate transition data – and often combinations of models are needed. 
  • Capacity building: Building this shared expertise across climate NGOs, AI practitioners – and with end users like financial institutions – will take significant resources. 

Huge thanks to all our participants for sharing your precious time and insight. Please reach out to us if you want to learn more and collaborate as this conversation moves forward.