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Digital developments in focus
| 3 minutes read

WEF: Without AI we won’t meet ESG Goals

The World Economic Forum has recently said that without AI, we won’t meet ESG goals and beat climate change. It is clear that advancements in AI are likely to lead to more developments in green technology generally, but how can AI help a company under pressure from its investors and customers to improve performance and transparency on ESG issues?

Examples of how AI can help

  • Setting a realistic ESG roadmap: Developing meaningful ESG goals, and then monitoring progress, is a challenge faced by many organisations around the world. The WEF suggests that AI can be a “game changer” in managing ESG efforts, for example by combining and processing huge swathes of data (e.g. ESG metrics and financial data) and by helping organisations to gain insights, cut through the noise, and set realistic roadmaps to meet genuine ESG goals.
  • Increased transparency and accountability: AI can also help provide ESG management solutions with comprehensive tracking and reporting systems and increased accountability. The WEF states, for example, that organisations with automated solutions for emissions measurements are nearly twice as likely to reduce emissions in line with their ambitions.

  • Reporting obligations: Greater transparency also helps with any reporting obligations organisations may have. The march toward requiring ever greater reporting on ESG continues worldwide, including in respect of supply chains. The expectations of regulators in this space also continues to harden. Despite efforts like those of the International Sustainability Standards Board (ISSB) to establish a global reporting baseline and increase standardisation, the data itself is still complex, growing in size and closely interrogated. As mentioned above, there are a variety of ways in which AI can help manage, and cut through, large amounts of data, and it is expected that organisations will become increasingly reliant on AI solutions to manage these burgeoning reporting demands. That said, at present AI solutions may still struggle with some of the more qualitative, narrative disclosures which are made (for example, in relation to TCFD disclosures which follow best practice) meaning human review is still often required.

  • Improved overall efficiency: AI solutions which are able to standardise and deliver on ESG metrics can, the WEF suggests, improve overall business efficiencies in areas like logistics, supply chain management, demand forecasting and optimising workflows generally.

  • Impact on investments, divestments and M&A: ESG-related due diligence is becoming increasingly important as investors and organisations conducting M&A want to understand the ESG credentials of any target companies. Recent PCW research also shows that almost half of investors surveyed said they may be willing to divest from companies that aren’t taking sufficient action on ESG issues. AI tools can assist with the information gathering process around any sale.

  • Combatting claims of greenwashing: AI solutions which provide good reporting, transparency and accountability may also help organisations avoid (or combat) any claims of greenwashing.

That said, when procuring AI, whether for ESG purposes or otherwise, it is important to understand some of the risk areas that the WEF article does not cover. 

What for example, are the green credentials of the AI tool itself (given it is likely to be storing and processing large quantities of data)? 

Do you understand how it works? In a world where transparency is important, a black-box AI solution can make explainability hard. 

Do you trust its results? AI is really all about the data, and if poor quality or biased data is used in the models, this can lead to a poor outcome. 

Will the AI provider stand behind its product and accept sufficient contractual liability? 

And do you understand the impact of using AI on other ESG considerations? The WEF article focusses heavily on the ‘E’ part of ESG but when thinking about the ‘S’ (social) for example, algorithmic bias is a concern on the radar of regulators like the UK’s Information Commissioner and Competition and Markets Authority.

While AI solutions can assist companies in meeting, tracking and demonstrating compliance with their ESG goals, it is therefore important that the usual risk analysis and procurement processes are followed and that organisations understand both the benefits, and limitations, of the AI tools they use.  

"Without AI, we won’t meet ESG goals and address climate change" (World Economic Forum)


ai, emerging tech