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| 2 minutes read

ESG - disputes, supply chain transparency and generative AI

Spurred by a rising focus on a company’s downstream impact, companies are facing new pressure to ensure transparency and sustainability throughout their supply chains. One way this pressure has materialized is through ESG related enforcement actions, consumer litigation and disputes with business partners. While advances in Generative Artificial Intelligence (AI) can enhance supply line transparency and reduce the risks of these disputes, continued communication and due diligence is required to ensure companies are best poised for the future.  

The rise of ESG-related litigation and disputes

Litigation, regulatory enforcement actions and other disputes related to ESG policies, practices, and disclosures have increased in the past several years. Several notable disputes, specifically related to sustainability and supply chain transparency, include:

  • Watch-dog investigation into fast-fashion retailer Shien’s factories, found to have violated Chinese labor laws by having manufacturers run informal factories set up in residential buildings.
  • Lawsuit against Ben & Jerry’s alleging the company fraudulently claimed its products were ethically sourced while using migrant child labor.

Generative AI and supply chain transparency

Given the rise of supply chain-related ESG disputes, ensuring transparency is key to recognizing and avoiding potential compliance issues and risks. Generative AI has shown the potential to revolutionize supply chain traceability to further assist in achieving ESG goals and mitigating risks, including through

  • Data analysis and pattern recognition — Generative AI algorithms can analyze large volumes of data collected throughout the supply chain, identifying patterns and anomalies that might be challenging for humans to detect.
  • Sustainable supplier selection — By analyzing a wide range of factors, including supplier certifications, environmental performance, and ethical practices, generative AI algorithms can identify suppliers that align with a company’s sustainability goals.
  • Risk management and mitigation — By analyzing data from various sources such as weather conditions, geopolitical factors, and market trends, generative AI algorithms can help companies proactively identify and respond to potential disruptions.
  • Life-cycle assessment and product design — By considering factors such as materials used, manufacturing processes, transportation, and end-of-life disposal, generative AI algorithms can help optimize product design to minimize environmental footprints.

Understanding the limits of Generative AI

Although the aforementioned generative AI solutions are promising, there remain barriers to ensuring full transparency and accountability across supply chains, and in turn, limiting ESG related disputes. Chief among these barriers is the multiplicity of systems within a manufacturer’s “technology stack” and the lack of communication and interconnectivity between those systems. As comprehensive as these Generative AI solutions may be, without enhanced organizational communications their effectiveness in enhancing transparency and minimizing ESG disputes will be minimal. 

ESG disputes will continue as regulators and company shareholders are empowered to bring legal action against companies that fail to identify and address their material ESG risks. Adapting to this trend, through utilizing new technologies and enhancing transparency, will be increasingly necessary. 


esg, supply chain