Risks and Benefits of Generative AI for Pharma Supply Chain Management

As the life sciences sector grapples with complex challenges around sustainability and Environmental and Social Governance (ESG) compliance, Generative AI (GenAI) is emerging as a potentially powerful tool for enhancing efficiency and sustainability. Michele Tagliaferri, Eva von Mühlenen, and Anna-Shari Melin explain.

GenAI is emerging as a potentially powerful technology for revolutionizing supply chain processes and for helping companies to align with ESG requirements and navigate a complex legal terrain. This technology has the potential to greatly assist life sciences companies, which, in many parts of the world, are now facing requirements to look more closely at their supply chains.

In the EU, the Corporate Sustainability Reporting Directive (CSRD) requires companies to report on the environmental and human rights impacts of their supply chains. In advance of CSRD’s implementation, some EU countries, including France, Germany, and Norway, have already introduced due diligence and reporting requirements in their national laws. In the U.S., California has brought in a climate accountability package of laws that demand extensive disclosure of climate-related risks. In China, new ESG disclosure rules stipulate that the largest companies will need to publish sustainability reports by 2026.

These global developments place a significant regulatory burden on life sciences companies, many of which are currently attempting to determine what ESG means for their businesses, what the sustainability/ESG requirements impacting them are, and whether any corporate governance changes will be needed.

The life sciences industry has particularly complex supply chains. Raw materials or active pharmaceutical ingredients (APIs) for drug manufacturing are often sourced from companies in developing countries whose environmental impact and human rights records can be challenging to assess. Sterile packaging requirements are onerous, requiring large amounts of single-use plastic. On the logistics side, threats to drug distribution — for example, from the closure of international shipping lanes — can be a matter of life or death.

Yet, there are also benefits for life sciences companies in having a better understanding of their international supply chains. For instance, knowing that a particular raw ingredient or API comes from an area that may be impacted by climate change allows companies to consider alternative sources to preserve manufacturing capacity and ultimately continue to meet the demand for life-saving medicinal products.

Emerging GenAI software can significantly improve the management of supply chains. A GenAI tool can be trained to monitor thousands of news and media channels in hundreds of languages for potential environmental and human rights risks to a company’s supply chain. It can then filter and feed information regarding any environment-related risks, or any potential human rights impacts that the company’s supply chain might be facing. GenAI can streamline the acquisition and processing of the necessary data to fulfil ESG-related requirements.

Some larger pharma companies are currently considering deploying GenAI for supply chain management. However, life sciences companies need to be wary of the legal risks associated with AI-generated outputs, such as potential inaccuracies or biases that could lead to non-compliance or reputational damage. GenAI tools for supply chain monitoring should be used with caution, primarily serving as a support function, while adhering to the below guidelines, particularly in relation to human oversight and data review.

Using GenAI tools does not automatically guarantee compliance with legal standards and good practice (GxP) requirements. Any relevant information received from GenAI tools must be critically reviewed by a human, taking into account both the sourcing of the data and GenAI’s assessment of the risk represented by the data, in order to ensure its accuracy and reliability. This involves:

• Assessing the credibility of information sourced from various jurisdictions, especially those with restrictions on press freedom or where data may be subject to manipulation;
• Considering the impact of geopolitical factors that may influence sustainability data, such as potential increases in global oil prices or geopolitical tensions that may add noise to the underlying data in the long or short term;
• Evaluating risks associated with conflicts or economic measures, such as tariffs and sanctions, and implementing safeguards to address both human rights- and environment-related risks; and
• Ethically managing and utilizing GenAI-derived data to avoid and mitigate human rights issues and conflicts of interest, rather than purely to design supply chains more economically.

GenAI tools certainly have the potential to enhance ESG compliance and operational efficiency. By leveraging the power of AI, life sciences companies can not only meet the stringent demands of today’s regulatory environment, but make significant progress in satisfying ESG requirements.

This post is as of the posting date stated above. Sidley Austin LLP assumes no duty to update this post or post about any subsequent developments having a bearing on this post.