Artificial intelligence is all the rage, but is it coming into conflict with environmental, social and governance (ESG) concerns? Guy Courtin, vice president of industry and global alliances with Tecsys, explains the apparent disconnect.

Two of the biggest topics in supply chain management today are artificial intelligence and ESG — environmental, social and governance. And in some instances, they’re on a collision course. 

AI and machine learning have the potential to solve many issues in supply chain today, says Courtin, “but the dirty side of it is ESG.” It takes an immense amount of energy and computational power to run queries through generative AI, and it’s important that users of the tool be aware of that. With the increase in global warming, “we have to take this into consideration,” he says, “and we just don’t talk about it.”

It starts with understanding the environmental consequences, through metrics that track carbon dioxide emission. U.S. and European Union regulators are already requiring that reporting mechanisms be put in place, and NGOs are setting their own standards. With that information at hand, users can at least make a decision about whether AI applications are always desirable. “I can’t manage what I can’t measure,” Courtin says.

The irony is that AI can itself provide a means of measuring its own environmental consequences. Even then, though, something constructive must be done with the data.

Digitization has been touted as another solution for addressing the environmental impact of technology. What many people miss, Courtin says, is that they still must deal with a physical supply chain. “At the end of the day,” he says, “it’s about moving goods. We live in a physical world.”

Even a digital innovator like Amazon.com is building warehouses and acquiring trucks, ships and planes to get product to e-commerce shoppers, Courtin says.