Sustainability might once have once seemed like a luxury: nice to have, but not a strategic imperative. Those days are over. With intensifying regulatory scrutiny and consumer pressure, sustainable sourcing is fast becoming an implied, if not explicit, requirement across a spectrum of industries.

Companies now set sustainability goals, issue annual reports, and vow to reach net-zero emissions. Yet many are also grappling with the scope and complexity of that very transition, with a series of critical questions, including:

• How can companies more holistically measure environmental and social performance?
• How can they optimize supply chains, while also making sure to collaborate with sustainable suppliers?
• How can they ensure transparency in procurement, and make certain that the suppliers they use stay engaged in sustainable practices?
• How can they stay abreast of the myriad of changing legal and regulatory landscapes? 

Getting the answers to these questions wrong can bring existential company risk. From fines and reputational damage to supply chain disruptions and even business shutdowns, the stakes are indeed getting higher. Having the right tools can not only provide an effective path forward, but can also offer a competitive advantage. 

Enter generative artificial intelligence. As sustainability goes further mainstream, and business leaders place it higher on their agendas, GenAI is uniquely positioned to aid in the transition. Everything from risk mitigation to increased efficiency is on offer, with unprecedented transparency in supply-chain sourcing.

Following are three core areas of potential AI-fueled improvement.

Measurement and reporting. By introducing automation into carbon-footprint calculation, GenAI tools can monitor emissions in real-time and generate timely recommendations, including the full lifecycle of any given product. From the harvesting of raw materials to its eventual disposal, problems up and down the supply chain can be quickly identified, addressed and optimized over time. Through the use of unorganized and otherwise wasted information, companies can redefine goals, measurements, reporting and forecasting, resulting in a more comprehensive and data-driven approach.  

Like the adage goes: “What gets measured gets done.” 

Governance and visualization. Sustainability data is vast and growing. To make good decisions, leadership needs better visibility into supply chains. This includes summary reports for the expert that can generate recommendations for the layman based on data, as well as real-time analysis of rival companies. GenAI can help solve these issues and align stakeholders to better set and meet sustainability goals. From there, the tech can issue recommendations that fit within regulatory frameworks, then adjust them in real time as the frameworks themselves evolve. Such changes might include legislative action, court decisions or even company changes in ethical standards, which can be evaluated and automated. Meanwhile, AI-powered technology can generate digestible visualizations for internal decision-makers, regulators and consumers alike.

Supply chain optimization. Sources, routes and distribution methods are all critical to the supply chain. Hidden patterns, however, can thwart even the best of sustainability efforts. With across-the-board mappings of supply chains, Al-powered tools can begin to identify those trends and close the gaps. That includes evaluations of everything from weather and economics to political protests and social trends that might cause disruptions, or otherwise be linked to unsustainable practices.

Meanwhile, feeding historical data into the algorithm can provide a more representative picture of the entire process. For example, if a company aims to avoid polluting a river or the logging of a protected forest, knowing and evaluating the status of the locale’s existing ecology can make subsequent actions more measurable, and therefore more correctable. Such data can then be quickly assembled and summarized to empower executive decision-making.  

While AI can decode complex data sets and support the organization’s sustainability goals, leadership must be cognizant of the potential risks. Algorithms themselves can suffer from data bias and transparency concerns, making regular updates and human oversight necessary to avoid prejudicial or unsustainable sourcing practices. Meanwhile, the sheer volume of newly gleaned information can make companies subject to cyberattacks, which have greatly increased in number in recent years. Independent oversight and cybersecurity regimens should be part of any AI-fueled implementation plan. Employee training and continuous education should also be prioritized to help prevent misuse.

Despite such concerns, the benefits of generative AI-powered strategies can’t be ignored in 2024. With proper oversight, these tools can start to bring companies far closer to their sourcing and sustainability goals.

Kirsten Loegering is vice president of product management for finance and supply chain workflows with ServiceNow.