Organizational supply chains have evolved from linear chains into complex, multi-layered supply webs, which has significantly increased the difficulty of conducting effective supply chain due diligence. As these networks grow more intricate and global, companies face mounting pressure to identify, monitor, and mitigate a wider range of risks originating from their suppliers and other third parties. The stakes for ethical conduct, regulatory compliance, and operational resilience continue to rise as dependencies between suppliers deepen and become less transparent.
To address this challenge, some organizations are exploring the use of generative artificial intelligence and large language models to help make sense of vast and fragmented data across their supply webs. The core idea is to use these tools to analyze program assessment data and similar inputs to generate a clearer picture of the overall risk landscape. By processing and interpreting information at scale, generative artificial intelligence and large language models can help organizations more accurately extrapolate their total universe of inherent and residual risk, turning raw assessment data into actionable insight for risk management and due diligence.
The conversation is framed around how ethics and compliance leaders can integrate these emerging technologies into established risk management frameworks without losing sight of core ethical standards. Craig Moss, executive vice president of measurement at Ethisphere, brings additional perspective from his roles at the Digital Supply Chain Institute, the Cyber Readiness Institute, and the Association of Professional Social Compliance Auditors. His background highlights the growing convergence of digital transformation, cybersecurity readiness, and social compliance in managing supplier risk across complex supply webs, and underscores the importance of aligning artificial intelligence enabled analysis with responsible business practices.
