On-time delivery rates for companies that leverage risk management technology( Deloitte)

CO2 lowered per shipment with AI-enhanced SRM( McKinsey)

Embedding ESG data directly into supply chain AI models, such as carbon emissions, energy usage and supplier labour conditions, helps ensure that recommendations reflect both commercial priorities and ESG goals. This is especially important when selecting or ranking suppliers, managing inventory or planning logistics routes.

Regular bias audits and transparency reporting can uncover unintended risks early, particularly in areas like supplier diversity, regional sourcing or resource utilisation. Maintaining human oversight at key decision points – for instance, when onboarding new vendors or shifting sourcing strategies – adds an essential layer of accountability.

Finally, collaboration across procurement, sustainability, compliance and operations teams is vital. These stakeholders bring critical context to AI-driven insights, helping ensure recommendations are commercially sound and socially responsible. This approach not only supports ESG targets, but also strengthens supply chain resilience, brand reputation and stakeholder trust.

122 May 2025