AI governance in transport and logistics.
Transport and logistics organisations deploy AI for route optimisation, autonomous vehicles, warehouse automation, demand forecasting, and supply chain visibility. These systems operate in safety-critical environments, affect workers and communities, and increasingly face sector-specific regulation around autonomous systems, emissions, and labour conditions.
Key governance challenges
Autonomous vehicles and safety
Autonomous trucks, drones, and warehouse robots operate in environments shared with human workers and the public. Safety case governance — validation, testing, monitoring, incident response, and regulatory reporting — is essential and increasingly mandated by transport regulators.
Supply chain transparency
AI systems that optimise supply chains must account for sanctions compliance, forced labour risk, environmental due diligence, and provenance tracking. The EU Corporate Sustainability Due Diligence Directive and similar frameworks create governance obligations for AI-driven sourcing decisions.
Worker impact and labour rights
AI-driven scheduling, performance monitoring, and workload allocation in logistics warehouses and delivery networks directly affect working conditions. Governance must address employment law obligations, consultation requirements, and algorithmic management transparency.
Emissions and sustainability
AI systems optimising fleet routing, fuel consumption, and modal shift generate data that feeds emissions reporting and climate commitments. Governance must ensure these systems are accurate, auditable, and aligned with regulatory reporting requirements.