Why Logistics AI Needs Human Experts in the Loop

The logistics industry faces a critical decision as AI tools proliferate across operations: how much autonomy to grant to artificial intelligence versus maintaining human oversight. Alfonso Quijano, CTO of Lean Solutions Group, argues that the optimal approach is the "experts in the loop" model, which positions human judgment not as a backstop for AI failures, but as the essential ingredient that makes AI valuable in the first place. Quijano identifies specific, high-value use cases where AI excels: document-heavy workflows like invoice processing, bill of lading discrepancies, email routing, and accounts payable work.

In these domains, AI systems achieve 65-70 hours of reclaimed time per operator monthly, or roughly 25 percent of working hours. However, the remaining 20-30 percent of exceptions still require trained human judgment, particularly in domains like carrier sales where relationship trust cannot be fully automated. The paradigm shift involves treating standard operating procedures as "living documents" that evolve continuously through AI-human feedback loops, similar to how AI agents use coordination files internally.

This structural change in how operational knowledge is maintained will reshape workforce roles, transforming experts from core-process operators into coaches and ambassadors for AI systems. For supply chain executives, the takeaway is clear: success requires careful workflow mapping to identify where exceptions live, understanding the autonomy spectrum of available tools, and planning for permanent human involvement in exception management.