Recent disruptions in key trade routes, from commercial shipping issues in the Red Sea to automotive production delays following floods in Europe, have exposed weaknesses in our supply chains. Traditional automation has delivered efficiency gains but introduced a hidden fragility: Systems that work perfectly within normal parameters can still break down exactly when we need them most. Having spent over a decade building and investing in AI-driven logistics and data infrastructure, I’ve seen how systems optimized for efficiency can crumble in the face of real-world volatility—unless they’re designed to adapt in real time. I believe the biggest impact AI can have on supply chain management is in crisis response and operational issue recovery. In the world of non-AI tech, process automation often goes hand-in-hand with rigidity. When things remain within normal parameters, these systems deliver efficiency and profitability. But when real-world complications arise, the solution can default to “pick up the phone, call your counterparty and figure it out.” The rise of generative AI has fundamentally changed this dynamic. These systems can self-solve a significantly larger number of problems, and I’ve found them to be uniquely suited to handling ambiguous situations.

Full opinion : AI’s Role In Creating Agile, Resilient Supply Chains.